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

{"title":"Demographic Differences in a Growth Mindset Incentive Structure for Educational Games","authors":"Eleanor O'Rourke, Yvonne Chen, K. Haimovitz, C. Dweck, Zoran Popovic","doi":"10.1145/2724660.2728686","DOIUrl":"https://doi.org/10.1145/2724660.2728686","url":null,"abstract":"Video games have great potential to motivate students in environments for learning at scale. However, little is known about how to design in-game incentive structures to maximize learning and engagement. In this work, we expand on our previous research that introduced a new \"brain points\" incentive structure designed to promote the growth mindset, or the belief that intelligence is malleable. We replicate our original findings, showing that brain points increase student persistence and use of strategy. We also explore how brain points impact students from different demographic groups. We find that brain points are less engaging for low-income students, and discuss methods of improving our design in the future.","PeriodicalId":20664,"journal":{"name":"Proceedings of the Second (2015) ACM Conference on Learning @ Scale","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89498449","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}

{"title":"Assessment of KNOWLA: Knowledge Assembly for Learning and Assessment","authors":"Meredith M. Thompson, Eric J. Braude, Christopher D. Canfield, Jay Halfond, Aparaita Sengupta","doi":"10.1145/2724660.2728673","DOIUrl":"https://doi.org/10.1145/2724660.2728673","url":null,"abstract":"The assessment of learning in large online courses such as Massive Online Open Courses, or MOOCs, requires tools that are valid, reliable, and can be automatically administered and scored. We have developed and assessed a tool called Knowledge Assembly for Learning and Assessment, or KNOWLA. The tool measures a student's knowledge in a particular subject by having her assemble a set of scrambled phrases into a logical order. Initial testing indicates that KNOWLA is reliable, and can be used to measure learning gains. KNOWLA also shows promise as a learning tool.","PeriodicalId":20664,"journal":{"name":"Proceedings of the Second (2015) ACM Conference on Learning @ Scale","volume":"134 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78669497","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}

{"title":"Adding Third-Party Authentication to Open edX: A Case Study","authors":"John Cox, P. Simakov","doi":"10.1145/2724660.2728675","DOIUrl":"https://doi.org/10.1145/2724660.2728675","url":null,"abstract":"In this document, we describe the third-party authentication system we added to Open edX. With this system, Open edX administrators can allow their users to sign in with a large array of external authentication providers. We outline the features and advantages of the system, describe how it can be extended and customized, and highlight reusable design principles that can be applied to other authentication implementations in online education.","PeriodicalId":20664,"journal":{"name":"Proceedings of the Second (2015) ACM Conference on Learning @ Scale","volume":"145 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77669023","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}

{"title":"Uncovering Trajectories of Informal Learning in Large Online Communities of Creators","authors":"Seungwon Yang, C. Domeniconi, Matt Revelle, Mack Sweeney, Ben U. Gelman, Chris Beckley, A. Johri","doi":"10.1145/2724660.2724674","DOIUrl":"https://doi.org/10.1145/2724660.2724674","url":null,"abstract":"We analyzed informal learning in Scratch Online -- an online community with over 4.3 million users and 6.7 million user-generated content. Users develop projects, which are graphical interfaces involving manipulation of programming blocks. We investigated two fundamental questions: how can we model informal learning, and what patterns of informal learning emerge. We proceeded in two phases. First, we modeled learning as a trajectory of cumulative programming block usage by long-term users who created at least 50 projects. Second, we applied K-means++ clustering to uncover patterns of learning and corresponding subpopulations. We found four groups of users manifesting four different patterns of learning, ranging from the smallest to the largest improvement. At one end of the spectrum, users learned more and in a faster manner. At the opposite end, users did not show much learning, even after creating dozens of projects. The modeling and clustering of trajectory patterns that enabled us to quantitatively analyze informal learning may be applicable to other similar communities. The results can also support administrators of online communities in implementing customized interventions for specific subpopulations.","PeriodicalId":20664,"journal":{"name":"Proceedings of the Second (2015) ACM Conference on Learning @ Scale","volume":"129 11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79598506","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}

{"title":"Does Online Q&A Activity Vary Based on Topic: A Comparison of Technical and Non-technical Stack Exchange Forums","authors":"Saif Ahmed, Seungwon Yang, A. Johri","doi":"10.1145/2724660.2728701","DOIUrl":"https://doi.org/10.1145/2724660.2728701","url":null,"abstract":"With the increasing demand on knowledge sharing and problem solving, there is a growing participation on online Question & Answer (Q&A) forums in the recent past. We classify the online community participation on Stack Exchange into two different genres, one is technical and another is non-technical. Though several studies have measured community activity, studies that compare activity across forums within different topic areas are limited. In this work we examine the effect of incentives on contributions by exploring the differences between technical and non-technical communities in terms of user's participation. Given the increased attention on discussion forums as part of online learning, especially MOOCs, we believe that our findings can assist with providing better support for learners across different content areas.","PeriodicalId":20664,"journal":{"name":"Proceedings of the Second (2015) ACM Conference on Learning @ Scale","volume":"68 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83865526","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}

