Modeling and predicting students’ engagement behaviors using mixture Markov models

Author:

Maqsood RabiaORCID,Ceravolo PaoloORCID,Romero CristóbalORCID,Ventura SebastiánORCID

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Hardware and Architecture,Human-Computer Interaction,Information Systems,Software

Reference54 articles.

1. Akaike H (1998) Information theory and an extension of the maximum likelihood principle. Selected papers of Hirotugu Akaike. Springer, Berlin, pp 199–213

2. Anderson E (2017) Measurement of online student engagement: Utilization of continuous online student behaviors as items in a partial credit Rasch model. PhD thesis, Morgridge College of Education, University of Denver, USA, Electronic Theses and Dissertations. 1248

3. Beal CR, Qu L, Lee H (2006) Classifying learner engagement through integration of multiple data sources. In: AAAI, pp 151–156

4. Beal C, Mitra S, Cohen P (2007) Modeling learning patterns of students with a tutoring system using hidden Markov model. In: Luckin R et al (eds) Proceedings of the 13th international conference on Artificial intelligence in education (AIED). Marina del Rey

5. Biernacki C, Celeux G, Govaert G (2003) Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate gaussian mixture models. Comput Stat Data Anal 41(3–4):561–575

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. IoT-Based Behavioral Analysis Model for Smart Learning;2023 International Conference on Sustainable Communication Networks and Application (ICSCNA);2023-11-15

2. A black-box model for predicting difficulty of word puzzle games: a case study of Wordle;Knowledge and Information Systems;2023-10-14

3. Personality classification from text using bidirectional long short-term memory model;Multimedia Tools and Applications;2023-09-08

4. Detection of Abnormal Behavioral States in Student Learning Based on Video Surveillance;2023 International Conference on Data Science and Network Security (ICDSNS);2023-07-28

5. Predicting Students' Behavior Towards their Degree using Machine Learning Techniques;2022 3rd International Conference on Innovations in Computer Science & Software Engineering (ICONICS);2022-12-14

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3