T. Staubitz, Dominic Petrick, Matthias Bauer, Jan Renz, C. Meinel
Massive Open Online Courses (MOOCs) have revolutionized higher education by offering university-like courses for a large amount of learners via the Internet. The paper at hand takes a closer look on peer assessment as a tool for delivering individualized feedback and engaging assignments to MOOC participants. Benefits, such as scalability for MOOCs and higher order learning, and challenges, such as grading accuracy and rogue reviewers, are described. Common practices and the state-of-the-art to counteract challenges are highlighted. Based on this research, the paper at hand describes a peer assessment workflow and its implementation on the openHPI and openSAP MOOC platforms. This workflow combines the best practices of existing peer assessment tools and introduces some small but crucial improvements.
{"title":"Improving the Peer Assessment Experience on MOOC Platforms","authors":"T. Staubitz, Dominic Petrick, Matthias Bauer, Jan Renz, C. Meinel","doi":"10.1145/2876034.2876043","DOIUrl":"https://doi.org/10.1145/2876034.2876043","url":null,"abstract":"Massive Open Online Courses (MOOCs) have revolutionized higher education by offering university-like courses for a large amount of learners via the Internet. The paper at hand takes a closer look on peer assessment as a tool for delivering individualized feedback and engaging assignments to MOOC participants. Benefits, such as scalability for MOOCs and higher order learning, and challenges, such as grading accuracy and rogue reviewers, are described. Common practices and the state-of-the-art to counteract challenges are highlighted. Based on this research, the paper at hand describes a peer assessment workflow and its implementation on the openHPI and openSAP MOOC platforms. This workflow combines the best practices of existing peer assessment tools and introduces some small but crucial improvements.","PeriodicalId":20739,"journal":{"name":"Proceedings of the Third (2016) ACM Conference on Learning @ Scale","volume":"111 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80618820","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 this talk, Sugata Mitra will take us through the origins of schooling as we know it, to the dematerialisation of institutions as we know them. Thirteen years of experiments in children's education takes us through a series of startling results -- children can self organise their own learning, they can achieve educational objectives on their own, can read by themselves. Finally, the most startling of them all: Groups of children with access to the Internet can learn anything by themselves. From the slums of India, to the villages of India and Cambodia, to poor schools in Chile, Argentina, Uruguay, the USA and Italy, to the schools of Gateshead and the rich international schools of Washington and Hong Kong, Sugata's experimental results show a strange new future for learning. Using the TED Prize, he has now built seven "Schools in the Cloud", of which some glimpses will be provided in the talk.
{"title":"The Future of Learning","authors":"Sugata Mitra","doi":"10.1145/2876034.2876053","DOIUrl":"https://doi.org/10.1145/2876034.2876053","url":null,"abstract":"In this talk, Sugata Mitra will take us through the origins of schooling as we know it, to the dematerialisation of institutions as we know them. Thirteen years of experiments in children's education takes us through a series of startling results -- children can self organise their own learning, they can achieve educational objectives on their own, can read by themselves. Finally, the most startling of them all: Groups of children with access to the Internet can learn anything by themselves. From the slums of India, to the villages of India and Cambodia, to poor schools in Chile, Argentina, Uruguay, the USA and Italy, to the schools of Gateshead and the rich international schools of Washington and Hong Kong, Sugata's experimental results show a strange new future for learning. Using the TED Prize, he has now built seven \"Schools in the Cloud\", of which some glimpses will be provided in the talk.","PeriodicalId":20739,"journal":{"name":"Proceedings of the Third (2016) ACM Conference on Learning @ Scale","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75583902","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}
This work-in-progress paper elaborates on a gradually evolving approach to design of open learning and the design principles used by the Open University of the Netherlands in short open courses - online masterclasses and in Massive Open Online Courses -- delivered in the learning environment of the Open University and in the experimental multilingual MOOC aggregator EMMA as part of a European project. As the paper will demonstrate, these principles can be seen as building blocks of open scalable design of active and engaging learning.
