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Credentialing

Nearly 1 out of 5 classes in california taught by underprepared teachers, diana lambert , daniel j. willis , and yuxuan xie, june 30, 2022.

Most California teachers have the appropriate credentials and training to teach the subjects and students in their classes, but many do not, according to new statewide data on teacher assignments released Thursday.

While 83% of K-12 classes in the 2020-21 school year were taught by teachers credentialed to teach that course, 17% were taught by teachers who were not.  

Teachers are required to have either a multiple-subject, single-subject or special education credential to teach, depending on the grade level and coursework, but an ongoing statewide teacher shortage has meant that most school districts have had to rely on teachers who are not fully prepared to teach at least some classes on their schedule. Often that has meant teachers working with various emergency-style permits or waivers. 

Map shows the percentage of classes taught by teachers with full credentials labeled by the state as clear.

“There is no question that well-qualified teachers are among the most important contributors to a student’s educational experience,” said State Board of Education President Linda Darling-Hammond. “California is committed to ensuring that every student has teachers who are well-prepared to teach challenging content to diverse learners in effective ways and are fully supported in their work. With this data, we can focus on measures to assist our educator workforce as they strive to provide high-quality teaching to all students, especially our most vulnerable students.” 

The new Teacher Assignment Monitoring Outcome data is the state’s newest tool in its battle to end a long and enduring teacher shortage. It is expected to guide state and local leaders on how best to use resources to recruit and retain teachers and will inform California residents about teacher assignments in their local schools. It also allows California to finally meet federal Every Student Succeeds Act requirements.

“The release of the teacher data is a milestone achievement, years in the making,” said John Affeldt, managing attorney at Public Advocates, a public interest law firm. “We wish it had been here years ago but now the state will finally will have data capturing the quality of teaching force statewide down to the school level.”

The data will reveal disparities between low-income and wealthier schools in staffing fully prepared  teachers , he said.  Research by the Learning Policy Institute  shows that the gaps have widened in California since the pandemic.

Students are more likely to have underprepared teachers in small rural districts where teachers are more difficult to recruit, according to the data. At Big Lagoon Union Elementary School in Humboldt County, 97% of the courses in 2020-21 were taught by interns, who generally have not completed the tests, coursework and student teaching required for a preliminary or clear credential. The school serves 24 students and has two teachers and a principal, according to state data.

Of the 10 school districts with the largest number of classes being taught by underprepared teachers, Oakland Unified has the largest enrollment — 35,352 students. Almost a third of the classes in the district that year were being taught by teachers working without the correct credential or training, according to an EdSource analysis of the state data that excluded charter schools.

The new data categorizes teacher assignments as “clear,” “out-of-field,” “ineffective,” “interns,” “incomplete” or “unknown.”

It s hows that 83.1% of the assignments that school year were clear because classes were taught by teachers with the appropriate credentials. Another 4.4% of the teaching assignments were deemed out-of-field because classes were taught by teachers who were credentialed but hadn’t passed required tests or coursework that demonstrate competence to teach the course or the student population in the class. Interns taught 1.5% of classes. Teaching assignments were labeled ineffective if they were taught by people without authorization to teach in California, or who were teaching outside their credential or permit without authorization from the state. Some 4.1% of courses had that designation.

Elementary schools had the highest percentage of clear teaching assignments — 90.6%, while media arts courses had the highest percentage of ineffective assignments at 34%.

Los Angeles Unified, the state’s largest district, was in line with the state average with 85% of its assignments clear, 3.3% out-of-field and 3.5% ineffective.

Other districts had a much higher number of teachers assigned to classes they weren’t fully prepared to teach. Maricopa Unified, Konocti Unified, Sierra-Plumas Joint Unified, Alpaugh Unified, Needles Unified, Oakland Unified, Chualar Union, Vineland Elementary, East Nicolaus Joint Union High and Borrego Springs Unified had 29% to 41% of their classes taught by an underprepared teacher in 2020-21 — the highest percentage among districts with more than  250 students.

There has long been concern about teacher assignments at schools in high-poverty communities. Oakland Unified, which has 72% of its students on free and reduced-priced lunches, was among the districts with the highest number of underprepared teachers.

Oakland Unified has had a teacher shortage for decades. It has been made worse by the pandemic. Over the past five years the district has averaged over 500 teacher vacancies each year. The complexity of the credentialing process, teacher diversity and the national teacher shortage have all played a part in the teacher shortages in Oakland, according to a press release from the district.

“I have the utmost respect for all of our teachers, whether they are currently credentialed, teaching outside of their subject area or in the process of getting their credential,” says Superintendent Kyla Johnson-Trammell, who noted she started her career with an emergency teaching credential.

In recent years district officials have increased beginning teacher salaries and increased recruitment and retention efforts.

School officials have numerous options that allow them to assign teachers to classes they aren’t credentialed to teach. Teachers who have not completed testing, coursework and student teaching can work with provisional intern permits and intern credentials. Credentialed teachers can teach classes outside their credential with limited assignment permits and waivers in order to meet staffing needs. School districts also can use the local assignment option to assign a teacher with a different teaching credential to a class when they can’t find an educator with the proper credential. 

“Amidst a nationwide staffing shortage, school districts are struggling to find teachers for classes and sometimes must utilize the local assignment option to place high-quality teachers in assignments that they aren’t credentialed to teach, yet they are proving to be highly effective in,” said Kindra Britt, spokeswoman for California County Superintendents Educational Services Association.

Court and community schools run by county offices of education have a particularly difficult time filling positions, she said.  

 “ We are putting the most qualified person in front of students,” she said. “The data doesn’t really support that.”

Darling-Hammond calls the shortage of appropriately credentialed teachers in some communities worrisome, but she is confident that recent state initiatives to recruit and retain teachers will increase the number of teachers in the state. The initiatives include $500 million for Golden State Teacher Grants, $350 million for teacher residency programs and $1.5 billion for the Educator Effectiveness Block Grant. 

But there is still work to be done, Darling-Hammond said. “A lot of people are beginning to recognize that retention is the name of the game,” she said. “It’s not about recruitment. Nine out of 10 positions are open because people left the year before .”

There won’t be any punitive action from the state if they have too many teachers without the correct credentials, although they may feel more public pressure now that the data is publicly available, Darling-Hammond said.

The data collection was mandated by Assembly Bill 1219, which passed in 2019. It also is the result of two years of collaboration between the Commission on Teacher Credentialing and the California Department of Education. 

The information will be used to inform state and local education officials about where teaching shortages exist and how deep they are so that resources can be targeted to places with the most need, Darling-Hammond said. The data also can help the state improve programs by tracking the attrition rates of teachers who completed residency or other teacher preparation pathways, she said.

“As we begin to emerge from a global pandemic, this data is an important tool to drive conversations about how we can best serve students,” said Mary Nicely, chief deputy superintendent of public instruction at the California Department of Education. “By launching this annual report, we are providing a new level of transparency to support schools, students and families as we find ways to navigate today’s challenges to public education, including statewide education workforce shortages.” 

The data is submitted to the state from school districts each fall, based on teaching assignments on the first Wednesday in October. The teaching assignments are then compared to teachers’ credentials by Commission on Teacher Credentialing staff. If a teacher’s assignment doesn’t match his or her credential, the school district and a state monitor will review the case, said Cindy Kazanis, division director at the California Department of Education.

More than 3,000 school employees were trained to use the new database at more than 30 in-person sessions and through several webinars, said state officials at a news conference on Wednesday.

But not all necessary employees had training or knew how to enter the codes correctly, resulting in many school or district entries being designated as incomplete, Britt said. The California Department of Education won’t correct the data, she said.

“Despite the confusing labels, our educators are effective; this issue is semantic, and we need steps to remediate the incorrect data,” Britt said. “I ’m a little concerned about the damage that can be done to an already strained education workforce.”

Schools had time to review the data, including almost six months to submit, review, correct and certify their teacher assignment data, said Maria Clayton, director of communications for the California Department of Education. They then had four months in late 2021 to review the results after teacher credentials were compared to teaching assignments.

Britt said the California County Superintendents Educational Services Association is advocating for more training options for county offices.

The information on teacher assignments will be available to the public on the California Department of Education’s Dataquest website , and will be used in several other state and local reports including each School Accountability Report Card, the California School Dashboard, the Federal Teacher Equity Plan and the Williams Monitoring criteria.

Affeldt and other equity advocates are hoping the state board will include the information as a metric on the school dashboard to compel districts to address disparities among schools in teacher assignments. The board plans to examine the issue after the California Department of Information has released a second year of data.

EdSource reporter John Fensterwald also contributed to this story.

teacher assignment monitoring outcome data

Poorer students still get the least qualified teachers, but California has made progress

August 4, 2022.

There are 40% more teachers in poor schools who lack the required qualifications than in the richest schools, an EdSource analysis found.

Diana Lambert , John Fensterwald , And Daniel J. Willis

teacher assignment monitoring outcome data

Oakland, with among lowest ratio of fully prepared, rightly assigned teachers, has a strategy to address teacher churn

The district is hoping a "grow your own" approach to recruiting teaching candidates from the community will pay dividends OUSD. But living costs are high, and nearby districts can compete with offer higher salaries.

John Fensterwald

teacher assignment monitoring outcome data

Find your school's teacher qualifications | Database

Database shows percentage of classes taught by teachers with the different qualifications.

Daniel J. Willis

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Tia Davenport 1 year ago 1 year ago

I found this article to be very telling in many ways, but the fact is that California needs teachers. As a National Board Certified Teacher, I’m not given the respect or compensation for my work to earn this top certification, and I earned NBCT status as an “effective” teacher. There has to be a better pipeline for new teachers and offer support for the many substitute teachers. We have to do better.

Marcy 2 years ago 2 years ago

To address the teacher shortage how about university teacher education programs offering asynchronous/synchronous online courses to meet the needs of nontraditional students. Evening courses typically start at 4-6pm at CSUs. Teacher interns work in the classroom, enduring long commutes to attend in person classes is a problem. With schools ending at 3:30 PM, candidates will have added stress. Another barrier would-be teachers face is paying expensive fees to take CBEST, CSET and RICA … Read More

To address the teacher shortage how about university teacher education programs offering asynchronous/synchronous online courses to meet the needs of nontraditional students. Evening courses typically start at 4-6pm at CSUs. Teacher interns work in the classroom, enduring long commutes to attend in person classes is a problem. With schools ending at 3:30 PM, candidates will have added stress.

Another barrier would-be teachers face is paying expensive fees to take CBEST, CSET and RICA tests. Most do not pass the exams the first time. The governor should allow The CTC to evaluate course transcripts for subject matter or eliminate expensive CBEST, CSET and RICA examinations. California should look to other states by losing the excessive requirements to teach. If a candidate has a master’s, doctorate or bachelor’s degree and enrolled in a teacher education program that should be sufficient to teach.

The teacher education programs are not attracting would-be teachers. Teacher education programs are unaffordable. Most would-be candidates may not qualify for Pell grants to return to school because of prior education. Most working adults cannot afford to take off work for unpaid student teaching. Student teachers should receive compensation or the option of working as a paid paraprofessional.

Until state officials, universities and credentialing agencies address issues, teacher shortages will persist. Teaching is a noble profession, but low teacher salary and excessive teaching requirements do not make the profession attractive.

Ted 2 years ago 2 years ago

17% is much closer to 1 in 6. “Nearly 1 in 5” is stretching it. I suggest revising.

David 2 years ago 2 years ago

There is an assumption in this discussion that is going unaddressed. It is assumed that teaching credentials are necessary in order to have effective teaching. That is not the case. Having credentials means that you've taken some courses and know what the "best practices" are, not that you have the ability and desire to convey your subject matter in a compelling way so that students of various backgrounds and abilities will absorb and make … Read More

There is an assumption in this discussion that is going unaddressed. It is assumed that teaching credentials are necessary in order to have effective teaching. That is not the case. Having credentials means that you’ve taken some courses and know what the “best practices” are, not that you have the ability and desire to convey your subject matter in a compelling way so that students of various backgrounds and abilities will absorb and make it their own.

Far better than requiring teaching credentials would be to audition prospective teachers. They still would need to pass a thorough background and subject matter check and, I think, have a BA degree, but requiring only that rather than the onerous amount of coursework that is currently needed to get a teaching credential would open up the field to many qualified college graduates who, were it not for that hurdle, could be excellent teachers.

Once on the job, their teaching effectiveness (using objective tests) would need to be monitored as, at present, it is not. Alas, there is a huge industry today that is economically supported by “teacher credentialing” and there are many ineffective teachers who are accustomed to being left alone, and these groups would naturally oppose such a change, but, if we really want to improve K-12 education, standing up to such opposition should not dissuade us. This is something we should be willing to at least try.

Jorge Ramirez 2 years ago 2 years ago

The data in this article seems incomplete. I’m an avid reader of Ed source articles, but this time I feel that your staff drop the ball in getting the whole story. The data charts just don’t add up.

Dr. Bill Conrad 2 years ago 2 years ago

No amount of bureaucratic credentialing rigamarole is going to address a fundamental problem that extraordinarily week colleges of education attract the least qualified candidates and train them very poorly in content, pedagogy, and assessment skills. Just look at the abysmal student achievement results in California! Teaching is still considered charity work! Over 80% of K-12 teachers are white women. Nuf said. Read The Fog of Education!

Dori 2 years ago 2 years ago

As an experienced and sucessful teacher coming in from MN, I was told by the county rep that my credential wouldn't transfer over because it was based on a U of M major program for interdisciplinary history and lit which wasn't recognized by CA. I had to take more classes to focus on a single angle discipline and retake all the pedagogy classes. Since the school needed me immediately, I signed on as a long-term … Read More

As an experienced and sucessful teacher coming in from MN, I was told by the county rep that my credential wouldn’t transfer over because it was based on a U of M major program for interdisciplinary history and lit which wasn’t recognized by CA. I had to take more classes to focus on a single angle discipline and retake all the pedagogy classes. Since the school needed me immediately, I signed on as a long-term substitute then entered the internship program. Several of us were in the same situation – highly successful teachers who had to jump through California’s hoops because our credentials wouldn’t transfer over for one reason or another.

For those claiming “interns need to be babysat,” I guarantee that my fellow interns went through rigorous classes through the Fortune School of Education, but some were treated very poorly by their fellow teachers. Instead of getting the on-campus mentorship they deserved, they were left with little to no PD despite the placement school’s promises of appropriate support.

