Stefan Slater, R. Baker, M. Almeda, Alex J. Bowers, N. Heffernan
Student knowledge modeling is an important part of modern personalized learning systems, but typically relies upon valid models of the structure of the content and skill in a domain. These models are often developed through expert tagging of skills to items. However, content creators in crowdsourced personalized learning systems often lack the time (and sometimes the domain knowledge) to tag skills themselves. Fully automated approaches that rely on the covariance of correctness on items can lead to effective skill-item mappings, but the resultant mappings are often difficult to interpret. In this paper we propose an alternate approach to automatically labeling skills in a crowdsourced personalized learning system using correlated topic modeling, a natural language processing approach, to analyze the linguistic content of mathematics problems. We find a range of potentially meaningful and useful topics within the context of the ASSISTments system for mathematics problem-solving.
{"title":"Using correlational topic modeling for automated topic identification in intelligent tutoring systems","authors":"Stefan Slater, R. Baker, M. Almeda, Alex J. Bowers, N. Heffernan","doi":"10.1145/3027385.3027438","DOIUrl":"https://doi.org/10.1145/3027385.3027438","url":null,"abstract":"Student knowledge modeling is an important part of modern personalized learning systems, but typically relies upon valid models of the structure of the content and skill in a domain. These models are often developed through expert tagging of skills to items. However, content creators in crowdsourced personalized learning systems often lack the time (and sometimes the domain knowledge) to tag skills themselves. Fully automated approaches that rely on the covariance of correctness on items can lead to effective skill-item mappings, but the resultant mappings are often difficult to interpret. In this paper we propose an alternate approach to automatically labeling skills in a crowdsourced personalized learning system using correlated topic modeling, a natural language processing approach, to analyze the linguistic content of mathematics problems. We find a range of potentially meaningful and useful topics within the context of the ASSISTments system for mathematics problem-solving.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"310 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122784562","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 paper presents a study of temporal analytics and discourse analysis of an online discussion, through investigation of a group of 13 in-service teachers and 2 instructors. A discussion forum consisting of 281 posts on an online collaborative learning environment was investigated. A text-mining tool was used to discover keywords from the discourse, and through social network analysis based on these keywords, a significant presence of relevant and promising ideas within discourse was revealed. However, uncovering the key ideas alone is insufficient to clearly explain students' level of understanding regarding the discussed topics. A more thorough analysis was thus performed by using temporal analytics with step-wise discourse analysis to trace the ideas and determine their impact on communal discourse. The results indicated that most ideas within the discourse could be traced to the origin of a set of improvable ideas, which impacted and also increased the community's level of interest in sharing and discussing ideas through discourse.
{"title":"Temporal analytics with discourse analysis: tracing ideas and impact on communal discourse","authors":"Vwen Yen Lee, S. Tan","doi":"10.1145/3027385.3027386","DOIUrl":"https://doi.org/10.1145/3027385.3027386","url":null,"abstract":"This paper presents a study of temporal analytics and discourse analysis of an online discussion, through investigation of a group of 13 in-service teachers and 2 instructors. A discussion forum consisting of 281 posts on an online collaborative learning environment was investigated. A text-mining tool was used to discover keywords from the discourse, and through social network analysis based on these keywords, a significant presence of relevant and promising ideas within discourse was revealed. However, uncovering the key ideas alone is insufficient to clearly explain students' level of understanding regarding the discussed topics. A more thorough analysis was thus performed by using temporal analytics with step-wise discourse analysis to trace the ideas and determine their impact on communal discourse. The results indicated that most ideas within the discourse could be traced to the origin of a set of improvable ideas, which impacted and also increased the community's level of interest in sharing and discussing ideas through discourse.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131271637","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 following paper is a proof-of-concept demonstration of a novel Bayesian framework for making inferences about individual students and the context in which they are learning. It has implications for both efforts to automate personalized instruction and to probabilistically model educational context. By modelling students as Bayesian learners, individuals who weigh their prior belief against current circumstantial data to reach conclusions, it becomes possible to both generate estimates of performance and the impact of the educational environment in probabilistic terms. This framework is tested through a Bayesian algorithm that can be used to characterize student prior knowledge in course material and predict student performance. This is demonstrated using both simulated data. The algorithm generates estimates that behave qualitatively as expected on simulated data and predict student performance substantially better than chance. A discussion of the results and the conceptual benefits of the framework follow.
