Proceedings of the Seventh ACM Conference on Learning @ Scale最新文献_第8页

Students' Achievement of Personalized Learning Objectives in MOOCs mooc中学生个性化学习目标的实现

Proceedings of the Seventh ACM Conference on Learning @ Scale Pub Date : 2020-08-12 DOI: 10.1145/3386527.3405918

Tobias Rohloff, Dominic Sauer, C. Meinel

{"title":"Students' Achievement of Personalized Learning Objectives in MOOCs","authors":"Tobias Rohloff, Dominic Sauer, C. Meinel","doi":"10.1145/3386527.3405918","DOIUrl":"https://doi.org/10.1145/3386527.3405918","url":null,"abstract":"Massive Open Online Courses (MOOCs) provide the opportunity to offer free and open education at scale. Thousands of students with different social and cultural backgrounds from all over the world can enroll for a course. This diverse audience comes with varying motivations and intentions from their personal or professional life. However, course instructors cannot offer individual support and guidance at this scale and therefore usually provide a one-size-fits-all approach. Students have to follow weekly-structured courses and their success is measured with the achievement of a certificate at the end. To better address the varying learning needs, technical support for goal-oriented and self-regulated learning is desired but very limited to date. Both learning strategies are proven to be key factors for students' achievement in large-scale online learning environments. Therefore, this paper presents a continuative study of personalized learning objectives in MOOCs to encourage goal-oriented and self-regulated learning. Based on the previously well-perceived acceptance and usefulness of the concept of personalized learning objectives, this study examines which learners select an objective and how successful they complete objectives. Concerning the learners' socio-demographic and geographical background, we could not identify any practical significant difference between students with selected learning objectives and the total course population. However, we have identified promising objective achievement rates, and we have observed a practical significant improvement of the certification rates comparing the total course population and students who selected an objective that included a graded certificate. This has also demonstrated a method for calculating more reasonable completion rates in MOOCs.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88348720","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: 7

Global Learning @ Scale 全球学习@ Scale

Proceedings of the Seventh ACM Conference on Learning @ Scale Pub Date : 2020-08-12 DOI: 10.1145/3386527.3405956

David A. Joyner, M. Carlon, J. Cross, Eduardo Corpeño, Rocael Hernández, Oscar Rodas, Dhawal Shah, Manoel Cortes Mendez, T. Staubitz, José A. Ruipérez Valiente

引用次数: 1

PARQR

Proceedings of the Seventh ACM Conference on Learning @ Scale Pub Date : 2020-08-12 DOI: 10.1145/3386527.3405914

India Irish, Roy Finkelberg, Daniel K. Nkemelu, Swar Gujrania, Aadarsh Padiyath, Sumedha Raman, Chirag Tailor, Rosa I. Arriaga, Thad Starner

引用次数: 5

Sensing Affect to Empower Students: Learner Perspectives on Affect-Sensitive Technology in Large Educational Contexts 感知情感赋予学生权力:大型教育环境中情感敏感技术的学习者视角

Proceedings of the Seventh ACM Conference on Learning @ Scale Pub Date : 2020-08-12 DOI: 10.1145/3386527.3405917

Qiaosi Wang, Shan Jing, David A. Joyner, Lauren Wilcox, Hong Li, T. Plötz, Betsy Disalvo

{"title":"Sensing Affect to Empower Students: Learner Perspectives on Affect-Sensitive Technology in Large Educational Contexts","authors":"Qiaosi Wang, Shan Jing, David A. Joyner, Lauren Wilcox, Hong Li, T. Plötz, Betsy Disalvo","doi":"10.1145/3386527.3405917","DOIUrl":"https://doi.org/10.1145/3386527.3405917","url":null,"abstract":"Large-scale educational settings have been common domains for affect detection and recognition research. Most research emphasizes improvements in the accuracy of affect measurement to enhance instructors' efficiency in managing large numbers of students. However, these technologies are not designed from students' perspectives, nor designed for students' own usage. To identify the unique design considerations for affect sensors that consider student capacities and challenges, and explore the potential of affect sensors to support students' self-learning, we conducted semi-structured interviews and surveys with both online students and on-campus students enrolled in large in-person classes. Drawing on these studies we: (a) propose using affect data to support students' self-regulated learning behaviors through a \"scaling for empowerment'' design perspective, (b) identify design guidelines to mitigate students' concerns regarding the use of affect data at scale, (c) provide design recommendations for the physical design of affect sensors for large educational settings.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":"139 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76862946","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: 14

Examining Sources of Variation in Student Confusion in College Classes 大学课堂上学生困惑的变异来源研究

Proceedings of the Seventh ACM Conference on Learning @ Scale Pub Date : 2020-08-12 DOI: 10.1145/3386527.3405939

Youjie Chen, René F. Kizilcec

引用次数: 0

Cold Start Knowledge Tracing with Attentive Neural Turing Machine 基于细心神经图灵机的冷启动知识跟踪

Proceedings of the Seventh ACM Conference on Learning @ Scale Pub Date : 2020-08-12 DOI: 10.1145/3386527.3406741

