Exercise Recommendation with Preferences and Expectations Based on Ability Computation  

作　　者：Mengjuan Li Lei Niu 

机构地区：[1]Faculty of Artificial Intelligence in Education,Central China Normal University,Wuhan,430079,China

出　　处：《Computers, Materials & Continua》2023年第10期263-284,共22页计算机、材料和连续体（英文）

基　　金：supported by the National Natural Science Foundation of China(No.62006090);Research Funds of Central China Normal University(CCNU)under Grants 31101222211 and 31101222212.

摘　　要：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 students’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,students’learning stage preferences are combined with filtering the exercises that meet their learning progress and preferences.Then students’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 students’expectations again by students’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 students’personalized learning needs and is more accurate than other exercise recommendation systems under various metrics on real online education public datasets.

关 键 词：Cognitive computing personalized learning forgetting behavior exercise recommendation 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]