Interest in temporal analytics---analytics that probe temporal aspects of learning so as to gain insights into the processes through which learning occurs---continues to grow. The relationships of temporal patterns to learning outcomes is a central area of interest. However, while the literature on temporal analyses is developing, there has been less consideration of the methods by which temporal analyses might be translated to actionable insights and thus, put into use in educational practice. Emerging temporal analysis techniques present both theoretical and practical challenges for producing and interpreting results. Synergetic actions are needed in order to support practitioners.
{"title":"Putting temporal analytics into practice: the 5th international workshop on temporality in learning data","authors":"Bodong Chen, A. Wise, Simon Knight, B. Cheng","doi":"10.1145/2883851.2883865","DOIUrl":"https://doi.org/10.1145/2883851.2883865","url":null,"abstract":"Interest in temporal analytics---analytics that probe temporal aspects of learning so as to gain insights into the processes through which learning occurs---continues to grow. The relationships of temporal patterns to learning outcomes is a central area of interest. However, while the literature on temporal analyses is developing, there has been less consideration of the methods by which temporal analyses might be translated to actionable insights and thus, put into use in educational practice. Emerging temporal analysis techniques present both theoretical and practical challenges for producing and interpreting results. Synergetic actions are needed in order to support practitioners.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132650763","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}
Many pedagogical models in the field of learning analytics are implicit and do not overtly direct learner behavior. While this allows flexibility of use, this could also result in misaligned practice, and there are calls for more explicit pedagogical models in learning analytics. This paper presents an explicit pedagogical model, the Team and Self Diagnostic Learning (TSDL) framework, in the context of collaborative inquiry tasks. Key informing theories include experiential learning, collaborative learning, and the learning analytics process model. The framework was trialed through a teamwork competency awareness program for 14 year old students. A total of 272 students participated in the program. This paper foregrounds students' and teachers' evaluative accounts of the program. Findings reveal positive perceptions of the stages of the TSDL framework, despite identified challenges, which points to its potential usefulness for teaching and learning. The TSDL framework aims to provide theoretical clarity of the learning process, and foster alignment between learning analytics and the learning design. The current work provides trial outcomes of a teamwork competency awareness program that used dispositional analytics, and further efforts are underway to develop the discourse layer of the analytic engine. Future work will also be dedicated to application and refinement of the framework for other contexts and participants, both learners and teachers alike.
{"title":"A pedagogical framework for learning analytics in collaborative inquiry tasks: an example from a teamwork competency awareness program","authors":"Elizabeth Koh, A. Shibani, J. Tan, Helen Hong","doi":"10.1145/2883851.2883914","DOIUrl":"https://doi.org/10.1145/2883851.2883914","url":null,"abstract":"Many pedagogical models in the field of learning analytics are implicit and do not overtly direct learner behavior. While this allows flexibility of use, this could also result in misaligned practice, and there are calls for more explicit pedagogical models in learning analytics. This paper presents an explicit pedagogical model, the Team and Self Diagnostic Learning (TSDL) framework, in the context of collaborative inquiry tasks. Key informing theories include experiential learning, collaborative learning, and the learning analytics process model. The framework was trialed through a teamwork competency awareness program for 14 year old students. A total of 272 students participated in the program. This paper foregrounds students' and teachers' evaluative accounts of the program. Findings reveal positive perceptions of the stages of the TSDL framework, despite identified challenges, which points to its potential usefulness for teaching and learning. The TSDL framework aims to provide theoretical clarity of the learning process, and foster alignment between learning analytics and the learning design. The current work provides trial outcomes of a teamwork competency awareness program that used dispositional analytics, and further efforts are underway to develop the discourse layer of the analytic engine. Future work will also be dedicated to application and refinement of the framework for other contexts and participants, both learners and teachers alike.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"55 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130738901","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 study addresses the issues of overload and chaos in MOOC discussion forums by developing a model to categorize and identify threads based on whether or not they are substantially related to the course content. Content-related posts were defined as those that give/seek help for the learning of course material and share/comment on relevant resources. A linguistic model was built based on manually-coded starting posts in threads from a statistics MOOC (n=837) and tested on thread starting posts from the second offering of the same course (n=304) and a different statistics course (n=298). The number of views and votes threads received were tested to see if they helped classification. Results showed that content-related posts in the statistics MOOC had distinct linguistic features which appeared to be unrelated to the subject-matter domain; the linguistic model demonstrated good cross-course reliability (all recall and precision > .77) and was useful across all time segments of the courses; number of views and votes were not helpful for classification.