{"title":"Designing for Open Learning: Design Principles and Scalability Affordances in Practice","authors":"O. Firssova, F. Brouns, M. Kalz","doi":"10.1145/2876034.2893426","DOIUrl":"https://doi.org/10.1145/2876034.2893426","url":null,"abstract":"This work-in-progress paper elaborates on a gradually evolving approach to design of open learning and the design principles used by the Open University of the Netherlands in short open courses - online masterclasses and in Massive Open Online Courses -- delivered in the learning environment of the Open University and in the experimental multilingual MOOC aggregator EMMA as part of a European project. As the paper will demonstrate, these principles can be seen as building blocks of open scalable design of active and engaging learning.","PeriodicalId":20739,"journal":{"name":"Proceedings of the Third (2016) ACM Conference on Learning @ Scale","volume":"288 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79402791","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}
M. Falakmasir, José P. González-Brenes, Geoffrey J. Gordon, K. DiCerbo
Student assessments are important because they allow collecting evidence about learning. However, time spent on evaluating students may be otherwise used for instructional activities. Computer-based learning platforms provide the opportunity for unobtrusively gathering students' digital learning footprints. This data can be used to track learning progress and make inference about student competencies. We present a novel data analysis pipeline, Student Proficiency Inferrer from Game data (SPRING), that allows modeling game playing behavior in educational games. Unlike prior work, SPRING is a fully data-driven method that does not require costly domain knowledge engineering. Moreover, it produces a simple interpretable model that not only fits the data but also predicts learning outcomes. We validate our framework using data collected from students playing 11 educational mini-games. Our results suggest that SPRING can predict math assessments accurately on withheld test data (Correlation=0.55, Spearman rho=0.51).
{"title":"A Data-Driven Approach for Inferring Student Proficiency from Game Activity Logs","authors":"M. Falakmasir, José P. González-Brenes, Geoffrey J. Gordon, K. DiCerbo","doi":"10.1145/2876034.2876038","DOIUrl":"https://doi.org/10.1145/2876034.2876038","url":null,"abstract":"Student assessments are important because they allow collecting evidence about learning. However, time spent on evaluating students may be otherwise used for instructional activities. Computer-based learning platforms provide the opportunity for unobtrusively gathering students' digital learning footprints. This data can be used to track learning progress and make inference about student competencies. We present a novel data analysis pipeline, Student Proficiency Inferrer from Game data (SPRING), that allows modeling game playing behavior in educational games. Unlike prior work, SPRING is a fully data-driven method that does not require costly domain knowledge engineering. Moreover, it produces a simple interpretable model that not only fits the data but also predicts learning outcomes. We validate our framework using data collected from students playing 11 educational mini-games. Our results suggest that SPRING can predict math assessments accurately on withheld test data (Correlation=0.55, Spearman rho=0.51).","PeriodicalId":20739,"journal":{"name":"Proceedings of the Third (2016) ACM Conference on Learning @ Scale","volume":"382 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80709348","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}
Steven Ritter, M. Yudelson, Stephen E. Fancsali, Susan R. Berman
Nearly every adaptive learning system aims to present students with materials personalized to their level of understanding (Enyedy, 2014). Typically, such adaptation follows some form of mastery learning (Bloom, 1968), in which students are asked to master one topic before proceeding to the next topic. Mastery learning programs have a long history of success (Guskey and Gates, 1986; Kulik, Kulik & Bangert-Drowns, 1990) and have been shown to be superior to alternative instructional approaches. Although there is evidence for the effectiveness of mastery learning when it is well supported by teachers, mastery learning's effectiveness is crucially dependent on the ability and willingness of teachers to implement it properly. In particular, school environments impose time constraints and set goals for curriculum coverage that may encourage teachers to deviate from mastery-based instruction. In this paper we examine mastery learning as implemented in Carnegie Learning's Cognitive Tutor. Like in all real-world systems, teachers and students have the ability to violate mastery learning guidance. We investigate patterns associated with violating and following mastery learning over the course of the full school year at the class and student level. We find that violations of mastery learning are associated with poorer student performance, especially among struggling students, and that this result is likely attributable to such violations of mastery learning.
