{"title":"Delivering an Omnibus-style Syllabus Online with Google Classroom: A case study of Compulsory Foundational Education English 1 Classes","authors":"J. Collins","doi":"10.51034/JASLA.4.0_18","DOIUrl":"https://doi.org/10.51034/JASLA.4.0_18","url":null,"abstract":"","PeriodicalId":36754,"journal":{"name":"Journal of Learning Analytics","volume":"4 1","pages":"18-27"},"PeriodicalIF":3.9,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70968879","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}
In his keynote, Neil Selwyn not only acknowledged his role as ‘outsider’ to the field of learning analytics, but also intentionally assumed the role of “idiot”. In my commentary I assume that Selwyn’s embrace of being an idiot was more than just self-deprecating humour or a disclaimer aimed to prepare the audience for his provocations. In a Medieval carnival, the clown, fool or community idiot was crowned king, and for the duration of the carnival, could make fun of the royal household, blaspheme and provoke, all licenced by his or her role at that moment in time. Selwyn acknowledged that his own position was and continue to be informed by Critical Data Studies (CDS), an emerging research focus and discourse aimed at troubling much of current accepted and unquestioned assumptions and practices in the broader context of data science. I reflect and comment on Selwyn’s keynote by firstly mapping some of the key tenets of CDS, before addressing some aspects of the keynote and two aspect of his “learning analytics wish-list” namely “giving students control” and “seeing ethics in terms of power, not in terms of protection."
{"title":"Commentary on Neil Selwyn's LAK18 Keynote Address","authors":"P. Prinsloo","doi":"10.18608/JLA.2019.63.4","DOIUrl":"https://doi.org/10.18608/JLA.2019.63.4","url":null,"abstract":"In his keynote, Neil Selwyn not only acknowledged his role as ‘outsider’ to the field of learning analytics, but also intentionally assumed the role of “idiot”. In my commentary I assume that Selwyn’s embrace of being an idiot was more than just self-deprecating humour or a disclaimer aimed to prepare the audience for his provocations. In a Medieval carnival, the clown, fool or community idiot was crowned king, and for the duration of the carnival, could make fun of the royal household, blaspheme and provoke, all licenced by his or her role at that moment in time. Selwyn acknowledged that his own position was and continue to be informed by Critical Data Studies (CDS), an emerging research focus and discourse aimed at troubling much of current accepted and unquestioned assumptions and practices in the broader context of data science. I reflect and comment on Selwyn’s keynote by firstly mapping some of the key tenets of CDS, before addressing some aspects of the keynote and two aspect of his “learning analytics wish-list” namely “giving students control” and “seeing ethics in terms of power, not in terms of protection.\"","PeriodicalId":36754,"journal":{"name":"Journal of Learning Analytics","volume":"6 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2019-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67542546","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}
C. Herodotou, B. Rienties, Barry Verdin, Avinash Boroowa
Predictive Learning Analytics (PLA) aim to improve learning by identifying students at risk of failing their studies. Yet, little is known about how best to integrate and scaffold PLA initiatives into higher education institutions. Towards this end, it becomes essential to capture and analyze the perceptions of relevant educational stakeholders (i.e., managers, teachers, students) about PLA. This paper presents an “at scale” implementation of PLA at a distance learning higher education institution and details, in particular, the perspectives of 20 educational managers involved in the implementation. It concludes with a set of recommendations about how best to adopt and apply large-scale PLA initiatives in higher education.
