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}
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}
J. Schulte, Pedro Fernandez de Mendonca, Roberto Martínez Maldonado, S. B. Shum
For students, in particular freshmen, the degree pathway from semester to semester is not that transparent, although students have a reasonable idea what courses are expected to be taken each semester. An often-pondered question by students is: "what can I expect in the next semester?" More precisely, given the commitment and engagement I presented in this particular course and the respective performance I achieved, can I expect a similar outcome in the next semester in the particular course I selected? Are the demands and expectations in this course much higher so that I need to adjust my commitment and engagement and overall workload if I expect a similar outcome? Is it better to drop a course to manage expectations rather than to (predictably) fail, and perhaps have to leave the degree altogether? Degree and course advisors and student support units find it challenging to provide evidence based advise to students. This paper presents research into educational process mining and student data analytics in a whole university scale approach with the aim of providing insight into the degree pathway questions raised above. The beta-version of our course level degree pathway tool has been used to shed light for university staff and students alike into our university's 1,300 degrees and associated 6 million course enrolments over the past 20 years.
{"title":"Large scale predictive process mining and analytics of university degree course data","authors":"J. Schulte, Pedro Fernandez de Mendonca, Roberto Martínez Maldonado, S. B. Shum","doi":"10.1145/3027385.3029446","DOIUrl":"https://doi.org/10.1145/3027385.3029446","url":null,"abstract":"For students, in particular freshmen, the degree pathway from semester to semester is not that transparent, although students have a reasonable idea what courses are expected to be taken each semester. An often-pondered question by students is: \"what can I expect in the next semester?\" More precisely, given the commitment and engagement I presented in this particular course and the respective performance I achieved, can I expect a similar outcome in the next semester in the particular course I selected? Are the demands and expectations in this course much higher so that I need to adjust my commitment and engagement and overall workload if I expect a similar outcome? Is it better to drop a course to manage expectations rather than to (predictably) fail, and perhaps have to leave the degree altogether? Degree and course advisors and student support units find it challenging to provide evidence based advise to students. This paper presents research into educational process mining and student data analytics in a whole university scale approach with the aim of providing insight into the degree pathway questions raised above. The beta-version of our course level degree pathway tool has been used to shed light for university staff and students alike into our university's 1,300 degrees and associated 6 million course enrolments over the past 20 years.","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":"125423968","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}
Bodong Chen, Yizhou Fan, Guogang Zhang, Qiong Wang
The present study examines behavioral patterns, motivations, and self-regulated learning strategies of returning learners---a special learner subpopulation in massive open online courses (MOOCs). To this end, data were collected from a teacher professional development MOOC that has been offered for seven iterations during 2014--2016. Data analysis identified more than 15% of all registrants as returning learners. Findings from click log analysis identified possible motivations of re-enrollment including improving grades, refreshing theoretical understanding, and solving practical problems. Further analysis uncovered evidence of self-regulated learning strategies among returning learners. Taken together, this study contributes to ongoing inquiry into MOOCs learning pathways, informs future MOOC design, and sheds light on the exploration of MOOCs as a viable option for teacher professional development.
