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}
R. Azevedo, Garrett C. Millar, M. Taub, Nicholas V. Mudrick, Amanda E. Bradbury, Megan J. Price
Emotions play a critical role during learning and problem solving with advanced learning technologies (ALTs). Despite their importance, relatively few attempts have been made to understand learners' emotional monitoring and regulation by using data visualizations of their own (and others') cognitive, affective, metacognitive, and motivational (CAMM) self-regulated learning (SRL) processes to potentially foster their emotion regulation (ER). We present a theoretically based and empirically driven conceptual framework that addresses ER by proposing the use of visualizations of one's own and others' CAMM SRL multichannel data to facilitate learners' monitoring and regulation of emotions during learning with ALTs. We use an example with eye-tracking data to illustrate the mapping between theoretical assumptions, ER strategies, and the types of data visualizations that can enhance learners' ER, including key processes such as emotion flexibility, emotion adaptivity, and emotion efficacy. We conclude with future directions leading to a systematic interdisciplinary research agenda that addresses outstanding ER-related issues by integrating models, theories, methods, and analytical techniques for the cognitive, learning, and affective sciences; human- computer interaction (HCI); data visualization; big data; data mining; and SRL.
{"title":"Using data visualizations to foster emotion regulation during self-regulated learning with advanced learning technologies: a conceptual framework","authors":"R. Azevedo, Garrett C. Millar, M. Taub, Nicholas V. Mudrick, Amanda E. Bradbury, Megan J. Price","doi":"10.1145/3027385.3027440","DOIUrl":"https://doi.org/10.1145/3027385.3027440","url":null,"abstract":"Emotions play a critical role during learning and problem solving with advanced learning technologies (ALTs). Despite their importance, relatively few attempts have been made to understand learners' emotional monitoring and regulation by using data visualizations of their own (and others') cognitive, affective, metacognitive, and motivational (CAMM) self-regulated learning (SRL) processes to potentially foster their emotion regulation (ER). We present a theoretically based and empirically driven conceptual framework that addresses ER by proposing the use of visualizations of one's own and others' CAMM SRL multichannel data to facilitate learners' monitoring and regulation of emotions during learning with ALTs. We use an example with eye-tracking data to illustrate the mapping between theoretical assumptions, ER strategies, and the types of data visualizations that can enhance learners' ER, including key processes such as emotion flexibility, emotion adaptivity, and emotion efficacy. We conclude with future directions leading to a systematic interdisciplinary research agenda that addresses outstanding ER-related issues by integrating models, theories, methods, and analytical techniques for the cognitive, learning, and affective sciences; human- computer interaction (HCI); data visualization; big data; data mining; and SRL.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"7 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":"117252075","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}
Misato Oi, M. Yamada, Fumiya Okubo, Atsushi Shimada, H. Ogata
In this paper, we examined whether previous findings on educational big data consisting of e-book logs from a given academic course can be reproduced with different data from other academic courses. The previous findings showed that (1) students who attained consistently good achievement more frequently browsed different e-books and their pages than low achievers and that (2) this difference was found only for logs of preparation for course sessions (preview), not for reviewing material (review). Preliminarily, we analyzed e-book logs from four courses. The results were reproduced in only one course and only partially, that is, (1) high achievers more frequently changed e-books than low achievers (2) for preview. This finding suggests that to allow effective usage of learning and teaching analyses, we need to carefully construct an educational environment to ensure reproducibility.
