Learning Analytics framework for measuring students’ performance and teachers’ involvement through problem based learning in engineering education
Akhila Joshi, Padmashree Desai, P.G. Tewari
Abstract
Learning Analytics (LA) is a systematic and interdisciplinary field that uses methods and analysis techniques to measure the effectiveness of different pedagogical approaches such as problem based learning (PBL), project based learning etc. Analysis of data helps to bring improvements in the curriculum and content delivery in higher education. According to recent predictions, for educational technology, research seeks to analyze the learners' behavior, engagement time in learning, feedback, problem solving skills, ability to participate in team discussion. Teacher's involvement in designing an activity, keeping the student motivated and to induce them to participate in the activity sessions is a challenging task. Students' performance is always directly proportional to teachers' involvement. Combining PBL and LA helps to collect data and analyze in a structured way to make decisions for an academic purpose. The benefits include targeted course offerings to the students, students' learning outcomes, their behavior, performance, improved curriculum development, and improved personalized learning. Hence, this research enriches the application of PBL pedagogy with LA in engineering education.