Stereotype Modeling for Problem-Solving Performance Predictions in MOOCs and Traditional Courses
Author:
Affiliation:
1. University of Pittsburgh, Pittsburgh, PA, USA
2. Carnegie Mellon University, Pittsburgh, PA, USA
3. University of Helsinki, Helsinki, Finland
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3079628.3079672
Reference29 articles.
1. Engaging with massive online courses
2. Sequential PAttern mining using a bitmap representation
3. Studying learning in the worldwide classroom: Research into edX's first MOOC;Breslow Lori;Research & Practice in Assessment,2013
4. John Champaign Kimberly F Colvin Alwina Liu Colin Fredericks Daniel Seaton and David E Pritchard. 2014. Correlating skill and improvement in 2 MOOCs with a student's time on tasks. In Learning@scale Conf. 11--20. 10.1145/2556325.2566250 John Champaign Kimberly F Colvin Alwina Liu Colin Fredericks Daniel Seaton and David E Pritchard. 2014. Correlating skill and improvement in 2 MOOCs with a student's time on tasks. In Learning@scale Conf. 11--20. 10.1145/2556325.2566250
Cited by 16 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. Integrating Stereotype User Models for Adaptive Scenarios in Game Playing within Immersive Virtual Environments;2023 18th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP)18th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP 2023);2023-09-25
2. Investigating Student's Problem-solving Approaches in MOOCs using Natural Language Processing;LAK23: 13th International Learning Analytics and Knowledge Conference;2023-03-13
3. Computing Education Research in Finland;Past, Present and Future of Computing Education Research;2023
4. Study Behavior in Computing Education—A Systematic Literature Review;ACM Transactions on Computing Education;2022-03-31
5. Exploring Behavioral Patterns for Data-Driven Modeling of Learners' Individual Differences;Frontiers in Artificial Intelligence;2022-02-15
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