I was lucky because I was an intern in name only. I do agree that interns should not be given a full slate of classes from the very beginning. Ideally, they would have 2 classes where they co-teach the first semester, teach 1-2 alone the second semester and co-teach 2 others. Year 2 – give them up to 4 classes until they finish the program. They get a full slate after they clear their credential.

John 2 years ago 2 years ago

Pay the teachers more money. The current pay condemns teachers to a life without homeownership and trips to the food bank with their children.

Pay master teachers 6 figure salaries no doubt. However they must demonstrate expertise in content knowledge, pedagogy, and assessment skills. And they can demonstrate significant measurable growth in academic knowledge and skills. No more free money for time as a teacher! Those days are over! Students and families expect and deserve second to none service. No more free money for teachers with ridiculous demands foe small class sizes. Focus on improving and aligning professional practices.

Peter 2 years ago 2 years ago

Besides the teacher shortage and attrition, how confident are we in the quality of the credentialed teachers? I am well aware that many teacher preparation programs in CA graduate underprepared teachers.

Dominee Marchus 2 years ago 2 years ago

It appears your definition of ineffective teachers is the former definition which was revised and expanded November 2019 to include teachers who are legally authorized, see CDE website, Updated Teacher Equity Definitions; https://www.cde.ca.gov/pd/ee/teacherequitydefinitions.asp

Allison Nofzinger 2 years ago 2 years ago

California takes way too long to clear certified teachers from other states. I had 3 different state credentials and it took over 1.5yrs. Seriously and thus was before the pandemic. Now most don't even need to take the credential tests. It's ridiculous. I don't think it's right for intern teachers to just come in either. We don't have time to train them during the day. I get they are excited to be in the schools … Read More

California takes way too long to clear certified teachers from other states. I had 3 different state credentials and it took over 1.5yrs. Seriously and thus was before the pandemic. Now most don’t even need to take the credential tests. It’s ridiculous. I don’t think it’s right for intern teachers to just come in either. We don’t have time to train them during the day.

I get they are excited to be in the schools … but it’s not just babysitting. The pay for California is so low for teachers as well. There is no way to even live here on what we get paid. I’ve lived Hawaii and MD and made more than here. Seriously, get with the program! You want to keep us pay us !

veronica thomas 2 years ago 2 years ago

Do we have similar data for charter schools?

John Fensterwald 2 years ago 2 years ago

Yes, and EdSource plans to include them soon.

Leonard Isenberg 2 years ago 2 years ago

LAUSD and other districts have created the shortage of qualified teacher by bringing false charges and getting rid of more expensive high seniority teachers and replacing them with fresh out of college untrained teachers working on emergency credentialed "teachers" working for $35,000 a year for 3 years, only to be replaced by another set of emergency credentialed untrained "teachers" when the 3 years are up. Public education is no longer about education, rather it's about … Read More

LAUSD and other districts have created the shortage of qualified teacher by bringing false charges and getting rid of more expensive high seniority teachers and replacing them with fresh out of college untrained teachers working on emergency credentialed “teachers” working for $35,000 a year for 3 years, only to be replaced by another set of emergency credentialed untrained “teachers” when the 3 years are up. Public education is no longer about education, rather it’s about vendor profits. http://www.perdaily.com

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  • Review Article
  • Open access
  • Published: 22 June 2020

Teaching analytics, value and tools for teacher data literacy: a systematic and tripartite approach

  • Ifeanyi Glory Ndukwe 1 &
  • Ben Kei Daniel 1  

International Journal of Educational Technology in Higher Education volume  17 , Article number:  22 ( 2020 ) Cite this article

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Teaching Analytics (TA) is a new theoretical approach, which combines teaching expertise, visual analytics and design-based research to support teacher’s diagnostic pedagogical ability to use data and evidence to improve the quality of teaching. TA is now gaining prominence because it offers enormous opportunities to the teachers. It also identifies optimal ways in which teaching performance can be enhanced. Further, TA provides a platform for teachers to use data to reflect on teaching outcome. The outcome of TA can be used to engage teachers in a meaningful dialogue to improve the quality of teaching. Arguably, teachers need to develop their teacher data literacy and data inquiry skills to learn about teaching challenges. These skills are dependent on understanding the connection between TA, LA and Learning Design (LD). Additionally, they need to understand how choices in particular pedagogues and the LD can enhance their teaching experience. In other words, teachers need to equip themselves with the knowledge necessary to understand the complexity of teaching and the learning environment. Providing teachers access to analytics associated with their teaching practice and learning outcome can improve the quality of teaching practice. This research aims to explore current TA related discussions in the literature, to provide a generic conception of the meaning and value of TA. The review was intended to inform the establishment of a framework describing the various aspects of TA and to develop a model that can enable us to gain more insights into how TA can help teachers improve teaching practices and learning outcome. The Tripartite model was adopted to carry out a comprehensive, systematic and critical analysis of the literature of TA. To understand the current state-of-the-art relating to TA, and the implications to the future, we reviewed published articles from the year 2012 to 2019. The results of this review have led to the development of a conceptual framework for TA and established the boundaries between TA and LA. From the analysis the literature, we proposed a Teaching Outcome Model (TOM) as a theoretical lens to guide teachers and researchers to engage with data relating to teaching activities, to improve the quality of teaching.

Introduction

Educational institutions today are operating in an information era, where machines automatically generate data rather than manually; hence, the emergence of big data in education ( Daniel 2015 ). The phenomenon of analytics seeks to acquire insightful information from data that ordinarily would not be visible by the ordinary eyes, except with the application of state-of-the-art models and methods to reveal hidden patterns and relationships in data. Analytics plays a vital role in reforming the educational sector to catch up with the fast pace at which data is generated, and the extent to which such data can be used to transform our institutions effectively. For example, with the extensive use of online and blended learning platforms, the application of analytics will enable educators at all levels to gain new insights into how people learn and how teachers can teach better. However, the current discourses on the use of analytics in Higher Education (HE) are focused on the enormous opportunities analytics offer to various stakeholders; including learners, teachers, researchers and administrators.

In the last decade, extensive literature has proposed two weaves of analytics to support learning and improve educational outcomes, operations and processes. The first form of Business Intelligence introduced in the educational industry is Academic Analytics (AA). AA describes data collected on the performance of academic programmes to inform policy. Then, Learning Analytics (LA), emerged as the second weave of analytics, and it is one of the fastest-growing areas of research within the broader use of analytics in the context of education. LA is defined as the "measurement, collection, analysis and reporting of data about the learner and their learning contexts for understanding and optimising learning and the environments in which it occurs" ( Elias 2011 ). LA was introduced to attend to teaching performance and learning outcome ( Anderson 2003 ; Macfadyen and Dawson 2012 ). Typical research areas in LA, include student retention, predicting students at-risk, personalised learning which in turn are highly student-driven ( Beer et al. 2009 ; Leitner et al. 2017 ; Pascual-Miguel et al. 2011 ; Ramos and Yudko 2008 ). For instance, Griffiths ( Griffiths 2017 ), employed LA to monitor students’ engagements and behavioural patterns on a computer-supported collaborative learning environment to predict at-risk students. Similarly, Rienties et al. ( Rienties et al. 2016 ) looked at LA approaches in their capacity to enhance the learner’s retention, engagement and satisfaction. However, in the last decade, LA research has focused mostly on the learner and data collections, based on digital data traces from Learning Management Systems (LMS) ( Ferguson 2012 ), not the physical classroom.

Teaching Analytics (TA) is a new theoretical approach that combines teaching expertise, visual analytics and design-based research, to support the teacher with diagnostic and analytic pedagogical ability to improve the quality of teaching. Though it is a new phenomenon, TA is now gaining prominence because it offers enormous opportunities to the teachers.

Research on TA pays special attention to teacher professional practice, offering data literacy and visual analytics tools and methods ( Sergis et al. 2017 ). Hence, TA is the collection and use of data related to teaching and learning activities and environments to inform teaching practice and to attain specific learning outcomes. Some authors have combined the LA, and TA approaches into Teaching and Learning Analytics (TLA) ( Sergis and Sampson 2017 ; Sergis and Sampson 2016 ). All these demonstrate the rising interest in collecting evidence from educational settings for awareness, reflection, or decision making, among other purposes. However, the most frequent data that have been collected and analysed about TA focus on the students (e.g., different discussion and learning activities and some sensor data such as eye-tracking, position or physical actions) ( Sergis and Sampson 2017 ), rather than monitoring teacher activities. Providing teachers access to analytics of their teaching, and how they can effectively use such analytics to improve their teaching process is a critical endeavour. Also, other human-mediated data gathering in the form of student feedback, self and peer observations or teacher diaries can be employed to enrich TA further. For instance, visual representations such as dashboards can be used to present teaching data to help teachers reflect and make appropriate decisions to inform the quality of teaching. In other words, TA can be regarded as a reconceptualisation of LA for teachers to improve teaching performance and learning outcome. The concept of TA is central to the growing data-rich technology-enhanced learning and teaching environment ( Flavin 2017 ; Saye and Brush 2007 ). Further, it provides teachers with the opportunity to engage in data-informed pedagogical improvement.

While LA is undeniably an essential area of research in educational technology and the learning sciences, automatically extracted data from an educational platform mainly provide an overview of student activities, and participation. Nevertheless, it hardly indicates the role of the teacher in these activities, or may not otherwise be relevant to teachers’ individual needs (for Teaching Professional Development (TPD) or improvement of their classroom practice). Many teachers generally lack adequate data literacy skills ( Sun et al. 2016 ). Teacher data literacy skill and teacher inquiry skill using data are the foundational concepts underpinning TA ( Kaser and Halbert 2014 ). The development of these two skills is dependent on understanding the connection between TA, LA and Learning Design (LD). In other words, teachers need to equip themselves with knowledge through interaction with sophisticated data structures and analytics. Hence, TA is critical to improving teachers’ low efficacy towards educational data.

Additionally, technology has expanded the horizon of analytics to various forms of educational settings. As such, the educational research landscape needs efficient tools for collecting data and analyzing data, which in turn requires explicit guidance on how to use the findings to inform teaching and learning ( McKenney and Mor 2015 ). Increasing the possibilities for teachers to engage with data to assess what works for the students and courses they teach is instrumental to quality ( Van Harmelen and Workman 2012 ). TA provides optimal ways of performing the analysis of data obtained from teaching activities and the environment in which instruction occurs. Hence, more research is required to explore how teachers can engage with data associated with teaching to encourage teacher reflection, improve the quality of teaching, and provide useful insights into ways teachers could be supported to interact with teaching data effectively. However, it is also essential to be aware that there are critical challenges associated with data collection. Moreover, designing the information flow that facilitates evidence-based decision-making requires addressing issues such as the potential risk of bias; ethical and privacy concerns; inadequate knowledge of how to engage with analytics effectively.

To ensure that instructional design and learning support is evidence-based, it is essential to empower teachers with the necessary knowledge of analytics and data literacy. The lack of such knowledge can lead to poor interpretation of analytics, which in turn can lead to ill-informed decisions that can significantly affect students; creating more inequalities in access to learning opportunities and support regimes. Teacher data literacy refers to a teachers’ ability to effectively engage with data and analytics to make better pedagogical decisions.

The primary outcome of TA is to guide educational researchers to develop better strategies to support the development of teachers’ data literacy skills and knowledge. However, for teachers to embrace data-driven approaches to learning design, there is a need to implement bottom-up approaches that include teachers as main stakeholders of a data literacy project, rather than end-users of data.

The purpose of this research is to explore the current discusses in the literature relating to TA. A vital goal of the review was to extend our understanding of conceptions and value of TA. Secondly, we want to contextualise the notion of TA and develop various concepts around TA to establish a framework that describes multiple aspects of TA. Thirdly, to examine different data collections/sources, machine learning algorithms, visualisations and actions associated with TA. The intended outcome is to develop a model that would provide a guide for the teacher to improve teaching practice and ultimately enhance learning outcomes.

The research employed a systematic and critical analysis of articles published from the year 2012 to 2019. A total of 58 publications were initially identified and compiled from the Scopus database. After analysing the search results, 31 papers were selected for review. This review examined research relating to the utilisation of analytics associated with teaching and teacher activities and provided conceptual clarity on TA. We found that the literature relating to conception, and optimisation of TA is sporadic and scare, as such the notion of TA is theoretically underdeveloped.

Methods and procedures

This research used the Tripartite model ( Daniel and Harland 2017 ), illustrated in Fig.  1 , to guide the systematic literature review. The Tripartite model draws from systematic review approaches such as the Cochrane, widely used in the analyses of rigorous studies, to provide the best evidence. Moreover, the Tripartite model offers a comprehensive view and presentation of the reports. The model composes of three fundamental components; descriptive (providing a summary of the literature), synthesis (logically categorising the research based on related ideas, connections and rationales), and critique (criticising the novel, providing evidence to support, discard or offer new ideas about the literature). Each of these phases is detailed fully in the following sections.

figure 1

Tripartite Model. The Tripartite Model: A Systematic Literature Review Process ( Daniel and Harland 2017 )

To provide clarity; the review first focused on describing how TA is conceptualised and utilised. Followed by the synthesis of the literature on the various tools used to harvest, analyse and present teaching-related data to the teachers. Then the critique of the research which led to the development of a conceptual framework describing various aspects of TA. Finally, this paper proposes a Teaching Outcome Model (TOM). TOM is intended to offer teachers help on how to engage and reflect on teaching data.

TOM is a TA life cycle which starts with the data collection stage; where the focus is on teaching data. Then the data analysis stage; the application of different Machine Learning (ML) techniques to the data to discover hidden patterns. Subsequently, the data visualisation stage, where data presentation is carried out in the form of a Teaching Analytics Dashboard (TAD) for the teacher. This phase is where the insight generation, critical thinking and teacher reflection are carried out. Finally, the action phase, this is where actions are implemented by teachers to improve teaching practice. Some of these actions include improving the LD, changing teaching method, providing appropriate feedback and assessment or even carrying out more research. This research aims to inform the future work in the advancement of TA research field.

Framing research area for review

As stated in the introduction, understanding current research on TA can be used to provide teachers with strategies that can help them utilise various forms of data to optimise teaching performance and outcome. Framing the review was guided by some questions and proposed answers to address those questions (see Table  1 )

Inclusion and exclusion criteria

The current review started with searching through the Scopus database using the SciVal visualisation and analytical tool. The rationale for choosing the Scopus database is that it contains the largest abstract and citation database of peer-reviewed research literature with diverse titles from publishers worldwide. Hence, it is only conceivable to search for and find a meaningful balance of the published content in the area of TA. Also, the review included peer-reviewed journals and conference proceedings. We excluded other documents and source types, such as book series, books, editorials, trade publications on the understanding that such sources might lack research on TA. Also, this review excluded articles published in other languages other than English.

