M. Sahami, Jace Kohlmeier, Peter Norvig, A. Paepcke, A. Saberi
The software platforms that mediate online learning experiences are the common ground where learning science and computer science intersect. This panel will discuss the affordances of current online learning platforms and lessons learned in using them with students. The goal of the panel is to help learning scientists and computer scientists understand each others' needs and how they might be effectively addressed in these platforms. The panelists, who have experience creating/using these platforms and interacting with learning scientists, will discuss how current platforms for learning at scale might evolve to better serve the community.
{"title":"Panel: online learning platforms and data science","authors":"M. Sahami, Jace Kohlmeier, Peter Norvig, A. Paepcke, A. Saberi","doi":"10.1145/2556325.2579110","DOIUrl":"https://doi.org/10.1145/2556325.2579110","url":null,"abstract":"The software platforms that mediate online learning experiences are the common ground where learning science and computer science intersect. This panel will discuss the affordances of current online learning platforms and lessons learned in using them with students. The goal of the panel is to help learning scientists and computer scientists understand each others' needs and how they might be effectively addressed in these platforms. The panelists, who have experience creating/using these platforms and interacting with learning scientists, will discuss how current platforms for learning at scale might evolve to better serve the community.","PeriodicalId":20830,"journal":{"name":"Proceedings of the first ACM conference on Learning @ scale conference","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80069181","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}
Daniel Armendariz, Zachary MacHardy, Daniel D. Garcia
The Online Course Tool for Adaptive Learning (OCTAL) is an adaptive exercise system that customizes the progression of question topics to each student. By creating a concept dependency graph of topics in a course and modeling a student's knowledge state, the tool presents questions that test knowledge within a student's zone of proximal development. We intend OCTAL to be a formative assessment tool that is not tied to any specific course by providing language-agnostic questions on computer science concepts. While the tool will be generalizable for many courses, our first prototype includes a concept map and question set for UC Berkeley's introductory computer science course, CS10: The Beauty and Joy of Computing. Using the tool, we will launch an experiment in the spring to investigate metacognitive improvements in the identification of knowledge gaps by presenting online course material in a nonlinear fashion.
{"title":"OCTAL: online course tool for adaptive learning","authors":"Daniel Armendariz, Zachary MacHardy, Daniel D. Garcia","doi":"10.1145/2556325.2567849","DOIUrl":"https://doi.org/10.1145/2556325.2567849","url":null,"abstract":"The Online Course Tool for Adaptive Learning (OCTAL) is an adaptive exercise system that customizes the progression of question topics to each student. By creating a concept dependency graph of topics in a course and modeling a student's knowledge state, the tool presents questions that test knowledge within a student's zone of proximal development. We intend OCTAL to be a formative assessment tool that is not tied to any specific course by providing language-agnostic questions on computer science concepts. While the tool will be generalizable for many courses, our first prototype includes a concept map and question set for UC Berkeley's introductory computer science course, CS10: The Beauty and Joy of Computing. Using the tool, we will launch an experiment in the spring to investigate metacognitive improvements in the identification of knowledge gaps by presenting online course material in a nonlinear fashion.","PeriodicalId":20830,"journal":{"name":"Proceedings of the first ACM conference on Learning @ scale conference","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85336999","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}
Web-CAT, the Web-based Center for Automated Testing, is the most widely used open-source automated grading system for programming assignments in the world. Web-CAT is customizable and extensible, allowing it to support a wide variety of programming languages and assessment strategies. Web-CAT is most well known as the system that "grades students on how well they test their own code," with experimental evidence that it offers greater learning benefits than more traditional output-comparison grading. This work-in-progress demonstration will show how Web-CAT can be used to automatically grade student work, assess conformance with coding style guidelines, provide students with feedback on how well they have tested their own code, and allow instructors to provide directed hints to students on where to focus their attention for improvements.