几乎每一个自适应学习系统都旨在为学生提供个性化的材料，以满足他们的理解水平(Enyedy, 2014)。通常，这种适应遵循某种形式的掌握学习(Bloom, 1968)，在这种学习中，学生被要求在学习下一个主题之前掌握一个主题。掌握式学习项目有着悠久的成功历史(Guskey and Gates, 1986;Kulik, Kulik & Bangert-Drowns, 1990)，并且已被证明优于其他教学方法。虽然有证据表明，在教师的大力支持下，掌握学习是有效的，但掌握学习的有效性关键取决于教师正确实施掌握学习的能力和意愿。特别是，学校环境施加了时间限制，并设定了课程覆盖的目标，这可能会鼓励教师偏离以掌握为基础的教学。在本文中，我们考察了掌握学习作为卡内基学习的认知导师的实施。我们调查了在整个学年的课程中，在班级和学生层面上违反和遵循精通学习的模式。我们发现，违反精通学习与较差的学生表现有关，特别是在挣扎的学生中，这一结果可能归因于这种违反精通学习。
{"title":"How Mastery Learning Works at Scale","authors":"Steven Ritter, M. Yudelson, Stephen E. Fancsali, Susan R. Berman","doi":"10.1145/2876034.2876039","DOIUrl":"https://doi.org/10.1145/2876034.2876039","url":null,"abstract":"Nearly every adaptive learning system aims to present students with materials personalized to their level of understanding (Enyedy, 2014). Typically, such adaptation follows some form of mastery learning (Bloom, 1968), in which students are asked to master one topic before proceeding to the next topic. Mastery learning programs have a long history of success (Guskey and Gates, 1986; Kulik, Kulik & Bangert-Drowns, 1990) and have been shown to be superior to alternative instructional approaches. Although there is evidence for the effectiveness of mastery learning when it is well supported by teachers, mastery learning's effectiveness is crucially dependent on the ability and willingness of teachers to implement it properly. In particular, school environments impose time constraints and set goals for curriculum coverage that may encourage teachers to deviate from mastery-based instruction. In this paper we examine mastery learning as implemented in Carnegie Learning's Cognitive Tutor. Like in all real-world systems, teachers and students have the ability to violate mastery learning guidance. We investigate patterns associated with violating and following mastery learning over the course of the full school year at the class and student level. We find that violations of mastery learning are associated with poorer student performance, especially among struggling students, and that this result is likely attributable to such violations of mastery learning.","PeriodicalId":20739,"journal":{"name":"Proceedings of the Third (2016) ACM Conference on Learning @ Scale","volume":"47 186 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83278900","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}
Chaohua Ou, Ashok K. Goel, David A. Joyner, Daniel F. Haynes
Despite the ubiquitous use of videos in online learning and enormous literature on designing online learning, there has been relatively little research on what pedagogical strategies should be used to make the most of video lessons and what constitutes an effective video for student learning. We experimented with a model of incorporating four pedagogical strategies, four instructional phases, and four production guidelines-in designing and developing video lessons for an online graduate course. In this paper, we share our experience as well as students' perceptions of their effectiveness. We also discuss what needs to be done for future research.
{"title":"Designing Videos with Pedagogical Strategies: Online Students' Perceptions of Their Effectiveness","authors":"Chaohua Ou, Ashok K. Goel, David A. Joyner, Daniel F. Haynes","doi":"10.1145/2876034.2893391","DOIUrl":"https://doi.org/10.1145/2876034.2893391","url":null,"abstract":"Despite the ubiquitous use of videos in online learning and enormous literature on designing online learning, there has been relatively little research on what pedagogical strategies should be used to make the most of video lessons and what constitutes an effective video for student learning. We experimented with a model of incorporating four pedagogical strategies, four instructional phases, and four production guidelines-in designing and developing video lessons for an online graduate course. In this paper, we share our experience as well as students' perceptions of their effectiveness. We also discuss what needs to be done for future research.","PeriodicalId":20739,"journal":{"name":"Proceedings of the Third (2016) ACM Conference on Learning @ Scale","volume":"54 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90686595","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}
Anna Kasunic, Jessica Hammer, R. Kraut, M. Massimi, A. Ogan
Although xMOOCs are not designed to directly engage students via social media platforms, some students in these courses join MOOC-associated Facebook groups. This study explores the prevalence of Facebook groups associated with courses from MITx and HarvardX, the geographic distribution of students in such groups as compared to the courses at large, and the extent to which such groups are location and/or language homophilous. Results suggests that a non-trivial number of MOOC students engage in Facebook groups, that learners from a number of non-U.S. locations are disproportionately likely to participate in such groups, and that the groups display both location and language homophily. These findings have implications for how MOOCs and social media platforms can support learners from non-English speaking contexts.