{"title":"Opportunities for personalization in modeling students as Bayesian learners","authors":"Charles Lang","doi":"10.1145/3027385.3027410","DOIUrl":"https://doi.org/10.1145/3027385.3027410","url":null,"abstract":"The following paper is a proof-of-concept demonstration of a novel Bayesian framework for making inferences about individual students and the context in which they are learning. It has implications for both efforts to automate personalized instruction and to probabilistically model educational context. By modelling students as Bayesian learners, individuals who weigh their prior belief against current circumstantial data to reach conclusions, it becomes possible to both generate estimates of performance and the impact of the educational environment in probabilistic terms. This framework is tested through a Bayesian algorithm that can be used to characterize student prior knowledge in course material and predict student performance. This is demonstrated using both simulated data. The algorithm generates estimates that behave qualitatively as expected on simulated data and predict student performance substantially better than chance. A discussion of the results and the conceptual benefits of the framework follow.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"744 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132705759","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}
Vitomir Kovanovíc, Srécko Joksimovíc, Oleksandra Poquet, T. Hennis, S. Dawson, D. Gašević
In this poster, we present the results of the study which examined the relationship between student differences in their use of the available technology and their perceived levels of cognitive presence within the MOOC context. The cognitive presence is a construct used to measure the level of practical inquiry in the Communities of Inquiry model. Our results revealed the existence of three clusters based on student technology use. The clusters significantly differed in terms of their levels of cognitive presence, most notably they differed on the levels of problem resolution.
{"title":"Understanding the relationship between technology use and cognitive presence in MOOCs","authors":"Vitomir Kovanovíc, Srécko Joksimovíc, Oleksandra Poquet, T. Hennis, S. Dawson, D. Gašević","doi":"10.1145/3027385.3029471","DOIUrl":"https://doi.org/10.1145/3027385.3029471","url":null,"abstract":"In this poster, we present the results of the study which examined the relationship between student differences in their use of the available technology and their perceived levels of cognitive presence within the MOOC context. The cognitive presence is a construct used to measure the level of practical inquiry in the Communities of Inquiry model. Our results revealed the existence of three clusters based on student technology use. The clusters significantly differed in terms of their levels of cognitive presence, most notably they differed on the levels of problem resolution.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133743683","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}
Recent research using learning analytics data to explore student performance over the course of a term suggests that a substantial percentage of students who are classified as academically struggling manage to recover. In this study, we report the result of a hazard analysis based on students' behavioral engagement with different digital instructional technologies over the course of a semester. We observe substantially different adoption and use behavior between students who did and did not experience academic difficulty in the course. Students who experienced moderate academic difficulty benefited the most from using tools that helped them plan their study behaviors. Students who experienced more severe academic difficulty benefited from tools that helped them prepare for exams. We observed that students adopted most tools and system features before they experienced academic difficulty, and students who adopted early were more likely to recover.
{"title":"Don't call it a comeback: academic recovery and the timing of educational technology adoption","authors":"M. Brown, R. DeMonbrun, Stephanie D. Teasley","doi":"10.1145/3027385.3027393","DOIUrl":"https://doi.org/10.1145/3027385.3027393","url":null,"abstract":"Recent research using learning analytics data to explore student performance over the course of a term suggests that a substantial percentage of students who are classified as academically struggling manage to recover. In this study, we report the result of a hazard analysis based on students' behavioral engagement with different digital instructional technologies over the course of a semester. We observe substantially different adoption and use behavior between students who did and did not experience academic difficulty in the course. Students who experienced moderate academic difficulty benefited the most from using tools that helped them plan their study behaviors. Students who experienced more severe academic difficulty benefited from tools that helped them prepare for exams. We observed that students adopted most tools and system features before they experienced academic difficulty, and students who adopted early were more likely to recover.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132108846","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}
Despite their importance for educational practice, reflective writings are still manually analysed and assessed, posing a constraint on the use of this educational technique. Recently, research started to investigate automated approaches for analysing reflective writing. Foundational to many automated approaches is the knowledge of words that are important for the genre. This research presents keywords that are specific to several categories of a reflective writing model. These keywords have been derived from eight datasets, which contain several thousand instances using the log-likelihood method. Both performance measures, the accuracy and the Cohen's κ, for these keywords were estimated with ten-fold cross validation. The results reached an accuracy of 0.78 on average for all eight categories and a fair to good interrater reliability for most categories even though it did not make use of any sophisticated rule-based mechanisms or machine learning approaches. This research contributes to the development of automated reflective writing analytics that are based on data-driven empirical foundations.
