We present the Beetle-Grow intelligent tutoring system, which combines active experimentation, self-explanation, and formative feedback using natural language interaction. It runs in a standard web browser and has a fresh, engaging design. The underlying back-end system has previously been shown to be highly effective in teaching basic electricity and electronics concepts. Beetle-Grow has been designed to capture student interaction and indicators of learning in a form suitable for data mining, and to support future work on building tools for interactive tutoring that improve after experiencing interaction with students, as human tutors do.
{"title":"Beetle-Grow: An Effective Intelligent Tutoring System for Data Collection","authors":"Elaine Farrow, M. Dzikovska, Johanna D. Moore","doi":"10.1145/2876034.2893408","DOIUrl":"https://doi.org/10.1145/2876034.2893408","url":null,"abstract":"We present the Beetle-Grow intelligent tutoring system, which combines active experimentation, self-explanation, and formative feedback using natural language interaction. It runs in a standard web browser and has a fresh, engaging design. The underlying back-end system has previously been shown to be highly effective in teaching basic electricity and electronics concepts. Beetle-Grow has been designed to capture student interaction and indicators of learning in a form suitable for data mining, and to support future work on building tools for interactive tutoring that improve after experiencing interaction with students, as human tutors do.","PeriodicalId":20739,"journal":{"name":"Proceedings of the Third (2016) ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75833927","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}
We are exploring the effects of social incentives and motivation on learner performance in a massive open online course. In the preliminary study that we report here, we asked learners if they wanted to be considered for a community TAship in a subsequent offering of the course, if they finished in the top 20% of those who completed the current course instance. We prompted students near the beginning of the course and in the middle of the course. This prompt appears to have had a significant, albeit small effect on learner completion when given early in the course. The prompt had no significant effect when given later in the course. We also discuss our plans to follow-up this study.
{"title":"Exploring the Effects of Lightweight Social Incentives on Learner Performance in MOOCs","authors":"Katherine A. Brady, D. Fisher, G. Narasimham","doi":"10.1145/2876034.2893438","DOIUrl":"https://doi.org/10.1145/2876034.2893438","url":null,"abstract":"We are exploring the effects of social incentives and motivation on learner performance in a massive open online course. In the preliminary study that we report here, we asked learners if they wanted to be considered for a community TAship in a subsequent offering of the course, if they finished in the top 20% of those who completed the current course instance. We prompted students near the beginning of the course and in the middle of the course. This prompt appears to have had a significant, albeit small effect on learner completion when given early in the course. The prompt had no significant effect when given later in the course. We also discuss our plans to follow-up this study.","PeriodicalId":20739,"journal":{"name":"Proceedings of the Third (2016) ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77886634","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}
It is our great pleasure to present the Proceedings of the Third Annual ACM Conference on Learning at Scale, L@S 2016, held on April 25-26 at the University of Edinburgh, UK, the first time for the conference to be held outside of North America. This conference series is a venue for discussion of the highest quality research on how learning and teaching can be transformed when done at scale. This conference was created by the Association for Computing Machinery (ACM), inspired by the emergence of Massive Open Online Courses (MOOCs) and the accompanying shift in thinking about education. This area of research is interdisciplinary, sitting at the intersection of the learning sciences, education, computer science, educational data mining, and learning analytics. "Learning at Scale" refers to new approaches to teaching and learning that involve large numbers of students, thousands or even tens of thousands. It covers face-to-face settings as well as settings in which learners work remotely, whether synchronous or asynchronous. It is concerned with the challenges and affordances of scale: What are innovative forms of learning and instruction that can be orchestrated with very large numbers of learners? Specific topics include, but are not limited to: Pedagogies that enhance learning as scale; personalization and adaptation of learning at scale; selfand co-regulation of learning at scale; platforms, tools, and architectures for learning at scale; usability studies; tools for automated feedback and grading; learning analytics; analysis of log data; studies of application of learning theory; and finally, investigation of student behavior and correlation with learning outcomes, depth and retention of learning, and motivational and affective outcomes. The call for papers attracted submissions from all over the world, covering a broad range of topics from the theoretical to the pragmatic. All papers were reviewed according to stringent criteria. Full Papers were