Search strategy

This review used several keywords and combinations to search on terms related to TA. For instance: ’Teaching Analytics’ AND ’Learning Analytics’ OR ’Teacher Inquiry’ OR ’Data Literacy’ OR ’Learning Design’ OR ’Computer-Supported Collaborative Learning’ OR ’Open Learner Model’ OR ’Visualisation’ OR ’Learning Management System’ OR ’Intelligent Tutoring System’ OR ’Student Evaluation on Teaching’ OR ’Student Ratings’.

This review searched articles published between 2012 to 2019. The initial stage of the literature search yielded 58 papers. After the subsequent screening of previous works and removing duplicates and titles that did not relate to the area of research, 47 articles remained. As such, a total of 36 studies continued for full-text review. Figure  2 , shows the process of finalising the previous studies of this review.

figure 2

Inclusion Exclusion Criteria Flowchart. The selection of previous studies

Compiling the abstracts and the full articles

The review ensured that the articles identified for review were both empirical and conceptual papers. The relevance of each article was affirmed by requiring that chosen papers contained various vital phrases all through the paper, as well as, title, abstract, keywords and, afterwards, the entire essay. In essence, were reviewed giving particular cognisance and specific consideration to those section(s) that expressly related to the field of TA. In doing as such, to extract essential points of view on definitions, data sources, tools and technologies associated with analytics for the teachers. Also, this review disregarded papers that did not, in any way, relate to analytics in the context of the teachers. Finally, 31 articles sufficed for this review.

Systematic review: descriptive

Several studies have demonstrated that TA is an important area of inquiry ( Flanders 1970 ; Gorham 1988 ; Pennings et al. 2014 ; Schempp et al. 2004 ), that enables researchers to explore analytics associated with teaching process systematically. Such analytics focus on data related to the teachers, students, subjects taught and teaching outcomes. The ultimate goal of TA is to improve professional teaching practice ( Huang 2001 ; Sergis et al. 2017 ). However, there is no consensus on what constitutes TA. Several studies suggest that TA is an approach used to analyse teaching activities ( Barmaki and Hughes 2015 ; Gauthier 2013 ; KU et al. 2018 ; Saar et al. 2017 ), including how teachers deliver lectures to students, tools usage pattern, or dialogue. While various other studies recognise TA as the ability to applying analytical methods to improve teacher awareness of student activities for appropriate intervention ( Ginon et al. 2016 ; Michos and Hernández Leo 2016 ; Pantazos et al. 2013 ; Taniguchi et al. 2017 ; Vatrapu et al. 2013 ). A hand full of others indicate TA as analytics that combines both teachers and students activities ( Chounta et al. 2016 ; Pantazos and Vatrapu 2016 ; Prieto et al. 2016 ; Suehiro et al. 2017 ). Hence, it is particularly problematic and challenging to carry out a systematic study in the area of analytics for the teachers to improve teaching practice, since there is no shared understanding of what constitutes analytics and how best to approach TA.

Researchers have used various tools to automatically harvest important episodes of interactive teacher and student behaviour during teaching, for teacher reflection. For instance, KU et al. ( 2018 ), utilised instruments such as; Interactive Whiteboard (IWB), Document Camera (DC), and Interactive Response System (IRS) to collect classroom instructional data during instruction. Similarly, Vatrapu et al. ( 2013 ) employed eye-tracking tools to capture eye-gaze data on various visual representations. Thomas ( 2018 ) also extracted multimodal features from both the speaker and the students’ audio-video data, using digital devices such as cameras and high-definition cameras. Data collected from some of these tools not only provide academics with real-time data but also attract more details about teaching and learning than the teacher may realise. However, the cost of using such digital tools for large-scale verification is high, and cheaper alternatives are sort after. For instance, Suehiro et al. ( 2017 ) proposed a novel approach of using e-books to extract teaching activity logs in a face-to-face class efficiently.

Vatrapu ( 2012 ) considers TA as a subset of LA dedicated to supporting teachers to understand the learning and teaching process. However, this definition does not recognise that both the learning and teaching processes are intertwined. Also, most of the research in LA collects data about the student learning or behaviour, to provide feedback to the teacher ( Vatrapu et al. 2013 ; Ginon et al. 2016 ; Goggins et al. 2016 ; Shen et al. 2018 ; Suehiro et al. 2017 ), see, for example, the iKlassroom conceptual proposal by Vatrapu et al. ( 2013 ), which highlights a map of the classroom to help contextualise real-time data about the learners in a lecture. Although, a few research draw attention to the analysis of teacher-gathering and teaching practice artefacts, such as lesson plans. Xu and Recker ( 2012 ) examined teachers tool usage patterns. Similarly, Gauthier ( 2013 ) extracted the analysis of the reasoning behind the expert teacher and used such data to improve the quality of teaching.

Multimodal analytics is an emergent trend used to complement available digital trace with data captured from the physical world ( Prieto et al. 2017 ). Isolated examples include the smart school multimodal dataset conceptual future proposal by Prieto et al. ( 2017 ), which features a plan of implementing a smart classroom to help contextualise real-time data about both the teachers and learners in a lecture. Another example, Prieto et al. ( 2016 ), explored the automatic extraction of orchestration graphs from a multimodal dataset gathered from only one teacher, classroom space, and a single instructional design. Results showed that ML techniques could achieve reasonable accuracy towards automated characterisation in teaching activities. Furthermore, Prieto et al. ( 2018 ) applied more advanced ML techniques to an extended version of the previous dataset to explore the different relationships that exist between datasets captured by multiple sources.

Previous studies have shown that teachers want to address common issues such as improving their TPD and making students learn effectively ( Charleer et al. 2013 ; Dana and Yendol-Hoppey 2019 ; Pennings et al. 2014 ). Reflection on teaching practice plays an essential role in helping teachers address these issues during the process of TPD ( Saric and Steh 2017 ; Verbert et al. 2013 ). More specifically, reflecting on personal teaching practice provides opportunities for teachers to re-examine what they have performed in their classes ( Loughran 2002 ; Mansfield 2019 ; Osterman and Kottkamp 1993 ). Which, in turn, helps them gain an in-depth understanding of their teaching practice, and thus improve their TPD. For instance, Gauthier ( 2013 ), used a visual teach-aloud method to help teaching practitioners reflect and gain insight into their teaching practices. Similarly, Saar et al. ( 2017 ) talked about a self-reflection as a way to improve teaching practice. Lecturers can record and observe their classroom activities, analyse their teaching and make informed decisions about any necessary changes in their teaching method.

The network analysis approach is another promising field of teacher inquiry, especially if combined with systematic, effective qualitative research methods ( Goggins et al. 2016 ). However, researchers and teacher who wish to utilise social network analysis must be specific about what inquiry they want to achieve. Such queries must then be checked and validated against a particular ontology for analytics ( Goggins 2012 ). Goggins et al. ( 2016 ), for example, aimed at developing an awareness of the types of analytics that could help teachers in Massive Open Online Courses (MOOCs) participate and collaborate with student groups, through making more informed decisions about which groups need help, and which do not. Network theory offers a particularly useful framework for understanding how individuals and groups respond to each other as they evolve. Study of the Social Network (SNA) is the approach used by researchers to direct analytical studies informed by network theory. SNA has many specific forms, each told by graph theory, probability theory, and algebraic modelling to various degrees. There are gaps in our understanding of the link between analytics and pedagogy. For example, which unique approaches to incorporating research methods for qualitative and network analysis would produce useful information for teachers in MOOCs? A host of previous work suggests a reasonable path to scaling analytics for MOOCs will involve providing helpful TA perspectives ( Goggins 2012 ; Goggins et al. 2016 ; Vatrapu et al. 2012 ).

Teacher facilitation is considered a challenging and critical aspect of active learning ( Fischer et al. 2014 ). Both educational researchers and practitioners have paid particular attention to this process, using different data gathering and visualisation methods, such as classroom observation, student feedback, audio and video recordings, or teacher self-reflection. TA enables teachers to perform analytics through visual representations to enhance teachers’ experience ( Vatrapu et al. 2011 ). As in a pedagogical environment, professionals have to monitor several data such as questions, mood, ratings, or progress. Hence, dashboards have become an essential factor in improving and conducting successful teaching. Dashboards are visualisation tools enable teachers to monitor and observe teaching practice to enhance teacher self-reflection ( Yigitbasioglu and Velcu 2012 ). While a TAD is a category of dashboard meant for teachers and holds a unique role and value [62]. First, TAD could allow teachers to access students learning in an almost real-time and scalable manner ( Mor et al. 2015 ), consequently, enabling teachers to improve their self-knowledge by monitoring and observing students activities. TAD assists the teachers in obtaining an overview of the whole classroom as well as drill down into details about individual and groups of students to identify student competencies, strengths and weaknesses. For instance, Pantazos and Vatrapu ( 2016 ) described TAD for repertory grid data to enable teachers to conduct systematic visual analytics of classroom learning data for formative assessment purposes. Second, TAD also allows for tracking on teacher self-activities ( van Leeuwen et al. 2019 ), as well as students feedback about their teaching practice. For example,Barmaki and Hughes ( 2015 ) explored a TAD that provides automated real-time feedback based on speakers posture, to support teachers practice classroom management and content delivery skills. It is a pedagogical point that dashboards can motivate teachers to reflect on teaching activities, help them improve teaching practice and learning outcome ( 2016 ). The literature has extensively described extensively, different teaching dashboards. For instance, Dix and Leavesley ( 2015 ), broadly discussed the idea of TAD and how they can represent visual tools for academics to interface with learning analytics and other academic life. Some of these academic lives may include schedules such as when preparing for class or updating materials, or meeting times such as meeting appointments with individual or collective group of students. Similarly, Vatrapu et al. ( 2013 ) explored TAD using visual analytics techniques to allow teachers to conduct a joint analysis of students personal constructs and ratings of domain concepts from the repertory grids for formative assessment application.

Systematic review: synthesis

In this second part of the review process, we extracted selected ideas from previous studies. Then group them based on data sources, analytical methods used, types of visualisations performed and actions.

Data sources and tools

Several studies have used custom software and online applications such as employing LMS and MOOCs to collect online classroom activities ( Goggins et al. 2016 ; KU et al. 2018 ; Libbrecht et al. 2013 ; Müller et al. 2016 ; Shen et al. 2018 ; Suehiro et al. 2017 ; Vatrapu et al. 2013 ; Xu and Recker 2012 ). Others have used modern devices including eye-tracker, portable electroencephalogram (EEG), gyroscope, accelerometer and smartphones ( Prieto et al. 2016 ; Prieto et al. 2018 ; Saar et al. 2017 ; Saar et al. 2018 ; Vatrapu et al. 2013 ), and conventional instruments such as video and voice recorders ( Barmaki and Hughes 2015 ; Gauthier 2013 ; Thomas 2018 ), to record classroom activities. However, some authors have pointed out several issues with modern devices such as expensive equipment, high human resource and ethical concerns ( KU et al. 2018 ; Prieto et al. 2017 ; Prieto et al. 2016 ; Suehiro et al. 2017 ).

In particular, one study by Chounta et al. ( 2016 ) recorded classroom activities using humans to code tutor-student dialogue manually. However, they acknowledged that manual coding of lecture activities is complicated and cumbersome. Some authors also subscribe to this school of thought and have attempted to address this issue by applying Artificial Intelligence (AI) techniques to automate and scale the coding process to ensure quality in all platforms ( Prieto et al. 2018 ; Saar et al. 2017 ; Thomas 2018 ). Others have proposed re-designing TA process to automate the process of data collection as well as making the teacher autonomous in collecting data about their teaching ( Saar et al. 2018 ; Shen et al. 2018 ). Including using technology that is easy to set up, effortless to use, does not require much preparation and at the same time, not interrupting the flow of the class. In this way, they would not require researcher assistance or outside human observers. Table  2 , summarises the various data sources as well as tools that are used to harvest teaching data with regards to TA.

The collection of evidence from both online and real classroom practice is significant both for educational research and TPD. LA deals mostly with data captured from online and blended learning platforms (e.g., log data, social network and text data). Hence, LA provides teachers with data to monitor and observe students online class activities (e.g., discussion boards, assignment submission, email communications, wiki activities and progress). However, LA neglects to capture physical occurrences of the classroom and do not always address individual teachers’ needs. TA requires more adaptable forms of classroom data collection (e.g., through video- recordings, sensor recording or by human observers) which are tedious, human capital intensive and costly. Other methods have been explored to balance the trade-off between data collected online, and data gathered from physical classroom settings by implementing alternative designs approach ( Saar et al. 2018 ; Suehiro et al. 2017 ).

Analysis methods

Multimodal analytics is the emergent trend that will complement readily available digital traces, with data captured from the physical world. Several articles in the literature have used multimodal approaches to analyse teaching processes in the physical world ( Prieto et al. 2016 ; Prieto et al. 2017 ; Prieto et al. 2018 ; Saar et al. 2017 ; Thomas 2018 ). In university settings, unobtrusive computer vision approaches to assess student attention from their facial features, and other behavioural signs have been applied ( Thomas 2018 ). Most of the studies that have ventured into multimodal analytics applied ML algorithms to their captured datasets to build models of the phenomena under investigation ( Prieto et al. 2016 ; Prieto et al. 2018 ). Apart from research areas that involve multimodal analytics, other areas of TA research have also applied in ML techniques such as teachers tool usage patterns ( Xu and Recker 2012 ), online e-books ( Suehiro et al. 2017 ), students written-notes ( Taniguchi et al. 2017 ). Table  3 outlines some of the ML techniques applied from previous literature in TA.

Visualisation methods

TA allows teachers to apply visual analytics and visualisation techniques to improve TPD. The most commonly used visualisation techniques in TA are statistical graphs such as line charts, bar charts, box plots, or scatter plots. Other visualisation techniques include SNA, spatial, timeline, static and real-time visualisations. An essential visualisation factor for TA is the number of users represented in a visualisation technique. Serving single or individual users allows the analyst to inspect the viewing behaviour of one participant. Visualising multiple or group users at the same time can allow one to find strategies of groups. However, these representations might suffer from visual clutter if too much data displays at the same time. Here, optimisation strategies, such as averaging or bundling of lines might be used, to achieve better results. Table  4 represents the visualisation techniques mostly used in TA.

Systematic review: critique

Student evaluation on teaching (set) data.

