Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale最新文献_第4页

Informing the Design of Collaborative Activities in MOOCs using Actionable Predictions 基于可操作预测的mooc协同活动设计

Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale Pub Date : 2019-06-24 DOI: 10.1145/3330430.3333640

Erkan Er, E. Gómez-Sánchez, Miguel L. Bote-Lorenzo, Juan I. Asensio-Pérez, Y. Dimitriadis

引用次数: 2

Via: Illuminating Academic Pathways at Scale 通过:照亮学术道路的规模

Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale Pub Date : 2019-06-24 DOI: 10.1145/3330430.3333623

Geoffrey Angus, Richard Diehl Martinez, M. Stevens, A. Paepcke

引用次数: 7

Designing Digital Peer Assessment for Second Language Learning in Low Resource Learning Settings 设计低资源环境下第二语言学习的数字同伴评估

Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale Pub Date : 2019-06-24 DOI: 10.1145/3330430.3333626

Maletsabisa Molapo, C. S. Moodley, I. Akhalwaya, Toby Kurien, Jay Kloppenberg, R. Young

引用次数: 2

Graded Team Assignments in MOOCs: Effects of Team Composition and Further Factors on Team Dropout Rates and Performance MOOCs分级小组作业:小组组成及其他因素对小组辍学率和绩效的影响

Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale Pub Date : 2019-06-24 DOI: 10.1145/3330430.3333619

T. Staubitz, C. Meinel

引用次数: 15

An Automated Feedback System to Support Student Learning in Writing-to-Learn Activities 支持学生在写作学习活动中学习的自动反馈系统

Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale Pub Date : 2019-06-24 DOI: 10.1145/3330430.3333657

Ye Xiong, Y. Wu

引用次数: 2

Apples to Apples: Differences in Viewer Retention When Longer Content is Chopped into Smaller Bites 苹果对苹果:当较长的内容被分割成小块时，观众留存率的差异

Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale Pub Date : 2019-06-24 DOI: 10.1145/3330430.3333617

J. Kay, Colin Lambe, Tyler J. Nolan, Thomas M. Grello, Anthony F. Breitzman

{"title":"Apples to Apples: Differences in Viewer Retention When Longer Content is Chopped into Smaller Bites","authors":"J. Kay, Colin Lambe, Tyler J. Nolan, Thomas M. Grello, Anthony F. Breitzman","doi":"10.1145/3330430.3333617","DOIUrl":"https://doi.org/10.1145/3330430.3333617","url":null,"abstract":"Numerous studies have concluded that viewer retention decreases as video length increases. However, we are not aware of any prior work in which a set of longer MOOC (Massive Open Online Course) videos are compared with the same content split into multiple shorter videos. We are fortunate to be in the unique position to have two separate MOOCs that teach essentially the same content using two different platforms (the LEGO Mindstorms NXT and EV3 robots). In our NXT MOOC, videos are quite long, with over 20% of the videos having a running time of more than ten minutes. The EV3 MOOC has very similar content; EV3 MOOC scripts were written by modifying NXT scripts as appropriate. However, many of the EV3 lessons were split into two or three shorter videos in place of a single longer one. NXT videos that are very close in terms of both content and duration to EV3 videos have similar average percentage viewed. This suggests that the two populations watching the videos are similar and that we have a promising setup for analyzing the relationship between NXT lessons whose EV3 counterparts consist of multiple shorter videos. We present an analysis of our data, along with various interpretations some, but not all, of which support the \"shorter videos are better\" hypothesis.","PeriodicalId":20693,"journal":{"name":"Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81920314","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

LiveDataLab

Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale Pub Date : 2019-06-24 DOI: 10.1145/3330430.3333665

Aaron Green, ChengXiang Zhai

引用次数: 0

Mining Students Pre-instruction Beliefs for Improved Learning 挖掘学生的教学前信念以改善学习

Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale Pub Date : 2019-06-24 DOI: 10.1145/3330430.3333637

