Exercise Recommendation with Preferences and Expectations Based on Ability Computation

Abstract

In the era of artificial intelligence, cognitive computing, based on cognitive science; and supported by machine learning and big data, brings personalization into every corner of our social life. Recommendation systems are essential applications of cognitive computing in educational scenarios. They help learners personalize their learning better by computing student and exercise characteristics using data generated from relevant learning progress. The paper introduces a Learning and Forgetting Convolutional Knowledge Tracking Exercise Recommendation model (LFCKT-ER). First, the model computes studentsʼ ability to understand each knowledge concept, and the learning progress of each knowledge concept, and the model consider their forgetting behavior during learning progress. Then, studentsʼ learning stage preferences are combined with filtering the exercises that meet their learning progress and preferences. Then studentsʼ ability is used to evaluate whether their expectations of the difficulty of the exercises are reasonable. Then, the model filters the exercises that best match studentsʼ expectations again by studentsʼ expectations. Finally, we use a simulated annealing optimization algorithm to assemble a set of exercises with the highest diversity. From the experimental results, the LFCKT-ER model can better meet studentsʼ personalized learning needs and is more accurate than other exercise recommendation systems under various metrics on real online education public datasets.