{"title":"Bringing order to chaos in MOOC discussion forums with content-related thread identification","authors":"A. Wise, Yi Cui, Jovita M. Vytasek","doi":"10.1145/2883851.2883916","DOIUrl":"https://doi.org/10.1145/2883851.2883916","url":null,"abstract":"This study addresses the issues of overload and chaos in MOOC discussion forums by developing a model to categorize and identify threads based on whether or not they are substantially related to the course content. Content-related posts were defined as those that give/seek help for the learning of course material and share/comment on relevant resources. A linguistic model was built based on manually-coded starting posts in threads from a statistics MOOC (n=837) and tested on thread starting posts from the second offering of the same course (n=304) and a different statistics course (n=298). The number of views and votes threads received were tested to see if they helped classification. Results showed that content-related posts in the statistics MOOC had distinct linguistic features which appeared to be unrelated to the subject-matter domain; the linguistic model demonstrated good cross-course reliability (all recall and precision > .77) and was useful across all time segments of the courses; number of views and votes were not helpful for classification.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133311952","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}
An increasing number of researchers are taking learning design into consideration when predicting learning behavior and outcomes across different modules. This study builds on preliminary learning design work that was presented at LAK2015 by the Open University UK. In this study we linked 151 modules and 111.256 students with students' satisfaction and performance using multiple regression models. Our findings strongly indicate the importance of learning design in predicting and understanding performance of students in blended and online environments. In line with proponents of social learning analytics, our primary predictor for academic retention was the amount of communication activities, controlling for various institutional and disciplinary factors. Where possible, appropriate communication tasks that align with the learning objectives of the course may be a way forward to enhance academic retention.
{"title":"The impact of 151 learning designs on student satisfaction and performance: social learning (analytics) matters","authors":"B. Rienties, Lisette Toetenel","doi":"10.1145/2883851.2883875","DOIUrl":"https://doi.org/10.1145/2883851.2883875","url":null,"abstract":"An increasing number of researchers are taking learning design into consideration when predicting learning behavior and outcomes across different modules. This study builds on preliminary learning design work that was presented at LAK2015 by the Open University UK. In this study we linked 151 modules and 111.256 students with students' satisfaction and performance using multiple regression models. Our findings strongly indicate the importance of learning design in predicting and understanding performance of students in blended and online environments. In line with proponents of social learning analytics, our primary predictor for academic retention was the amount of communication activities, controlling for various institutional and disciplinary factors. Where possible, appropriate communication tasks that align with the learning objectives of the course may be a way forward to enhance academic retention.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133422466","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 widespread adoption of Learning Analytics (LA) and Educational Data Mining (EDM) has somewhat stagnated recently, and in some prominent cases even been reversed following concerns by governments, stakeholders and civil rights groups about privacy and ethics applied to the handling of personal data. In this ongoing discussion, fears and realities are often indistinguishably mixed up, leading to an atmosphere of uncertainty among potential beneficiaries of Learning Analytics, as well as hesitations among institutional managers who aim to innovate their institution's learning support by implementing data and analytics with a view on improving student success. In this paper, we try to get to the heart of the matter, by analysing the most common views and the propositions made by the LA community to solve them. We conclude the paper with an eight-point checklist named DELICATE that can be applied by researchers, policy makers and institutional managers to facilitate a trusted implementation of Learning Analytics.
{"title":"Privacy and analytics: it's a DELICATE issue a checklist for trusted learning analytics","authors":"H. Drachsler, W. Greller","doi":"10.1145/2883851.2883893","DOIUrl":"https://doi.org/10.1145/2883851.2883893","url":null,"abstract":"The widespread adoption of Learning Analytics (LA) and Educational Data Mining (EDM) has somewhat stagnated recently, and in some prominent cases even been reversed following concerns by governments, stakeholders and civil rights groups about privacy and ethics applied to the handling of personal data. In this ongoing discussion, fears and realities are often indistinguishably mixed up, leading to an atmosphere of uncertainty among potential beneficiaries of Learning Analytics, as well as hesitations among institutional managers who aim to innovate their institution's learning support by implementing data and analytics with a view on improving student success. In this paper, we try to get to the heart of the matter, by analysing the most common views and the propositions made by the LA community to solve them. We conclude the paper with an eight-point checklist named DELICATE that can be applied by researchers, policy makers and institutional managers to facilitate a trusted implementation of Learning Analytics.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131725112","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}
S. Bull, B. Ginon, J. Kay, M. Kickmeier-Rust, Matthew D. Johnson
With the arrival of 'big data; in education, the potential was recognised for learning analytics to track students' learning, to reveal patterns in their learning, or to identify at-risk students, in addition to guiding reform and supporting educators in improving teaching and learning processes [1]. Learning Analytics dashboards have been used at all levels, including institutional, regional and national level [2]. In classroom use, while learning visualisations are often based on counts of activity data or interaction patterns, there is increasing recognition that learning analytics relate to learning, and should therefore provide pedagogically useful information [3]. While increasing numbers of technology-enhanced learning applications are embracing the potential of learning analytics at the classroom level, often these are aimed at teachers. However, learners can also benefit from learning analytics data (e.g. [4][5]).