Although the literature has extensively reported various data sources used for TA, this study also draws attention to student feedback on teaching, as another form of data that originates from the classroom. The analytics of student feedback on teaching could support teacher reflection on teaching practice and add value to TA. Student feedback on teaching is also known as student ratings, or SET is a form of textual data. It can be described as a combination of both quantitative and qualitative data that express students opinions about particular areas of teaching performance. It has existed since the 1920s ( Marsh 1987 ; Remmers and Brandenburg 1927 ), and used as a form of teacher feedback. In addition to serving as a source of input for academic improvement ( Linse 2017 ), many universities also rely profoundly on SET for hiring, promoting and firing instructors ( Boring et al. 2016 ; Harland and Wald 2018 ).

Technological advancement has enabled institutions of Higher Education (HE) to administer course evaluations online, forgoing the traditional paper-and-pencil ( Adams and Umbach 2012 ). There has been much research around online teaching evaluations. Asare and Daniel ( 2017 ) investigated the factors influencing the rate at which students respond to online SET. While there is a verity of opinions as to the validity of SET as a measure of teaching performance, many teaching academics and administrators perceive that SET is still the primary measure that fills this gap ( Ducheva et al. 2013 ; Marlin Jr and Niss 1980 ). After all, who experiences teaching more directly than students? These evaluations generally consist of questions addressing the instructor’s teaching, the content and activities of the paper, and the students’ own learning experience, including assessment. However, it appears these schemes gather evaluation data and pass on the raw data to the instructors and administrators, stopping short of deriving value from the data to facilitate improvements in the instruction and the learning experiences. This measure is especially critical as some teachers might have the appropriate data literacy skills to interpret and use such data.

Further, there are countless debates over the validity of SET data ( Benton and Cashin 2014 ; MacNell et al. 2015 ). These debates have highlighted some shortcomings of student ratings of teaching in light of the quality of instruction rated ( Boring 2015 ; Braga et al. 2014 ). For Edström, what matters is how the individual teacher perceives an evaluation. It could be sufficient to undermine TPD, especially if the teachers think they are the subjects of audit ( Edström 2008 ). However, SET is today an integral part of the universities evaluation process ( Ducheva et al. 2013 ). Research has also shown that there is substantial room for utilising student ratings for improving teaching practice, including, improving the quality of instruction, learning outcomes, and teaching and learning experience ( Linse 2017 ; Subramanya 2014 ). This research aligns to the side of the argument that supports using SET for instructional improvements, to the enhancement of teaching experience.

Systematically, analytics of SET could provide valuable insights, which can lead to improving teaching performance. For instance, visualising SET can provide some way, a teacher can benchmark his performance over a while. Also, SET could provide evidence to claim for some level of data fusion in TA, as argued in the conceptualisation subsection of TA.

Transformational TA

The growing research into big data in education has led to renewed interests in the use of various forms of analytics ( Borgman et al. 2008 ; Butson and Daniel 2017 ; Choudhury et al. 2002 ). Analytics seeks to acquire insightful information from hidden patterns and relationships in data that ordinarily would not be visible by the natural eyes, except with the application of state-of-the-art models and methods. Big data analytics in HE provides lenses on students, teachers, administrators, programs, curriculum, procedures, and budgets ( Daniel 2015 ). Figure  3 illustrates the types of analytics that applies to TA to transform HE.

figure 3

Types of analytics in higher education ( Daniel 2019 )

Descriptive Analytics Descriptive analytics aims to interpret historical data to understand better organisational changes that have occurred. They are used to answer the "What happened?" information regarding a regulatory process such as what are the failure rates in a particular program ( Olson and Lauhoff 2019 ). It applies simple statistical techniques such as mean, median, mode, standard deviation, variance, and frequency to model past behaviour ( Assunção et al. 2015 ; ur Rehman et al. 2016 ). Barmaki and Hughes ( 2015 ) carried out some descriptive analytics to know the mean view time, mean emotional activation, and area of interest analysis on the data generated from 27 stimulus images to investigate the notational, informational and emotional aspect of TA. Similarly, Michos and Hernández-Leo ( 2016 ) demonstrated how descriptive analytics could support teachers’ reflection and re-design their learning scenarios.

Diagnostic Analytics Diagnostic analytics is higher-level analytics that further diagnoses descriptive analytics ( Olson and Lauhoff 2019 ). They are used to answer the "Why it happened?". For example, a teacher may need to carry out diagnostic analytics to know why there is a high failure rate in a particular programme or why students rated a course so low for a specific year compared to the previous year. Diagnostic analytics uses some data mining techniques such as; data discovery, drill-down and correlations to further explore trends, patterns and behaviours ( Banerjee et al. 2013 ). Previous research has applied the repertory grid technique as a pedagogical method to support the teachers perform knowledge diagnostics of students about a specific topic of study ( Pantazos and Vatrapu 2016 ; Vatrapu et al. 2013 ).

Relational Analytics Relational analytics is the measure of relationships that exists between two or more variables. Correlation analysis is a typical example of relational analytics that measures the linear relationship between two variables ( Rayward-Smith 2007 ). For instance, Thomas ( 2018 ) applied correlation analysis to select the best features from the speaker and audience measurements. Some researchers have also referred to other forms of relational analytics, such as co-occurrence analysis to reveal students hidden abstract impressions from students written notes ( Taniguchi et al. 2017 ). Others have used relational analytics to differentiate critical formative assessment futures of an individual student to assist teachers in the understanding of the primary components that affect student performance ( Pantazos et al. 2013 ; Michos and Hernández Leo 2016 ). A few others have applied it to distinguish elements or term used to express similarities or differences as they relate to their contexts ( Vatrapu et al. 2013 ). Insights generated from this kind of analysis can be considered to help improve teaching in future lectures and also compare different teaching styles. Sequential pattern mining is also another type of relational analytics used to determine the relationship that exists between subsequent events ( Romero and Ventura 2010 ). It can be applied in multimodal analytics to cite the relationship between the physical aspect of the learning and teaching process such as the relationship between ambient factors and learning; or the investigation of robust multimodal indicators of learning, to help in teacher decision-making ( Prieto et al. 2017 ).

Predictive Analytics Predictive analytics aims to predict future outcomes based on historical and current data ( Gandomi and Haider 2015 ). Just as the name infers, predictive analytics attempts to predict future occurrences, patterns and trends under varying conditions ( Joseph and Johnson 2013 ). It makes use of different techniques such as regression analysis, forecasting, pattern matching, predictive modelling and multi-variant statistics ( Gandomi and Haider 2015 ; Waller and Fawcett 2013 ). In prediction, the goal is to predict students and teachers activities to generate information that can support decision-making by the teacher ( Chatti et al. 2013 ). Predictive analytics is used to answer the "What will happen". For instance, what are the interventions and preventive measures a teacher can take to minimise the failure rate? Herodotou et al. ( Herodotou et al. 2019 ) provided evidence on how predictive analytics can be used by teachers to support active learning. An extensive body of literature suggests that predictive analytics can help teachers improve teaching practice ( Barmaki and Hughes 2015 ; Prieto et al. 2016 ; Prieto et al. 2018 ; Suehiro et al. 2017 ) and also to identify group of students that might need extra support to reach desired learning outcomes ( Goggins et al. 2016 ; Thomas 2018 ).

Prescriptive Analytics Prescriptive analytics provides recommendations or can automate actions in a feedback loop that might modify, optimise or pre-empt outcomes ( Williamson 2016 ). It is used to answer the "How will it best happen?". For instance, how will teachers make the right interventions for students that have been perceived to be at risk to minimise the student dropout rate or what kinds of resources are needed to support students who might need them to succeed? It determines the optimal action that enhances the business processes by providing the cause-effect relationship and applying techniques such as; graph analysis, recommendation engine, heuristics, neural networks, machine learning and Markov process ( Bihani and Patil 2014 ; ur Rehman et al. 2016 ). For example, applying curriculum Knowledge graph and learning Path recommendation to support teaching and learners learning process ( Shen et al. 2018 ).

Actionable Analytics Actionable analytics refers to analytics that prompt action ( Gudivada et al. 2016 ; Gudivada et al. 2018 ; Winkler and Söllner 2018 ). Norris et al. ( 2008 ) used the term action analytics to describe "the emergence of a new generation of tools, solutions, and behaviours that are giving rise to more powerful and effective utilities through which colleges and universities can measure performance and provoke pervasive actions to improve it". The educational sector can leverage some of these innovative, new and cutting edge technologies and techniques such as Natural Language Processing (NLP) ( Sergis and Sampson 2016 ; Taniguchi et al. 2017 ), big data analytics ( Goggins et al. 2016 ) and deep learning ( Prieto et al. 2018 ) to support teacher in both the teaching and learning processes.

Institutional Transformation Data in themselves are not useful; they only become valuable if they can be used to generate insight. In other words, analytics can be applied to institutional data to optimise productivity and performance of the institutional operations, thereby providing value that can transform the institutional practices. In education, there are various purposes of analytics, ranging from those that provide institutions with an overview or deep-down microscopic view of individual students, faculty, curriculum, programs, operations and budgets, to those capable of predicting future trends. Unveiling the value of TA empowers the teachers to identify issues and transform difficulties into opportunities. These opportunities can be employed to optimises the institutional processes, enhance learner experiences and improve teaching performance. TA and LA both play a vital role in effectively reforming and transforming the educational sector to catch up with the fast pace at which data generates. For example, with the extensive use of online and blended learning platforms, the application of analytics will enable institutional stakeholders at all levels to gain new insights into educational data. Today, the HE sector is at crossroads, where there is a need for synergies in learning research and data analytics to transform the way teaching and learning are fundamentally carried out.

The link between TA, LA and LD

Primarily, TA aims to link the centrepiece of LA and remodel them to address teaching challenges. More specifically, TA argues that connecting and analysing insights generated from LA methods and tools with those generated from in-class methods and tools, through TA tools could support teacher reflection and improve TPD based on evidence. Hence, this concept is presented further in the next subsection.

Conceptual framework of TA

Based on the different perceptions of TA described in previous reviews, this study proposes a conceptual framework for TA to model the complex interaction existing around TA. Three nodes (LA, TA and LD) are interconnected to each other forming a triadic network with the teacher at the centre, performing value-added interactions to make informed based decisions. Each part of this interconnection forms a triangle, totalling three triangles (A, B and C) (see Fig.  4 ).

figure 4

Conceptualisation of TA. Triadic TA Conceptual Framework

The proposed framework is not bound to any particular implementation of learning or design technology. Instead, the point is to describe the elements of analytics and data sources that are key for each domain to guide the use of analytical methods, tools and technology to support the multiple dimensions of learning design successfully.

This triad illustrates the interaction occurring between the teacher, the LA and the LD, to inform TPD. Hernández-Leo et al. ( 2019 ) argued that LD could contribute to structuring and orchestrating the design intent with learners digital trace patterns, advancing the knowledge and interpretation of LA. LA tailored to fit the design intent could be considered by teachers as contributing to the enhancement of the LD in subsequent design interactions. For example, LA could be an information tool to inform the tutors or designers of pedagogical decision making ( Persico and Pozzi 2015 ). Hence, a teacher may want to utilise LA to make just-in-time pedagogical decisions, such as grouping students based on their performance.

Similarly, a teacher may want to investigate if the estimated time taken for students to carry out learning tasks is reasonable or whether adjustments need to be made to the course design ( Hernández-Leo et al. 2019 ; Pozzi and Persico 2013 ). This domain can also provide teachers with analytics regarding the challenges and difficulties students face in the problem-solving phase while performing a task. In return, they give the teacher information in the form of TAD summarising the various challenges students encountered with that activity. They may also provide solutions on how to address them. For example, an early alert system that instantiates a dashboard for instructors using some metrics calculations such as login counts and page views ( Thille and Zimmaro 2017 ). The data sources in the LA node can improve teachers’ awareness, which could also lead to the improvement of LD and help to distinguish design elements that could modify future designs. Data collection in this domain is mostly automatic through virtual learning environments (e.g., LMS, MOOCs). Other forms of data collection may include social media platforms (e.g., Facebook, Tweeter), wearable sensors (e.g., eye-trackers, EEG), software tools that support and collect data related to specific student activities and attendance ( Bakharia et al. 2016 ; Bos and Brand-Gruwel 2016 ).

This triangle represents the relationship between the teacher, the LD and TA. While experiencing LD, TA endeavours to handle continues teachers’ engagement, progression, achievement and learners satisfaction ( Bakharia et al. 2016 ; Sergis and Sampson 2017 ). For example, exploring the impact of video shot on instructor performance and student learning. Using MOOC AB testing, teachers could experiment whether a difference in video production setting would have any impact on the instructors acting performance, or whether any changes in format and instructors performance will result in detectable differences in student viewing behaviour ( Chen et al. 2016 ).

Further, data sources in TA could assist teacher reflection on the impacts of their LD. Data collection could also be automatic by the use of wearable sensors on the teachers while performing teaching activities, also known as in-class analytics. Several institutions now record video contents of their face-to-face classes. Some others even go a step further by collecting their physiological data. These datasets, as mentioned earlier, have a way of exemplifying and illustrating things that ordinarily, a book of pedagogy cannot convey, in providing systematic feedback for the teachers. It involves capturing data during a traditional in-class, face-to-face teacher-centric instruction or teacher-student interaction (where students learn by directly or indirectly interacting with instructors in a lab or lecture hall) and analysing data to identify areas of possible improvements. The kind of data usually captured in this setting are audio, video, body movement, brain activity, cortex activity, to mention just a few. For example, a teacher can perform diagnostic analysis on class recorded videos to expose what is intrinsic during his lecture. This kind of diagnostic analysis could help teachers understand more about their teaching and discover areas of further improvement. SET is another form of data about the teachers; they are collected via the institutional application platforms ( Hernández-Leo et al. 2019 ) and can be visualised to improve teaching performance..

Analytics that happens in the LD involves the visualisation of teaching design to facilitate teacher reflection on the lesson plan, visualisation of the extent to which the lesson plan aligns with the educational objectives, and finally, validation of the lesson plan to highlight potential inconsistencies in the teaching design. For example, a teacher can visualise the number of assessment activities of the lesson plan or the various types of educational resources used in the lesson plan, to know if they are still valid or obsolete. Similarly, a teacher could analyse the time allocated for each lesson activity, to find out if the time allocated for each activity is good enough, or visualise the level of inconsistencies of time misappropriations and imbalances between the overall lesson plan and the individual lesson activities.

