Educational Robotics for Absolute Beginners is a MOOC designed to introduce K-12 teachers with no prior computer science or robotics experience to the basics of LEGO NXT Robot programming. The course was developed following several successful in-person workshops on the same topic. This paper introduces some of the issues that arose as we transitioned the material to a MOOC, describes some of the unique challenges we faced by incorporating specialized hardware into a MOOC, and presents some preliminary data evaluating the success of our approach.
{"title":"The challenges of using a MOOC to introduce \"absolute beginners\" to programming on specialized hardware","authors":"J. Kay, Tom McKlin","doi":"10.1145/2556325.2567886","DOIUrl":"https://doi.org/10.1145/2556325.2567886","url":null,"abstract":"Educational Robotics for Absolute Beginners is a MOOC designed to introduce K-12 teachers with no prior computer science or robotics experience to the basics of LEGO NXT Robot programming. The course was developed following several successful in-person workshops on the same topic. This paper introduces some of the issues that arose as we transitioned the material to a MOOC, describes some of the unique challenges we faced by incorporating specialized hardware into a MOOC, and presents some preliminary data evaluating the success of our approach.","PeriodicalId":20830,"journal":{"name":"Proceedings of the first ACM conference on Learning @ scale conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73262367","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}
Learning programming at scale underlies computer science education ranging from basic programming to advanced software engineering topics. There are strong needs of providing effective system supports for learning programming at scale. Among various desirable characteristics of such system supports, system supports shall allow students to write programs via an online Integrated Development Environment (IDE), allow students to get feedback on how they perform on the given programming exercises, etc. To aim for such effective system supports for learning programming at scale, research teams from Peking University have developed two systems: POP (denoting Peking University Online Programming System) and POJ (denoting Peking University Online Judge System). These two systems have achieved high impact among students around the world (especially those in China). In this paper, we present the overview of the two systems, along with our ongoing and future work on extending the systems for achieving higher effectiveness in supporting learning programming at scale.
{"title":"Educational programming systems for learning at scale","authors":"Qianxiang Wang, Wenxin Li, T. Xie","doi":"10.1145/2556325.2567868","DOIUrl":"https://doi.org/10.1145/2556325.2567868","url":null,"abstract":"Learning programming at scale underlies computer science education ranging from basic programming to advanced software engineering topics. There are strong needs of providing effective system supports for learning programming at scale. Among various desirable characteristics of such system supports, system supports shall allow students to write programs via an online Integrated Development Environment (IDE), allow students to get feedback on how they perform on the given programming exercises, etc. To aim for such effective system supports for learning programming at scale, research teams from Peking University have developed two systems: POP (denoting Peking University Online Programming System) and POJ (denoting Peking University Online Judge System). These two systems have achieved high impact among students around the world (especially those in China). In this paper, we present the overview of the two systems, along with our ongoing and future work on extending the systems for achieving higher effectiveness in supporting learning programming at scale.","PeriodicalId":20830,"journal":{"name":"Proceedings of the first ACM conference on Learning @ scale conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80019993","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}
The invention of the movie camera was initially seen as a way to reach "massive" by filming plays; we are in an equivalent stage with our early MOOCs. Thinking outside the box of "teaching" is essential to realizing learning at scale. Virtual worlds and augmented realities can complement digitized classroom instruction through simulated apprenticeships, embedded support for learning everywhere, and transformed social interactions. Going big also requires thinking small: analyzing diagnostic micro-patterns to customize individual learning, sifting through millions of participants to find the ideal partners to aid each other's growth. To reach massive with universal access and powerful outcomes, we must creatively expand our visions of platforms, pedagogy, and financing.