reviewed by at least three program committee members, Work-In-Progress Papers and Demo Descriptions by two. Final decisions for acceptance of Full Papers were made by the program committee as a whole, often after extensive discussion of the merits of the paper. Whereas Full Papers present work that is innovative and mature, WiPs and Demos offer a forum for the newest and emerging work at earlier stages, offering pointers to future directions. As such, they fulfill a key role in this fast moving area. An industry session reflects the importance of L@S for the commercial world and for real world deployment. The overall submission numbers did not differ substantially from those of the previous year. Thus, the conference is successfully migrating from the continent of its birth, indicating its international relevance. How could it be different, as Learning at Scale is a truly international phenomenon? We are fortunate to have three outstanding keynote speakers. Sugata Mitra, Professor of
{"title":"Proceedings of the Third (2016) ACM Conference on Learning @ Scale","authors":"J. Haywood, V. Aleven, J. Kay, Ido Roll","doi":"10.1145/2876034","DOIUrl":"https://doi.org/10.1145/2876034","url":null,"abstract":"It is our great pleasure to present the Proceedings of the Third Annual ACM Conference on Learning at Scale, L@S 2016, held on April 25-26 at the University of Edinburgh, UK, the first time for the conference to be held outside of North America. \u0000 \u0000This conference series is a venue for discussion of the highest quality research on how learning and teaching can be transformed when done at scale. This conference was created by the Association for Computing Machinery (ACM), inspired by the emergence of Massive Open Online Courses (MOOCs) and the accompanying shift in thinking about education. This area of research is interdisciplinary, sitting at the intersection of the learning sciences, education, computer science, educational data mining, and learning analytics. \u0000 \u0000\"Learning at Scale\" refers to new approaches to teaching and learning that involve large numbers of students, thousands or even tens of thousands. It covers face-to-face settings as well as settings in which learners work remotely, whether synchronous or asynchronous. It is concerned with the challenges and affordances of scale: What are innovative forms of learning and instruction that can be orchestrated with very large numbers of learners? Specific topics include, but are not limited to: Pedagogies that enhance learning as scale; personalization and adaptation of learning at scale; selfand co-regulation of learning at scale; platforms, tools, and architectures for learning at scale; usability studies; tools for automated feedback and grading; learning analytics; analysis of log data; studies of application of learning theory; and finally, investigation of student behavior and correlation with learning outcomes, depth and retention of learning, and motivational and affective outcomes. \u0000 \u0000The call for papers attracted submissions from all over the world, covering a broad range of topics from the theoretical to the pragmatic. All papers were reviewed according to stringent criteria. Full Papers were reviewed by at least three program committee members, Work-In-Progress Papers and Demo Descriptions by two. Final decisions for acceptance of Full Papers were made by the program committee as a whole, often after extensive discussion of the merits of the paper. Whereas Full Papers present work that is innovative and mature, WiPs and Demos offer a forum for the newest and emerging work at earlier stages, offering pointers to future directions. As such, they fulfill a key role in this fast moving area. An industry session reflects the importance of L@S for the commercial world and for real world deployment. \u0000 \u0000The overall submission numbers did not differ substantially from those of the previous year. Thus, the conference is successfully migrating from the continent of its birth, indicating its international relevance. How could it be different, as Learning at Scale is a truly international phenomenon? \u0000 \u0000We are fortunate to have three outstanding keynote speakers. Sugata Mitra, Professor of ","PeriodicalId":20739,"journal":{"name":"Proceedings of the Third (2016) ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81712048","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}
We present Elice, an online CS (computer science) education platform, and Elivate, a system for (i) taking student learning data from Elice, (ii) inferring their progress through an educational taxonomy tailored for programming education, and (iii) generating the real-time assistance for students and lecturers. Online courses suffer from high average attrition rates, and early prediction can enable early personalized feedback to motivate and assist students who may be having difficulties. Elice captures detailed student learning activities including intermediate revisions of code as students make progress toward completing their programming exercises and timestamps of student logins and submissions. Elivate then takes those data to analyze each student's progress and estimate the time to completion. In doing so, Elivate uses a learning taxonomy and automatic clustering of source code revisions. Using more than 240,000 code revisions generated by 1,000 students, we demonstrate how Elivate processes large-scale student data and generates appropriate real-time feedback for students.