This area presents the communication between the teacher, the LA and the TA. Chinchu Thomas ( 2018 ) explored the correlation between student ratings on teaching and student physiological data. Similarly, Schmidlin ( 2015 ) established how to analyse and cross-reference data without decrypting the data sources. Hence, we argue that SET could be linked with LA such as student digital traces from LMS ( Stier et al. 2019 ) and other forms of data (such as attendance data), without compromising privacy. This claim for data fusion could support the teachers to make informed-decisions in new ways. For example, analytics performed on linked datasets could quickly reveal those student opinions that may not count at the end of the semester courses.

Visualisations that could quickly realise students with low participation rates and link it to their opinions, without revealing any identity. Additionally, teachers may be interested in comparing the view of students with low participation rate with those of high participation rate. This kind of information may lead teachers towards making explicit judgements with evidence. A tutor may choose to disregard the opinions of those students that participated less than 20 per cent in-class activities and assignments, as well as had a low attendance rate. Hence, narrowing concentration more on the opinions of students that participated in improving teaching practice.

However, considering ethical concerns, data fusion at the individual level still requires explicit and informed consent from the students whose data are collected ( Menchen-Trevino 2016 ). Other issues such as privacy concerns, data fusion can be problematic as this usually requires that the teachers know student identities. However, from a programmatic perspective, extra measures can be put in place to address this concern. Algorithms can be interfaced to mask student identities to some other unique identities to make them anonymous but linked ( Schmidlin et al. 2015 ) to provide a richer set of data for the teacher to make informed decisions.

Teachers can get a better picture towards improving the context in which learning happens, only if they can be informed about both how they teach and how students learn. Hence, this framework aims to continually provide teachers with interesting information from intelligent feedback based on data generated from users and learning context to improve their learning design and teaching outcome continuously.

Teaching Outcome Model (TOM)

Design-based research advances instructional design work, theory, and implementation as iterative, participatory, and located rather than processes "owned and operated" by designers of instructions ( Wang and Hannafin 2005 ). TOM is an iterative process that follows a design-based research approach to guide teachers, researchers, faculty and administrators on how to utilise data to improve the quality of teaching and learning outcome. This model enables teachers to investigate and evaluate their work using data. Consequently, improving the teacher use of data to inform teaching practice. To build more awareness with regards to teaching data, TOM models TA through iterative cycles of data collection, data analysis, data visualisation and action stages which are interdependent of each other (see Fig.  5 ). Design-based research, as a pragmatic methodology, can guide TOM while generating insights that can support teacher reflections on teaching and student learning. Conversely, TOM ensures that design-based research methodologies can be operational and systemised. Following the various stages outlined in the model, teachers can regularly identify, match and adjust teaching practice, and learning design to all the learners need.

figure 5

Teaching Outcome Model. TA Life cycle

In the data collection stage, a constant stream of data accumulates from the digital traces relating to teaching daily activities and engagements, including structured and unstructured data, visual and non-visual data, historical and real-time data. It is also important to note that the rate at which diverse data accumulates in our educational system will keep growing. According to Voithofer and Golan ( 2018 ), there are several ways to mine teaching and learning data without professional knowledge that is beyond the necessary teacher training experience in data literacy, administering learning design and class orchestration. Subscribing to this school of thought, adopting Big data infrastructure in our institutions will guarantee easy access to data by the various stakeholders, this will also mitigate the bottleneck of disparate data points existing in our educational sector. Therefore, enabling educators to focus more attention on instruction, setting up interactive class activities, and participating more on discussions that will create more data for evidence-based decision making. Also, the misuse of data is a broad primary concern ( Roberts et al. 2017 ). One critical matter is identifying the types of data that can be collected, analysed and visualized; to ensure that the right people have access to the data for the right purpose. As such, implementing data governance policies around institutional data such as; ’open definition of purpose, scope and boundaries, even if that is broad and in some respects, open-ended’ is critical ( Kay et al. 2012, p 6 ). This sort of measure will introduce clarity and address issues around who controls what data as well as security and privacy issues around data.

Analysis stage

This step involves the different ways of working with data to ensure data quality. Professionals such as data scientists, programmers, engineers and researchers need to work together with the teachers at this level. They can apply data mining techniques, statistical methods, complex algorithms, and AI techniques (such as NLP, AI, ML, deep learning) to adequately transform data into the useful analytical process. Analytics in the education space presents in diverse forms including, descriptive, diagnostic, predictive and prescriptive. These different forms of analytics can be utilised to offer a high-level view or fine-grained view of individual learners, teacher, faculty and their various activities, engagements and behaviours. Unravelling the value of data analytics empowers teachers and researchers to identify problems and transform challenges into opportunities that can be utilised to support teacher reflection and enrich teacher data-literacy experiences. For example, teachers can apply NLP on text data to gather topics from discussion posts, contributions participants have made within collaborative projects and their sentiments.

Furthermore, ML techniques could be combined with TA to enhance teaching outcome. For instance, chatbots could support the teacher by acting as a teacher assistant in large classes. An essential consideration in analytics, however, is that data can be easily de-identified ( Roberts et al. 2017 ; Cumbley and Church 2013 ), especially when data sets increase in size and scope and are combined to generate big data. To resolve these concerns, a particular university introduced a two-stage method of data de-identification coupled with data governance to restrict data access ( De Freitas et al. 2015 ).

Visualisation stage

This stage ensures data presentation in useful and meaningful ways to teachers. Empowering teachers with interactive visual interfaces and dashboards that facilitate teacher cognition and promote reflection about pre-processed and fine-grained teaching and learning activities. Through TAD, can project real-time and historical information from different data sources that might not be necessarily interoperable, and results summarised ( Moore 2018 ). However, visualisation is "what you see is what you get"; meaning that information presentation method may affect its interpretation, and consequently, may influence decision-making. Hence, it is necessary to address issues around visualisations in diverse forms such as; visual analytics and exploratory data analysis to create room for visual interactivity, exploratory visualisation to discover trends, patterns, relationships and behaviours. For example, a teacher can use a TAD to monitor student engagement. When the student engagement is poor, it may prompt the teacher to take necessary actions such as; changing teaching material and making it more interactive. Additionally, there are also questions around privacy, such as who has access to visualisations relevant to an instructor, such as other faculty members participating in the course, directly or indirectly, administrators, researchers, potential employees of other institutions.

Action stage

At this stage, informed-decision leads to action and actions unavoidably reshape our environment; subsequently, regenerate new data. Additionally, there is a to create tools that will be useful to the teacher to understand and make meaning of data quickly. Actions taken by teachers can be used to improve the course design and assessment (value-added formative assessment). In any case, predictive analytics prompts an epistemological question; how should we ensure effective action by the teacher based on flawed predictions such that the system does not collapse?

Discussion and conclusion

This article presents the result of a systematic literature review aimed at describing the conception, and synthesis of the current research on the notion of TA, to provide insight into how TA can be used to improve the quality of teaching. The first part of the article described what is meant by TA to consolidate the divergent discourse on TA. The review showed that TA applies to analytics on teaching activities as well as methods of improving teachers’ awareness on students’ activities, including supporting the teachers to understand student learning behaviours to provide adequate feedback to teachers. In essence, the primary goal of TA is to improve teaching performance. The literature also revealed the several tools and methods are available for extracting digital traces associated with teaching in addition to traditional student evaluation tools. However, one of the main challenges recognised was the cost associated with some devices used to capture in-class activities, and ML techniques have been proposed to minimise this challenge.

The literature has also recognised teacher inquiry as a promising area of research in TA and came to a consensus that methods, like multimodal analytics and SNA, could help promote teacher inquiry and teacher reflection. Visualisations and visual analytics techniques are very significant in TA and also encourage teacher inquiry. The use of visualisation dashboards and TAD are essential tools that the modern-day teachers require to carry out a continuous and efficient reflection on teaching practice.

The emphasis of the synthesis of TA was clearly on data collection, analysis and visualisation, as illustrated in Fig.  6 . In the literature, the various kinds of data collected and used to improve teaching practice, include:

Digital trace data; "records of activity (trace data) undertaken through an online information system (thus, digital)" [119]. They incorporate various activities generated from custom applications and learning environments that leave digital footprints.

Image data are photographic or trace objects that represent the underlying pixel data of an area of an image element.

Physiological data are body measurement based on body-mounted sensors ( Lazar et al. 2017 ), used to extract data from teachers while performing classroom teaching activities.

Audio-video stream data or recorded lecturer data with captured physical teaching activities and students learning activities. Hence, attainable with mounted cameras, computer or mobile cameras connected to applications like Zoom and Skype, eye tracks with recording capabilities and digital cameras connected to learning environments such as Eco365.

Social data are data with online social activities, including utilising the repertory grid technique to collect students’ assessment data from social media sites.

Text data, including quantitative and qualitative data, data generated from text documents such as discussion forums, students essay or articles, emails and chat messages.

figure 6

Dimensions of TA. Illustration of TA based on the literature

Analysis in this context refers to the application of Educational Data Mining (EDM) and deep learning techniques mostly used to process data. EDM approaches is a complicated process that requires an interweaving of various specialised knowledge and ML algorithms, especially to improve teaching and learning ( Chen 2019 ). NLP and classification are the two main EDM techniques applied in TA. However, the review also recognised the use of other methods such as clustering and deep learning techniques, to support teachers.

As commonly said, a picture is worth more than a thousand words; visualisation can effectively communicate and reveal structures, patterns and trends in variables and their interconnections. Research in TA has applied several visualisation techniques including Network, Timeline, Spatial, Table and Statistical Graphs. For instance, SNA is a form of visual analytics that is used to support teachers to determine how different groups interact and engage with course resources. Identifying differences in interaction patterns for different groups of students may result in different learning outcomes, such as, how access patterns of successful groups of students differ from that of unsuccessful students. Applying visualisation techniques can support teachers in areas such as advising underperforming students about effective ways to approach study. Visualisation can enable teachers to identify groups of students that might need assistance and discover new and efficient means of using collaborative systems to achieve group work that can be taught explicitly to students.

However, while acknowledging the incomplete nature of data and complexities associated with data collection, analysis and use, teachers should take caution to avoid bais. Data collected in one context may not be directly applicable to another or have both benefits and cost for individuals or groups from which data was harvested. Therefore, key stakeholders, including teachers, course directors, unit coordinators and researchers must pay proper attention to predictive models and algorithms and take extra care to ensure that the contexts of data analysed are carefully considered. There are also privacy concerns, such as who has access to view analytics relating to a teacher, including other faculty members both directly or indirectly involved in the course, administrators, researchers, future employees of other institutions. It will be useful for institutions to have clear guidelines as to who has access to what and who views what. Other issues around data include how long should data remain accessible ( Siemens 2013 ), with big data technology and infrastructure, data should be kept for as long as it can exist. Pardo and Siemens ( 2014 ) acknowledged that the use of analytics in higher education research has no clear interpretation of the right to privacy. They seem opposed to the need for absolute privacy, on the basis that the use of historical data enhances research with potential rewards for the future of teaching professional development and student outcome.

The review provided in the current article highlighted the significant limitations in the existing literature on teaching analytics. The TAD is proposed to guide teachers, developers, and researchers to understand and optimise teaching and the learning environments. The critical aspect of this review is establishing the link between LA, TA and LD and its value in informing teachers’ inquiry process. Also, the review describes the relationship between LA, TA and LD. Finally, the article proposes TOM, which draws from a research-based approach to guide teachers on how to utilise data to improve teaching. The outcome of this model is a TAD that provides actionable insights for teacher reflection and informed decision-making. Therefore, showing the value that TA brings to pedagogic interventions and teacher reflection.

Theoretical implications

The analysis of data collected from the interaction of teachers with technology and students is a promising approach for advancing our understanding of the teaching process and how it can be supported. Teachers can use data obtained from their teaching to reflect on their pedagogical design and optimise the learning environment to meet students’ diverse needs and expectations.

Teacher-centric learning design can improve the utility of new technologies and subsequent acceptance of the use of these technologies to improve the quality of teaching and enhance students learning experience. TAD is one class of tools that can be designed in such a way that will improve teaching practice.

Research on learning analytics has revealed useful insights about students’ learning and the context in which they learn. While the ability to track, harvest and analyse various forms of learning analytics can reveal useful insights about learners’ engagement with learning environments, our review suggests that there is limited focus on analytics relating to the teacher, their teaching approaches and activities. Also, there has been increasing advances in the design of learner and teaching dashboards. However, many teachers still struggle with understanding and interpreting dashboards partly because they lack data literacy skills, and mostly because most the design of many of the tools does not include teachers as partners.

Although, TAD enable teachers to inspect, and understand the processes and progress relating to their teaching, the current implementations of TAD in general, does not adequately provide teachers with the details they need or want in a readily usable format. Educational technology developers can utilise our proposed model to design better tools for improving teaching practice. For example, a TAD can be designed to perform text analytics on students qualitative comments about a course taught, and results presented to the teacher in the form of themes, sentiments and classification; such that it will support the instructor’s needs and preferences for insight generation and reflection.

Teachers monitor, observe and track both teaching and learning activities to make appropriate decisions. Moreover, it is also important to note that visualisations can be misrepresented, misinterpreted or misused by the viewer [122]. Hence, perception and cognition remain a significant challenge in TAD. Consequently, it becomes necessary to design and write algorithms that extract information visualisation, in such a way that allows adequate understanding by teachers. It is also crucial for dashboards to integrate multiple sources such as combining both the learning and teaching activities into a TAD, to create room for teachers to comprehend, reflect on and act upon the presented information quickly.

Also, the current state of technology shows little progress in taking TA, raising concerns about the accurate validity and scalability of innovations such as predictive analytics and TAD. Furthermore, the ethical issues of data use are not considered sufficient to establish institutional policies which incorporate TA as part of quality education models.

Finally, consideration of the framework’s three layers as a whole raises new questions and opportunities. For example, linking educational performance and satisfaction to specific learning design involves consideration of elements of all three layers. This review has shown that TA is a new and essential area of analytics in education. The study also suggests that the conceptualisation of teaching analytics is still at its infancy. However, the practical and successful use of teaching analytics is highly dependent on the development of conceptual and theoretical foundations into consideration.

Implications for practice

This review has uncovered the value of TA and its role in fostering data literacy skills in teachers to support evidence-based teaching. The purpose of TOM is to guide the development of teaching dashboard, and for researchers to develop strategies that help meaningful ways in which data can be presented to teachers. Teacher dashboards can empower the teachers with tools that create new opportunities to make data-informed strategic decisions, utilising the power of analytics and visualisation techniques. Consequently, increasing the efficiency and effectiveness of the institution, including, improving teaching practice, curriculum development and improvement, active learning engagement and improved students’ success. TOM also presents a platform for teaching academics who may have the best understanding of their course contexts, to provide a significant contribution to a culture of data-informed teaching practice within an institution.

