Abstract
Students of initial level programming courses generally face difficulties while learning the programming concepts. The learning analytics studies, in these courses, are mostly anecdotal on the aspect of assessment as less or no attention is given to assess learning at various cognitive levels of specific concepts. Furthermore, the existing work reflects deficiencies in examining the effect of learners’ cognitive performance on subsequent stages of the course. This gap needs to be addressed by introducing more granular and methodical approaches of cognitive analysis for sustaining the programming courses effectively in computer science and associated disciplines. In this article, a framework-based approach is proposed for cognitive learning analytics on the concepts taught in initial level programming courses. The framework serves as a platform that provides structure to the concept data using the technique of concept mapping and examines learners’ cognitive propagation on related concepts using assessment data. Learners’ performance prediction has been examined on relatively higher-level programming concepts through the metrics established from the cognitive maps of learners, acquired by deploying the related layers of framework. Overall maximum prediction accuracy range obtained was 64.81% to 90.86%, which was better than the prediction accuracies presented in most of the related studies.
Highlights
The analysis of educational data has been an area of interests for researchers as it reveals information that leads to channelize the efforts of both acquiring and imparting knowledge
Concept analysis is performed to examine learning at various levels of cognition by applying Bloom’s taxonomy [8,9]; work is still required in the direction of analyzing the cognitive propagation of learning of the subsequent higher-level concepts of programming
We have presented a structured approach of assessing cognitive propagation on a number of programming concepts, which emphasize the granularity and specificity of learning analytics using Bloom’s taxonomy-based assessment data
Summary
The analysis of educational data has been an area of interests for researchers as it reveals information that leads to channelize the efforts of both acquiring and imparting knowledge. The learning analysis performed through assessment data is mostly anecdotal where the impact of analysis is examined on final exams or grades [4,5] while less work is done to find impact of analysis on subsequent stages of the course covering one or more concepts [6,7]. Concept analysis is performed to examine learning at various levels of cognition by applying Bloom’s taxonomy [8,9]; work is still required in the direction of analyzing the cognitive propagation of learning of the subsequent higher-level concepts of programming. The framework provides a systematic and structured mechanism for transforming the continuously producing data through assessments of programming courses to find any meaningful information that could be useful for improvements
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