Student Modeling and Analysis in Adaptive Instructional Systems

doi:10.1109/ACCESS.2022.3178744

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 平行智能技术与系统团队

	Student Modeling and Analysis in Adaptive Instructional Systems
	Liang, Jing 1; Hare, Ryan 2; Chang, Tianyu 3; Xu, Fangli 1; Tang, Ying 2,4,5; Wang, Fei-Yue4,5 ; Peng, Shimeng 6; Lei, Mingyu 7
发表期刊	IEEE ACCESS
ISSN	2169-3536
	2022
卷号	10 页码:59359-59372
通讯作者	Tang, Ying(tang@rowan.edu)
摘要	There is a growing interest in developing and implementing adaptive instructional systems to improve, automate, and personalize student education. A necessary part of any such adaptive instructional system is a student model used to predict or analyze learner behavior and inform adaptation. To help inform researchers in this area, this paper presents a state-of-the-art review of 11 years of research (2010-2021) in student modeling, focusing on learner characteristics, learning indicators, and foundational aspects of dissimilar models. We mainly emphasize increased prediction accuracy when using multidimensional learner data to create multimodal models in real-world adaptive instructional systems. In addition, we discuss challenges inherent in real-world multimodal modeling, such as uncontrolled data collection environments leading to noisy data and data sync issues. Finally, we reinforce our findings and conclusions through an industry case study of an adaptive instructional system. In our study, we verify that adding multiple data modalities increases our model prediction accuracy from 53.3% to 69%. At the same time, the challenges encountered with our real-world case study, including uncontrolled data collection environment with inevitably noisy data, calls for synchronization and noise control strategies for data quality and usability.
关键词	Adaptation models Artificial intelligence Predictive models Analytical models Adaptive systems Ontologies Mathematical models Adaptive instructional systems student modeling multimodal learning analytics
DOI	10.1109/ACCESS.2022.3178744
关键词[WOS]	MULTIMODAL DATA ; FUZZY-LOGIC
收录类别	SCI
语种	英语
资助项目	National Science Foundation[1913809] ; Division of Undergraduate Education (DUE)
项目资助者	National Science Foundation ; Division of Undergraduate Education (DUE)
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000809361700001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/49618
专题	多模态人工智能系统全国重点实验室_平行智能技术与系统团队
通讯作者	Tang, Ying
作者单位	1.Squirrel AI Learning Yixue Educ Inc, Highland Pk, NJ 08904 USA 2.Rowan Univ, Dept Elect & Comp Engn, Glassboro, NJ 08028 USA 3.Columbia Univ, Teachers Coll, New York, NY 10027 USA 4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 5.Qingdao Acad Intelligent Ind, Inst Smart Educ, Qingdao 266000, Peoples R China 6.Nagoya Univ, Grad Sch Informat, Dept Intelligent Syst, Nagoya, Aichi 4648601, Japan 7.China Acad Informat & Commun Technol, Beijing 100191, Peoples R China
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Liang, Jing,Hare, Ryan,Chang, Tianyu,et al. Student Modeling and Analysis in Adaptive Instructional Systems[J]. IEEE ACCESS,2022,10:59359-59372.
APA	Liang, Jing.,Hare, Ryan.,Chang, Tianyu.,Xu, Fangli.,Tang, Ying.,...&Lei, Mingyu.(2022).Student Modeling and Analysis in Adaptive Instructional Systems.IEEE ACCESS,10,59359-59372.
MLA	Liang, Jing,et al."Student Modeling and Analysis in Adaptive Instructional Systems".IEEE ACCESS 10(2022):59359-59372.