The responsibility for managing the systems that provide the analytics usually falls within the control and supervision of the institution’s information technology (IT) department, and often, they have little to no knowledge of their pedagogical applications to teaching and learning. Likewise, academics and their fields of learning support are often deprived of IT skills and have little to no professional understanding of how software systems work. TOM provides opportunities for the teachers to be involved in the design of TA by providing significant interaction and collaboration between the IT and the other sectors that interpret and act upon the information flow.

Additionally, institutions need to provide teaching staff with the necessary training that fosters the development of data literacy skills, and in the use of data and analytical or visualisation dashboards to monitor their teaching practice. Based on some of the challenges identified in the present review, it is imperative institutions ensure that data is collected transparently, with the awareness of all the stakeholders involved, and informed consent of individuals where appropriate. With the advancements in computing technology, data collection, analysis and use have significantly increased, large amounts of data can be continually pulled from different sources and processed at fast speeds. Big data offers institutions the opportunity to implement big data infrastructures and utilise the full potential of data analytics and visualisation. However, institutions also need to consider implementing a data governance framework to guide the implementation and practice of analytics.

The conceptual framework of TA was established to demonstrate the relationship between LA, TA and LD, which can be useful knowledge to various institutional stakeholders, including the learners, teachers, researchers and administrators. However, there are also issues around data ownership, intellectual property rights, and licensing for data re-use (the students, the instructor, the researcher or the institution). For instance, the same data sources can be shared amongst the various stakeholders, but with different level of access, as such data sharing agreement would be needed to guide sharability without infringing on rights, violating privacy or disadvantaging individuals. The implementation of data sharing agreement would require the building of institutional, group as well as individual trust, which would include guidelines on sharing data within the institution and between third parties, such as external organisations and other institutions. In general, stricter data management policies that guide data collection, analysis and use is essential for every institution.

Limitations and future research

Teaching analytics is an emergent phenomenon in the learning analytics and data science literature, with a limited body of published work in the area, as such conclusions drawn from the review are limited to the databases interrogated and articles reviewed. Further, findings in the review are likely to be influenced by our interpretation of the literature and untestable assumptions. For example, linking LA, TA and LD and their underlying assumptions is not grounded in empirical work. The review serves as an advocacy for teacher data literacy and the ability to work with various forms of data. However, working with a single data point may not be publicly accessible to teachers.

Moreover, the combination of analytics on the several data points may lead to some level of identification, and this would require navigating issues around access, protecting privacy, and obtaining appropriate consents. Therefore, it is almost impossible for individual teachers to comprehend not only the scope of data collected, analysed and used but also the consequences of the different layers of collection, analysis and use. Consequently, making it challenging for teachers to make use of the full potentials of data to make informed choices in learning design. No matter how straightforward or transparent institutional policies around data are, the sheer complexity of the collection, analysis and use has made it impossible, posing a fundamental issue for the stakeholders trying to use analytics to enhance teaching practice and learning outcome across an institution.

In future research, we hope to carry out more extensive empirical research on how TOM could be applied to address issues with regards to ethical and privacy concerns about the utilization of TA. We are currently exploring how teaching analytics dashboards can be used to support teacher data literacy and use analytics to improve teaching practice and learning outcome.

Availability of data and materials

Not applicable.

Abbreviations

Academic analytics

Artificial intelligence

Educational data mining

Higher education

Interactive whiteboard

  • Learning analytics

Learning design

Learning management system

Machine learning

Massive open online courses

Natural language processing

Open learners model

Student evaluation of teaching

Social network analysis

  • Teaching analytics

Teaching analytics dashboard

Term frequency inverse document frequency

  • Teaching and learning analytics
  • Teaching outcome model

Technology, pedagogy, and content knowledge

Teacher professional development

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Acknowledgements

The research reported is part of an ongoing PhD research study in the area of Big Data Analytics in Higher Education. We also want to thank members of the Technology Enhanced Learning and Teaching (TELT) Committee of the University of Otago, New Zealand for support and for providing constructive feedback.

This research project was fully sponsored by Higher Education Development Centre, University of Otago, New Zealand.

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IGN conceived and presented the Conceptualisation of Teaching Analytics and Teachingv Outcome Model. BKD developed the Tripartite Approach that was utilised in this research. BKD encouraged IGN to perform a systematic review of teaching analytics that was guided by the Tripartite Approach. BKD supervised the findings of this work. IGN took the lead in writing the manuscript. All authors discussed the results, provided critical feedback and contributed to the final manuscript.

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Ndukwe, I.G., Daniel, B.K. Teaching analytics, value and tools for teacher data literacy: a systematic and tripartite approach. Int J Educ Technol High Educ 17 , 22 (2020). https://doi.org/10.1186/s41239-020-00201-6

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teacher assignment monitoring outcome data

teacher assignment monitoring outcome data

Teacher Quality

Teacher Quality Data

Since the Williams lawsuit was filed in 2000, Public Advocates has pressed state leaders to create a comprehensive data system to track teacher quality, assignments, and vacancies. In 2019, students and families won a major victory with the passage of AB 1219, which established the California State Assignment Accountability System (CalSAAS). The new system, known as Teacher Assignment Monitoring Outcomes (TAMO) was launched in 2022 and provides local and statewide data that can be used to identify the scope and impact of teacher vacancies and misassignments, in order to remedy inequities in access to quality teaching that impact low-income students and students of color. Find out if your classes are taught by the right teachers .

With the release of TAMO data, the State Board of Education (SBE) and the California Department of Education (CDE) will be able to establish objective criteria for a Teacher Quality Indicator on the California School Dashboard as mandated by the Legislature last year. The indicator will identify the extent to which teachers are appropriately assigned and fully credentialed at the school level. Public Advocates is serving on CDE’s Ad Hoc Task Force to develop this teacher quality indicator on the dashboard by the end of 2023. The dashboard which will assess school and district performance relative to statewide performance to measure access and equity to full prepare teachers.

Attracting and Retaining A Diverse Fully Prepared Educator Workforce

California is not just the most populous state, but one of the most diverse states in the nation. Unfortunately our teachers do not reflect the student communities they teach. Research shows that students derive both social-emotional and academic benefits from a diverse educator workforce. Public advocates, alongside our partners, Californians for Justice and Ed Trust-West, developed a community-informed educator diversity road map. Read our recommendations.

Earlier Teacher Quality Campaigns and Resources:

  • The Partnership for Future of Learning Teaching Profession Playbook : Building a Strong and Diverse Teaching Profession offers a comprehensive set of strategies that work together to recruit, prepare, develop, and retain high-quality teachers and bring greater racial, ethnic, and linguistic diversity to the profession
  • State Advocacy  – Making sure there are fully prepared teachers for low-income students and English Language Learners
  • Federal Advocacy  – Promoting teacher quality and education equity at the national level.
  • School Accountability Report Cards (SARC)  – Monitoring school’s reporting on enrollment, learning conditions and academic performance.
  • Historic Cases –  ( Renee v Duncan ; CFJ v CCTC ; AMAE v. CA )

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teacher assignment monitoring outcome data

The California Department of Education, in cooperation with the California Commission on Teacher Credentialing and the State Board of Education, announced June 29 the first-ever release of statewide Teaching Assignment Monitoring Outcome data.

This information, from the 2020–21 school year, provides a snapshot, broken down by county, district and school, that shows how teachers are authorized to teach their assigned courses based on a variety of factors, including the subject area of the course and the number of students enrolled in the course. The release creates a baseline data set that will inform state and local decisions over the coming years as agencies work to address teacher shortages, a long-term national issue exacerbated by COVID-19.

“As we begin to emerge from a global pandemic, this data is an important tool to drive conversations about how we can best serve students,” said Mary Nicely, Chief Deputy Superintendent of Public Instruction at the California Department of Education. “By launching this annual report, we are providing a new level of transparency to support schools, students, and families as we find ways to navigate today’s challenges to public education, including statewide education workforce shortages.”

“There is no question that well-qualified teachers are among the most important contributors to a student’s educational experience,” said State Board of Education President Linda Darling-Hammond. “California is committed to ensuring that every student has teachers who are well prepared to teach challenging content to diverse learners in effective ways and are fully supported in their work. With this data, we can focus on measures to assist our educator workforce as they strive to provide high- quality teaching to all students, especially our most vulnerable students.” To that end, California has invested more than $3.6 billion in the last four years to improve teacher recruitment, retention, and training.

According to the statewide data, 83.1 percent of teacher assignments are clear, meaning the class or course is taught by a teacher who has a credential and is fully authorized to teach the course. Another 4.4 percent of assignments are out-of-field, meaning the teacher has a credential but has not demonstrated subject matter competence in the subject area(s) or for the associated student population according to statewide standards; 1.5 percent of classes or courses are taught by teachers with an intern credential, meaning the teacher is still completing their training or other credential requirements while serving as the teacher of record; and 4.1 percent of assignments are considered ineffective, meaning the teacher is authorized by an emergency permit, or holds a teaching credential but is teaching outside of their credentialed area without authorization, or holds no credential, permit, or authorization to teach in California. More information about the assignment monitoring definitions can be found on the CDE website.

The data report is the result of extensive cooperation between the California Commission on Teacher Credentialing and the California Department of Education. Following the State Board’s approval of teacher assignment definitions in the federal Every Student Succeeds Act state plan, the agencies began developing a roadmap providing the public with the meaningful data released. Bringing the two data systems together was a two-year process.

“The Commission on Teacher Credentialing is pleased that, through this partnership with the Department, our new CalSAAS system is informing a yearly, comprehensive look at teacher preparation and assignment, from the state to the school site level,” said Mary Vixie Sandy, Director of the California Commission on Teacher Credentialing. “This collaboration and the Department’s new DataQuest tables are finally shining a light on this most important indicator of educational opportunity: a fully prepared and properly assigned teacher, for both the subjects and the students they are teaching.”

AB 1219 required the California Commission on Teacher Credentialing to develop an electronic teacher assignment monitoring system known as the California Statewide Assignment Accountability System (CalSAAS) for the purpose of annually monitoring teacher assignments.

Additionally, AB 1219 required the California Commission on Teacher Credentialing and the CDE to enter into a data sharing agreement to facilitate the annual monitoring of teacher assignments. As part of this data sharing agreement, the California Department of Education is required to provide the Commission on Teacher Credentialing with certificated staff assignment and course data that is submitted to the Department of Education by local educational agencies through the annual California Longitudinal Pupil Achievement Data System Fall 2 data submission.

“While this first-ever baseline data set shows that a vast majority of teaching assignments are properly filled, there is more work to be done to hire, train and retain teachers, especially in light of the national teacher shortage,” said State Board of Education President Darling-Hammond. “Recent statewide initiatives like the $500 million Golden State Teacher Grants, the $350 million investment in Teacher Residency programs, and the $1.5 billion Educator Effectiveness Block Grant are aimed at bringing more teachers into the pipeline and providing them with the effective training—steps that will move California toward a day when 100 percent of assignments are ‘clear.’”

The complete California Teaching Assignment Monitoring Outcome data can be found on the CDE DataQuest 2020–21 Teaching Assignment Monitoring Outcomes by Full-Time Equivalent web page.

More information about Assignment Monitoring Outcome can be found on the CDE Teaching AMO Report web page.

Data showing teacher assignments at school districts in the Santa Clarita Valley can be found below:

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Initial Thoughts

Perspectives & resources, what is data-based individualization.

  • Page 1: Overview of Data-Based Individualization

How can school personnel use data to make instructional decisions?

  • Page 2: Collecting and Evaluating Data

Page 3: Progress Monitoring

  • Page 4: Analyzing Progress Monitoring Data
  • Page 5: Diagnostic Assessment
  • Page 6: Error Analysis for Reading
  • Page 7: Error Analysis for Mathematics
  • Page 8: Making Data-Based Instructional Decisions for Reading
  • Page 9: Making Data-Based Instructional Decisions for Mathematics
  • Page 10: References & Additional Resources
  • Page 11: Credits

Recall that Step 2 and Step 5 of the DBI process involve progress monitoring—one of the best ways to measure a student’s response to instruction. The progress monitoring approach used most often in the DBI process is known as general outcome measurement (GOM). GOM is a type of formative assessment in which multiple related skills are measured on a regular basis to assess a student’s performance on those skills across time.

General outcome measures are:

  • Easy to implement
  • Quick to administer
  • Cost-effective
  • Designed to be administered frequently (e.g., once per week)
  • Sensitive to change in student performance

Description of DBI Steps Graphic

This graphic illustrates both the steps of data-based individualization, as well as they ways in which those steps interact. Step 1, “Validated Intervention Program,” is represented by an orange rectangle. This box connects via a vertical grey line to Step 2, “Progress Monitoring,” which is illustrated as a green oval. Both steps, in turn, are connected to a horizontal line with labeled circles at each of its ends. The circle on the left, “Nonresponsive,” has a red minus sign at its center, while the circle on the right, “Responsive,” has a red plus sign. A grey arrow connected to the “Nonresponsive” circle points toward Step 3 of the DBI process, “Diagnostic Academic Assessment/Functional Assessment,” which is represented as a green oval, similar to Step 2. The “Responsive” circle also has a grey arrow, this one pointing back up toward Step 2, “Progress Monitoring.”

Step 3 is connected via a vertical grey arrow to Step 4, “Intervention Adaptation,” represented as an orange rectangle. Another grey arrow connects Step 4 to Step 5, “Progress Monitoring,” another green oval. As above, these latter steps are connected to a horizontal line with labeled circles at each of its ends. The circle on the left, “Nonresponsive,” has a red minus sign at its center, while the circle on the right, “Responsive,” has a red plus sign. A large grey arrow connected to the “Nonresponsive” circle points back to Step 3, “Diagnostic Academic Assessment/Functional Assessment,” while the “Responsive” circle directs instructors back to Step 5, “Progress Monitoring.”

This module page focuses on Steps 2, 3, and 5, so those green ovals are highlighted whereas the rest of the graphic is slightly faded out.

vip_graphic_green

The first step in progress monitoring is to identify a measure to assess the skills targeted by the intervention. The type of progress monitoring measure a teacher uses will depend on the student’s instructional level rather than his or her grade level. For example, a third-grade student reading at a third-grade instructional level might be administered a passage reading fluency measure (or probe). However, a third-grade student reading at a first-grade level might be administered a word identification fluency probe.