{"title":"New wine in no bottles: immersive, personalized ubiquitous learning","authors":"C. Dede","doi":"10.1145/2556325.2578292","DOIUrl":"https://doi.org/10.1145/2556325.2578292","url":null,"abstract":"The invention of the movie camera was initially seen as a way to reach \"massive\" by filming plays; we are in an equivalent stage with our early MOOCs. Thinking outside the box of \"teaching\" is essential to realizing learning at scale. Virtual worlds and augmented realities can complement digitized classroom instruction through simulated apprenticeships, embedded support for learning everywhere, and transformed social interactions. Going big also requires thinking small: analyzing diagnostic micro-patterns to customize individual learning, sifting through millions of participants to find the ideal partners to aid each other's growth. To reach massive with universal access and powerful outcomes, we must creatively expand our visions of platforms, pedagogy, and financing.","PeriodicalId":20830,"journal":{"name":"Proceedings of the first ACM conference on Learning @ scale conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81519923","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}
Given a class of large number of students, each exhibiting a different ability level, how can we form teams of students so that the expected performance of team members improves due to team participation? We take a computational perspective and formally define two versions of such team-formation problem: the MAXTEAM and the MAXPARTITION problems. The first asks for the identification of a single team of students that improves the performance of most of the participating team members. The second asks for a partitioning of students into non-overlapping teams that also maximizes the benefit of the participating students. We show that the first problem can be solved optimally in polynomial time, while the second is NP-complete. For the MAXPARTITION problem, we also design an efficient approximate algorithm for solving it. Our experiments with generated data coming from different distributions demonstrate that our algorithm is significantly better than any of the popular strategies for dividing students in a class into sections.
{"title":"Forming beneficial teams of students in massive online classes","authors":"R. Agrawal, Behzad Golshan, Evimaria Terzi","doi":"10.1145/2556325.2567856","DOIUrl":"https://doi.org/10.1145/2556325.2567856","url":null,"abstract":"Given a class of large number of students, each exhibiting a different ability level, how can we form teams of students so that the expected performance of team members improves due to team participation? We take a computational perspective and formally define two versions of such team-formation problem: the MAXTEAM and the MAXPARTITION problems. The first asks for the identification of a single team of students that improves the performance of most of the participating team members. The second asks for a partitioning of students into non-overlapping teams that also maximizes the benefit of the participating students. We show that the first problem can be solved optimally in polynomial time, while the second is NP-complete. For the MAXPARTITION problem, we also design an efficient approximate algorithm for solving it. Our experiments with generated data coming from different distributions demonstrate that our algorithm is significantly better than any of the popular strategies for dividing students in a class into sections.","PeriodicalId":20830,"journal":{"name":"Proceedings of the first ACM conference on Learning @ scale conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81490843","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}
Computer-based learning environments can provide valuable resources for learning at scale, but students in these environments might learn without an instructor. Subgoal labels have been used in worked examples in STEM domains to help a learner understand the purpose of a set of steps, and this feature has increased problem solving performance [1]. Subgoal labels, however, have not been tested in instructional text. The present study explored this intervention. The results of the present study show that learners who received subgoal labels in both the text and example outperformed those in other conditions. When subgoal labeled text is paired with an unlabeled example, however, performance does not improve. These findings indicate that subgoal labeled instructional text when paired with subgoal labeled examples can improve performance in a computer-based learning environment.
{"title":"Improving problem solving performance in computer-based learning environments through subgoal labels","authors":"Lauren E. Margulieux, R. Catrambone","doi":"10.1145/2556325.2567853","DOIUrl":"https://doi.org/10.1145/2556325.2567853","url":null,"abstract":"Computer-based learning environments can provide valuable resources for learning at scale, but students in these environments might learn without an instructor. Subgoal labels have been used in worked examples in STEM domains to help a learner understand the purpose of a set of steps, and this feature has increased problem solving performance [1]. Subgoal labels, however, have not been tested in instructional text. The present study explored this intervention. The results of the present study show that learners who received subgoal labels in both the text and example outperformed those in other conditions. When subgoal labeled text is paired with an unlabeled example, however, performance does not improve. These findings indicate that subgoal labeled instructional text when paired with subgoal labeled examples can improve performance in a computer-based learning environment.","PeriodicalId":20830,"journal":{"name":"Proceedings of the first ACM conference on Learning @ scale conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83667179","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}
Education and learning are currently undergoing transformative changes due to the emergence of tablet devices, cloud computing, and abundant online content. These trends present opportunities to transform traditional paper-based textbooks into tablet-based electronic textbooks, and to further enrich the educational experience by augmenting them with relevant supplementary materials. A natural question is whether this educational intervention, namely, enriching textbooks with relevant web articles, images and videos, is effective. It turns out that designing an experiment at scale for this purpose is nontrivial. We report on progress in designing and carrying out such an experiment.