{"title":"Elivate: A Real-Time Assistant for Students and Lecturers as Part of an Online CS Education Platform","authors":"Suin Kim, Jae Won Kim, Jungkook Park, Alice H. Oh","doi":"10.1145/2876034.2893406","DOIUrl":"https://doi.org/10.1145/2876034.2893406","url":null,"abstract":"We present Elice, an online CS (computer science) education platform, and Elivate, a system for (i) taking student learning data from Elice, (ii) inferring their progress through an educational taxonomy tailored for programming education, and (iii) generating the real-time assistance for students and lecturers. Online courses suffer from high average attrition rates, and early prediction can enable early personalized feedback to motivate and assist students who may be having difficulties. Elice captures detailed student learning activities including intermediate revisions of code as students make progress toward completing their programming exercises and timestamps of student logins and submissions. Elivate then takes those data to analyze each student's progress and estimate the time to completion. In doing so, Elivate uses a learning taxonomy and automatic clustering of source code revisions. Using more than 240,000 code revisions generated by 1,000 students, we demonstrate how Elivate processes large-scale student data and generates appropriate real-time feedback for students.","PeriodicalId":20739,"journal":{"name":"Proceedings of the Third (2016) ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81904191","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}
Colin Fredericks, Glenn Lopez, V. Shnayder, Saif Rayyan, Daniel T. Seaton
Staff from edX, MIT, and Harvard will present two instructor dashboards for edX MOOCs. Current workflows will be described, from parsing and displaying data to using dashboards for course revision. A major focus will be lessons learned in the first two years of deployment.
{"title":"Instructor Dashboards In EdX","authors":"Colin Fredericks, Glenn Lopez, V. Shnayder, Saif Rayyan, Daniel T. Seaton","doi":"10.1145/2876034.2893405","DOIUrl":"https://doi.org/10.1145/2876034.2893405","url":null,"abstract":"Staff from edX, MIT, and Harvard will present two instructor dashboards for edX MOOCs. Current workflows will be described, from parsing and displaying data to using dashboards for course revision. A major focus will be lessons learned in the first two years of deployment.","PeriodicalId":20739,"journal":{"name":"Proceedings of the Third (2016) ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81932432","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}
Two major benefits of Massive Open Online Course platforms are their collection of fine grain data on student interactions with the course website and their capacity to give students immediate feedback on their work. We study the patterns of students' usage of immediate feedback in an undergraduate physics course that uses blended learning, and we present informative aggre-gate descriptives from this 474-student class. We find that overall student study strategies mirror those in "traditional" courses, that students strategically use the auto-checking feature of the platform, and that they extensively use the other content resources available to them on the platform. Several of these findings support educational research that has not had the benefit of the data MOOC platforms give us access to. Better understanding of how students engage with blended learning will aid residential instructors in tailoring in-class time and providing their students with recommendations for approaches to studying.
{"title":"Work in Progress: Student Behaviors Using Feedback in a Blended Physics Undergraduate Classroom","authors":"Jennifer DeBoer, L. Breslow","doi":"10.1145/2876034.2893421","DOIUrl":"https://doi.org/10.1145/2876034.2893421","url":null,"abstract":"Two major benefits of Massive Open Online Course platforms are their collection of fine grain data on student interactions with the course website and their capacity to give students immediate feedback on their work. We study the patterns of students' usage of immediate feedback in an undergraduate physics course that uses blended learning, and we present informative aggre-gate descriptives from this 474-student class. We find that overall student study strategies mirror those in \"traditional\" courses, that students strategically use the auto-checking feature of the platform, and that they extensively use the other content resources available to them on the platform. Several of these findings support educational research that has not had the benefit of the data MOOC platforms give us access to. Better understanding of how students engage with blended learning will aid residential instructors in tailoring in-class time and providing their students with recommendations for approaches to studying.","PeriodicalId":20739,"journal":{"name":"Proceedings of the Third (2016) ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81977089","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}
Adaptive learning is the core technology behind intelligent tutoring systems, which are responsible for estimating student knowledge and providing personalized instruction to students based on their skill level. In this paper, we present a new adaptive learning system architecture, which uses Artificial Neural Network to construct the Learner Model, which automatically models relationship between different concepts in the curriculum and beats Knowledge Tracing in predicting student performance. We also propose a novel method for selecting items of optimal difficulty, personalized to student's skill level and learning rate, which decreases their learning time by 26.5% as compared to standard pre-defined curriculum sequence item selection policy.
