A COGNITIVE FRAMEWORK FOR THE IMPROVEMENT OF LEARNING ANALYTICS EFFICIENCY IN INTRODUCTORY PROGRAMMING COURSES
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Date
2021
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Publisher
UMT, Lahore
Abstract
Learning analytics (LA) has become a popular discipline among educationists and
researchers as it has a potential to reveal new facets of teaching and learning that could
be utilized to improve the efficiencies of related learning environments. Introductory
programming courses (IPCs) hold special significance as these courses lay down the
foundation for subsequent higher level courses in computer science and associated
disciplines. The LA studies in IPCs are mostly anecdotal as less or no attention is given
to examine learning at various cognitive levels. This research is designed to find
improvements in learning analytics in IPCs by evaluating the cognitive aspects of
students’ learning. It aims to explore more granular technique of LA that could lead to
enhance the efficiency of LA in IPCs. The objectives of this work are addressed by
proposing a framework for cognitive learning analytics in IPCs which serves as a platform
that provides structure to the concept data using the technique of concept mapping and
examines proliferation of cognitive learning on related concepts using assessment data.
The framework is evaluated by predicting performance of learners on a number of IPC
concepts through the metrics established from cognitive maps of learners, acquired by
deploying the related layers of framework. It was identified that performance predictions
through proposed metrics helped in improving efficiency of learning analytics performed
in existing work. The research is concluded by presenting prediction accuracies acquired
while evaluating the framework which are comparable to the related studies where the
proposed technique showed better accuracies as compared to most of the related work.
This work contributes by proposing a framework of cognitive learning analytics in
introductory programming courses and presenting metrics to measure the cognitive
performances which predict the learning performances with improved accuracies.