{"title":"A Preliminary Look at MOOC-associated Facebook Groups: Prevalence, Geographic Representation, and Homophily","authors":"Anna Kasunic, Jessica Hammer, R. Kraut, M. Massimi, A. Ogan","doi":"10.1145/2876034.2893415","DOIUrl":"https://doi.org/10.1145/2876034.2893415","url":null,"abstract":"Although xMOOCs are not designed to directly engage students via social media platforms, some students in these courses join MOOC-associated Facebook groups. This study explores the prevalence of Facebook groups associated with courses from MITx and HarvardX, the geographic distribution of students in such groups as compared to the courses at large, and the extent to which such groups are location and/or language homophilous. Results suggests that a non-trivial number of MOOC students engage in Facebook groups, that learners from a number of non-U.S. locations are disproportionately likely to participate in such groups, and that the groups display both location and language homophily. These findings have implications for how MOOCs and social media platforms can support learners from non-English speaking contexts.","PeriodicalId":20739,"journal":{"name":"Proceedings of the Third (2016) ACM Conference on Learning @ Scale","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73545704","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}
Zhuo Wang, Jile Zhu, Xiang Li, Zhiting Hu, Ming Zhang
Massive Open Online Courses (MOOCs) provide an effective learning platform with various high-quality educational materials accessible to learners from all over the world. However, current MOOCs lack personalized learning guidance and intelligent assessment for individuals. Though a few recent attempts have been made to trace students' knowledge states by adapting the popular Bayesian Knowledge Tracing (BKT) model, they have largely ignored the rich structures and correlations among knowledge components (KCs) within a course. This paper proposes to model both the hierarchical and the temporal properties of the knowledge states in order to improve the modeling accuracy. Based on the content organization characteristics on the Coursera MOOC platform, we provide a well-defined KC model, and develop Multi-Grained-BKT and Historical-BKT to capture the above features effectively. Experiments on a Coursera course dataset show our approach significantly improves over previous vanilla BKT models on predicting students' quiz performance.
{"title":"Structured Knowledge Tracing Models for Student Assessment on Coursera","authors":"Zhuo Wang, Jile Zhu, Xiang Li, Zhiting Hu, Ming Zhang","doi":"10.1145/2876034.2893416","DOIUrl":"https://doi.org/10.1145/2876034.2893416","url":null,"abstract":"Massive Open Online Courses (MOOCs) provide an effective learning platform with various high-quality educational materials accessible to learners from all over the world. However, current MOOCs lack personalized learning guidance and intelligent assessment for individuals. Though a few recent attempts have been made to trace students' knowledge states by adapting the popular Bayesian Knowledge Tracing (BKT) model, they have largely ignored the rich structures and correlations among knowledge components (KCs) within a course. This paper proposes to model both the hierarchical and the temporal properties of the knowledge states in order to improve the modeling accuracy. Based on the content organization characteristics on the Coursera MOOC platform, we provide a well-defined KC model, and develop Multi-Grained-BKT and Historical-BKT to capture the above features effectively. Experiments on a Coursera course dataset show our approach significantly improves over previous vanilla BKT models on predicting students' quiz performance.","PeriodicalId":20739,"journal":{"name":"Proceedings of the Third (2016) ACM Conference on Learning @ Scale","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78223047","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}
Eleanor O'Rourke, E. Peach, C. Dweck, Zoran Popovic
Student retention is a central challenge in systems for learning at scale. It has been argued that educational video games could improve student retention by providing engaging experiences and informing the design of other online learning environments. However, educational games are not uniformly effective. Our recent research shows that player retention can be increased by using a brain points incentive structure that rewards behaviors associated with growth mindset, or the belief that intelligence can grow. In this paper, we expand on our prior work by providing new insights into how growth mindset behaviors can be effectively promoted in the educational game Refraction. We present results from an online study of 25,000 children who were exposed to five different versions of the brain points intervention. We find that growth mindset animations cause a large number of players to quit, while brain points encourage persistence. Most importantly, we find that awarding brain points randomly is ineffective; the incentive structure is successful specifically because it rewards desirable growth mindset behaviors. These findings have important implications that can support the future generalization of the brain points intervention to new educational contexts.