{"title":"Examining motivations and self-regulated learning strategies of returning MOOCs learners","authors":"Bodong Chen, Yizhou Fan, Guogang Zhang, Qiong Wang","doi":"10.1145/3027385.3029448","DOIUrl":"https://doi.org/10.1145/3027385.3029448","url":null,"abstract":"The present study examines behavioral patterns, motivations, and self-regulated learning strategies of returning learners---a special learner subpopulation in massive open online courses (MOOCs). To this end, data were collected from a teacher professional development MOOC that has been offered for seven iterations during 2014--2016. Data analysis identified more than 15% of all registrants as returning learners. Findings from click log analysis identified possible motivations of re-enrollment including improving grades, refreshing theoretical understanding, and solving practical problems. Further analysis uncovered evidence of self-regulated learning strategies among returning learners. Taken together, this study contributes to ongoing inquiry into MOOCs learning pathways, informs future MOOC design, and sheds light on the exploration of MOOCs as a viable option for teacher professional development.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"38 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":"130510573","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}
Jihyun Park, K. Denaro, F. Rodriguez, Padhraic Smyth, M. Warschauer
Student clickstream data can provide valuable insights about student activities in an online learning environment and how these activities inform their learning outcomes. However, given the noisy and complex nature of this data, an on-going challenge involves devising statistical techniques that capture clear and meaningful aspects of students' click patterns. In this paper, we utilize statistical change detection techniques to investigate students' online behaviors. Using clickstream data from two large university courses, one face-to-face and one online, we illustrate how this methodology can be used to detect when students change their previewing and reviewing behavior, and how these changes can be related to other aspects of students' activity and performance.
{"title":"Detecting changes in student behavior from clickstream data","authors":"Jihyun Park, K. Denaro, F. Rodriguez, Padhraic Smyth, M. Warschauer","doi":"10.1145/3027385.3027430","DOIUrl":"https://doi.org/10.1145/3027385.3027430","url":null,"abstract":"Student clickstream data can provide valuable insights about student activities in an online learning environment and how these activities inform their learning outcomes. However, given the noisy and complex nature of this data, an on-going challenge involves devising statistical techniques that capture clear and meaningful aspects of students' click patterns. In this paper, we utilize statistical change detection techniques to investigate students' online behaviors. Using clickstream data from two large university courses, one face-to-face and one online, we illustrate how this methodology can be used to detect when students change their previewing and reviewing behavior, and how these changes can be related to other aspects of students' activity and performance.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"27 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":"124969947","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 (ITSs) are commonly designed to enhance student learning. However, they are not typically designed to meet the needs of teachers who use them in their classrooms. ITSs generate a wealth of analytics about student learning and behavior, opening a rich design space for real-time teacher support tools such as dashboards. Whereas real-time dashboards for teachers have become popular with many learning technologies, we are not aware of projects that have designed dashboards for ITSs based on a broad investigation of teachers' needs. We conducted design interviews with ten middle school math teachers to explore their needs for on-the-spot support during blended class sessions, as a first step in a user-centered design process of a real-time dashboard. Based on multi-methods analyses of this interview data, we identify several opportunities for ITSs to better support teachers' needs, noting that the analytics commonly generated by existing teacher support tools do not strongly align with the analytics teachers expect to be most useful. We highlight key tensions and tradeoffs in the design of such real-time supports for teachers, as revealed by "Speed Dating" possible futures with teachers. This paper has implications for our ongoing co-design of a real-time dashboard for ITSs, as well as broader implications for the design of ITSs that can effectively collaborate with teachers in classroom settings.
{"title":"Intelligent tutors as teachers' aides: exploring teacher needs for real-time analytics in blended classrooms","authors":"Kenneth Holstein, B. McLaren, V. Aleven","doi":"10.1145/3027385.3027451","DOIUrl":"https://doi.org/10.1145/3027385.3027451","url":null,"abstract":"Intelligent tutoring systems (ITSs) are commonly designed to enhance student learning. However, they are not typically designed to meet the needs of teachers who use them in their classrooms. ITSs generate a wealth of analytics about student learning and behavior, opening a rich design space for real-time teacher support tools such as dashboards. Whereas real-time dashboards for teachers have become popular with many learning technologies, we are not aware of projects that have designed dashboards for ITSs based on a broad investigation of teachers' needs. We conducted design interviews with ten middle school math teachers to explore their needs for on-the-spot support during blended class sessions, as a first step in a user-centered design process of a real-time dashboard. Based on multi-methods analyses of this interview data, we identify several opportunities for ITSs to better support teachers' needs, noting that the analytics commonly generated by existing teacher support tools do not strongly align with the analytics teachers expect to be most useful. We highlight key tensions and tradeoffs in the design of such real-time supports for teachers, as revealed by \"Speed Dating\" possible futures with teachers. This paper has implications for our ongoing co-design of a real-time dashboard for ITSs, as well as broader implications for the design of ITSs that can effectively collaborate with teachers in classroom settings.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"34 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":"126028036","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}
A mixed methods approach was undertaken in this exploratory study to better understand how learners perceive and utilize learning analytics visualizations during online discussions activities. Internal conditions such as goal orientation and numeracy were measured alongside the external conditions created by the discussion structure and learning analytics. Our results emphasize key factors that should be considered when designing learning analytics tools.
