Learning Analytics Model and Bloom’s Taxonomy based Evaluation Framework for the Post Graduate Students’ Project Assessment – A Blended Project Based Learning Management System with Rubric Referenced Predictors

Abstract

Effective engagement and monitoring of students’ online self-learning capacity and application of their acquired knowledge in the final year project is a challenging task for the educators worldwide. The author builds an evaluation framework to assess the stage-wise performance of students in this undertaken project. The primary objective of this research study is to classify the rubric reference predictors of the stage wise project performance assessment metrics for the learning analytics based decision support system and effective academic decision making. The students of the post graduate computer applications degree programme, supervisors, external industry guide, internal faculty members of the peer review committee, external examiner and Head of the Department are the major stake holders of the proposed evaluation framework. The proposed framework computes the students’ individual as well as class attainment level of learning outcomes in their final year capstone projects. The author adopts blended Learning approach based on the principles of Blooms’ taxonomy using Google classroom as the LMS. The correlation matrices of the assessment predictors, mapping with course outcome and blooms’ levels are also obtained. The students’ attainment level of course outcomes are assessed using effective cognitive sequencing of the assessment methods and the respective rubric referenced predictors with the stage-wise feedback system.