{"title":"Predictive Learning Analytics 'At Scale': Guidelines to Successful Implementation in Higher Education","authors":"C. Herodotou, B. Rienties, Barry Verdin, Avinash Boroowa","doi":"10.18608/JLA.2019.61.5","DOIUrl":"https://doi.org/10.18608/JLA.2019.61.5","url":null,"abstract":"Predictive Learning Analytics (PLA) aim to improve learning by identifying students at risk of failing their studies. Yet, little is known about how best to integrate and scaffold PLA initiatives into higher education institutions. Towards this end, it becomes essential to capture and analyze the perceptions of relevant educational stakeholders (i.e., managers, teachers, students) about PLA. This paper presents an “at scale” implementation of PLA at a distance learning higher education institution and details, in particular, the perspectives of 20 educational managers involved in the implementation. It concludes with a set of recommendations about how best to adopt and apply large-scale PLA initiatives in higher education.","PeriodicalId":36754,"journal":{"name":"Journal of Learning Analytics","volume":"6 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2019-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.18608/JLA.2019.61.5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67542424","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}
{"title":"Book Review: Quantitative Ethnography by David Williamson Shaffer","authors":"S. B. Shum","doi":"10.18608/jla.2019.61.7","DOIUrl":"https://doi.org/10.18608/jla.2019.61.7","url":null,"abstract":"","PeriodicalId":36754,"journal":{"name":"Journal of Learning Analytics","volume":"6 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67542489","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 editorial introduces a special section of the Journal of Learning Analytics, for which Neil Selwyn’s keynote address to LAK ’18 has been written up as an article, “What’s the problem with learning analytics?” His claims and arguments are engaged in commentaries from Alfred Essa, Rebecca Ferguson, Paul Prinsloo, and Carolyn Rosé, who provide diverse perspectives on Selwyn’s proposals and arguments, from applause to refutation. Reflecting on the debate, I note some of the tensions to be resolved for learning analytics and social science critiques to engage productively, observing that central to the debate is how we understand the role of abstraction in the analysis of data about teaching and learning, and hence the opportunities and risks this entails.
{"title":"Critical Data Studies, Abstraction & Learning Analytics: Editorial to Selwyn's LAK keynote and invited commentaries","authors":"S. B. Shum","doi":"10.18608/jla.2019.63.2","DOIUrl":"https://doi.org/10.18608/jla.2019.63.2","url":null,"abstract":"This editorial introduces a special section of the Journal of Learning Analytics, for which Neil Selwyn’s keynote address to LAK ’18 has been written up as an article, “What’s the problem with learning analytics?” His claims and arguments are engaged in commentaries from Alfred Essa, Rebecca Ferguson, Paul Prinsloo, and Carolyn Rosé, who provide diverse perspectives on Selwyn’s proposals and arguments, from applause to refutation. Reflecting on the debate, I note some of the tensions to be resolved for learning analytics and social science critiques to engage productively, observing that central to the debate is how we understand the role of abstraction in the analysis of data about teaching and learning, and hence the opportunities and risks this entails.","PeriodicalId":36754,"journal":{"name":"Journal of Learning Analytics","volume":"6 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.18608/jla.2019.63.2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67542500","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}
{"title":"LAK16 Editorial","authors":"C. Rosé, S. Dawson, H. Drachsler","doi":"10.18608/jla.2017.41.2","DOIUrl":"https://doi.org/10.18608/jla.2017.41.2","url":null,"abstract":"","PeriodicalId":36754,"journal":{"name":"Journal of Learning Analytics","volume":"4 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67542399","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}
{"title":"Talk with Me: Student Pronoun Use as an Indicator of Discourse Health","authors":"Carrie Demmans Epp, K. Phirangee, Jim Hewitt","doi":"10.18608/jla.2017.43.4","DOIUrl":"https://doi.org/10.18608/jla.2017.43.4","url":null,"abstract":"","PeriodicalId":36754,"journal":{"name":"Journal of Learning Analytics","volume":"4 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67542413","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}
Online environments can cultivate what have been referred to as 21st century skil ls and capabilities, as youth contribute, pursue, share, and interact around work and ideas. Such environments also hold great potential for addressing digital divi des related to the devel opment of such skil ls by connecting youth in areas with fewer resources and opportunities to s oc i a l a nd material supports for learning. However, even with increasing attention to the importance of 21st century skil ls, there is sti l l relatively l ittle known about how to measure these sorts of competencies effectively. In this paper, we offer an exploratory approach for interpreting student user trace log data to reveal opportunities for creative production, self-directed learning, and social learning online. Our approach engages social learning analytics to code actions according to relationships between users and engages in self-report and ethnographic methods to supplement initial results. We share our methods; provide rich descripti on of the unique learning environment; present results of logged opportunities for creative production, self directed learning, and social learning across the sixth grade cohort; and explore these results through the lens of individual learners, including cohort self-reports of identity, interest, and perceptions, and qualitative case studies of two students.