{"title":"LAL workshop: learning analytics for learners","authors":"S. Bull, B. Ginon, J. Kay, M. Kickmeier-Rust, Matthew D. Johnson","doi":"10.1145/2883851.2883852","DOIUrl":"https://doi.org/10.1145/2883851.2883852","url":null,"abstract":"With the arrival of 'big data; in education, the potential was recognised for learning analytics to track students' learning, to reveal patterns in their learning, or to identify at-risk students, in addition to guiding reform and supporting educators in improving teaching and learning processes [1]. Learning Analytics dashboards have been used at all levels, including institutional, regional and national level [2]. In classroom use, while learning visualisations are often based on counts of activity data or interaction patterns, there is increasing recognition that learning analytics relate to learning, and should therefore provide pedagogically useful information [3]. While increasing numbers of technology-enhanced learning applications are embracing the potential of learning analytics at the classroom level, often these are aimed at teachers. However, learners can also benefit from learning analytics data (e.g. [4][5]).","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123775793","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}
Given the importance of reading proficiency and habits for young students, an online e-quiz bank, Reading Battle, was launched in 2014 to facilitate reading improvement for primary-school students. With more than ten thousand questions in both English and Chinese, the system has attracted nearly five thousand learners who have made about half a million question answering records. In an effort towards delivering personalized learning experience to the learners, this study aims to discover potentially useful knowledge from learners' reading and question answering records in the Reading Battle system, by applying association rule mining and clustering analysis. The results show that learners could be grouped into three clusters based on their self-reported reading habits. The rules mined from different learner clusters can be used to develop personalized recommendations to the learners. Implications of the results on evaluating and further improving the Reading Battle system are also discussed.
{"title":"Towards personalizing an e-quiz bank for primary school students: an exploration with association rule mining and clustering","authors":"Xiao Hu, Yinfei Zhang, S. Chu, Xiaobo Ke","doi":"10.1145/2883851.2883959","DOIUrl":"https://doi.org/10.1145/2883851.2883959","url":null,"abstract":"Given the importance of reading proficiency and habits for young students, an online e-quiz bank, Reading Battle, was launched in 2014 to facilitate reading improvement for primary-school students. With more than ten thousand questions in both English and Chinese, the system has attracted nearly five thousand learners who have made about half a million question answering records. In an effort towards delivering personalized learning experience to the learners, this study aims to discover potentially useful knowledge from learners' reading and question answering records in the Reading Battle system, by applying association rule mining and clustering analysis. The results show that learners could be grouped into three clusters based on their self-reported reading habits. The rules mined from different learner clusters can be used to develop personalized recommendations to the learners. Implications of the results on evaluating and further improving the Reading Battle system are also discussed.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"207 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133893149","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 areas of educational data mining and learning analytics focus on the extraction of knowledge and actionable items from data sets containing detailed information about students. However, the potential impact from these techniques is increased when properly contextualized within a learning environment. More studies are needed to explore the connection between student interactions, approaches to learning, and academic performance. Self-regulated learning (SRL) is defined as the extent to which a student is able to motivationally, metacognitively, and cognitively engage in a learning experience. SRL has been the focus of research in traditional classroom learning and is also argued to play a vital role in the online or blended learning contexts. In this paper, we study how SRL affects students' online interactions with various learning activities and its influence in academic performance. The results derived from a naturalistic experiment among a cohort of first year engineering students showed that positive self-regulated strategies (PSRS) and negative self-regulated strategies (NSRS) affected both the interaction with online activities and academic performance. NSRS directly predicted academic outcomes, whereas PSRS only contributed indirectly to academic performance via the interactions with online activities. These results point to concrete avenues to promote self-regulation among students in this type of learning contexts.