For Your Information

  • For students with severe and persistent learning difficulties, progress monitoring data should be collected at least once a week, and more often if feasible.
  • One common type of GOM is curriculum-based measurement (CBM), a type of progress monitoring conducted on a regular basis to assess a student’s performance throughout an entire year’s curriculum.
  • Academic Progress Monitoring Tools Chart . This chart provides a variety of information about a wide range of progress monitoring measures. Some sources offer non-English language versions of the measures for linguistically diverse students and large-print versions for those with visual disabilities.
  • Progress Monitoring Handouts . This document contains information about reading and mathematics probes, including administration scripts and teacher scoring sheets.

Once the teacher has identified a progress monitoring measure, he or she is ready to begin evaluating the student’s performance. The steps below describe how to do this.

  • Collect baseline data : Determine each individual’s current level of performance, or baseline, for the targeted skill (e.g., reading fluency). If the student has been receiving Tier 2 instruction, the teacher can use the last three data points as a baseline. If not, the teacher can get a reliable estimate of a student’s level of performance by administering three probes within a week or so.

A typical or expected performance level in a given skill (e.g., reading) that serves as a general indicator of a student’s overall progress.

CBM_Drawing_Goal_Line_sm

Click for Larger View and Description

Create a graph : Use the commercially available progress monitoring graphing software that accompanies the progress monitoring measure or develop a teacher-made graph. The horizontal axis represents the number of weeks of instruction. The vertical axis represents the range of possible scores a child or student can obtain on the probe. After plotting the student’s baseline data (i.e., scores on three consecutive probes) on the graph, draw a goal line between the median of these scores and the goal.

Drawing the Goal Line

CBM_Drawing_Goal_Line_sm

Description

This graphic represents an example of a student’s goal line. A span of 15 “Instructional Weeks” forms the x-axis and is divided into one-week intervals. The y-axis is labeled “Correctly Identified Words Per Minute” and is divided ten-word increments from 0 to 100.

The goal is represented here by a blue line. The line begins at Week 1 at the 40 correct responses mark and rises slightly through the weeks until it terminates at the 60 correct responses line in Week 15. The end of the line is marked with an X and is labeled “Goal.” A yellow textbox reads “The goal line is drawn between the median score and the X.”

A short data line is included here, as well. It is represented as a red line. It begins in Week 1 at the 40 correct responses line, rises to 41 correct responses in Week 2, then falls to 37 correct responses in Week 3.

On a chart of an individual’s progress in obtaining knowledge or skills, the line that connects the median of an individual’s initial baseline data to an expected goal or benchmark.

The score that falls in the middle of a list of ranked scores. To determine the median, arrange the list of numbers in order from lowest to highest. The number in the middle is the median. For example, to determine the median of the numbers 7, 12, and 9, you would first put them in order (i.e., 7,9,12). The number in the middle (i.e., 9) is the median.

PastedGraphic-1

Click here to view a sample probe that has been scored and to learn how to score a passage reading fluency probe.

To score this passage, the teacher records any errors the student makes (e.g., misread words, omitted words, or added words). She calculates the number of words read correctly in one minute by subtracting the errors from the total number of words read. She uses the numbers at the end of each line in the passage to help. For example, Natalia made eleven mistakes. The last word she read was “boy.” The teacher looks at the line before the last word. There are 75 words. She then counts the number of words in the next line, 76, 77, 78, 79, 80, 81, 82, 83, 84. So Natalia read a total of 84 words. The teacher subtracts 11 from the total number of 84, which is a score of 73 words read correctly in one minute.

Adventure_on_Highway_66_revised

View as PDF

  • Graph scores : Every time a probe is administered, record the score on the graph and draw a line to connect it to the previous data point. Alternately, allow the student to graph the data: Research shows that students who do so are more aware of their performance and view themselves as more responsible for their learning.

The graph below shows Natalia’s progress monitoring data (Step 2). Note that a vertical, dashed line separates the baseline data from the progress monitoring data. Also note that there is no line connecting the baseline data points to the progress monitoring data points.

large_graph_2

Step 2: Progress Monitoring (Natalia’s Data)

This graph displays Natalia’s progress monitoring data over a span of eleven weeks, which here form the x-axis and which is divided by a vertical blue dotted line after Week 7. The left side of this line is labeled “Tier 2,” whereas the section after the line is labeled “Quantitative Changes.” The y-axis is labeled “Words Correct Per Minute” and is divided into five word increments with a gap between 0 and 40 where there is no data.

Natalia’s goal line is represented by a red line that runs through 42 words per minute for Week 1 and 67 words per minute for Week 11.Natalia’s actual correct words per minute are represented by a blue line that indicates the following numbers: Week 1—43 words per minute, Week 2—46 words per minute, Week 3—46 words per minute, Week 4—51 words per minute, Week 5—53 words per minute, Week 6—48 words per minute, Week 7—57 words per minute, Week 8—58 words per minute, Week 9—60 words per minute, Week 10—55 words per minute, Week 11—60 words per minute. At all points, Natalia progress is beneath her goal line.

Listen as Devin Kearns discusses the importance of collecting baseline data and of administering general outcome measures (time: 2:19).

View Transcript

Transcript: Devin Kearns, PhD

Narrator : Why do you start progress monitoring before you begin an intervention and what do you need to consider when selecting a progress monitoring measure to ensure you get the data you need to make decisions?

Devin Kearns : If you have a student who you’ve identified as potentially in need of intensive intervention, it’s essential to begin progress monitoring them on a weekly basis right away, even if you haven’t started your interventions, even if the team is not sure what they’re going to do. The reason is that you want to collect a lot of baseline data, which is to say the data you have before you actually make a decision about what to do, because that will allow you then to create an aim line to determine the rate of growth that a student is going to need in order to make adequate progress. If you don’t have baseline data, you can’t tell what’s a good estimate of the student’s current rate of performance and how much we should increase that. So it’s really critical to begin doing weekly progress monitoring as soon as you can.

One thing that’s important to state along with that is that there are two forms of progress monitoring, as you may know. One is mastery measurement, and one is general outcome measurement. And for reading and math in particular, it’s really important to do the general outcome measure at the student’s instructional level. So if you have a fourth-grade student who’s performing at a second-grade level in reading, you are going to use second-grade progress monitoring tools, and you’re going to administer those on a weekly basis to collect your baseline data. And it’s important to have this general outcome measure because if you just do, like, say a sight word measure, that is great, but you do not know enough about the student’s broader reading. You only know about one thing. So, again, it’s really important to begin the measurement right away as soon as you’re beginning to feel the student might be a student you are going to include in intensive intervention and might need data-based individualization even if you haven’t started yet.

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A Teacher’s Guide to Tracking Student Progress

Filling in bubbles on test assessment

Published: August 01, 2022

Every teacher knows there's more to quality education than simply ensuring you cover the curriculum requirements. Sometimes it's obvious when students need additional support. Other times, you're convinced they've mastered a concept or skill, but their summative assessments catch you by surprise.

So how can educators know when they're truly getting through? By tracking student progress!

Monitoring student progress benefits both teachers and learners alike. Students can view their grades and performance, and teachers can assess their own approach for improvement opportunities. Tracking academic progress also provides insights into classroom-, student-, and assignment-level data. That way, you know how, where, and when to make adjustments that enhance instruction and improve learning outcomes.

For example, if the latest formative assessment demonstrates class-wide comprehension, you know that students are on-track for success and can move on to the next lesson. On the other hand, if you see a specific student or group of learners struggling, you can create targeted interventions to support them.

To help you maximize student growth, here's everything you need to know about tracking student progress.

What is Student Progress Tracking?

As a part of data-driven instruction , student progress tracking enables you to capture learning data and evaluate academic progress toward school goals for individuals, groups, and the entire class. Everything from a daily quiz to end-of-year summative assessments can be used to monitor student success, providing valuable insights into the efficacy of assignments, lesson plans, teaching methods, and even the curriculum as a whole. 

According to the National Center on Student Progress Monitoring, over 200 empirical studies have shown the validity of Curriculum-Based Measurement (CBM) in assessing student achievement. CBM, now known as student progress tracking, was originally developed to monitor performance in special education students. Since then, it has become a reliable way to evaluate progress data in all classrooms.

However, not all student data is the same. While having enough information on your classrooms is essential, determining what types of data to track is just as important for effective monitoring. Otherwise, you risk overwhelming yourself with a mountain of information.

What Kinds of Data Should You Track in Your Classroom?

The kinds of data you'll collect will depend on a variety of factors, including your students' specific grade level, school year goals, and subject matter. That being said, there are a few common sources that you'll likely use, such as:

  • Quiz, test, and exam scores.
  • Assignment completion rates.
  • Attendance records.
  • Engagement metrics for online learning.
  • Behavioral observations.
  • Standardized test scores.
  • Cumulative records of student history.

While this might seem like a lot to handle at first, a student progress tracker can help you manage your data and inform your instruction.

What to Look For in a Student Progress Tracker

To help you find the right student progress tracker for your classroom, here are three key capabilities you should look for:

  • Enables School-Wide Initiatives Your student progress tracker should empower students and staff to achieve school-wide initiatives. For instance, if your school has a goal to improve English Language Arts (ELA) proficiency, you can use a tracker to keep a close eye on relevant academic data. Through focused monitoring of ELA performance, you can gain insights into learning progress and instructional methodologies.
  • Promotes Teacher Collaboration Teacher collaboration is a powerful driver of student success, demonstrating measurable improvements in learning outcomes, according to Frontiers in Education. To improve academic progress, your tracking software should facilitate data-sharing and collaborative discussions with other educators.
  • Empowers Tandem Tracking Managing multiple datasets at once can be challenging, especially with growing teacher workloads . Your student progress tracker should enable simultaneous, real-time tracking for multiple assignments, students, and classes. You'll also want filtering capabilities to narrow your analysis to specific groups or individual students.

How to Track Student Progress

As data tools and analysis reshape the way we think about classroom instruction, innovative student progress trackers have empowered teachers to monitor performance and maximize learning outcomes. This method of tracking student progress to inform teaching is known as data-driven instruction – a five-step cycle that improves student success and enhances instructional efficacy. It starts by:

Setting Learning Goals

The first step is to set short- and long-term learning goals for your students based on past performance. Some students may keep pace with the curriculum requirements while others might require an Individualized Education Plan (IEP). Whatever the case may be, your goals should be specific, measurable, and achievable. Work with your fellow educators and administrators to outline a path toward student success.

Creating a Lesson

With your learning objectives in mind, it's time to create a lesson plan that covers the required material in an engaging way. The best way to start is by determining the needs of your students. When drafting your lesson plan, you should consider:

  • What do your students already know?
  • Where are their comprehension gaps?
  • What is the best way for them to learn?

Administering Assessments

After you've developed your goal-oriented lesson plan, you need to gauge where your students are and how far they've progressed. Throughout the school year, you can use formative assessments to test their knowledge of core concepts without the high stakes of a formal exam. These smaller assignments also offer a granular view into student progress, enabling teachers to identify gaps and develop proactive interventions.

Tracking Student Progress Data

Once you've graded the assignments, you can use the data collected to measure and monitor student progress. Based on overall comprehension gaps, you can see where instruction was effective and where there are opportunities for improvement. You might also find class-wide student performance trends that can be fixed with simple modifications to the assessments. 

Turning Insights into Action

The final step is using the insights gained from tracking and analyzing student progress to inform instruction. That means going back to your goals and lesson plan and making adjustments based on student performance. For instance, if a group of students is struggling in class, you can provide targeted, differentiated support to improve any skill deficiencies. Any changes you make to instruction or goals should also be tracked to gauge their efficacy.

Track Your Student Progress Data

Are you looking for a student progress tracker for your classroom needs? Search no more!

Schoolytics is an all-in-one classroom data platform used by teachers and school districts across the country. Unify all your student data in one environment with real-time dashboards, time-saving tools, and dedicated customer support to track progress and maximize student learning.

To start tracking student progress with Schoolytics, sign up today!

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How data analysis help in teaching better

How data analysis help in teaching better

Ms Aditi, an enthusiastic educator, recognized the potential of data in her classroom. Using assessments and surveys, she delved into the data to gain valuable insights for improving her teaching methods. Diligently examining student performance, she pinpointed areas where they needed more support and identified individual requirements. Utilizing this information, she crafted customized lessons that catered to each student’s needs, incorporating engaging activities. 

Witnessing the wonders of personalized learning, the students enthusiastically embraced Ms Aditi’s data-driven approach. Their motivation surged, and day by day, their progress soared. Through her unwavering commitment and strategic utilization of data, Ms Aditi transformed her classroom into a dynamic centre of knowledge, igniting her students’ thirst for learning.

How data analysis help in teaching better

Working with data analysis limits guesswork and helps teachers rely on facts and figures. data analysis in teaching are packed with multiple benefits if used precisely.

After training 10,000+ Teachers in learning digital skills and 21st-century teaching skills, the expert trainers at upEducators have found the best data analysis practices for teachers and their benefits. In this blog, we throw some light on the benefits of using data analysis in teaching. So let’s dive in straight and look at some of the benefits.

Identifying Student Needs

Utilizing data analysis in teaching provides valuable insights into students’ needs and requirements. By examining various data points such as assessments, quizzes, and classroom observations, teachers can gain a comprehensive understanding of each student’s strengths, weaknesses, and learning styles. This information enables educators to identify knowledge gaps and areas where students may be struggling. With this knowledge, teachers can tailor their instructional strategies and lesson plans to address individual needs effectively.

Tracking student progress

By collecting and analyzing various forms of data, such as assessments, quizzes, and assignments, teachers can monitor individual student performance over time. This allows educators to identify trends, patterns, and areas of improvement or concern. With this information at hand, teachers can provide timely and targeted interventions to support struggling students and challenge those who are excelling. data analysis provide a clear picture of each student’s growth and development, enabling teachers to make informed instructional decisions and adjustments to optimize learning outcomes.

Assessing Learning Outcomes

By comparing the data against predetermined benchmarks or standards, teachers can determine the effectiveness of their instructional strategies and adjust their teaching methods accordingly. data analysis help teachers make evidence-based decisions about curriculum design, instructional techniques, and interventions to improve learning outcomes for their students. It provides a clear and objective measure of the impact of teaching efforts and helps in driving continuous improvement in the classroom.

Differentiating Instructions

Data-driven differentiation enables teachers to provide personalized learning experiences that foster student engagement, motivation, and academic growth. It ensures that students receive the necessary support and opportunities to succeed, regardless of their individual abilities or prior knowledge.

Identifying effective teaching strategies

Through data analysis, teachers can determine which teaching strategies and approaches are most effective in promoting student learning and achievement. They can identify patterns and trends in the data that highlight successful instructional techniques or areas that may need improvement.