{"title":"Best intentions: learner feedback on learning analytics visualization design","authors":"Halimat I. Alabi, M. Hatala","doi":"10.1145/3027385.3029487","DOIUrl":"https://doi.org/10.1145/3027385.3029487","url":null,"abstract":"A mixed methods approach was undertaken in this exploratory study to better understand how learners perceive and utilize learning analytics visualizations during online discussions activities. Internal conditions such as goal orientation and numeracy were measured alongside the external conditions created by the discussion structure and learning analytics. Our results emphasize key factors that should be considered when designing learning analytics tools.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"17 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":"125819238","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}
Fumiya Okubo, Takayoshi Yamashita, Atsushi Shimada, H. Ogata
In this paper, we propose a method for predicting final grades of students by a Recurrent Neural Network (RNN) from the log data stored in the educational systems. We applied this method to the log data from 108 students and examined the accuracy of prediction. From the experimental results, comparing with multiple regression analysis, it is confirmed that an RNN is effective to early prediction of final grades.
{"title":"A neural network approach for students' performance prediction","authors":"Fumiya Okubo, Takayoshi Yamashita, Atsushi Shimada, H. Ogata","doi":"10.1145/3027385.3029479","DOIUrl":"https://doi.org/10.1145/3027385.3029479","url":null,"abstract":"In this paper, we propose a method for predicting final grades of students by a Recurrent Neural Network (RNN) from the log data stored in the educational systems. We applied this method to the log data from 108 students and examined the accuracy of prediction. From the experimental results, comparing with multiple regression analysis, it is confirmed that an RNN is effective to early prediction of final grades.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"52 1 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":"126921021","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}
Roope Jaakonmäki, H. Drachsler, M. Kickmeier-Rust, S. Dietze, A. Fortenbacher, I. Marenzi
Learning Analytics is a melting pot for a multitude of research fields and origin of many developments about learning and its environment. There is a serious hype over the concepts of learning analytics, however, concrete solutions and applications are comparably scarce. Of course, data rich environments, such as MOOCs, come with statistical analytics dashboards, although the educational value is often limited. Practical solutions for scenarios in data-lean environments or for small-scale organizations are rarely adopted. The LA4S project is dedicated to gather practical solutions, provide a tool box for practitioners, and publish a cook book with concrete learning analytics recipes for everyone.
{"title":"Cooking with learning analytics recipes","authors":"Roope Jaakonmäki, H. Drachsler, M. Kickmeier-Rust, S. Dietze, A. Fortenbacher, I. Marenzi","doi":"10.1145/3027385.3029465","DOIUrl":"https://doi.org/10.1145/3027385.3029465","url":null,"abstract":"Learning Analytics is a melting pot for a multitude of research fields and origin of many developments about learning and its environment. There is a serious hype over the concepts of learning analytics, however, concrete solutions and applications are comparably scarce. Of course, data rich environments, such as MOOCs, come with statistical analytics dashboards, although the educational value is often limited. Practical solutions for scenarios in data-lean environments or for small-scale organizations are rarely adopted. The LA4S project is dedicated to gather practical solutions, provide a tool box for practitioners, and publish a cook book with concrete learning analytics recipes for everyone.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"130 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":"133119829","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：