{"title":"Evaluating educational interventions at scale","authors":"R. Agrawal, M. H. Jhaveri, K. Kenthapadi","doi":"10.1145/2556325.2567884","DOIUrl":"https://doi.org/10.1145/2556325.2567884","url":null,"abstract":"Education and learning are currently undergoing transformative changes due to the emergence of tablet devices, cloud computing, and abundant online content. These trends present opportunities to transform traditional paper-based textbooks into tablet-based electronic textbooks, and to further enrich the educational experience by augmenting them with relevant supplementary materials. A natural question is whether this educational intervention, namely, enriching textbooks with relevant web articles, images and videos, is effective. It turns out that designing an experiment at scale for this purpose is nontrivial. We report on progress in designing and carrying out such an experiment.","PeriodicalId":20830,"journal":{"name":"Proceedings of the first ACM conference on Learning @ scale conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74392248","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}
Julia Cambre, Chinmay Kulkarni, Michael S. Bernstein, Scott R. Klemmer
In the physical classroom, peer interactions motivate students and expand their perspective. We suggest that synchronous peer interaction can benefit massive online courses as well. Talkabout organizes students into video discussion groups and allows instructors to determine group composition and discussion content. Using Talkabout, students pick a discussion time that suits their schedule. The system groups the students into small video discussions based on instructor preferences such as gender or geographic balance. To date, 2,474 students in five massive online courses have used Talkabout to discuss topics ranging from prejudice to organizational theory. Talkabout discussions are diverse: in one course, the median six-person discussion group had students from four different countries. Students enjoyed discussing in these diverse groups: the average student participated for 66 minutes, twice the course requirement. Students in more geographically distributed groups also scored higher on the final, suggesting that distributed discussions have educational value.
{"title":"Talkabout: small-group discussions in massive global classes","authors":"Julia Cambre, Chinmay Kulkarni, Michael S. Bernstein, Scott R. Klemmer","doi":"10.1145/2556325.2567859","DOIUrl":"https://doi.org/10.1145/2556325.2567859","url":null,"abstract":"In the physical classroom, peer interactions motivate students and expand their perspective. We suggest that synchronous peer interaction can benefit massive online courses as well. Talkabout organizes students into video discussion groups and allows instructors to determine group composition and discussion content. Using Talkabout, students pick a discussion time that suits their schedule. The system groups the students into small video discussions based on instructor preferences such as gender or geographic balance. To date, 2,474 students in five massive online courses have used Talkabout to discuss topics ranging from prejudice to organizational theory. Talkabout discussions are diverse: in one course, the median six-person discussion group had students from four different countries. Students enjoyed discussing in these diverse groups: the average student participated for 66 minutes, twice the course requirement. Students in more geographically distributed groups also scored higher on the final, suggesting that distributed discussions have educational value.","PeriodicalId":20830,"journal":{"name":"Proceedings of the first ACM conference on Learning @ scale conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84677565","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}
Programming best-practices are a difficult subject to learn for beginner computer science students. In the classroom, these practices are appreciated and taught through a combination of lectures and group projects. Group projects, however, take time and are ill-suited for Massive Open Online Courses (MOOCs). This project aims to develop a web-based many-player programming game which addresses these issues by having large numbers of students code many small functions in parallel, give feedback on each other's implementations, and compose them into much larger programs. Gameplay will require only a few hours and should provide rapid and substantive feedback on the reusability and flexibility of a student's code. We have developed and playtested a small-scale prototype to determine if software engineering lessons could be learned through such a game. Further prototypes will test the game at MOOC scales and with different structures. We will develop a final version to deploy to MIT's online class 6.005x: Software Construction.
