Ruth Cobos Pérez, Silvia Gil, Ángel Lareo, Francisco A. Vargas
In this paper a learning analytics dashboard for MOOCs is proposed. It visualises the progress of learners' activity taking into account navigation, social interactions and interaction with educational resources. This approach was tested with the MOOCs created by the University Autonóma of Madrid (Spain) in the edX platform. Nowadays, the dashboard is being improved taking into account the received feedback from MOOCs instructors and assistants. Finally, a new version is presented to work along with edX and Open edX.
{"title":"Open-DLAs: An Open Dashboard for Learning Analytics","authors":"Ruth Cobos Pérez, Silvia Gil, Ángel Lareo, Francisco A. Vargas","doi":"10.1145/2876034.2893430","DOIUrl":"https://doi.org/10.1145/2876034.2893430","url":null,"abstract":"In this paper a learning analytics dashboard for MOOCs is proposed. It visualises the progress of learners' activity taking into account navigation, social interactions and interaction with educational resources. This approach was tested with the MOOCs created by the University Autonóma of Madrid (Spain) in the edX platform. Nowadays, the dashboard is being improved taking into account the received feedback from MOOCs instructors and assistants. Finally, a new version is presented to work along with edX and Open edX.","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":"90155252","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}
Rika Antonova, Joe Runde, Min Hyung Lee, E. Brunskill
We seek to automatically identify which items to include in a set of curriculum, and how to adaptively select these items, in order to maximize student performance on some specified set of learning objectives. Our experimental results with a histogram tutoring system suggest that Bayesian Optimization can quickly (with only a small amount of student data) find good parameters, and may help instructors identify misalignment between their course, and their desired learning objectives.
{"title":"Automatically Learning to Teach to the Learning Objectives","authors":"Rika Antonova, Joe Runde, Min Hyung Lee, E. Brunskill","doi":"10.1145/2876034.2893443","DOIUrl":"https://doi.org/10.1145/2876034.2893443","url":null,"abstract":"We seek to automatically identify which items to include in a set of curriculum, and how to adaptively select these items, in order to maximize student performance on some specified set of learning objectives. Our experimental results with a histogram tutoring system suggest that Bayesian Optimization can quickly (with only a small amount of student data) find good parameters, and may help instructors identify misalignment between their course, and their desired learning objectives.","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":"91467872","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":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":"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":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":"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}
Cheating in computer science classes can damage the reputation of institutions and their students. It is therefore essential to routinely authenticate student submissions with available software plagiarism detection algorithms such as Measure of Software Similarity (MOSS). Scaling this task for large classes where assignments are repeated each semester adds complexity and increases the instructor workload. The MOSS Tool for Addressing Plagiarism at Scale (MOSS-TAPS), organizes the MOSS submission task in courses that repeat coding assignments. In a recent use-case in the Online Master of Science in Computer Science (OMSCS) program at the Georgia Institute of Technology, the instructor time spent was reduced from 50 hours to only 10 minutes using the managed submission tool design presented here. MOSS-TAPS provides persistent configuration, supports a mixture of software languages and file organizations, and is implemented in pure Java for cross-platform compatibility.
{"title":"TAPS: A MOSS Extension for Detecting Software Plagiarism at Scale","authors":"Dana Sheahen, David A. Joyner","doi":"10.1145/2876034.2893435","DOIUrl":"https://doi.org/10.1145/2876034.2893435","url":null,"abstract":"Cheating in computer science classes can damage the reputation of institutions and their students. It is therefore essential to routinely authenticate student submissions with available software plagiarism detection algorithms such as Measure of Software Similarity (MOSS). Scaling this task for large classes where assignments are repeated each semester adds complexity and increases the instructor workload. The MOSS Tool for Addressing Plagiarism at Scale (MOSS-TAPS), organizes the MOSS submission task in courses that repeat coding assignments. In a recent use-case in the Online Master of Science in Computer Science (OMSCS) program at the Georgia Institute of Technology, the instructor time spent was reduced from 50 hours to only 10 minutes using the managed submission tool design presented here. MOSS-TAPS provides persistent configuration, supports a mixture of software languages and file organizations, and is implemented in pure Java for cross-platform compatibility.","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":"78725820","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}
José A. Ruipérez Valiente, Giora Alexandron, Zhongzhou Chen, David E. Pritchard
The study presented in this paper deals with copying answers in MOOCs. Our findings show that a significant fraction of the certificate earners in the course that we studied have used what we call harvesting accounts to find correct answers that they later submitted in their main account, the account for which they earned a certificate. In total, around 2.5% of the users who earned a certificate in the course obtained the majority of their points by using this method, and around 10% of them used it to some extent. This paper has two main goals. The first is to define the phenomenon and demonstrate its severity. The second is characterizing key factors within the course that affect it, and suggesting possible remedies that are likely to decrease the amount of cheating. The immediate implication of this study is to MOOCs. However, we believe that the results generalize beyond MOOCs, since this strategy can be used in any learning environments that do not identify all registrants.
{"title":"Using Multiple Accounts for Harvesting Solutions in MOOCs","authors":"José A. Ruipérez Valiente, Giora Alexandron, Zhongzhou Chen, David E. Pritchard","doi":"10.1145/2876034.2876037","DOIUrl":"https://doi.org/10.1145/2876034.2876037","url":null,"abstract":"The study presented in this paper deals with copying answers in MOOCs. Our findings show that a significant fraction of the certificate earners in the course that we studied have used what we call harvesting accounts to find correct answers that they later submitted in their main account, the account for which they earned a certificate. In total, around 2.5% of the users who earned a certificate in the course obtained the majority of their points by using this method, and around 10% of them used it to some extent. This paper has two main goals. The first is to define the phenomenon and demonstrate its severity. The second is characterizing key factors within the course that affect it, and suggesting possible remedies that are likely to decrease the amount of cheating. The immediate implication of this study is to MOOCs. However, we believe that the results generalize beyond MOOCs, since this strategy can be used in any learning environments that do not identify all registrants.","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":"78785072","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}
While MOOCs and other forms of large-scale learning are of growing importance, the vast majority of tertiary students still study in traditional face-to-face settings. This paper examines some of the challenges in attempting to apply the benefits of large-scale learning to these settings, building on a growing repository of cross-institutional data.