{"title":"Exploring the relation between self-regulation, online activities, and academic performance: a case study","authors":"A. Pardo, Feifei Han, R. Ellis","doi":"10.1145/2883851.2883883","DOIUrl":"https://doi.org/10.1145/2883851.2883883","url":null,"abstract":"The areas of educational data mining and learning analytics focus on the extraction of knowledge and actionable items from data sets containing detailed information about students. However, the potential impact from these techniques is increased when properly contextualized within a learning environment. More studies are needed to explore the connection between student interactions, approaches to learning, and academic performance. Self-regulated learning (SRL) is defined as the extent to which a student is able to motivationally, metacognitively, and cognitively engage in a learning experience. SRL has been the focus of research in traditional classroom learning and is also argued to play a vital role in the online or blended learning contexts. In this paper, we study how SRL affects students' online interactions with various learning activities and its influence in academic performance. The results derived from a naturalistic experiment among a cohort of first year engineering students showed that positive self-regulated strategies (PSRS) and negative self-regulated strategies (NSRS) affected both the interaction with online activities and academic performance. NSRS directly predicted academic outcomes, whereas PSRS only contributed indirectly to academic performance via the interactions with online activities. These results point to concrete avenues to promote self-regulation among students in this type of learning contexts.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128252992","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 affordances of learning analytics (LA) are being increasingly harnessed to enhance 21st century (21C) pedagogy and learning. Relatively rare, however, are use cases and empirically based understandings of students' actual experiences with LA tools and environments at fostering 21C literacies, especially in secondary schooling and Asian education contexts. This paper addresses this knowledge gap by 1) presenting a first iteration design of a computer-supported collaborative critical reading and LA environment and its 16-week implementation in a Singapore high school; and 2) foregrounding students' quantitative and qualitative accounts of the benefits and problematics associated with this learning innovation. We focus the analytic lens on the LA dashboard components that provided visualizations of students' reading achievement, 21C learning dispositions, critical literacy competencies and social learning network positioning within the class. The paper aims to provide insights into the potentialities, paradoxes and pathways forward for designing LA that take into consideration the voices of learners as critical stakeholders.
{"title":"Fostering 21st century literacies through a collaborative critical reading and learning analytics environment: user-perceived benefits and problematics","authors":"J. Tan, Simon Yang, Elizabeth Koh, C. Jonathan","doi":"10.1145/2883851.2883965","DOIUrl":"https://doi.org/10.1145/2883851.2883965","url":null,"abstract":"The affordances of learning analytics (LA) are being increasingly harnessed to enhance 21st century (21C) pedagogy and learning. Relatively rare, however, are use cases and empirically based understandings of students' actual experiences with LA tools and environments at fostering 21C literacies, especially in secondary schooling and Asian education contexts. This paper addresses this knowledge gap by 1) presenting a first iteration design of a computer-supported collaborative critical reading and LA environment and its 16-week implementation in a Singapore high school; and 2) foregrounding students' quantitative and qualitative accounts of the benefits and problematics associated with this learning innovation. We focus the analytic lens on the LA dashboard components that provided visualizations of students' reading achievement, 21C learning dispositions, critical literacy competencies and social learning network positioning within the class. The paper aims to provide insights into the potentialities, paradoxes and pathways forward for designing LA that take into consideration the voices of learners as critical stakeholders.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"256 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133592779","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}
Stefan T. Mol, Vladimer Kobayashi, G. Kismihók, Catherine Zhao
Despite the mounting evidence supporting the role that goal setting has on the learning process, there seems to be only a handful of studies that directly investigate goal setting in the context of Learning Analytics (LA). Although investigations have incorporated elements of goal setting, the attention afforded to theory and operationalization have been modest. In this workshop we plan to position goal setting at the forefront of LA research. The workshop will serve as a venue to bring together researchers interested in advancing Goal Setting (GS) research in the LA field. Topics include: (1) GS theory and measurement; (2) analysis and visualization of GS data; (3) strategies for integrating GS in the learning experience; and (4) implementation of GS technologies. Participants who need tools to execute their GS ideas and those who already have tools and are exploring better ways to integrate a goal setting feature can gain a lot from this workshop. Moreover, participants will have the opportunity to contribute to the conceptualization and staging of GS ideas in LA research.
{"title":"Learning through goal setting","authors":"Stefan T. Mol, Vladimer Kobayashi, G. Kismihók, Catherine Zhao","doi":"10.1145/2883851.2883859","DOIUrl":"https://doi.org/10.1145/2883851.2883859","url":null,"abstract":"Despite the mounting evidence supporting the role that goal setting has on the learning process, there seems to be only a handful of studies that directly investigate goal setting in the context of Learning Analytics (LA). Although investigations have incorporated elements of goal setting, the attention afforded to theory and operationalization have been modest. In this workshop we plan to position goal setting at the forefront of LA research. The workshop will serve as a venue to bring together researchers interested in advancing Goal Setting (GS) research in the LA field. Topics include: (1) GS theory and measurement; (2) analysis and visualization of GS data; (3) strategies for integrating GS in the learning experience; and (4) implementation of GS technologies. Participants who need tools to execute their GS ideas and those who already have tools and are exploring better ways to integrate a goal setting feature can gain a lot from this workshop. Moreover, participants will have the opportunity to contribute to the conceptualization and staging of GS ideas in LA research.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114184431","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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