J. Uchidiuno, A. Ogan, Evelyn Yarzebinski, Jessica Hammer
Massive Open Online Courses (MOOCs) have the potential to bridge education and literacy gaps by offering high quality, free courses to anyone with an Internet connection. MOOCs in their present state, however, may be relatively inaccessible to non-native English speakers, as a majority of MOOC content is in the English language. While a potential solution is to translate all MOOC content into all languages, it is not known whether this solution will satisfy the learning goals of all English as a Second Language (ESL) speakers. Through a series of interviews, we investigate ESL speakers' motivations for taking MOOCs and other online courses. Our findings show that ESL speakers have a variety of motivations for taking online courses that are not captured in current surveys, which implies that current one-size-fits-all approaches to increasing MOOC accessibility for learners with a first language other than English may not be effective. Rather, offering learners individualized tools based on their motivation and needs may be more effective.
{"title":"Understanding ESL Students' Motivations to Increase MOOC Accessibility","authors":"J. Uchidiuno, A. Ogan, Evelyn Yarzebinski, Jessica Hammer","doi":"10.1145/2876034.2893398","DOIUrl":"https://doi.org/10.1145/2876034.2893398","url":null,"abstract":"Massive Open Online Courses (MOOCs) have the potential to bridge education and literacy gaps by offering high quality, free courses to anyone with an Internet connection. MOOCs in their present state, however, may be relatively inaccessible to non-native English speakers, as a majority of MOOC content is in the English language. While a potential solution is to translate all MOOC content into all languages, it is not known whether this solution will satisfy the learning goals of all English as a Second Language (ESL) speakers. Through a series of interviews, we investigate ESL speakers' motivations for taking MOOCs and other online courses. Our findings show that ESL speakers have a variety of motivations for taking online courses that are not captured in current surveys, which implies that current one-size-fits-all approaches to increasing MOOC accessibility for learners with a first language other than English may not be effective. Rather, offering learners individualized tools based on their motivation and needs may be more effective.","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":"90977466","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 research reported in this paper used a researcher developed tool to categorize the pedagogical approaches used in MOOCs. The Assessing MOOC Pedagogies (AMP) tool characterized MOOC pedagogical approaches on ten dimensions. Preliminary testing on 20 different MOOCs demonstrated >= 80% inter-reliability and the facility of the measure to distinguish differing pedagogical patterns. The patterns distinguished crossed content areas and seemed to be related to what Sfard (1998) identified as metaphors for learning; acquisition and participation approaches seemed to distinguish the pedagogies of differing MOOCs. A third, arguably important, pattern related to self-direction was also distinguished.
{"title":"Metaphors for Learning and MOOC Pedagogies","authors":"Karen Swan, S. Day, L. Bogle","doi":"10.1145/2876034.2893385","DOIUrl":"https://doi.org/10.1145/2876034.2893385","url":null,"abstract":"The research reported in this paper used a researcher developed tool to categorize the pedagogical approaches used in MOOCs. The Assessing MOOC Pedagogies (AMP) tool characterized MOOC pedagogical approaches on ten dimensions. Preliminary testing on 20 different MOOCs demonstrated >= 80% inter-reliability and the facility of the measure to distinguish differing pedagogical patterns. The patterns distinguished crossed content areas and seemed to be related to what Sfard (1998) identified as metaphors for learning; acquisition and participation approaches seemed to distinguish the pedagogies of differing MOOCs. A third, arguably important, pattern related to self-direction was also distinguished.","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":"75431951","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}
Intelligent tutoring systems are known for providing customized learning opportunities for thousands of users. One feature of many systems is differentiating the amount of practice users receive. To do this, some systems rely on a threshold of consecutive correct responses. For instance, Khan Academy used to use ten correct in a row and now uses five correct in a row as the mastery threshold. The present research uses a series of randomized control trials, conducted in an online learning platform (eg., ASSISTments.org), to explore the effects of different thresholds of consecutive correct responses on learning. Results indicate that despite spending significantly more time practicing there is no significant difference on learning between two, three, four, or five consecutive correct responses. This suggests that systems, and MOOCS, can employ the simple rule of two or three consecutive correct responses when determining the amount of practice provided to users.
{"title":"Optimizing the Amount of Practice in an On-Line Platform","authors":"K. Kelly, N. Heffernan","doi":"10.1145/2876034.2893393","DOIUrl":"https://doi.org/10.1145/2876034.2893393","url":null,"abstract":"Intelligent tutoring systems are known for providing customized learning opportunities for thousands of users. One feature of many systems is differentiating the amount of practice users receive. To do this, some systems rely on a threshold of consecutive correct responses. For instance, Khan Academy used to use ten correct in a row and now uses five correct in a row as the mastery threshold. The present research uses a series of randomized control trials, conducted in an online learning platform (eg., ASSISTments.org), to explore the effects of different thresholds of consecutive correct responses on learning. Results indicate that despite spending significantly more time practicing there is no significant difference on learning between two, three, four, or five consecutive correct responses. This suggests that systems, and MOOCS, can employ the simple rule of two or three consecutive correct responses when determining the amount of practice provided to users.","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":"86333268","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}
Saijing Zheng, Kyungsik Han, M. Rosson, John Millar Carroll
The Massive Open Online Courses (MOOC) have experienced rapid development. However, high dropout rate has become a salient issue. Many studies have attempted to understand this phenomenon; other have explored mechanisms for enhancing retention. For instance, social media has been used to improve student engagement and retention. However there is a lack of (1) empirical studies of social media use and engagement compared to embedded MOOC forums; and (2) rationales for social media use from both instructors' and students' perspectives. We addressed these open issues through the collection and analysis of real usage data from three MOOC forums and their associated social media (i.e., Facebook) groups as well as conducting interviews of instructors and students. We found that students show higher engagement and retention in social media than in MOOC forums, and identified both instructors' and students' perspectives that lead to the results. We discuss design implications for future MOOC platforms.
