Proceedings of the Eighth ACM Conference on Learning @ Scale最新文献_第4页

Blending Peer Instruction with Just-In-Time Teaching: Jointly Optimal Task Scheduling with Feedback for Classroom Flipping 同侪指导与即时教学的融合:课堂翻转的联合最优任务调度与反馈

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-06-08 DOI: 10.1145/3430895.3460134

Jingting Li, Lin Ling, Chee-Wei Tan

{"title":"Blending Peer Instruction with Just-In-Time Teaching: Jointly Optimal Task Scheduling with Feedback for Classroom Flipping","authors":"Jingting Li, Lin Ling, Chee-Wei Tan","doi":"10.1145/3430895.3460134","DOIUrl":"https://doi.org/10.1145/3430895.3460134","url":null,"abstract":"Blended learning often requires alternating between asynchronous pre-class and synchronous in-class activities using online technologies to enhance the overall learning experience. Subject to constraints on desired learning outcome specifications and individual student preference, can we jointly optimize pre-class and in-class tasks to improve the two-way interaction between students and the instructor? We leverage ideas of self-assessment in Just-In-Time Teaching and Peer Instruction to propose an optimization-theoretic framework to analyze the optimal trade-off between the time invested in two different learning tasks for each individual student. We show that the problem can be formulated as a linear program, which can be efficiently solved to determine the optimal amount of time for pre-class and in-class learning. We develop a mobile chatbot software integrated with feedback data analytics to blend asynchronous pre-class quiz assessment together with the synchronous in-class poll-quiz routine of Peer Instruction to achieve classroom flipping that can be used for remote and hybrid teaching and learning.","PeriodicalId":125581,"journal":{"name":"Proceedings of the Eighth ACM Conference on Learning @ Scale","volume":"30 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":"126095240","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

Toward Effective Courseware at Scale: Investigating Automatically Generated Questions as Formative Practice 走向有效的大规模课件:调查自动生成的问题作为形成性实践

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-06-08 DOI: 10.1145/3430895.3460162

Rachel Van Campenhout, Noam Brown, Bill Jerome, Jeffrey S. Dittel, Benny G. Johnson

{"title":"Toward Effective Courseware at Scale: Investigating Automatically Generated Questions as Formative Practice","authors":"Rachel Van Campenhout, Noam Brown, Bill Jerome, Jeffrey S. Dittel, Benny G. Johnson","doi":"10.1145/3430895.3460162","DOIUrl":"https://doi.org/10.1145/3430895.3460162","url":null,"abstract":"Courseware is a comprehensive learning environment that engages students in a learning by doing approach while also giving instructors data-driven insights on their class, providing a scalable solution for many instructional models. However, courseware-and the volume of formative questions required to make it effective-is time-consuming and expensive to create. By using artificial intelligence for automatic question generation, we can reduce the time and cost of developing formative questions in courseware. However, it is critical that automatically generated (AG) questions have a level of quality on par with human-authored (HA) questions in order to be confident in their usage at scale. Therefore, our research question is: are student interactions with AG questions equivalent to HA questions with respect to engagement, difficulty, and persistence metrics? This paper evaluates data for AG and HA questions that students used as formative practice in their university Communication course. Analysis of AG and HA questions shows that our first generation of AG questions perform equally well as HA questions in multiple important respects.","PeriodicalId":125581,"journal":{"name":"Proceedings of the Eighth ACM Conference on Learning @ Scale","volume":"103 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":"115550316","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: 6

Exploring Additional Personalized Support While Attempting Exercise Problems in Online Learning Platforms 在尝试在线学习平台上的练习问题时，探索额外的个性化支持

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-06-08 DOI: 10.1145/3430895.3460145

Yuya Asano, Madhurima Dutta, Trisha Thakur, Jaemarie Solyst, Stephanie Cristea, Helena Jovic, Andrew Petersen, J. Williams

引用次数: 3

Personalization at Scale: Making Learning Personally Relevant in a Climate Science MOOC 大规模的个性化:在气候科学MOOC中使学习与个人相关

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-06-08 DOI: 10.1145/3430895.3460154

Ido Roll, Ilana Ram, S. Harris

引用次数: 1

Data-informed Decision-making in TEFA Processes: An Empirical Study of a Process Derived from Peer-Instruction TEFA过程中的数据知情决策:来自同伴指导的过程的实证研究

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-06-08 DOI: 10.1145/3430895.3460153

Rialy Andriamiseza, Franck Silvestre, J. Parmentier, J. Broisin

引用次数: 0

Learning Analytics Dashboard Research Has Neglected Diversity, Equity and Inclusion 学习分析仪表板研究忽视了多样性、公平性和包容性

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-06-08 DOI: 10.1145/3430895.3460160