{"title":"Reflective writing analytics: empirically determined keywords of written reflection","authors":"T. Ullmann","doi":"10.1145/3027385.3027394","DOIUrl":"https://doi.org/10.1145/3027385.3027394","url":null,"abstract":"Despite their importance for educational practice, reflective writings are still manually analysed and assessed, posing a constraint on the use of this educational technique. Recently, research started to investigate automated approaches for analysing reflective writing. Foundational to many automated approaches is the knowledge of words that are important for the genre. This research presents keywords that are specific to several categories of a reflective writing model. These keywords have been derived from eight datasets, which contain several thousand instances using the log-likelihood method. Both performance measures, the accuracy and the Cohen's κ, for these keywords were estimated with ten-fold cross validation. The results reached an accuracy of 0.78 on average for all eight categories and a fair to good interrater reliability for most categories even though it did not make use of any sophisticated rule-based mechanisms or machine learning approaches. This research contributes to the development of automated reflective writing analytics that are based on data-driven empirical foundations.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134532392","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}
Policy for learning analytics joins a stream of initiatives aimed at understanding the expanding world of information collection, storage, processing and dissemination that is being driven by computing technologies. This paper offers a information policy perspective on learning analytics, joining work by others on ethics and privacy in the management of learning analytics data [8], but extending to consider how issues play out across the information lifecycle and in the formation of policy. Drawing on principles from information policy both informs learning analytics and brings learning analytics into the information policy domain. The resulting combination can help inform policy development for educational institutions as they implement and manage learning analytics policy and practices. The paper begins with a brief summary of the information policy perspective, then addresses learning analytics with attention to various categories of consideration for policy development.
{"title":"An information policy perspective on learning analytics","authors":"C. Haythornthwaite","doi":"10.1145/3027385.3027389","DOIUrl":"https://doi.org/10.1145/3027385.3027389","url":null,"abstract":"Policy for learning analytics joins a stream of initiatives aimed at understanding the expanding world of information collection, storage, processing and dissemination that is being driven by computing technologies. This paper offers a information policy perspective on learning analytics, joining work by others on ethics and privacy in the management of learning analytics data [8], but extending to consider how issues play out across the information lifecycle and in the formation of policy. Drawing on principles from information policy both informs learning analytics and brings learning analytics into the information policy domain. The resulting combination can help inform policy development for educational institutions as they implement and manage learning analytics policy and practices. The paper begins with a brief summary of the information policy perspective, then addresses learning analytics with attention to various categories of consideration for policy development.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"776 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113982266","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}
Wikiglass is a learning analytic tool for visualizing collaborative work on Wikis built by groups of secondary or primary school students. This poster presents new features of Wikiglass developed recently based on requests from teachers, including flexible selection of date range, revision network, and thinking order detection. Currently the new features are used and evaluated in two secondary schools in Hong Kong.
{"title":"New features in Wikiglass, a learning analytic tool for visualizing collaborative work on wikis","authors":"Xiao Hu, C. Yang, Chen Qiao, Xiaoyu Lu, S. Chu","doi":"10.1145/3027385.3029489","DOIUrl":"https://doi.org/10.1145/3027385.3029489","url":null,"abstract":"Wikiglass is a learning analytic tool for visualizing collaborative work on Wikis built by groups of secondary or primary school students. This poster presents new features of Wikiglass developed recently based on requests from teachers, including flexible selection of date range, revision network, and thinking order detection. Currently the new features are used and evaluated in two secondary schools in Hong Kong.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115812561","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}
Charles Lang, Stephanie D. Teasley, John C. Stamper
Learning Analytics courses and degree programs both on-and offline have begun to proliferate over the last three years. As a result of this growth in interest from students, university administrators, researchers and instructors we believe it is a good time to review how these educational efforts are impacting the field, how synergy between instructors might be developed to greater serve the field and what kinds of best practices could be developed.
{"title":"Building the learning analytics curriculum: workshop","authors":"Charles Lang, Stephanie D. Teasley, John C. Stamper","doi":"10.1145/3027385.3029439","DOIUrl":"https://doi.org/10.1145/3027385.3029439","url":null,"abstract":"Learning Analytics courses and degree programs both on-and offline have begun to proliferate over the last three years. As a result of this growth in interest from students, university administrators, researchers and instructors we believe it is a good time to review how these educational efforts are impacting the field, how synergy between instructors might be developed to greater serve the field and what kinds of best practices could be developed.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123347212","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}
Xiao Hu, X. Hou, Chi-Un Lei, C. Yang, Tzi-Dong Jeremy Ng
This poster presents a cross-platform learning analytics dashboard on Moodle and Open edX for monitoring outcome-based learning progress. The dashboard visualizes students' interactions with the platforms in near real-time, aiming to help teachers and students monitor students' learning progress. The dashboard has been used in four large-size general education courses in a comprehensive university in Hong Kong, undergoing evaluation and improvement.
{"title":"An outcome-based dashboard for moodle and Open edX","authors":"Xiao Hu, X. Hou, Chi-Un Lei, C. Yang, Tzi-Dong Jeremy Ng","doi":"10.1145/3027385.3029483","DOIUrl":"https://doi.org/10.1145/3027385.3029483","url":null,"abstract":"This poster presents a cross-platform learning analytics dashboard on Moodle and Open edX for monitoring outcome-based learning progress. The dashboard visualizes students' interactions with the platforms in near real-time, aiming to help teachers and students monitor students' learning progress. The dashboard has been used in four large-size general education courses in a comprehensive university in Hong Kong, undergoing evaluation and improvement.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"372 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123491152","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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