An Intelligent Course Decision Assistant by Mining and Filtering Learners’ Personality Patterns

Abstract

For a student, determining how to choose from a set of courses is an important issue prior to learning. An appropriate learning guide can direct students toward an area of interest. The learning results produced by the student in this case are superior due to their strong interest in the subject matter. Although a number of methods have been proposed to address this issue, the effectiveness remains unsatisfactory. To this end, we created an effective system, called the personality-driven course decision assistant, to help students determine the courses they should select by mining and filtering learners’ personality patterns. For learner pattern mining, the relationships between the students’ learning results and the referred personalities are discovered to provide the learners with valuable information before learning. For filtering learner personality patterns, students with similar personality patterns are filtered to predict the potential learning results. Through the actual system, a number of subjective and objective evaluations were conducted, and the evaluation results reveal that the proposed system is highly effective and reliable.

Multi-Objective Design of Profit Volumes and Closeness Ratings Using MBHS Optimizing Based on the PrefixSpan Mining Approach (PSMA) for Product Layout in Supermarkets

“…The PrefixSpan algorithm was compared with sequential patterns, such as the Apriori algorithm, GSP algorithm, and Freespan algorithm. The performance of the PrefixSpan algorithm is significantly higher than the others [62,63]. Moreover, the PrefixSpan had a consuming time that was faster than the others because the proposed algorithm was not generated from the frequency of items within the search space in the projected databases.…”

Actionable Pattern-Driven Analytics and Prediction

“…The fourth paper [4] builds an effective system, called the personality-driven course decision assistant, to help students determine the courses they should select by mining and filtering learners' personality patterns. For learner pattern mining, the relationships between the students' learning results and the referred personalities are discovered to provide the learners with valuable information before learning commences.…”

Encoding Text Information with Graph Convolutional Networks for Personality Recognition

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