{"title":"Work-in-progress: program grading and feedback generation with Web-CAT","authors":"S. Edwards","doi":"10.1145/2556325.2567888","DOIUrl":"https://doi.org/10.1145/2556325.2567888","url":null,"abstract":"Web-CAT, the Web-based Center for Automated Testing, is the most widely used open-source automated grading system for programming assignments in the world. Web-CAT is customizable and extensible, allowing it to support a wide variety of programming languages and assessment strategies. Web-CAT is most well known as the system that \"grades students on how well they test their own code,\" with experimental evidence that it offers greater learning benefits than more traditional output-comparison grading. This work-in-progress demonstration will show how Web-CAT can be used to automatically grade student work, assess conformance with coding style guidelines, provide students with feedback on how well they have tested their own code, and allow instructors to provide directed hints to students on where to focus their attention for improvements.","PeriodicalId":20830,"journal":{"name":"Proceedings of the first ACM conference on Learning @ scale conference","volume":"47 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91455632","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}
A. Cross, B. Ashok, S. Bala, Edward Cutrell, Naren Datha, Rahul Kumar, Viraj Kumar, P. Madhusudan, Siddharth Prakash, S. Rajamani, Satish Sangameswaran, Deepika Sharma, W. Thies
Due to the recent emergence of massive open online courses (MOOCs), students and teachers are gaining unprecedented access to high-quality educational content. However, many questions remain on how best to utilize that content in a classroom environment. In this small-scale, exploratory study, we compared two ways of using a recorded video lecture. In the online learning condition, students viewed the video on a personal computer, and also viewed a follow-up tutorial (a quiz review) on the computer. In the blended learning condition, students viewed the video as a group in a classroom, and received the follow-up tutorial from a live lecturer. We randomly assigned 102 students to these conditions, and assessed learning outcomes via a series of quizzes. While we saw significant learning gains after each session conducted, we did not observe any significant differences between the online and blended learning groups. We discuss these findings as well as areas for future work.
{"title":"Online learning versus blended learning: an exploratory study","authors":"A. Cross, B. Ashok, S. Bala, Edward Cutrell, Naren Datha, Rahul Kumar, Viraj Kumar, P. Madhusudan, Siddharth Prakash, S. Rajamani, Satish Sangameswaran, Deepika Sharma, W. Thies","doi":"10.1145/2556325.2567869","DOIUrl":"https://doi.org/10.1145/2556325.2567869","url":null,"abstract":"Due to the recent emergence of massive open online courses (MOOCs), students and teachers are gaining unprecedented access to high-quality educational content. However, many questions remain on how best to utilize that content in a classroom environment. In this small-scale, exploratory study, we compared two ways of using a recorded video lecture. In the online learning condition, students viewed the video on a personal computer, and also viewed a follow-up tutorial (a quiz review) on the computer. In the blended learning condition, students viewed the video as a group in a classroom, and received the follow-up tutorial from a live lecturer. We randomly assigned 102 students to these conditions, and assessed learning outcomes via a series of quizzes. While we saw significant learning gains after each session conducted, we did not observe any significant differences between the online and blended learning groups. We discuss these findings as well as areas for future work.","PeriodicalId":20830,"journal":{"name":"Proceedings of the first ACM conference on Learning @ scale conference","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91111891","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}
Videos are a widely-used kind of resource for online learning. This paper presents an empirical study of how video production decisions affect student engagement in online educational videos. To our knowledge, ours is the largest-scale study of video engagement to date, using data from 6.9 million video watching sessions across four courses on the edX MOOC platform. We measure engagement by how long students are watching each video, and whether they attempt to answer post-video assessment problems. Our main findings are that shorter videos are much more engaging, that informal talking-head videos are more engaging, that Khan-style tablet drawings are more engaging, that even high-quality pre-recorded classroom lectures might not make for engaging online videos, and that students engage differently with lecture and tutorial videos. Based upon these quantitative findings and qualitative insights from interviews with edX staff, we developed a set of recommendations to help instructors and video producers take better advantage of the online video format. Finally, to enable researchers to reproduce and build upon our findings, we have made our anonymized video watching data set and analysis scripts public. To our knowledge, ours is one of the first public data sets on MOOC resource usage.
{"title":"How video production affects student engagement: an empirical study of MOOC videos","authors":"Philip J. Guo, Juho Kim, Rob Rubin","doi":"10.1145/2556325.2566239","DOIUrl":"https://doi.org/10.1145/2556325.2566239","url":null,"abstract":"Videos are a widely-used kind of resource for online learning. This paper presents an empirical study of how video production decisions affect student engagement in online educational videos. To our knowledge, ours is the largest-scale study of video engagement to date, using data from 6.9 million video watching sessions across four courses on the edX MOOC platform. We measure engagement by how long students are watching each video, and whether they attempt to answer post-video assessment problems. Our main findings are that shorter videos are much more engaging, that informal talking-head videos are more engaging, that Khan-style tablet drawings are more engaging, that even high-quality pre-recorded classroom lectures might not make for engaging online videos, and that students engage differently with lecture and tutorial videos. Based upon these quantitative findings and qualitative insights from interviews with edX staff, we developed a set of recommendations to help instructors and video producers take better advantage of the online video format. Finally, to enable researchers to reproduce and build upon our findings, we have made our anonymized video watching data set and analysis scripts public. To our knowledge, ours is one of the first public data sets on MOOC resource usage.","PeriodicalId":20830,"journal":{"name":"Proceedings of the first ACM conference on Learning @ scale conference","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90461025","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}
Arti Ramesh, Dan Goldwasser, Bert Huang, Hal Daumé, L. Getoor
Maintaining and cultivating student engagement is a prerequisite for MOOCs to have broad educational impact. Understanding student engagement as a course progresses helps characterize student learning patterns and can aid in minimizing dropout rates, initiating instructor intervention. In this paper, we construct a probabilistic model connecting student behavior and class performance, formulating student engagement types as latent variables. We show that our model identifies course success indicators that can be used by instructors to initiate interventions and assist students.