{"title":"The Role of Social Media in MOOCs: How to Use Social Media to Enhance Student Retention","authors":"Saijing Zheng, Kyungsik Han, M. Rosson, John Millar Carroll","doi":"10.1145/2876034.2876047","DOIUrl":"https://doi.org/10.1145/2876034.2876047","url":null,"abstract":"The Massive Open Online Courses (MOOC) have experienced rapid development. However, high dropout rate has become a salient issue. Many studies have attempted to understand this phenomenon; other have explored mechanisms for enhancing retention. For instance, social media has been used to improve student engagement and retention. However there is a lack of (1) empirical studies of social media use and engagement compared to embedded MOOC forums; and (2) rationales for social media use from both instructors' and students' perspectives. We addressed these open issues through the collection and analysis of real usage data from three MOOC forums and their associated social media (i.e., Facebook) groups as well as conducting interviews of instructors and students. We found that students show higher engagement and retention in social media than in MOOC forums, and identified both instructors' and students' perspectives that lead to the results. We discuss design implications for future MOOC platforms.","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":"87795993","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 rising number of Massive Open Online Courses (MOOCs) enable people to advance their knowledge and competencies in a wide range of fields. Learning though is only the first step, the transfer of the taught concepts into practice is equally important and often neglected in the investigation of MOOCs. In this paper, we consider the specific case of FP101x (a functional programming MOOC on edX) and the extent to which learners alter their programming behaviour after having taken the course. We are able to link about one third of all FP101x learners to GitHub, the most popular social coding platform to date and contribute a first exploratory analysis of learner behaviour beyond the MOOC platform. A detailed longitudinal analysis of GitHub log traces reveals that (i) more than 8% of engaged learners transfer, and that (ii) most existing transfer learning findings from the classroom setting are indeed applicable in the MOOC setting as well.
{"title":"Learning Transfer: Does It Take Place in MOOCs? An Investigation into the Uptake of Functional Programming in Practice","authors":"Guanliang Chen, Dan Davis, C. Hauff, G. Houben","doi":"10.1145/2876034.2876035","DOIUrl":"https://doi.org/10.1145/2876034.2876035","url":null,"abstract":"The rising number of Massive Open Online Courses (MOOCs) enable people to advance their knowledge and competencies in a wide range of fields. Learning though is only the first step, the transfer of the taught concepts into practice is equally important and often neglected in the investigation of MOOCs. In this paper, we consider the specific case of FP101x (a functional programming MOOC on edX) and the extent to which learners alter their programming behaviour after having taken the course. We are able to link about one third of all FP101x learners to GitHub, the most popular social coding platform to date and contribute a first exploratory analysis of learner behaviour beyond the MOOC platform. A detailed longitudinal analysis of GitHub log traces reveals that (i) more than 8% of engaged learners transfer, and that (ii) most existing transfer learning findings from the classroom setting are indeed applicable in the MOOC setting as well.","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":"84567413","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}
Mehdi S. M. Sajjadi, Morteza Alamgir, U. V. Luxburg
Peer grading is the process of students reviewing each others' work, such as homework submissions, and has lately become a popular mechanism used in massive open online courses (MOOCs). Intrigued by this idea, we used it in a course on algorithms and data structures at the University of Hamburg. Throughout the whole semester, students repeatedly handed in submissions to exercises, which were then evaluated both by teaching assistants and by a peer grading mechanism, yielding a large dataset of teacher and peer grades. We applied different statistical and machine learning methods to aggregate the peer grades in order to come up with accurate final grades for the submissions (supervised and unsupervised, methods based on numeric scores and ordinal rankings). Surprisingly, none of them improves over the baseline of using the mean peer grade as the final grade. We discuss a number of possible explanations for these results and present a thorough analysis of the generated dataset.
{"title":"Peer Grading in a Course on Algorithms and Data Structures: Machine Learning Algorithms do not Improve over Simple Baselines","authors":"Mehdi S. M. Sajjadi, Morteza Alamgir, U. V. Luxburg","doi":"10.1145/2876034.2876036","DOIUrl":"https://doi.org/10.1145/2876034.2876036","url":null,"abstract":"Peer grading is the process of students reviewing each others' work, such as homework submissions, and has lately become a popular mechanism used in massive open online courses (MOOCs). Intrigued by this idea, we used it in a course on algorithms and data structures at the University of Hamburg. Throughout the whole semester, students repeatedly handed in submissions to exercises, which were then evaluated both by teaching assistants and by a peer grading mechanism, yielding a large dataset of teacher and peer grades. We applied different statistical and machine learning methods to aggregate the peer grades in order to come up with accurate final grades for the submissions (supervised and unsupervised, methods based on numeric scores and ordinal rankings). Surprisingly, none of them improves over the baseline of using the mean peer grade as the final grade. We discuss a number of possible explanations for these results and present a thorough analysis of the generated dataset.","PeriodicalId":20739,"journal":{"name":"Proceedings of the Third (2016) ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83109146","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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