{"title":"Reproducibility of findings from educational big data: a preliminary study","authors":"Misato Oi, M. Yamada, Fumiya Okubo, Atsushi Shimada, H. Ogata","doi":"10.1145/3027385.3029445","DOIUrl":"https://doi.org/10.1145/3027385.3029445","url":null,"abstract":"In this paper, we examined whether previous findings on educational big data consisting of e-book logs from a given academic course can be reproduced with different data from other academic courses. The previous findings showed that (1) students who attained consistently good achievement more frequently browsed different e-books and their pages than low achievers and that (2) this difference was found only for logs of preparation for course sessions (preview), not for reviewing material (review). Preliminarily, we analyzed e-book logs from four courses. The results were reproduced in only one course and only partially, that is, (1) high achievers more frequently changed e-books than low achievers (2) for preview. This finding suggests that to allow effective usage of learning and teaching analyses, we need to carefully construct an educational environment to ensure reproducibility.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"12 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":"117030780","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 number of studies have demonstrated links between linguistic knowledge and performance in math. Studies examining these links in first language speakers of English have traditionally relied on correlational analyses between linguistic knowledge tests and standardized math tests. For second language (L2) speakers, the majority of studies have compared math performance between proficient and non-proficient speakers of English. In this study, we take a novel approach and examine the linguistic features of student language while they are engaged in collaborative problem solving within an on-line math tutoring system. We transcribe the students' speech and use natural language processing tools to extract linguistic information related to text cohesion, lexical sophistication, and sentiment. Our criterion variables are individuals' pretest and posttest math performance scores. In addition to examining relations between linguistic features of student language production and math scores, we also control for a number of non-linguistic factors including gender, age, grade, school, and content focus (procedural versus conceptual). Linear mixed effect modeling indicates that non-linguistic factors are not predictive of math scores. However, linguistic features related to cohesion affect and lexical proficiency explained approximately 30% of the variance (R2 = .303) in the math scores.
{"title":"Predicting math performance using natural language processing tools","authors":"S. Crossley, Ran Liu, D. McNamara","doi":"10.1145/3027385.3027399","DOIUrl":"https://doi.org/10.1145/3027385.3027399","url":null,"abstract":"A number of studies have demonstrated links between linguistic knowledge and performance in math. Studies examining these links in first language speakers of English have traditionally relied on correlational analyses between linguistic knowledge tests and standardized math tests. For second language (L2) speakers, the majority of studies have compared math performance between proficient and non-proficient speakers of English. In this study, we take a novel approach and examine the linguistic features of student language while they are engaged in collaborative problem solving within an on-line math tutoring system. We transcribe the students' speech and use natural language processing tools to extract linguistic information related to text cohesion, lexical sophistication, and sentiment. Our criterion variables are individuals' pretest and posttest math performance scores. In addition to examining relations between linguistic features of student language production and math scores, we also control for a number of non-linguistic factors including gender, age, grade, school, and content focus (procedural versus conceptual). Linear mixed effect modeling indicates that non-linguistic factors are not predictive of math scores. However, linguistic features related to cohesion affect and lexical proficiency explained approximately 30% of the variance (R2 = .303) in the math scores.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"1999 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":"128053240","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}
Learning analytics empowered educational technologies (LA-ET) in primary classrooms allow for blended learning scenarios with teacher-lead instructions, class-paced and individually-paced practice. This quasi-experimental study investigates the effects of a LA-ET on the development of students' arithmetic skills over one schoolyear. Children learning in a traditional paper & pencil condition were compared to learners using a LA-ET on tablet computers in grade 4. The educational technology combined teacher dashboards (extracted analytics) and class and individually paced assignments (embedded analytics). The results indicated that children in the LA-ET condition made significantly more progress on arithmetic skills in one schoolyear compared to children in the paper & pencil condition.
{"title":"The effects of a learning analytics empowered technology on students' arithmetic skill development","authors":"I. Molenaar, C. K. Campen, F. Hasselman","doi":"10.1145/3027385.3029488","DOIUrl":"https://doi.org/10.1145/3027385.3029488","url":null,"abstract":"Learning analytics empowered educational technologies (LA-ET) in primary classrooms allow for blended learning scenarios with teacher-lead instructions, class-paced and individually-paced practice. This quasi-experimental study investigates the effects of a LA-ET on the development of students' arithmetic skills over one schoolyear. Children learning in a traditional paper & pencil condition were compared to learners using a LA-ET on tablet computers in grade 4. The educational technology combined teacher dashboards (extracted analytics) and class and individually paced assignments (embedded analytics). The results indicated that children in the LA-ET condition made significantly more progress on arithmetic skills in one schoolyear compared to children in the paper & pencil condition.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"97 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":"124746247","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 workshop aims to promote strategic planning for learning analytics in higher education through developing institutional policies. While adoption of learning analytics is predominantly seen in small-scale and bottom-up patterns, it is believed that a systemic implementation can bring the widest impact to the education system and lasting benefits to learners. However, the success of it highly depends on the adopted strategy that meets the needs of various stakeholders and systematically pushes the institution towards achieving its targets. It is imperative to develop a learning analytics policy that ensures a practice that is valid, effective and ethical. The workshop involves two components. The first component includes a set of presentations about the state of learning analytics in higher education, drawing on results from an Australian and a European project examining institutional learning analytics policy and adoption processes. The second component is an interactive session where participants are encouraged to share their motivations for adopting learning analytics and the diversity of challenges they perceive impede analytics adoption in their institution. Using the RAPID Outcome Mapping Approach (ROMA), participants will create a draft policy that articulates how the various challenges can be addressed. This workshop aims to further develop our understanding of how learning analytics operates in an organizational system and promote a cultural change in how such analytics are adopted in higher education.