{"title":"A multiplayer online game for teaching software engineering practices","authors":"David Xiao, Rob Miller","doi":"10.1145/2556325.2567858","DOIUrl":"https://doi.org/10.1145/2556325.2567858","url":null,"abstract":"Programming best-practices are a difficult subject to learn for beginner computer science students. In the classroom, these practices are appreciated and taught through a combination of lectures and group projects. Group projects, however, take time and are ill-suited for Massive Open Online Courses (MOOCs). This project aims to develop a web-based many-player programming game which addresses these issues by having large numbers of students code many small functions in parallel, give feedback on each other's implementations, and compose them into much larger programs. Gameplay will require only a few hours and should provide rapid and substantive feedback on the reusability and flexibility of a student's code. We have developed and playtested a small-scale prototype to determine if software engineering lessons could be learned through such a game. Further prototypes will test the game at MOOC scales and with different structures. We will develop a final version to deploy to MIT's online class 6.005x: Software Construction.","PeriodicalId":20830,"journal":{"name":"Proceedings of the first ACM conference on Learning @ scale conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86339102","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}
The current generation of Massive Open Online Courses (MOOCs) attract a diverse student audience from all age groups and over 196 countries around the world. Researchers, educators, and the general public have recently become interested in how the learning experience in MOOCs differs from that in traditional courses. A major component of the learning experience is how students navigate through course content. This paper presents an empirical study of how students navigate through MOOCs, and is, to our knowledge, the first to investigate how navigation strategies differ by demographics such as age and country of origin. We performed data analysis on the activities of 140,546 students in four edX MOOCs and found that certificate earners skip on average 22% of the course content, that they frequently employ non-linear navigation by jumping backward to earlier lecture sequences, and that older students and those from countries with lower student-teacher ratios are more comprehensive and non-linear when navigating through the course. From these findings, we suggest design recommendations such as for MOOC platforms to develop more detailed forms of certification that incentivize students to deeply engage with the content rather than just doing the minimum necessary to earn a passing grade. Finally, to enable other researchers to reproduce and build upon our findings, we have made our data set and analysis scripts publicly available.
{"title":"Demographic differences in how students navigate through MOOCs","authors":"Philip J. Guo, Katharina Reinecke","doi":"10.1145/2556325.2566247","DOIUrl":"https://doi.org/10.1145/2556325.2566247","url":null,"abstract":"The current generation of Massive Open Online Courses (MOOCs) attract a diverse student audience from all age groups and over 196 countries around the world. Researchers, educators, and the general public have recently become interested in how the learning experience in MOOCs differs from that in traditional courses. A major component of the learning experience is how students navigate through course content. This paper presents an empirical study of how students navigate through MOOCs, and is, to our knowledge, the first to investigate how navigation strategies differ by demographics such as age and country of origin. We performed data analysis on the activities of 140,546 students in four edX MOOCs and found that certificate earners skip on average 22% of the course content, that they frequently employ non-linear navigation by jumping backward to earlier lecture sequences, and that older students and those from countries with lower student-teacher ratios are more comprehensive and non-linear when navigating through the course. From these findings, we suggest design recommendations such as for MOOC platforms to develop more detailed forms of certification that incentivize students to deeply engage with the content rather than just doing the minimum necessary to earn a passing grade. Finally, to enable other researchers to reproduce and build upon our findings, we have made our data set and analysis scripts publicly available.","PeriodicalId":20830,"journal":{"name":"Proceedings of the first ACM conference on Learning @ scale conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83571358","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}
Video games are increasingly recognized as a compelling platform for instruction that could be leveraged to teach students at scale. Hint systems that provide personalized feedback to students in real time are a central component of many effective interactive learning environments, however little is known about how hints impact player behavior and motivation in educational games. In this work, we study the effectiveness of hints by comparing four designs based on successful hint systems in intelligent tutoring systems and commercial games. We present results from a study of 50,000 students showing that all four hint systems negatively impacted performance compared to a baseline condition with no hints. These results suggest that traditional hint systems may not translate well into the educational game environment, highlighting the importance of studying student behavior to understand the impact of new interactive learning technologies.
{"title":"Hint systems may negatively impact performance in educational games","authors":"Eleanor O'Rourke, Christy Ballweber, Zoran Popovic","doi":"10.1145/2556325.2566248","DOIUrl":"https://doi.org/10.1145/2556325.2566248","url":null,"abstract":"Video games are increasingly recognized as a compelling platform for instruction that could be leveraged to teach students at scale. Hint systems that provide personalized feedback to students in real time are a central component of many effective interactive learning environments, however little is known about how hints impact player behavior and motivation in educational games. In this work, we study the effectiveness of hints by comparing four designs based on successful hint systems in intelligent tutoring systems and commercial games. We present results from a study of 50,000 students showing that all four hint systems negatively impacted performance compared to a baseline condition with no hints. These results suggest that traditional hint systems may not translate well into the educational game environment, highlighting the importance of studying student behavior to understand the impact of new interactive learning technologies.","PeriodicalId":20830,"journal":{"name":"Proceedings of the first ACM conference on Learning @ scale conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87383955","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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