Data Analytics in the Classroom

Using data for making decisions is not new; companies use complex computations on customer data for business intelligence or analytics. Business intelligence techniques can discern historical patterns and trends from data and can create models that predict future trends and patterns. Analytics, broadly defined, comprises applied techniques from computer science, mathematics, and statistics for extracting usable information from very large datasets example web data, company business data, education data.

Using Data analytics in education is relatively a new concept. Educational Data Analytics is emerging as a research area with computational, psychological and research approaches for understanding how students learn. New computer-supported interactive learning methods and tools—intelligent tutoring systems, simulations, games— have opened up opportunities to collect and analyse student data, to discover patterns and trends in those data, and to make new discoveries and test hypotheses about how students learn.

Data in Education sector is multidimensional. Hence it become “Big Data” i.e. Big Data is Data that contains greater variety, arriving in increasing volumes and with more velocity. This is also known as the three Vs. Put simply, big data is larger, more complex data sets, especially from new data sources.

Features of Classroom Data

Hierarchic: Data at the student’s response level, the session level, the student level, the classroom level, the teacher level, and the school level are nested inside one another.

Time: As progress of student can decided on the bases on longitudinal data. Hence, time is important to capture data, such as length of practice sessions or time to learn.

Sequential: Sequence represents how concepts build on one another and how practice and tutoring should be ordered.

Context: Context is important for explaining results and knowing where a teaching model may or may not work.

Goals of Data Analytics in Classroom

Classroom data analytic researches view the following as goals:

• Predicting students’ future learning behaviour by creating student models that incorporate such detailed information as students’ knowledge, motivation, metacognition, and attitudes.
• Discovering or improving domain models that characterize the content to be learned and optimal instructional sequences.
• Studying the effects of different kinds of pedagogical support that can be provided by learning software e.g. Self-paced learning apps BYJU, Khan Academy, Quizlet, Vedantu, Wonderschool, Meritnation.
• To accomplish these goals, four types of analytics can be used to align with data sources.

Descriptive Analytics

Descriptive analytics can tell us “what happened.” This is by far the most common type of data used in classroom.  When teachers give a test at the end of a unit and assign a mark or compile semester reports, they are generating descriptive data. Other kinds of descriptive data schools might collect are attendance records, participation in co-curricular activities, literacy progressions, feedback surveys, etc.

Teachers and schools invest extraordinary resources collecting such information, so it’s unfortunate that these only provide a simple “backward glance”.  Careful analysis of such descriptive data across a school can help identify variability among teachers and faculties, identify patterns for specific student cohorts, and compare academic achievement in the context of things like school engagement and well-being.

Diagnostic Analytics

Diagnostic analytics offer more in-depth insights into student performance or ability. When people think of diagnostic data in education, what comes to mind are standardised or “high stakes” tests (e.g. SAT, GRE, GMAT) but at classroom level, diagnostic data is a form of pre-assessment that allows a teacher to determine students’ individual strengths, weaknesses, knowledge, and skills prior to instruction. It is primarily used to diagnose students’ difficulties and to guide lesson and curriculum planning. It is the crucial for teaching-learning process where teacher has to “diagnose” and prepare tailor made instructional material for “remedial teaching” to ensure the desired quality of learning.

Taking advantage of the diagnostic assessments used in a school is a challenge, mostly because the data is buried and not easily visualised. When teachers, faculties, grade-levels teams and leaders can quickly see such gaps and strengths across core skills at the student, class and cohort levels, each stakeholder is empowered to support targeted solutions.

Predictive Analytics

Predictive analytics can offer insights into “what is likely to happen.” It uses the results of descriptive and diagnostic analytics to predict future trends. As such, predictive analytics forecast likely outcomes to be pursued or avoided.  Many schools employ data consultants to conduct statistical reviews of past performance in high-stakes measures, especially Year 12 results such as Advanced Placement tests.

Clearly, because solid predictive analytics is based on equally solid descriptive and diagnostic data, it is more complex to gather and analyse.  The reason schools often outsource this analysis to data experts is because of the statistical analysis required and also the savvy to integrate appropriate data sets. Equally complex is the interpretation of the analysis so that patterns aren’t seen as causative when they might really only correlate (e.g., high participation in co-curricular activities may correlate with high achieving students, not cause the high achievement).

Besides employing data scientists, schools can begin use of predictive analytics by identifying what information they most value about students and their learning. This might require introducing new descriptive and diagnostic measures and then taking several years to grow these data sets.

Prescriptive Analytics

The fourth type of analytics results in actual prescriptions of “what action(s) to take” to solve a problem in the future or to take full advantage of promising trends seen in the predictive analysis. Many schools engage in pedagogical initiatives such as Reading Recovery, Maker Spaces, STEM, writing across the curriculum or wellbeing programs, but such decisions typically lack a robust foundation in hard data. It’s not that such initiatives are bad, but when they are begun without targeting an evidence-based issue with aligned pedagogical solutions, measuring success is ad hoc at best. One downside of such initiatives lacking credible success criteria is the drain on staff morale and change fatigue. We are all motivated when we perceive ourselves as being effective.  Without measures, this is difficult.

Like predictive analytics, prescriptive is also based on data from each of the other types and thus requires both quality data and insights. Obviously, the value-add of such predictive analytics is great, but the complexity needed to combine and model the data is comparably great.

Both the value and complexity of prescriptive analytics suggests not only the participation of experts, but also technology. In fact, this is where many tech companies get involved and apply Artificial Intelligence to the rapid modelling of data.  “Big data” sets are needed for algorithms to apply both unstructured and structured analysis to tease out reliably demonstrated outcomes. A good example is the increasing sophistication and accuracy of tech giants like Google and Amazon to make suggestions based upon constructed profiles for each user.  In this way AI can move beyond more simple “if/then” predictions to an assurance that specific actions will lead to the desired outcome. Extensive work is being done in this area at the university level.

Using these techniques, educational data mining researchers can build models to answer such questions as:

• What sequence of topics is most effective for a specific student?
• What student actions are associated with more learning (e.g., higher course grades)?
• What student actions indicate satisfaction, engagement, learning progress, etc.?
• What features of an online learning environment lead to better learning?
• What will predict student success?

Leveraging classroom data to achieve these benefits begins with a solid plan of action. It is to be made sure to do the following before implementing any data analytics solution:

Create Goals –data analytics platform will open up a lot of possibilities, so it could become overwhelming if we don’t enter into the new process without clear-cut goals in mind.

Set Expectations –it will need teachers to be on board with your data analytics platform and strategies, which is why there is need of open communication and set expectations for whole team before implementing a solution. Bypassing teachers can lead to dis-satisfaction among teachers.

Make it Accessible – For full transparency, teachers must have access to data where and when they need it. That means setting the appropriate permissions and organizing data properly.

So, in conclusion it can be said in this data driven world, Education sector must not collect data just keep records. The data of classroom can give much more meaning full insight into ways of learning and methods of teaching. Which can further lead to more efficiency of teacher and better learning outcomes for students.