{"title":"LA policy: developing an institutional policy for learning analytics using the RAPID outcome mapping approach","authors":"Yi-Shan Tsai, D. Gašević, P. Merino, S. Dawson","doi":"10.1145/3027385.3029424","DOIUrl":"https://doi.org/10.1145/3027385.3029424","url":null,"abstract":"This workshop aims to promote strategic planning for learning analytics in higher education through developing institutional policies. While adoption of learning analytics is predominantly seen in small-scale and bottom-up patterns, it is believed that a systemic implementation can bring the widest impact to the education system and lasting benefits to learners. However, the success of it highly depends on the adopted strategy that meets the needs of various stakeholders and systematically pushes the institution towards achieving its targets. It is imperative to develop a learning analytics policy that ensures a practice that is valid, effective and ethical. The workshop involves two components. The first component includes a set of presentations about the state of learning analytics in higher education, drawing on results from an Australian and a European project examining institutional learning analytics policy and adoption processes. The second component is an interactive session where participants are encouraged to share their motivations for adopting learning analytics and the diversity of challenges they perceive impede analytics adoption in their institution. Using the RAPID Outcome Mapping Approach (ROMA), participants will create a draft policy that articulates how the various challenges can be addressed. This workshop aims to further develop our understanding of how learning analytics operates in an organizational system and promote a cultural change in how such analytics are adopted in higher education.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"51 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":"129596164","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}
Maren Scheffel, H. Drachsler, K. Kreijns, J. Kraker, M. Specht
The collaborative learning processes of students in online learning environments can be supported by providing learning analytics-based visualisations that foster awareness and reflection about an individual's as well as the team's behaviour and their learning and collaboration processes. For this empirical study we implemented an activity widget into the online learning environment of a live five-months Master course and investigated the predictive power of the widget indicators towards the students' grades and compared the results to those from an exploratory study with data collected in previous runs of the same course where the widget had not been in use. Together with information gathered from a quantitative as well as a qualitative evaluation of the activity widget during the course, the findings of this current study show that there are indeed predictive relations between the widget indicators and the grades, especially those regarding responsiveness, and indicate that some of the observed differences in the last run could be attributed to the implemented activity widget.
{"title":"Widget, widget as you lead, I am performing well indeed!: using results from an exploratory offline study to inform an empirical online study about a learning analytics widget in a collaborative learning environment","authors":"Maren Scheffel, H. Drachsler, K. Kreijns, J. Kraker, M. Specht","doi":"10.1145/3027385.3027428","DOIUrl":"https://doi.org/10.1145/3027385.3027428","url":null,"abstract":"The collaborative learning processes of students in online learning environments can be supported by providing learning analytics-based visualisations that foster awareness and reflection about an individual's as well as the team's behaviour and their learning and collaboration processes. For this empirical study we implemented an activity widget into the online learning environment of a live five-months Master course and investigated the predictive power of the widget indicators towards the students' grades and compared the results to those from an exploratory study with data collected in previous runs of the same course where the widget had not been in use. Together with information gathered from a quantitative as well as a qualitative evaluation of the activity widget during the course, the findings of this current study show that there are indeed predictive relations between the widget indicators and the grades, especially those regarding responsiveness, and indicate that some of the observed differences in the last run could be attributed to the implemented activity widget.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"217 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":"132275286","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}
At Stenden University students from all over the world study together; all these different nationalities and cultures result in different ideas concerning academic success. The basis of this project was to develop a personalized dashboard for students via Microsoft Office 365 Power BI in which students can set their own personal KPI's. The raw data from the Student Information System (SIS) was transformed into clear visualizations that will help students gain better insight into their academic performance. This information can be used either independently or in consultation with their student advisor.
