Proceedings of the Second (2015) ACM Conference on Learning @ Scale最新文献_第6页

Autonomously Generating Hints by Inferring Problem Solving Policies 通过推理问题解决策略自动生成提示

Proceedings of the Second (2015) ACM Conference on Learning @ Scale Pub Date : 2015-03-14 DOI: 10.1145/2724660.2724668

C. Piech, M. Sahami, Jonathan Huang, L. Guibas

引用次数: 139

Connecting the Dots: Predicting Student Grade Sequences from Bursty MOOC Interactions over Time 连接点:预测学生成绩序列从突发MOOC互动随着时间的推移

Proceedings of the Second (2015) ACM Conference on Learning @ Scale Pub Date : 2015-03-14 DOI: 10.1145/2724660.2728669

Tanmay Sinha, Justine Cassell

{"title":"Connecting the Dots: Predicting Student Grade Sequences from Bursty MOOC Interactions over Time","authors":"Tanmay Sinha, Justine Cassell","doi":"10.1145/2724660.2728669","DOIUrl":"https://doi.org/10.1145/2724660.2728669","url":null,"abstract":"In this work, we track the interaction of students across multiple Massive Open Online Courses (MOOCs) on edX. Leveraging the ``burstiness\" factor of three of the most commonly exhibited interaction forms made possible by online learning (i.e, video lecture viewing, coursework access and discussion forum posting), we take on the task of predicting student performance (operationalized as grade) across these courses. Specifically, we utilize the probabilistic framework of Conditional Random Fields (CRF) to formalize the problem of predicting the sequence of grades achieved by a student in different MOOCs, taking into account the contextual dependency of this outcome measure on students' general interaction trend across courses. Based on a comparative analysis of the combination of interaction features, our best CRF model can achieve a precision of 0.581, recall of 0.660 and a weighted F-score of 0.560, outweighing several baseline discriminative classifiers applied at each sequence position. These findings have implications for initiating early instructor intervention, so as to engage students along less active interaction dimensions that could be associated with low grades.","PeriodicalId":20664,"journal":{"name":"Proceedings of the Second (2015) ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74543506","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: 25

moocRP: An Open-source Analytics Platform moocRP:一个开源分析平台

Proceedings of the Second (2015) ACM Conference on Learning @ Scale Pub Date : 2015-03-14 DOI: 10.1145/2724660.2724683

Z. Pardos, Kevin Kao

引用次数: 35

Clustering Student Programming Assignments to Multiply Instructor Leverage 聚类学生编程作业，以增加教师的杠杆作用

Proceedings of the Second (2015) ACM Conference on Learning @ Scale Pub Date : 2015-03-14 DOI: 10.1145/2724660.2728695

Hezheng Yin, J. Moghadam, A. Fox

{"title":"Clustering Student Programming Assignments to Multiply Instructor Leverage","authors":"Hezheng Yin, J. Moghadam, A. Fox","doi":"10.1145/2724660.2728695","DOIUrl":"https://doi.org/10.1145/2724660.2728695","url":null,"abstract":"A challenge in introductory and intermediate programming courses is understanding how students approached solving a particular programming problem, in order to provide feedback on how they might improve. In both Massive Open Online Courses (MOOCs) and large residential courses, such feedback is difficult to provide for each student individually. To multiply the instructor's leverage, we would like to group student submissions according to the general problem-solving strategy they used, as the first stage of a ``feedback pipeline''. We describe ongoing explorations of a variety of clustering algorithms and similarity metrics using a corpus of over 800 student submissions to a simple programming assignment from a programming MOOC. We find that for a majority of submissions, it is possible to automatically create clusters such that an instructor ``eyeballing'' some representative submissions from each cluster can readily describe qualitatively what the common elements are in student submissions in that cluster. This information can be the basis for feedback to the students or for comparing one group of students' approach with another's.","PeriodicalId":20664,"journal":{"name":"Proceedings of the Second (2015) ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85987787","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: 21

A Modern Student Experience inSystems Programming 系统编程的现代学生体验

Proceedings of the Second (2015) ACM Conference on Learning @ Scale Pub Date : 2015-03-14 DOI: 10.1145/2724660.2728665

Vaishaal Shankar, D. Culler

引用次数: 1

Item Ordering Effects with Qualitative Explanations using Online Adaptive Tutoring Data 项目排序效应与在线自适应辅导数据的定性解释

Proceedings of the Second (2015) ACM Conference on Learning @ Scale Pub Date : 2015-03-14 DOI: 10.1145/2724660.2728682

Steven Tang, Elizabeth A. McBride, H. Gogel, Z. Pardos

{"title":"Item Ordering Effects with Qualitative Explanations using Online Adaptive Tutoring Data","authors":"Steven Tang, Elizabeth A. McBride, H. Gogel, Z. Pardos","doi":"10.1145/2724660.2728682","DOIUrl":"https://doi.org/10.1145/2724660.2728682","url":null,"abstract":"Online computer adaptive learning is increasingly being used in classrooms as a way to provide guided learning for students. Such tutors have the potential to provide tailored feedback based on specific student needs and misunderstandings. Bayesian knowledge tracing (BKT) is used to model student knowledge when knowledge is assumed to be changing throughout a single assessment period; in contrast, traditional Item Response Theory (IRT) models assume student knowledge to be constant within an assessment period. The basic BKT model assumes that the chance a student transitions from \"not knowing\" to \"knowing\" after each item is the same, and problems are considered learning opportunities. It could be the case, however, that learning is actually context sensitive, where students' learning might be improved when the items and their associated tutoring content are delivered to the student in a particular order. In this paper, we use BKT models to find such context sensitive transition probabilities from real data delivered by an online tutoring system, ASSISTments. After empirically deriving orderings that lead to better learning, we qualitatively analyze the items and their tutoring content to uncover any mechanisms that might explain why such orderings are modeled to have higher learning potential.","PeriodicalId":20664,"journal":{"name":"Proceedings of the Second (2015) ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82965108","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

Towards Capturing Learners Sentiment and Context 捕捉学习者的情绪和语境

Proceedings of the Second (2015) ACM Conference on Learning @ Scale Pub Date : 2015-03-14 DOI: 10.1145/2724660.2728662

Jaye Clarkes-Nias, Juliet Mutahi, Andrew Kinai, Oliver E. Bent, Komminist Weldemariam, Saurabh Srivastava

引用次数: 3

Attrition and Achievement Gaps in Online Learning 在线学习中的损耗和成就差距

Proceedings of the Second (2015) ACM Conference on Learning @ Scale Pub Date : 2015-03-14 DOI: 10.1145/2724660.2724680

René F. Kizilcec, Sherif A. Halawa

引用次数: 251

Learner-Sourcing in an Engineering Class at Scale 大规模工程课程中的学习者资源

Proceedings of the Second (2015) ACM Conference on Learning @ Scale Pub Date : 2015-03-14 DOI: 10.1145/2724660.2728694

Elena L. Glassman, C. Terman, Rob Miller

引用次数: 4

Staggered Versus All-At-Once Content Release in Massive Open Online Courses: Evaluating a Natural Experiment 在大规模开放在线课程中，交错与一次性内容发布:评估一个自然实验

Proceedings of the Second (2015) ACM Conference on Learning @ Scale Pub Date : 2015-03-14 DOI: 10.1145/2724660.2724663

Tommy Mullaney, J. Reich

引用次数: 18