{"title":"Achieving 96% Mastery at National Scale through Inspired Learning and Generative Adaptivity","authors":"Zoran Popovic","doi":"10.1145/2724660.2724684","DOIUrl":"https://doi.org/10.1145/2724660.2724684","url":null,"abstract":"Most of the current research on improving learning outcomes focuses on a small subset of variables of an immensely multi-dimensional space of the learning ecosystem. Most digital learning tools primarily focus on individual students, other research focuses only on teacher professional development, or only on curriculum improvement. In this talk I will describe our efforts on how to discover optimal parameters of the entire ecosystem that considers student factors (engagement and mastery), classroom factors (blended learning variations and group learning variations), curriculum factors (multidimensional variation of existing curricula), and teacher factors (in-class tools that mitigate weaknesses, and promote teacher development). I will describe our work on algorithms to discover optimal learning pathways in this high-dimensional space. I will conclude with the outcomes of deploying a portion of our platform on algebra challenges conducted on two US states and the country of Norway. Zoran Popovic is a Director of Center for Game Science at University of Washington and founder of Enlearn. Trained as a computer scientist his research focus is on creating interactive engaging environments for learning and scientific discovery. His laboratory created Foldit, a biochemistry game that produced three Nature publications in just two years, an award-winning math learning games played by over five million learners worldwide. He is currently focusing on engaging methods that can rapidly develop experts in arbitrary domains with particular focus on revolutionizing K-12 math education. His Algebra Challenges conducted in Washington, Minnesota, and Norway, have shown that 96% of children even in elementary school can learn key algebra concepts in 1.5 hours. He has recently founded Enlearn to apply his work on generative adaptation to any curricula towards the goal of achieving full mastery by 95% of students. His contributions to the field of interactive computer graphics have been recognized by a number of awards including the NSF CAREER Award, Alfred P. Sloan Fellowship and ACM SIGGRAPH Significant New Researcher Award.","PeriodicalId":20664,"journal":{"name":"Proceedings of the Second (2015) ACM Conference on Learning @ Scale","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81949192","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}

{"title":"Session details: Opening Keynote Address","authors":"G. Kiczales","doi":"10.1145/3077548.3257987","DOIUrl":"https://doi.org/10.1145/3077548.3257987","url":null,"abstract":"","PeriodicalId":20664,"journal":{"name":"Proceedings of the Second (2015) ACM Conference on Learning @ Scale","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86904123","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}

{"title":"The Visible and Invisible in a MOOC Discussion Forum","authors":"Eni Mustafaraj, Jessie Bu","doi":"10.1145/2724660.2728691","DOIUrl":"https://doi.org/10.1145/2724660.2728691","url":null,"abstract":"Online discussion forums in a MOOC setting allow students to become aware of other students enrolled in the course. However, what is (usually) visible in the forums is the output of ``active'' students who engage in asking and answering questions. In addition to such active participants, there is (as always in online communities) a large group of ``passive'' users (so-called lurkers), who might find the forum useful to their learning, and read it regularly, despite remaining ``invisible''. Our analysis of a large MOOC online forum shows that for every active participant in the forum there are two passive ones. 30% of active participants complete the course, compared to only 6.6% of the passive participants. Vice-versa, 67% of students who complete the course are also active in the forum. However, ``invisible activity'' (e.g. reading or searching the forum) is something that both groups practice equally and more frequently, while only 3.3% of forum actions are visible.","PeriodicalId":20664,"journal":{"name":"Proceedings of the Second (2015) ACM Conference on Learning @ Scale","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75717201","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}

{"title":"Behavior Prediction in MOOCs using Higher Granularity Temporal Information","authors":"Cheng Ye, J. Kinnebrew, Gautam Biswas, Brent J. Evans, D. Fisher, G. Narasimham, Katherine A. Brady","doi":"10.1145/2724660.2728687","DOIUrl":"https://doi.org/10.1145/2724660.2728687","url":null,"abstract":"In this paper, we present early research evaluating the predictive power of a variety of temporal features across student subpopulations with distinctive behaviors at the beginning of the course. Initial results illustrate that these features predict important differences across the subpopulations and over time in the courses. Ultimately, these results have implications for effectively targeting adaptive scaffolding tailored to the particular intentions and goals of subpopulations in MOOCs.","PeriodicalId":20664,"journal":{"name":"Proceedings of the Second (2015) ACM Conference on Learning @ Scale","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75968106","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}

{"title":"AutoStyle: Toward Coding Style Feedback at Scale","authors":"J. Moghadam, R. R. Choudhury, Hezheng Yin, A. Fox","doi":"10.1145/2724660.2728672","DOIUrl":"https://doi.org/10.1145/2724660.2728672","url":null,"abstract":"While large-scale automatic grading of student programs for correctness is widespread, less effort has focused on automating feedback for good programming style:} the tasteful use of language features and idioms to produce code that is not only correct, but also concise, elegant, and revealing of design intent. We hypothesize that with a large enough (MOOC-sized) corpus of submissions to a given programming problem, we can observe a range of stylistic mastery from naïve to expert, and many points in between, and that we can exploit this continuum to automatically provide hints to learners for improving their code style based on the key stylistic differences between a given learner's submission and a submission that is stylistically slightly better. We are developing a methodology for analyzing and doing feature engineering on differences between submissions, and for learning from instructor-provided feedback as to which hints are most relevant. We describe the techniques used to do this in our prototype, which will be deployed in a residential software engineering course as an alpha test prior to deploying in a MOOC later this year.","PeriodicalId":20664,"journal":{"name":"Proceedings of the Second (2015) ACM Conference on Learning @ Scale","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77843587","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}