Jinjin Zhao, Shreyansh P. Bhatt, Candace Thille, Neelesh Gattani, D. Zimmaro

{"title":"Cold Start Knowledge Tracing with Attentive Neural Turing Machine","authors":"Jinjin Zhao, Shreyansh P. Bhatt, Candace Thille, Neelesh Gattani, D. Zimmaro","doi":"10.1145/3386527.3406741","DOIUrl":"https://doi.org/10.1145/3386527.3406741","url":null,"abstract":"Deep learning based knowledge tracing approaches achieve high accuracy in mastery prediction with pattern extraction on a large learning behavior data set. However, when there is little training data available, these approaches either fail to extract the key patterns or result in over fitting. Ideally, we aim to provide a similar learning experience to both the first group of learners, who interact with a new course or a new activity with little learning behavior data to provide personalized guidance, and the learners who interact with the course later. We propose a novel architecture, Attentive Neural Turing Machine (ANTM), to solve the cold start knowledge tracing problem. The proposed ANTM comprises an attentive controller module and differential reading and writing processes with extra memory bank. Accuracy (ACC) and Area Under Curve (AUC) measures are used for model performance comparison. Results show the proposed approach can learn fast and generalize well to unseen data. It achieves around 95% ACC trained with only 3 learners, while conventional deep learning based approaches achieve only 65% ACC with over prediction issues.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87143202","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

What My Little Pony Can Teach Us About Interest-Driven Learning 我的小马教我们兴趣驱动的学习

Proceedings of the Seventh ACM Conference on Learning @ Scale Pub Date : 2020-08-12 DOI: 10.1145/3386527.3406207

K. Davis

{"title":"What My Little Pony Can Teach Us About Interest-Driven Learning","authors":"K. Davis","doi":"10.1145/3386527.3406207","DOIUrl":"https://doi.org/10.1145/3386527.3406207","url":null,"abstract":"With its roots dating to popular television shows of the 1960s such as Star Trek, fanfiction has blossomed into an extremely widespread form of creative expression. In the past 20 years, amateur fanfiction writers, often young people between the ages of 13 and 25, have published over 61.5 billion words of fiction in online repositories, an amount that rivals the Google Books English fiction corpus of 80 billion words covering the past five centuries. Far from mere shallow repositories of pop culture, these sites are accumulating significant evidence that sophisticated informal learning is taking place online in novel and unexpected ways. Dr. Katie Davis will discuss insights from her book, Writers in the Secret Garden: Fanfiction, Youth, and New Forms of Mentoring (Aragon & Davis, 2019). Davis will describe how young people are utilizing new forms of technology to mentor each other in writing fanfiction, and developing their writing skills in the process. Over the course of five years, Davis and her co-author Dr. Cecilia Aragon conducted original mixed-methods research of online fanfiction repositories, combining their respective skills in data science and education. During the course of their research, they discovered a new kind of mentoring, which they call distributed mentoring, that is uniquely suited to networked communities, where people of all ages and experience levels engage with and support one another through a complex, interwoven tapestry of interactive, cumulatively sophisticated advice and informal instruction. Davis will use the insights from this research to reflect on what it is, exactly, about networked publics that can so effectively support interest-driven learning, and she will consider whether it's possible to apply these lessons to formal education environments.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":"179 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88070543","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: 1

Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing 面向知识跟踪的查询、键和值计算

Proceedings of the Seventh ACM Conference on Learning @ Scale Pub Date : 2020-02-14 DOI: 10.1145/3386527.3405945

Youngduck Choi, Youngnam Lee, Junghyun Cho, Jineon Baek, Byungsoo Kim, Yeongmin Cha, Dongmin Shin, Chan Bae, Jaewe Heo

{"title":"Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing","authors":"Youngduck Choi, Youngnam Lee, Junghyun Cho, Jineon Baek, Byungsoo Kim, Yeongmin Cha, Dongmin Shin, Chan Bae, Jaewe Heo","doi":"10.1145/3386527.3405945","DOIUrl":"https://doi.org/10.1145/3386527.3405945","url":null,"abstract":"In this paper, we propose a novel Transformer-based model for knowledge tracing, SAINT: Separated Self-AttentIve Neural Knowledge Tracing. SAINT has an encoder-decoder structure where the exercise and response embedding sequences separately enter, respectively, the encoder and the decoder. The encoder applies self-attention layers to the sequence of exercise embeddings, and the decoder alternately applies self-attention layers and encoder-decoder attention layers to the sequence of response embeddings. This separation of input allows us to stack attention layers multiple times, resulting in an improvement in area under receiver operating characteristic curve (AUC). To the best of our knowledge, this is the first work to suggest an encoder-decoder model for knowledge tracing that applies deep self-attentive layers to exercises and responses separately. We empirically evaluate SAINT on a large-scale knowledge tracing dataset, EdNet, collected by an active mobile education application, Santa, which has 627,347 users, 72,907,005 response data points as well as a set of 16,175 exercises gathered since 2016. The results show that SAINT achieves state-of-the-art performance in knowledge tracing with an improvement of 1.8% in AUC compared to the current state-of-the-art model.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78717489","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: 96

L@S'20: Seventh ACM Conference on Learning @ Scale, Virtual Event, USA, August 12-14, 2020 L@S'20:第七届ACM学习大会@ Scale，虚拟活动，美国，2020年8月12日至14日

Proceedings of the Seventh ACM Conference on Learning @ Scale Pub Date : 2020-01-01 DOI: 10.1145/3386527

引用次数: 0

Human Languages in Source Code: Auto-Translation for Localized Instruction 源代码中的人类语言:本地化指令的自动翻译

Proceedings of the Seventh ACM Conference on Learning @ Scale Pub Date : 2019-09-10 DOI: 10.1145/3386527.3405916

C. Piech, Sami Abu-El-Haija

引用次数: 3