{"title":"Brain Points: A Deeper Look at a Growth Mindset Incentive Structure for an Educational Game","authors":"Eleanor O'Rourke, E. Peach, C. Dweck, Zoran Popovic","doi":"10.1145/2876034.2876040","DOIUrl":"https://doi.org/10.1145/2876034.2876040","url":null,"abstract":"Student retention is a central challenge in systems for learning at scale. It has been argued that educational video games could improve student retention by providing engaging experiences and informing the design of other online learning environments. However, educational games are not uniformly effective. Our recent research shows that player retention can be increased by using a brain points incentive structure that rewards behaviors associated with growth mindset, or the belief that intelligence can grow. In this paper, we expand on our prior work by providing new insights into how growth mindset behaviors can be effectively promoted in the educational game Refraction. We present results from an online study of 25,000 children who were exposed to five different versions of the brain points intervention. We find that growth mindset animations cause a large number of players to quit, while brain points encourage persistence. Most importantly, we find that awarding brain points randomly is ineffective; the incentive structure is successful specifically because it rewards desirable growth mindset behaviors. These findings have important implications that can support the future generalization of the brain points intervention to new educational contexts.","PeriodicalId":20739,"journal":{"name":"Proceedings of the Third (2016) ACM Conference on Learning @ Scale","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83670044","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 why and how students interact with educational videos is essential to further improve the quality of MOOCs. In this paper, we look at the complexity of videos to explain two related aspects of student behavior: the dwelling time (how much time students spend watching a video) and the dwelling rate (how much of the video they actually see). Building on a strong tradition of psycholinguistics, we formalize a definition for information complexity in videos. Furthermore, building on recent advancements in time-on-task measures we formalize dwelling time and dwelling rate based on click-stream trace data. The resulting computational model of video complexity explains 22.44% of the variance in the dwelling rate for students that finish watching a paragraph of a video. Video complexity and student dwelling show a polynomial relationship, where both low and high complexity increases dwelling. These results indicate why students spend more time watching (and possibly contemplating about) a video. Furthermore, they show that even fairly straightforward proxies of student behavior such as dwelling can already have multiple interpretations; illustrating the challenge of sense-making from learning analytics.
{"title":"Explaining Student Behavior at Scale: The Influence of Video Complexity on Student Dwelling Time","authors":"F. V. D. Sluis, Jasper Ginn, T. Zee","doi":"10.1145/2876034.2876051","DOIUrl":"https://doi.org/10.1145/2876034.2876051","url":null,"abstract":"Understanding why and how students interact with educational videos is essential to further improve the quality of MOOCs. In this paper, we look at the complexity of videos to explain two related aspects of student behavior: the dwelling time (how much time students spend watching a video) and the dwelling rate (how much of the video they actually see). Building on a strong tradition of psycholinguistics, we formalize a definition for information complexity in videos. Furthermore, building on recent advancements in time-on-task measures we formalize dwelling time and dwelling rate based on click-stream trace data. The resulting computational model of video complexity explains 22.44% of the variance in the dwelling rate for students that finish watching a paragraph of a video. Video complexity and student dwelling show a polynomial relationship, where both low and high complexity increases dwelling. These results indicate why students spend more time watching (and possibly contemplating about) a video. Furthermore, they show that even fairly straightforward proxies of student behavior such as dwelling can already have multiple interpretations; illustrating the challenge of sense-making from learning analytics.","PeriodicalId":20739,"journal":{"name":"Proceedings of the Third (2016) ACM Conference on Learning @ Scale","volume":"96 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85885567","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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