{"title":"Challenge and Potential of Fine Grain, Cross-Institutional Learning Data","authors":"A. Dix","doi":"10.1145/2876034.2893429","DOIUrl":"https://doi.org/10.1145/2876034.2893429","url":null,"abstract":"While MOOCs and other forms of large-scale learning are of growing importance, the vast majority of tertiary students still study in traditional face-to-face settings. This paper examines some of the challenges in attempting to apply the benefits of large-scale learning to these settings, building on a growing repository of cross-institutional data.","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":"86313470","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}
Stephen Cummins, A. Beresford, Ian P. Davies, A. Rice
Online educational tools often generate learning data, and sharing such data between tutors and students can often improve learning outcomes. Unfortunately the process of sharing learning data today is not always transparent to students. Our aim is to improve the transparency and user control aspects of sharing data whilst maintaining the educational utility of data sharing between tutors and students. To do so, we start by surveying the possible methods of sharing data, and we use this to design a token-based scheme for facilitating data sharing. We implemented our scheme and observed it in use by 7,798 students over the course of one year. We find that our proposed scheme provides a good balance between transparency, user control, educational utility and scalability.
{"title":"Supporting Scalable Data Sharing in Online Education","authors":"Stephen Cummins, A. Beresford, Ian P. Davies, A. Rice","doi":"10.1145/2876034.2893376","DOIUrl":"https://doi.org/10.1145/2876034.2893376","url":null,"abstract":"Online educational tools often generate learning data, and sharing such data between tutors and students can often improve learning outcomes. Unfortunately the process of sharing learning data today is not always transparent to students. Our aim is to improve the transparency and user control aspects of sharing data whilst maintaining the educational utility of data sharing between tutors and students. To do so, we start by surveying the possible methods of sharing data, and we use this to design a token-based scheme for facilitating data sharing. We implemented our scheme and observed it in use by 7,798 students over the course of one year. We find that our proposed scheme provides a good balance between transparency, user control, educational utility and scalability.","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":"82798290","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}
Modern deep neural networks have achieved impressive results in a variety of automated tasks, such as text generation, grammar learning, and speech recognition. This paper discusses how education research might leverage recurrent neural network architectures in two small case studies. Specifically, we train a two-layer Long Short-Term Memory (LSTM) network on two distinct forms of education data: (1) essays written by students in a summative environment, and (2) MOOC clickstream data. Without any features specified beforehand, the network attempts to learn the underlying structure of the input sequences. After training, the model can be used generatively to produce new sequences with the same underlying patterns exhibited by the input distribution. These early explorations demonstrate the potential for applying deep learning techniques to large education data sets.
{"title":"Deep Neural Networks and How They Apply to Sequential Education Data","authors":"Steven Tang, Joshua C. Peterson, Z. Pardos","doi":"10.1145/2876034.2893444","DOIUrl":"https://doi.org/10.1145/2876034.2893444","url":null,"abstract":"Modern deep neural networks have achieved impressive results in a variety of automated tasks, such as text generation, grammar learning, and speech recognition. This paper discusses how education research might leverage recurrent neural network architectures in two small case studies. Specifically, we train a two-layer Long Short-Term Memory (LSTM) network on two distinct forms of education data: (1) essays written by students in a summative environment, and (2) MOOC clickstream data. Without any features specified beforehand, the network attempts to learn the underlying structure of the input sequences. After training, the model can be used generatively to produce new sequences with the same underlying patterns exhibited by the input distribution. These early explorations demonstrate the potential for applying deep learning techniques to large education data sets.","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":"84231530","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}
Tina Papathoma, Rebecca Ferguson, A. Littlejohn, Angela Coe
Professional learning is a critical component of the ongoing improvement, innovation and adoption of new practices that support learning at scale. In this context, educators must learn how to apply digital technologies and work effectively in digital networks. This study examines how higher education professionals adapted their practice to enable more open and flexible work processes. A case study carried out using Activity Theory showed that teams involved in the development of a module all need access to a range of expertise both practical and academic. At each stage, they need to be clear about the learning outcomes of the module, the responsibilities of each team and its constraints. Teams need to be willing to agree ways to shift those constraints in order to develop a module effectively.
{"title":"Making the Production of Learning at Scale More Open and Flexible","authors":"Tina Papathoma, Rebecca Ferguson, A. Littlejohn, Angela Coe","doi":"10.1145/2876034.2893432","DOIUrl":"https://doi.org/10.1145/2876034.2893432","url":null,"abstract":"Professional learning is a critical component of the ongoing improvement, innovation and adoption of new practices that support learning at scale. In this context, educators must learn how to apply digital technologies and work effectively in digital networks. This study examines how higher education professionals adapted their practice to enable more open and flexible work processes. A case study carried out using Activity Theory showed that teams involved in the development of a module all need access to a range of expertise both practical and academic. At each stage, they need to be clear about the learning outcomes of the module, the responsibilities of each team and its constraints. Teams need to be willing to agree ways to shift those constraints in order to develop a module effectively.","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":"81498070","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：