The task of the faculty while training a student for competitive examinations or board examinations has many elements: it involves the provision of a broad context of knowledge within which students can locate and understand the content of their more specific studies; it involves the creation of a learning environment in which students are motivated to think carefully and critically and express their thoughts, and in which they wish to confront and solve difficulties rather than ignore them, it involves monitoring and reflecting on the operations of teaching and student understanding and seeking to work on them. Most challenging of all involves helping students to achieve their own aims, and adopt the notion that underlies these examinations: that students’ learning requires from them commitment, hard-work, responsibility for their own learning, and a willingness to take risks, and that this operation has its rewards.
By using learning analytics, teachers are able to understand the learning level and the ability of each student better and can then personalize the learning experience for each student according to their needs. Essentially, this allows them to recognize particular needs of each student and make critical, data-driven decisions about how to foster student learning in the most effective way.
Descriptive analysis measures the student’s past performances and aims to understand the student data to obtain patterns in the student’s learning progress. Descriptive analytics explains students’ results in the past allowing teachers to make strategic modification in the teaching style for each student, make it personalized.
Teachers measure student learning through both formal and informal forms of assessment, like exams and class participation. In this student-centred classroom, teaching and assessment are connected because student learning is continuously measured during teacher instruction. This approach promises results as there is constant evaluation and correction.
Predictive analytics give ideas about the future trends in the students’ understanding of the coursework. This module utilizes the student’s past performance data and recent data to determine what is likely to happen next. This is a great analytics framework to identify students who are ‘low-performing’ or ‘low-engaging.’ This allows teachers to implement methods specifically meant to assist those average students get them back on track and reach their full potential.
At SpeedLabs, we use predictive analytics to determine which all students need greater attention. They are motivated and corrected before they start to fall behind. Over the process, we see the students gather pace and pick up full momentum to a dedicated path to preparation. We use a high-tech approach to teaching which uses various technologies to aid students in their classroom learning.
Prescriptive Analytics not only provides teachers with data they can then utilise to make actionable decisions, but it provides suggestions to make their teaching more efficient. Prescriptive Analytics provides teachers with some knowledge into student understanding and adaptive instructional techniques based on student performance.
Also published on Medium.