Proceedings of the Eighth ACM Conference on Learning @ Scale最新文献

Experimental Design of Learning Analysis Dashboards for Teachers and Learners 教师与学习者学习分析仪表板的实验设计

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-06-08 DOI: 10.1145/3430895.3460990

Yassine Safsouf, K. Mansouri, F. Poirier

引用次数: 1

Second Workshop on Educational A/B Testing at Scale 第二次大规模教育A/B测试研讨会

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-06-08 DOI: 10.1145/3430895.3460876

Steven Ritter, N. Heffernan, J. Williams, Derek J. Lomas, K. Bicknell

引用次数: 1

Problem Solving Analytics (PSA) in the Web-Based Office Simulation LUCA 基于web的办公仿真LUCA中的问题解决分析(PSA)

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-06-08 DOI: 10.1145/3430895.3460877

Sabrina Ludwig, A. Rausch, V. Deutscher, Jürgen Seifried

{"title":"Problem Solving Analytics (PSA) in the Web-Based Office Simulation LUCA","authors":"Sabrina Ludwig, A. Rausch, V. Deutscher, Jürgen Seifried","doi":"10.1145/3430895.3460877","DOIUrl":"https://doi.org/10.1145/3430895.3460877","url":null,"abstract":"Open-ended e-learning environments allow for explorative behaviour in challenging scenarios and hence, foster problem-solving competences. The web-based office simulation LUCA (funded by the German Federal Ministry of Education and Research) addresses the domain-specific competences of students in commercial vocational education and training (VET). The office simulation provides authentic office tools such as a spreadsheet application and an ERP software to solve complex work scenarios. These scenarios are implemented via the \"LUCA Editor\" and can contain automated assistance based on evidence rules of certain behaviours (\"scaffolding\"). The real-time analysis of the resulting log files enables the analysis of individual problem-solving behaviour (\"Problem Solving Analytics\", PSA). Teachers and trainers can monitor their students' problem-solving efforts in the \"LUCA Dashboard\", where they can also provide individual assistance via a chat tool. In our contribution, the scientific foundations of PSA will be outlined, followed by a demonstration of the latest prototype of LUCA and visitors' interaction with the software. LUCA's alpha version will be released in September of this year and will be available for practitioners in vocational schools and companies.","PeriodicalId":125581,"journal":{"name":"Proceedings of the Eighth ACM Conference on Learning @ Scale","volume":"24 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":"121594199","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: 0

Scale: A Window onto Universal Laws of Growth, Living and Dying in Organisms, Cities and Companies 尺度:了解有机体、城市和公司中生长、生存和死亡的普遍规律的窗口

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-06-08 DOI: 10.1145/3430895.3462226

G. West

引用次数: 1

Examining the Effects of Student Participation and Performance on the Quality of Learnersourcing Multiple-Choice Questions 考察学生参与和表现对学习者源选择题质量的影响

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-06-08 DOI: 10.1145/3430895.3460140

Steven Moore, Huy A. Nguyen, John C. Stamper

{"title":"Examining the Effects of Student Participation and Performance on the Quality of Learnersourcing Multiple-Choice Questions","authors":"Steven Moore, Huy A. Nguyen, John C. Stamper","doi":"10.1145/3430895.3460140","DOIUrl":"https://doi.org/10.1145/3430895.3460140","url":null,"abstract":"While generating multiple-choice questions has been shown to promote deep learning, students often fail to realize this benefit and do not willingly participate in this activity. Additionally, the quality of the student-generated questions may be influenced by both their level of engagement and familiarity with the learning materials. Towards better understanding how students can generate high quality questions, we designed and deployed a multiple-choice question generation activity in seven college-level online chemistry courses. From these courses, we collected data on student interactions and their contribution to the question-generation task. A total of 201 students enrolled in the courses and 57 of them elected to generate a multiple-choice question. Our results indicated that students were able to contribute quality questions, with 67% of them being evaluated by experts as acceptable for use. We further identified several student behaviors in the online courses that are correlated to their participation in the task and the quality of their contribution. Our findings can help teachers and students better understand the benefits of student-generated questions and effectively implement future learnersourcing activities.","PeriodicalId":125581,"journal":{"name":"Proceedings of the Eighth ACM Conference on Learning @ Scale","volume":"11 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":"116015166","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: 11

A Machine Learning Approach for Suggesting Feedback in Textual Exercises in Large Courses 大型课程文本练习中建议反馈的机器学习方法

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-06-08 DOI: 10.1145/3430895.3460135