Furthermore, data analysis help teachers to assess the effectiveness of specific tools, resources, or technologies used in the classroom. They can determine whether certain interventions or instructional materials have a positive impact on student learning outcomes.

Enhancing Parent-Teacher Communication

By utilizing data, teachers can provide evidence-based insights to parents about their child’s academic progress, strengths, and areas for improvement.

Sharing data-driven information allows teachers to have meaningful discussions with parents, enabling them to provide a comprehensive understanding of the student’s performance. This data can include assessment results, classwork samples, and observations that support the teacher’s assessment of the child’s progress.

Additionally, data analysis can highlight patterns or trends that may require additional attention or intervention. Teachers can use this information to initiate discussions with parents and collaboratively develop strategies to support the student’s learning and development.

Furthermore, data provides a basis for productive conversations regarding goal setting and progress monitoring. Teachers can use data to illustrate how a student is progressing towards specific learning objectives and engage parents in the goal-setting process.

Making informed decisions about instructional content and methods

By collecting and analyzing student data, teachers gain valuable insights into individual strengths, weaknesses, and progress. This information guides the selection of appropriate instructional materials, pacing, and differentiation strategies to meet diverse student needs. Analyzing data also helps identify common misconceptions or gaps in understanding, enabling targeted interventions. Furthermore, it allows teachers to assess the effectiveness of their instructional strategies and make data-driven adjustments.

Identifying areas of professional development for teachers using data

By collecting and analyzing data related to student performance, classroom observations, and feedback, teachers can gain insights into their instructional practices and areas for growth. These data-driven insights can be used to identify specific areas where teachers may benefit from additional training, resources, or support. For example, if the data reveals a need for improvement in differentiating instruction for diverse learners, teachers can seek professional development opportunities focused on inclusive teaching strategies. By leveraging data to inform their professional development goals, teachers can continuously enhance their instructional skills and effectiveness, ultimately benefiting their students’ learning outcomes.

Data analysis serves as invaluable tools in the pursuit of effective teaching. By leveraging data-driven insights, educators can make informed decisions about instructional content, methods, and professional development. This enables them to tailor their teaching to individual student needs, identify areas for improvement, and continuously enhance their teaching practices for better student outcomes.

If you too want to learn how to use data analysis in teaching, then the Professional Certificate in Innovative Teaching Practices course by upEducators is the right course for you. Through this course, you learn modern and innovative teaching practices like data analysis and you can implement these practices to make learning better in your classroom.

Author : This article is written by Samiya Rashid for upEducators blog.

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Promoting Equitable Access to Teachers

Access to a fully-prepared and stable teacher workforce is essential to educational opportunity. Research has shown higher levels of teacher preparedness has positive impacts on student achievement.

The California Department of Education (CDE) has developed the Promoting Equitable Access to Teachers (PEAT) Program to assist LEAs to identify and address local disparities, or equity gaps. A key element of the PEAT Program is a suite of equity tools designed to guide LEAs as they collect and analyze the appropriate data, conduct data analyses to identify potential equity gaps, conduct a root cause analysis and consider various strategies to address disparities, while engaging stakeholders throughout the process. For an overview of the PEAT Program, see Video 1 of this seven-part series . Current guidance regarding requirements for educator assignment monitoring under ESSA is available in this January 2021 letter .

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Who should i contact for credentialing and/or teacher placement (staffing) questions (non-charter).

Questions related to teacher credentialing and/or teacher placement should be directed to the California Commission on Teacher Credentialing (CTC). The CTC can be contacted by phone at 916-322-4974, Option 1 (Monday through Friday from 12:30 to 4:30pm Pacific Standard Time) or by e-mail at [email protected] .

Who should I contact for credentialing and/or teacher placement (staffing) questions for charter schools?

For questions related to charter school staffing, please contact the chartering authority of the charter school. The CTC cannot address any specific questions for charter school staffing as the monitoring of these schools does not fall under the authority of the Commission. Also, the California Department of Education has developed a charter school FAQ page which includes staffing information.

What authorization must a charter school teacher hold?

Teachers in charter schools shall hold a CTC credential, permit, or other document equivalent to that which a teacher in all other public schools would be required to hold. An equivalent credential, permit, or other document would mean that the teacher has the appropriate authorization for their assignment. Per California Education Code Section 47605(l), it is the intent of the Legislature that charter schools be given flexibility with regard to noncore, noncollege preparatory courses.

Are charter schools required to complete the Title I, Part A—Educator Equity section of the Local Control Accountability Plan (LCAP) Addendum which requires Local educational agencies (LEAs) applying for Title I funds to describe how the LEA will identify and address any disparities that result in low-income and minority students being taught at higher rates than other students by ineffective, inexperienced, or out-of-field teachers as required by the Every Student Succeeds Act (ESSA) Section 1112(b)(2) ?

Yes. If a charter school is applying for Title I funding, it is required to follow any and all conditions for receiving federal funding, which includes responding to the Title I, Part A—Educator Equity section of the LCAP Addendum.

Are local educational agencies (LEAs) required to ensure that all teachers of core academic subjects in the state are “highly qualified”?

No. Under the ESSA, the NCLB highly qualified teacher requirements were eliminated and replaced with applicable State certification and licensure requirements. Thus, teachers must meet applicable State certification and licensure requirements, including any requirements for certification obtained through alternative routes to certification, or, with regard to special education teachers, the qualifications described in section 612(a)(14)(C) of the Individuals with Disabilities Education Act (20U.S.C. 1412(a)(14)(C).

Are LEAs that receive Title I funds required under the ESSA to notify parents at the beginning of each school year that they may request information regarding the professional qualifications of their student’s classroom teachers?

Under the essa, can teachers still use the high objective uniform state standard of evaluation (housse) document to demonstrate highly qualified status, under the essa, can teachers in special settings still use the verification process for special settings (vpss) to be eligible to teach outside their credential area.

No. The VPSS process was an option under the NCLB for teachers assigned to special settings to demonstrate subject matter competency (one of the highly qualified teacher requirements) to allow them to teach outside their credential area. Under the ESSA, the NCLB highly qualified teacher requirements were eliminated and replaced with applicable State certification and licensure requirements. Therefore, the VPSS process is not applicable under the ESSA. All teachers must meet state certification and licensure requirements as stated in ESSA Sections 1111(g)(2)(J) and 1112(c)(6). LEAs may choose to use VPSS programs that are still available as an option for professional learning.

Do I need a Certificate of Compliance to apply for a teaching position in California?

No. The Certificate of Compliance was a document used to demonstrate that a teacher met all of the highly qualified teacher requirements under the NCLB Act. Under the ESSA, the NCLB highly qualified teacher requirements were eliminated and replaced with applicable State certification and licensure requirements. Therefore, the Certificate of Compliance is not applicable under the ESSA. All teachers must meet state certification and licensure requirements as stated in ESSA Sections 1111(g)(2)(J) and 1112(c)(6).

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IMAGES

  1. Designing Assessments

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  2. Revised teacher monitoring sheet

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  3. Overview: Teacher Assignment Monitoring Outcome

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  4. 50 Printable Teacher Evaluation Forms [Free] ᐅ TemplateLab

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  5. 4th Grade Assignment Monitoring Document by Custom Core Creations

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  6. Teacher or Paraprofessional Student Data Tracking Task Progress

    teacher assignment monitoring outcome data

VIDEO

  1. Monitoring Tool for Teacher Performance

  2. Teacher perspective: using progress monitoring writing data to author IEP goals

  3. Webinar: Using Data To Improve Teacher Preparation

  4. Goals, Objectives, and Learning Outcomes

  5. How to interpret progress monitoring data for new intervention teachers

  6. Special Education Setup

COMMENTS

  1. Teaching Assignment Monitoring Outcomes

    Overview of the Teaching Assignment Monitoring Outcome (AMO) by Full-Time Equivalency (FTE) data, including data sources, DataQuest reports, downloadable files, definitions, and a description of the methodology and business rules for processing the data. Promoting Equitable Access to Teachers

  2. Teacher Assignment Monitoring Outcomes

    This is an overview of the Teaching Assignment Monitoring Outcome (AMO) data and reports available from the California Department of Education's Dataquest site. This overview includes background, key terms, uses and limitations of the reports, as well as complementary resources from the department.

  3. Information about the Teaching AMO Report

    The CDE assignment data and the CTC assignment monitoring outcome data provide the basis for the DataQuest Teaching Assignment Monitoring (AMO) by Full-Time Equivalency (FTE) report to meet the requirements established under California's ESSA Consolidated State Plan. Data Sources and Timelines

  4. Overview: Teacher Assignment Monitoring Outcome

    This is an overview of the Teacher Assignment Monitoring Outcome (TAMO) data and reports available from the California Department of Education's Dataquest site. This overview includes...

  5. California Educator Assignment Monitoring

    The following is educator assignment data resulting from monitoring within the system, pursuant to Education Code §44258.9. Definitions and outcomes may not be directly comparable to other teacher data reports, including the California Department of Education's Teaching Assignment Monitoring Outcomes (TAMO) report.

  6. Nearly 1 out of 5 classes in California taught by underprepared teachers

    The new Teacher Assignment Monitoring Outcome data is the state's newest tool in its battle to end a long and enduring teacher shortage. It is expected to guide state and local leaders on how best to use resources to recruit and retain teachers and will inform California residents about teacher assignments in their local schools.

  7. Joint Statement on the Publication of Teaching Assignment Monitoring

    Joint Statement on the Publication of Teaching Assignment Monitoring Outcomes (TAMO) Data FOR IMMEDIATE RELEASE June 30, 2022 Contact: Sumeet Bal, Communications Director, [email protected] 917.647.1952

  8. Teaching analytics, value and tools for teacher data literacy: a

    Teaching Analytics (TA) is a new theoretical approach, which combines teaching expertise, visual analytics and design-based research to support teacher's diagnostic pedagogical ability to use data and evidence to improve the quality of teaching. TA is now gaining prominence because it offers enormous opportunities to the teachers. It also identifies optimal ways in which teaching performance ...

  9. Teacher Quality

    The new system, known as Teacher Assignment Monitoring Outcomes (TAMO) was launched in 2022 and provides local and statewide data that can be used to identify the scope and impact of teacher vacancies and misassignments, in order to remedy inequities in access to quality teaching that impact low-income students and students of color.

  10. California Announces First-Ever Interagency Release of Teacher

    The complete California Teaching Assignment Monitoring Outcome data can be found on the CDE DataQuest 2020-21 Teaching Assignment Monitoring Outcomes by Full-Time Equivalent web page....

  11. PDF Report to the Legislature on Credentialing Related to Educator

    not be directly comparable to other teacher data reports such as DEs Teacher Assignment Monitoring Outcomes (TAMO) Report. This report is intended to provide an overview of CalSAAS, monitoring results, and recommendations that could further enhance the assignment monitoring process and outcomes. A summary of the contents can be found below:

  12. PDF Teaching Assignment Monitoring Outcome Data 2022 Dashboard Toolkit

    Teaching Assignment Monitoring Outcome Data The California School Dashboard (Dashboard) includes local indicators that are founded on the Local Control Funding Formula (LCFF). Of the local indicators, LCFF Priority 1 indicates data on Basic Services and Conditions that local educational agencies (LEAs) report on the Dashboard.

  13. Teaching Assignment Monitoring Outcome Data for LCAP and ...

    5.27K subscribers Subscribe Like 106 views 7 months ago TAMO Video Series Recorded October 2022 webinar on using new teacher data to inform Local Control and Accountability Plans (LCAP),...

  14. IRIS

    Page 3: Progress Monitoring. Recall that Step 2 and Step 5 of the DBI process involve progress monitoring—one of the best ways to measure a student's response to instruction. The progress monitoring approach used most often in the DBI process is known as general outcome measurement (GOM). GOM is a type of formative assessment in which ...

  15. Teacher assignment monitoring, Cal. Ed. Code

    (1) The Legislature finds and declares that continued monitoring of educator assignments by the commission and the county superintendents of schools and continued reporting of educator assignments by the department will help ensure that local educational agencies meet state and federal reporting requirements, including the requirements of the fe...

  16. A Teacher's Guide to Tracking Student Progress

    What is Student Progress Tracking? As a part of data-driven instruction, student progress tracking enables you to capture learning data and evaluate academic progress toward school goals for individuals, groups, and the entire class.

  17. PDF RTI Toolkit: A Practical Guide for Schools Classroom Data Collection

    products, or self-monitoring into objective formative data that can be charted over time to track the outcomes of classroom interventions. Step 2: Select a Data Tool. Teachers have a variety of tools that they can access to collect behavioral or academic information and monitor classroom interventions. This 'look-up' chart provides a review ...

  18. Teaching AMO Downloadable Data Files

    Last Reviewed: Friday, June 30, 2023 Downloadable data files about teaching Assignment Monitoring Outcome (AMO) data disaggregated by subject, school type, school grade span, teacher credential level, and teacher experience level. Data are reported in full-time equivalency (FTE) units.

  19. PDF Monitoring tools and information systems for teacher management

    These data provide the basic information required by human resource managers for planning (e.g. forecasting staff ) or management (numbers e.g. teacher allocation) activities. Adapted planning and monitoring tools as well as techniques are therefore necessary in order to take decisions on teacher policies.

  20. Data Analysis for Teachers to Unlocking the Power of Teaching

    By collecting and analyzing various forms of data, such as assessments, quizzes, and assignments, teachers can monitor individual student performance over time. This allows educators to identify trends, patterns, and areas of improvement or concern. ... Assessing Learning Outcomes. By comparing the data against predetermined benchmarks or ...

  21. Improving Data for Better Teaching Assignment Outcomes

    Topics include how teacher assignment data is collected in CALPADS; how that data informs CalSAAS and the assignment monitoring process; and how ESSA's teacher equity definitions affect this data reporting for accountability (Dashboard, SARC, Williams, and LCAP Federal Addendum).

  22. Promoting Equitable Access to Teachers

    Current guidance regarding requirements for educator assignment monitoring under ESSA is available in this January 2021 letter. PEAT Equity Tools Teacher AMO Teacher Credentialing Local Control Teacher Requirements Paraprofessionals PEAT Equity Tools PEAT Overview and Tools Video Series Updated Teacher Equity Definitions

  23. PDF Behavior Management: Show Me the Data. What are feasible 'go-to

    The teacher selects progress-monitoring tool(s) that can be converted to numeric data—and charted. Activity: Think of a student... Think of a student whom you work with that displays challenging classroom behaviors. • Discuss this student with your group.