Proceedings of the Eighth ACM Conference on Learning @ Scale最新文献_第6页

Applying the Behavior Change Technique Taxonomy from Public Health Interventions to Educational Research 从公共卫生干预到教育研究的行为改变技术分类的应用

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-06-08 DOI: 10.1145/3430895.3460138

J. Cho, René F. Kizilcec

引用次数: 1

Characterizing Amateur Tutoring Behavior on a Large Online Learning Platform 大型在线学习平台业余辅导行为特征研究

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-06-08 DOI: 10.1145/3430895.3460148

Dapeng Shan, Prasanta Bhattacharya, B. Kao, T. Phan

引用次数: 0

Analyzing Student Reflection Sentiments and Problem-Solving Procedures in MOOCs mooc学生反思情绪与问题解决过程分析

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-06-08 DOI: 10.1145/3430895.3460150

Alexander Shashkov, Robert S. Gold, Erik Hemberg, ByeongJo Kong, Ana Bell, Una-May O’Reilly

引用次数: 2

Toward Personalizing Students' Education with Crowdsourced Tutoring 用众包辅导实现学生个性化教育

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-06-08 DOI: 10.1145/3430895.3460130

Ethan Prihar, Thanaporn Patikorn, Anthony F. Botelho, Adam C. Sales, N. Heffernan

{"title":"Toward Personalizing Students' Education with Crowdsourced Tutoring","authors":"Ethan Prihar, Thanaporn Patikorn, Anthony F. Botelho, Adam C. Sales, N. Heffernan","doi":"10.1145/3430895.3460130","DOIUrl":"https://doi.org/10.1145/3430895.3460130","url":null,"abstract":"As more educators integrate their curricula with online learning, it is easier to crowdsource content from them. Crowdsourced tutoring has been proven to reliably increase students' next problem correctness. In this work, we confirmed the findings of a previous study in this area, with stronger confidence margins than previously, and revealed that only a portion of crowdsourced content creators had a reliable benefit to students. Furthermore, this work provides a method to rank content creators relative to each other, which was used to determine which content creators were most effective overall, and which content creators were most effective for specific groups of students. When exploring data from TeacherASSIST, a feature within the ASSISTments learning platform that crowdsources tutoring from teachers, we found that while overall this program provides a benefit to students, some teachers created more effective content than others. Despite this finding, we did not find evidence that the effectiveness of content reliably varied by student knowledge-level, suggesting that the content is unlikely suitable for personalizing instruction based on student knowledge alone. These findings are promising for the future of crowdsourced tutoring as they help provide a foundation for assessing the quality of crowdsourced content and investigating content for opportunities to personalize students' education.","PeriodicalId":125581,"journal":{"name":"Proceedings of the Eighth ACM Conference on Learning @ Scale","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132342261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Student Perceptions of Social Support in the Transition to Emergency Remote Instruction 过渡到紧急远程教学的学生对社会支持的感知

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-06-08 DOI: 10.1145/3430895.3460158

J. Cho, Ian Wilkie Tomasik, Huiyun Yang, René F. Kizilcec

引用次数: 2

Knowledge Sharing in Tension: Interacting and Documenting on Stack Overflow 紧张中的知识共享:堆栈溢出的交互和文档

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-04-15 DOI: 10.1145/3430895.3460981

Thomas Hillman, Alena Seredko, Markus Nivala, Tanya Osborne

引用次数: 1

Student Barriers to Active Learning in Synchronous Online Classes: Characterization, Reflections, and Suggestions 同步在线课堂中学生主动学习的障碍:表征、反思和建议

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-04-10 DOI: 10.1145/3430895.3460126

Reza Hadi Mogavi, Yankun Zhao, E. Haq, Pan Hui, Xiaojuan Ma

引用次数: 17

Should College Dropout Prediction Models Include Protected Attributes? 大学辍学预测模型是否应包括受保护的属性？

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-03-28 DOI: 10.1145/3430895.3460139

Renzhe Yu, Hansol Lee, René F. Kizilcec

{"title":"Should College Dropout Prediction Models Include Protected Attributes?","authors":"Renzhe Yu, Hansol Lee, René F. Kizilcec","doi":"10.1145/3430895.3460139","DOIUrl":"https://doi.org/10.1145/3430895.3460139","url":null,"abstract":"Early identification of college dropouts can provide tremendous value for improving student success and institutional effectiveness, and predictive analytics are increasingly used for this purpose. However, ethical concerns have emerged about whether including protected attributes in these prediction models discriminates against underrepresented student groups and exacerbates existing inequities. We examine this issue in the context of a large U.S. research university with both residential and fully online degree-seeking students. Based on comprehensive institutional records for the entire student population across multiple years (N = 93,457), we build machine learning models to predict student dropout after one academic year of study and compare the overall performance and fairness of model predictions with or without four protected attributes (gender, URM, first-generation student, and high financial need). We find that including protected attributes does not impact the overall prediction performance and it only marginally improves the algorithmic fairness of predictions. These findings suggest that including protected attributes is preferable. We offer guidance on how to evaluate the impact of including protected attributes in a local context, where institutional stakeholders seek to leverage predictive analytics to support student success.","PeriodicalId":125581,"journal":{"name":"Proceedings of the Eighth ACM Conference on Learning @ Scale","volume":"158 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126593179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 39