{"title":"Uncovering hidden engagement patterns for predicting learner performance in MOOCs","authors":"Arti Ramesh, Dan Goldwasser, Bert Huang, Hal Daumé, L. Getoor","doi":"10.1145/2556325.2567857","DOIUrl":"https://doi.org/10.1145/2556325.2567857","url":null,"abstract":"Maintaining and cultivating student engagement is a prerequisite for MOOCs to have broad educational impact. Understanding student engagement as a course progresses helps characterize student learning patterns and can aid in minimizing dropout rates, initiating instructor intervention. In this paper, we construct a probabilistic model connecting student behavior and class performance, formulating student engagement types as latent variables. We show that our model identifies course success indicators that can be used by instructors to initiate interventions and assist students.","PeriodicalId":20830,"journal":{"name":"Proceedings of the first ACM conference on Learning @ scale conference","volume":"168 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87620057","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}
Understanding motivations for enrolling in MOOCs is key for personalizing and scaling the online learning experience. We develop a standardized survey item for measuring learners' reasons to enroll, based on a corpus of open-ended responses from previous course surveys. Online coders were employed in the iterative development of response options. The item was designed to minimize response biases by adhering to best practices from survey design research.
{"title":"\"Why did you enroll in this course?\": developing a standardized survey question for reasons to enroll","authors":"Emily Schneider, René F. Kizilcec","doi":"10.1145/2556325.2567852","DOIUrl":"https://doi.org/10.1145/2556325.2567852","url":null,"abstract":"Understanding motivations for enrolling in MOOCs is key for personalizing and scaling the online learning experience. We develop a standardized survey item for measuring learners' reasons to enroll, based on a corpus of open-ended responses from previous course surveys. Online coders were employed in the iterative development of response options. The item was designed to minimize response biases by adhering to best practices from survey design research.","PeriodicalId":20830,"journal":{"name":"Proceedings of the first ACM conference on Learning @ scale conference","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74312991","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}
In large programming classes, MOOCs or online communities, it is challenging to find peers and mentors to help with learning specific programming concepts. In this paper we present first steps towards an automated, scalable system for matching learners with Python programmers who have expertise in different areas. The learner matching system builds a knowledge model for each programmer by analyzing their authored code and extracting features that capture domain knowledge and style. We demonstrate the feasibility of a simple model that counts the references to modules from the standard library and Python Package Index in a programmers' code. We also show that programmers exhibit self-selection using which we can extract the modules a programmer is best at, even though we may not have all of their code. In our future work we aim to extend the model to encapsulate more features, and apply it for skill matching in a programming class as well as personalizing answers on StackOverflow.
{"title":"Modeling programming knowledge for mentoring at scale","authors":"A. Pai, Philip J. Guo, Rob Miller","doi":"10.1145/2556325.2567871","DOIUrl":"https://doi.org/10.1145/2556325.2567871","url":null,"abstract":"In large programming classes, MOOCs or online communities, it is challenging to find peers and mentors to help with learning specific programming concepts. In this paper we present first steps towards an automated, scalable system for matching learners with Python programmers who have expertise in different areas. The learner matching system builds a knowledge model for each programmer by analyzing their authored code and extracting features that capture domain knowledge and style. We demonstrate the feasibility of a simple model that counts the references to modules from the standard library and Python Package Index in a programmers' code. We also show that programmers exhibit self-selection using which we can extract the modules a programmer is best at, even though we may not have all of their code. In our future work we aim to extend the model to encapsulate more features, and apply it for skill matching in a programming class as well as personalizing answers on StackOverflow.","PeriodicalId":20830,"journal":{"name":"Proceedings of the first ACM conference on Learning @ scale conference","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79159656","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":"Proceedings of the first ACM conference on Learning @ scale conference","authors":"","doi":"10.1145/2556325","DOIUrl":"https://doi.org/10.1145/2556325","url":null,"abstract":"","PeriodicalId":20830,"journal":{"name":"Proceedings of the first ACM conference on Learning @ scale conference","volume":"72 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85943002","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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