在斯坦德大学，来自世界各地的学生在一起学习;所有这些不同的民族和文化导致了对学业成功的不同看法。这个项目的基础是通过Microsoft Office 365 Power BI为学生开发一个个性化的仪表板，学生可以在其中设置自己的个人KPI。来自学生信息系统(SIS)的原始数据被转换成清晰的可视化，这将帮助学生更好地了解他们的学习成绩。这些信息可以独立使用，也可以咨询学生顾问。
{"title":"Business intelligence (BI) for personalized student dashboards","authors":"J. Sluijter, M. Otten","doi":"10.1145/3027385.3029458","DOIUrl":"https://doi.org/10.1145/3027385.3029458","url":null,"abstract":"At Stenden University students from all over the world study together; all these different nationalities and cultures result in different ideas concerning academic success. The basis of this project was to develop a personalized dashboard for students via Microsoft Office 365 Power BI in which students can set their own personal KPI's. The raw data from the Student Information System (SIS) was transformed into clear visualizations that will help students gain better insight into their academic performance. This information can be used either independently or in consultation with their student advisor.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"24 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":"129788423","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}
Jaclyn L. Ocumpaugh, R. Baker, M. O. S. Pedro, M. Hawn, Cristina Heffernan, N. Heffernan, Stefan Slater
Advances in the learning analytics community have created opportunities to deliver early warnings that alert teachers and instructors when a student is at risk of not meeting academic goals [6], [71]. Alert systems have also been developed for school district leaders [33] and for academic advisors in higher education [39], but other professionals in the K-12 system, namely guidance counselors, have not been widely served by these systems. In this study, we use college enrollment models created for the ASSISTments learning system [55] to develop reports that target the needs of these professionals, who often work directly with students, but usually not in classroom settings. These reports are designed to facilitate guidance counselors' efforts to help students to set long term academic and career goals. As such, they provide the calculated likelihood that a student will attend college (the ASSISTments College Prediction Model or ACPM), alongside student engagement and learning measures. Using design principles from risk communication research and student feedback theories to inform a co-design process, we developed reports that can inform guidance counselor efforts to support student achievement.
{"title":"Guidance counselor reports of the ASSISTments college prediction model (ACPM)","authors":"Jaclyn L. Ocumpaugh, R. Baker, M. O. S. Pedro, M. Hawn, Cristina Heffernan, N. Heffernan, Stefan Slater","doi":"10.1145/3027385.3027435","DOIUrl":"https://doi.org/10.1145/3027385.3027435","url":null,"abstract":"Advances in the learning analytics community have created opportunities to deliver early warnings that alert teachers and instructors when a student is at risk of not meeting academic goals [6], [71]. Alert systems have also been developed for school district leaders [33] and for academic advisors in higher education [39], but other professionals in the K-12 system, namely guidance counselors, have not been widely served by these systems. In this study, we use college enrollment models created for the ASSISTments learning system [55] to develop reports that target the needs of these professionals, who often work directly with students, but usually not in classroom settings. These reports are designed to facilitate guidance counselors' efforts to help students to set long term academic and career goals. As such, they provide the calculated likelihood that a student will attend college (the ASSISTments College Prediction Model or ACPM), alongside student engagement and learning measures. Using design principles from risk communication research and student feedback theories to inform a co-design process, we developed reports that can inform guidance counselor efforts to support student achievement.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"11 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":"130055169","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}
Predicting student learning outcomes is one of the prominent themes in Learning Analytics research. These studies varied to a significant extent in terms of the techniques being used, the contexts in which they were situated, and the consequent effectiveness of the prediction. This paper presented the preliminary results of a systematic review of studies in predictive learning analytics. With the goal to find out what methodologies work for what circumstances, this study will be able to facilitate future research in this area, contributing to relevant system developments that are of pedagogic values.
{"title":"A systematic review of studies on predicting student learning outcomes using learning analytics","authors":"Xiao Hu, C. Cheong, Wenwen Ding, M. Woo","doi":"10.1145/3027385.3029438","DOIUrl":"https://doi.org/10.1145/3027385.3029438","url":null,"abstract":"Predicting student learning outcomes is one of the prominent themes in Learning Analytics research. These studies varied to a significant extent in terms of the techniques being used, the contexts in which they were situated, and the consequent effectiveness of the prediction. This paper presented the preliminary results of a systematic review of studies in predictive learning analytics. With the goal to find out what methodologies work for what circumstances, this study will be able to facilitate future research in this area, contributing to relevant system developments that are of pedagogic values.","PeriodicalId":160897,"journal":{"name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","volume":"80 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":"121880851","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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