K. Williamson, René F. Kizilcec

{"title":"Learning Analytics Dashboard Research Has Neglected Diversity, Equity and Inclusion","authors":"K. Williamson, René F. Kizilcec","doi":"10.1145/3430895.3460160","DOIUrl":"https://doi.org/10.1145/3430895.3460160","url":null,"abstract":"Learning analytic dashboards (LADs) have become more prevalent in higher education to help students, faculty, and staff make data-informed decisions. Despite extensive research on the design and usability of LADs, few studies have examined them in relation to issues of diversity, equity, and inclusion. We conducted a critical literature review to address three research questions: How does LAD research contribute to improving diversity, equity, and inclusion? How might LADs contribute to maintaining or exacerbating inequitable outcomes? And what future opportunities exist in this research space? Our review showed little use of LADs to address or improve issues of diversity, equity, and inclusion in the literature thus far. We argue that excluding these issues from LAD research is not an isolated oversight and it risks reinforcing existing inequities within the higher education system. We argue that LADs can be designed, researched, and deployed intentionally to advance equitable outcomes and help dismantle inequities in education. We highlight opportunities for future LAD research to address issues of diversity, equity, and inclusion.","PeriodicalId":125581,"journal":{"name":"Proceedings of the Eighth ACM Conference on Learning @ Scale","volume":"68 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":"131510476","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: 6

Learning Engineering @ Scale 学习工程@ Scale

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-06-08 DOI: 10.1145/3430895.3460875

Jim Goodell, Aaron M. Kessler, Mary Ellen Wiltrout, Monika Avello

{"title":"Learning Engineering @ Scale","authors":"Jim Goodell, Aaron M. Kessler, Mary Ellen Wiltrout, Monika Avello","doi":"10.1145/3430895.3460875","DOIUrl":"https://doi.org/10.1145/3430895.3460875","url":null,"abstract":"Scaled learning requires a novel set of practices on the part of professionals developing and delivering systems of scaled learning. According to IEEE's Industry Connections Industry Consortium for Learning Engineering (ICICLE), \"Learning engineering is a process and practice that applies the learning sciences, using human-centered engineering design methodologies, and data-informed decision-making to support learners and their development[1].\" This workshop is designed as a boot camp for learning engineering. It is structured around a series of micro-learning activities around this definition. Topics include: Learning engineering is a process Learning engineering applies the learning sciences Learning engineering is human centered Learning engineering is engineering Learning engineering is data-driven Learning engineering is a team sport Learning engineering is ethical These learning experiences are organized based on and include materials from the forthcoming book the Learning Engineering Toolkit[2]. This workshop will also give conference participants an opportunity to give input into models for scaling the profession of learning engineering and formative feedback for iterative development of the Learning Engineering Toolkit.","PeriodicalId":125581,"journal":{"name":"Proceedings of the Eighth ACM Conference on Learning @ Scale","volume":"501 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":"123419103","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

Scaling Up Data Science Course Projects: A Case Study 扩大数据科学课程项目:个案研究

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-06-08 DOI: 10.1145/3430895.3460168

B. Bhavya, Jinfeng Xiao, ChengXiang Zhai

引用次数: 1

Classification of Discussions in MOOC Forums: An Incremental Modeling Approach MOOC论坛讨论分类:一种增量建模方法

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-06-08 DOI: 10.1145/3430895.3460137

Anastasios Ntourmas, Y. Dimitriadis, S. Daskalaki, N. Avouris

{"title":"Classification of Discussions in MOOC Forums: An Incremental Modeling Approach","authors":"Anastasios Ntourmas, Y. Dimitriadis, S. Daskalaki, N. Avouris","doi":"10.1145/3430895.3460137","DOIUrl":"https://doi.org/10.1145/3430895.3460137","url":null,"abstract":"Supervised classification models are commonly used for classifying discussions in a MOOC forum. In most cases these models require a tedious process for manual labeling the forum messages as training data. So, new methods are needed to reduce the human effort necessary for the preparation of such training datasets. In this study we follow an incremental approach in order to examine how soon after the beginning of a new course, we have collected enough data for training a supervised classification model. We show that by employing features that derive from a seeded topic modeling method, we achieve classifiers with reliable performance early enough in the course life, thus reducing significantly the human effort. The content of the MOOC platform is used to bias the topic extraction towards discussions related to (a) course content, (b) logistics, or (c) social interactions. Then, we develop a supervised model at the start of each week based on the topic features of all previous weeks and evaluate its performance in classifying the discussions for the rest of the course. Our approach was implemented in three different MOOCs of different subjects and different sizes. The findings reveal that supervised models are able to perform reliably quite early in a MOOC's life and retain a steady overall accuracy across the remaining weeks, without requiring to be trained with the entire forum dataset.","PeriodicalId":125581,"journal":{"name":"Proceedings of the Eighth ACM Conference on Learning @ Scale","volume":"23 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":"127135175","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: 3

Can Feedback based on Predictive Data Improve Learners' Passing Rates in MOOCs? A Preliminary Analysis 基于预测数据的反馈能提高mooc学习者的通过率吗?初步分析

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-06-08 DOI: 10.1145/3430895.3460991

M. Pérez-Sanagustín, Ronald Pérez-Álvarez, Jorge Maldonado-Mahauad, Esteban Villalobos, Isabel Hilliger, Josefina Hernandez, Diego Sapunar, Pedro Manuel Moreno-Marcos, P. Muñoz-Merino, C. D. Kloos, J. Marín

引用次数: 5