Ángel Pérez-Lemonche, J. Stewart, Byron Drury, Rachel Henderson, Alexander J. Shvonski, David E. Pritchard

{"title":"Mining Students Pre-instruction Beliefs for Improved Learning","authors":"Ángel Pérez-Lemonche, J. Stewart, Byron Drury, Rachel Henderson, Alexander J. Shvonski, David E. Pritchard","doi":"10.1145/3330430.3333637","DOIUrl":"https://doi.org/10.1145/3330430.3333637","url":null,"abstract":"In principle, learning can be increased by assessing the detailed state of student knowledge and mistaken knowledge with a pre-test and then optimizing instruction as measured by the post-test score. As a first step in this direction, we applied a Multidimensional Item Response Theory (MIRT) to 17,000 pre-instruction administrations of the Force Concept Inventory (FCI) to study students' initial knowledge in detail. Examination of Item Response Curves (IRCs) showed that even students scoring below chance are not randomly guessing, but instead preferentially select only one or two distractors. Two dimensional IRT applied to the entire set of 150 possible responses, rather than applied dichotomously to the thirty questions, revealed two skill dimensions of comparable variance. Perpendicular directions were identified within this space corresponding to Newtonian ability and propensity to select responses whose IRC's have a maximum at intermediate Newtonian ability rather than at the top of bottom of this dimension. These intermediate responses corresponded to known pre-Newtonian ideas, particularly the Medieval concept of impetus. The ability to measure the detailed misconceptions of individual students or classes will allow development and application of instructional interventions for such specific misunderstandings, which are typically unchanged by traditional instruction.","PeriodicalId":20693,"journal":{"name":"Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale","volume":"300 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74410951","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: 4

Semantic Matching Evaluation of User Responses to Electronics Questions in AutoTutor AutoTutor中用户回答电子问题的语义匹配评价

Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale Pub Date : 2019-06-24 DOI: 10.1145/3330430.3333649

Colin M. Carmon, Andrew J. Hampton, Brent Morgan, Zhiqiang Cai, Lijia Wang, A. Graesser

{"title":"Semantic Matching Evaluation of User Responses to Electronics Questions in AutoTutor","authors":"Colin M. Carmon, Andrew J. Hampton, Brent Morgan, Zhiqiang Cai, Lijia Wang, A. Graesser","doi":"10.1145/3330430.3333649","DOIUrl":"https://doi.org/10.1145/3330430.3333649","url":null,"abstract":"Relatedness between user input and an ideal response is a salient feature required for proper functioning of an Intelligent Tutoring System (ITS) using natural language processing. Improper assessment of text input causes maladaptation in ITSs. Meta-assessment of user responses in ITSs can improve instruction efficacy and user satisfaction. Therefore, this paper evaluates the quality of semantic matching between user input and the expected response in AutoTutor, an ITS which holds a conversation with the user in natural language. AutoTutor's dialogue is driven by the AutoTutor Conversation Engine (ACE), which uses a combination of Latent Semantic Analysis (LSA) and Regular Expressions (RegEx) to assess user input. We assessed ACE via responses from 219 Amazon Mechanical Turk users, who answered 118 electronics questions broken into 5202 response pairings (n = 5202). These analyses explore the relationship between RegEx and LSA, agreement between the two judges, and agreement between human judges and ACE. Additionally, we calculated precision and recall. As expected, regular expressions and LSA had a moderate, positive relationship, and the agreement between ACE and human was fair, but slightly lower than agreement between human.","PeriodicalId":20693,"journal":{"name":"Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale","volume":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90875556","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

PARQR: Augmenting the Piazza Online Forum to Better Support Degree Seeking Online Masters Students PARQR:扩大广场在线论坛，以更好地支持在线硕士学生申请学位

Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale Pub Date : 2019-06-24 DOI: 10.1145/3330430.3333662

Noah Bilgrien, Roy Finkelberg, Chirag Tailor, India Irish, Girish Murali, Abhishek Mangal, Niklas Gustafsson, Sumedha Raman, Thad Starner, R. Arriaga

引用次数: 2