Jan Philip Bernius, Stephan Krusche, B. Bruegge

{"title":"A Machine Learning Approach for Suggesting Feedback in Textual Exercises in Large Courses","authors":"Jan Philip Bernius, Stephan Krusche, B. Bruegge","doi":"10.1145/3430895.3460135","DOIUrl":"https://doi.org/10.1145/3430895.3460135","url":null,"abstract":"Open-ended textual exercises facilitate the comprehension of problem-solving skills. Students can learn from their mistakes when teachers provide individual feedback. However, courses with hundreds of students cause a heavy workload for teachers: providing individual feedback is mostly a manual, repetitive, and time-consuming activity. This paper presents CoFee, a machine learning approach designed to suggest computer-aided feedback in open-ended textual exercises. The approach uses topic modeling to split student answers into text segments and language embeddings to transform these segments. It then applies clustering to group the text segments by similarity so that the same feedback can be applied to all segments within the same cluster. We implemented this approach in a reference implementation called Athene and integrated it into Artemis. We used Athene to review 17 textual exercises in two large courses at the Technical University of Munich with 2,300 registered students and 53 teachers. On average, Athene suggested feedback for 26% of the submissions. Accordingly, 85% of these suggestions were accepted by the teachers, 5% were extended with a comment and then accepted, and 10% were changed.","PeriodicalId":125581,"journal":{"name":"Proceedings of the Eighth ACM Conference on Learning @ Scale","volume":"35 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":"126908891","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: 8

With or Without EU: Navigating GDPR Constraints in Human Subjects Research in an Education Environment 有或没有欧盟:在教育环境中人类受试者研究中导航GDPR约束

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-06-08 DOI: 10.1145/3430895.3460984

Alex Duncan, David A. Joyner

引用次数: 0

Automating the Assessment of Problem-solving Practices Using Log Data and Data Mining Techniques 使用日志数据和数据挖掘技术对问题解决实践的自动化评估

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-06-08 DOI: 10.1145/3430895.3460127

Karen D. Wang, S. Salehi, Max Arseneault, Krishnan Nair, C. Wieman

{"title":"Automating the Assessment of Problem-solving Practices Using Log Data and Data Mining Techniques","authors":"Karen D. Wang, S. Salehi, Max Arseneault, Krishnan Nair, C. Wieman","doi":"10.1145/3430895.3460127","DOIUrl":"https://doi.org/10.1145/3430895.3460127","url":null,"abstract":"Interactive simulations provide an exciting opportunity to assess and teach students the practices used by scientists and engineers to solve real-world problems. This study examines how the logged interaction data from a simulation-based task could be used to automate the assessment of complex problem-solving practices. A total of 73 college students worked on an interactive circuit puzzle embedded in a science simulation in an interview setting. Their problem-solving processes were videotaped and logged in the backend of the simulation. We extracted different sets of features from the log data and evaluated their effectiveness as predictors of students' problem-solving success and evidence for specific problem-solving practices. Our results indicate that the application of data mining techniques guided by knowledge gained from qualitative observation was instrumental in the discovery of semantically meaningful features from the raw log data. These knowledge-grounded features were significant predictors of students' overall problem-solving success and provided evidence on how well they adopted specific problem-solving practices, including decomposition, data collection, and data recording. The results point to promising directions for how scaffolding/feedback could be provided in educational simulations to enhance student learning in problem-solving skills.","PeriodicalId":125581,"journal":{"name":"Proceedings of the Eighth ACM Conference on Learning @ Scale","volume":"16 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":"127756809","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: 5

Comparative Analysis of the Feature Extraction Approaches for Predicting Learners Progress in Online Courses: MicroMasters Credential versus Traditional MOOCs 预测在线课程学习者学习进度的特征提取方法的比较分析:MicroMasters Credential与传统mooc

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-06-08 DOI: 10.1145/3430895.3460143

F. Soleimani, Jeonghyun Lee

{"title":"Comparative Analysis of the Feature Extraction Approaches for Predicting Learners Progress in Online Courses: MicroMasters Credential versus Traditional MOOCs","authors":"F. Soleimani, Jeonghyun Lee","doi":"10.1145/3430895.3460143","DOIUrl":"https://doi.org/10.1145/3430895.3460143","url":null,"abstract":"Although MicroMasters courses differ from traditional undergraduate level MOOCs in student demographics, course design, and outcomes, the various aspects of this type of program have not yet been sufficiently investigated. This study aims to pave the path towards enhancing the design of constituent courses of MicroMasters programs with the focus on the application of Machine Learning algorithms. Thereby, we use a large-scale clickstream edX database to explore the trends in the online engagement of learners in a MicroMasters program, detect clickstream events that are highly correlated with the students' progress, and investigate how the engagements differ from those in a classic individual MOOC. Contrary to the previous application of machine learning algorithms in learning analytics, we implement various well-known machine learning approaches such as stepwise regression and tree-based algorithms, evaluate their performance, and propose the best-performed approach. We elaborate on noticeable differences between the engagements of the considered two groups.","PeriodicalId":125581,"journal":{"name":"Proceedings of the Eighth ACM Conference on Learning @ Scale","volume":"60 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":"133550512","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: 5

Forecasting Undergraduate Majors Using Academic Transcript Data 利用成绩单数据预测本科专业

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-06-08 DOI: 10.1145/3430895.3460149

David Lang, Alex Wang, Nathan Dalal, A. Paepcke, M. Stevens

引用次数: 3