{"title":"Personalized Adaptive Learning using Neural Networks","authors":"Devendra Singh Chaplot, Eunhee Rhim, J. Kim","doi":"10.1145/2876034.2893397","DOIUrl":"https://doi.org/10.1145/2876034.2893397","url":null,"abstract":"Adaptive learning is the core technology behind intelligent tutoring systems, which are responsible for estimating student knowledge and providing personalized instruction to students based on their skill level. In this paper, we present a new adaptive learning system architecture, which uses Artificial Neural Network to construct the Learner Model, which automatically models relationship between different concepts in the curriculum and beats Knowledge Tracing in predicting student performance. We also propose a novel method for selecting items of optimal difficulty, personalized to student's skill level and learning rate, which decreases their learning time by 26.5% as compared to standard pre-defined curriculum sequence item selection policy.","PeriodicalId":20739,"journal":{"name":"Proceedings of the Third (2016) ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79895838","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}
We propose a comparative judgement scheme for grading short answer questions in an online class. The scheme works by asking students to answer short answer questions. Then a multiple choice question is created whose choices are the answers given by students. We show that we can formulate a probabilistic graphical model for this scheme which lets us infer each students proficiency for answering and grading questions.
{"title":"Peer Reviewing Short Answers using Comparative Judgement","authors":"P. Kolhe, M. Littman, C. Isbell","doi":"10.1145/2876034.2893424","DOIUrl":"https://doi.org/10.1145/2876034.2893424","url":null,"abstract":"We propose a comparative judgement scheme for grading short answer questions in an online class. The scheme works by asking students to answer short answer questions. Then a multiple choice question is created whose choices are the answers given by students. We show that we can formulate a probabilistic graphical model for this scheme which lets us infer each students proficiency for answering and grading questions.","PeriodicalId":20739,"journal":{"name":"Proceedings of the Third (2016) ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81409463","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}
Shuchi Grover, M. Bienkowski, J. Niekrasz, Matthias Hauswirth
Authentic problem solving tasks in digital environments are often open-ended with ill-defined pathways to a goal state. Scaffolds and formative feedback during this process help learners develop the requisite skills and understanding, but require assessing the problem-solving process. This paper describes a hybrid approach to assessing process at scale in the context of the use of computational thinking practices during programming. Our approach combines hypothesis-driven analysis, using an evidence-centered design framework, with discovery-driven data analytics. We report on work-in-progress involving novices and expert programmers working on Blockly games.
{"title":"Assessing Problem-Solving Process At Scale","authors":"Shuchi Grover, M. Bienkowski, J. Niekrasz, Matthias Hauswirth","doi":"10.1145/2876034.2893425","DOIUrl":"https://doi.org/10.1145/2876034.2893425","url":null,"abstract":"Authentic problem solving tasks in digital environments are often open-ended with ill-defined pathways to a goal state. Scaffolds and formative feedback during this process help learners develop the requisite skills and understanding, but require assessing the problem-solving process. This paper describes a hybrid approach to assessing process at scale in the context of the use of computational thinking practices during programming. Our approach combines hypothesis-driven analysis, using an evidence-centered design framework, with discovery-driven data analytics. We report on work-in-progress involving novices and expert programmers working on Blockly games.","PeriodicalId":20739,"journal":{"name":"Proceedings of the Third (2016) ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78962085","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}
Flashcards are a popular study tool for exploiting the spacing effect -- the phenomenon in which periodic, spaced review of educational content improves long-term retention. The Leitner system is a simple heuristic algorithm for scheduling reviews such that forgotten items are reviewed more frequently than recalled items. We propose a formalization of the Leitner system as a queueing network model, and formulate optimal review scheduling as a throughput-maximization problem. Through simulations and theoretical analysis, we find that the Leitner Queue Network (LQN) model has desirable properties and gives insight into general principles for spaced repetition.
{"title":"A Queueing Network Model for Spaced Repetition","authors":"S. Reddy, I. Labutov, Siddhartha Banerjee","doi":"10.1145/2876034.2893436","DOIUrl":"https://doi.org/10.1145/2876034.2893436","url":null,"abstract":"Flashcards are a popular study tool for exploiting the spacing effect -- the phenomenon in which periodic, spaced review of educational content improves long-term retention. The Leitner system is a simple heuristic algorithm for scheduling reviews such that forgotten items are reviewed more frequently than recalled items. We propose a formalization of the Leitner system as a queueing network model, and formulate optimal review scheduling as a throughput-maximization problem. Through simulations and theoretical analysis, we find that the Leitner Queue Network (LQN) model has desirable properties and gives insight into general principles for spaced repetition.","PeriodicalId":20739,"journal":{"name":"Proceedings of the Third (2016) ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79615038","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}
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