{"title":"Revealing Opportunities for 21st Century Learning: An Approach to Interpreting User Trace Log Data","authors":"C. K. Martin, Denise C. Nacu, Nichole Pinkard","doi":"10.18608/jla.2016.32.4","DOIUrl":"https://doi.org/10.18608/jla.2016.32.4","url":null,"abstract":"Online environments can cultivate what have been referred to as 21st century skil ls and capabilities, as youth contribute, pursue, share, and interact around work and ideas. Such environments also hold great potential for addressing digital divi des related to the devel opment of such skil ls by connecting youth in areas with fewer resources and opportunities to s oc i a l a nd material supports for learning. However, even with increasing attention to the importance of 21st century skil ls, there is sti l l relatively l ittle known about how to measure these sorts of competencies effectively. In this paper, we offer an exploratory approach for interpreting student user trace log data to reveal opportunities for creative production, self-directed learning, and social learning online. Our approach engages social learning analytics to code actions according to relationships between users and engages in self-report and ethnographic methods to supplement initial results. We share our methods; provide rich descripti on of the unique learning environment; present results of logged opportunities for creative production, self directed learning, and social learning across the sixth grade cohort; and explore these results through the lens of individual learners, including cohort self-reports of identity, interest, and perceptions, and qualitative case studies of two students.","PeriodicalId":36754,"journal":{"name":"Journal of Learning Analytics","volume":"3 1","pages":"37-87"},"PeriodicalIF":3.9,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.18608/jla.2016.32.4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67542861","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}
Designing learning experiences that support the development of coherent understanding of complex scientific phenomena is challenging. We sought to identify analytics that can guide such designs to also support retention of coherent understanding. Based on prior research that distributing study of material over time supports retention, we explored revisiting previously studied material as an analytic. We tested ways to operationalize revisiting: as a general propensity to revisit previously studied material; as a propensity to revisit specific curricular steps; as a general propensity to distribute study by revisiting previously studied material on different days; and as a propensity to distribute study by revisiting specific steps on different days. The specific steps identified as central to the learning design included a static illustration and a dynamic visualization. We modeled revisiting in a sample of 664 students taught by 7 different teachers using a Web-based Inquiry Science Environment unit. Analysis of log files and regression modeling revealed that a general propensity to revisit did not predict retention. Revisiting the dynamic visualization better supported retention than revisiting static material, but only for distributed revisiting. Our findings suggest that revisiting can be a useful analytic when aligned to the framework guiding learning design.
{"title":"Distributed Revisiting: An Analytic for Retention of Coherent Science Learning.","authors":"Vanessa Svihla, M. Wester, M. Linn","doi":"10.18608/JLA.2015.22.7","DOIUrl":"https://doi.org/10.18608/JLA.2015.22.7","url":null,"abstract":"Designing learning experiences that support the development of coherent understanding of complex scientific phenomena is challenging. We sought to identify analytics that can guide such designs to also support retention of coherent understanding. Based on prior research that distributing study of material over time supports retention, we explored revisiting previously studied material as an analytic. We tested ways to operationalize revisiting: as a general propensity to revisit previously studied material; as a propensity to revisit specific curricular steps; as a general propensity to distribute study by revisiting previously studied material on different days; and as a propensity to distribute study by revisiting specific steps on different days. The specific steps identified as central to the learning design included a static illustration and a dynamic visualization. We modeled revisiting in a sample of 664 students taught by 7 different teachers using a Web-based Inquiry Science Environment unit. Analysis of log files and regression modeling revealed that a general propensity to revisit did not predict retention. Revisiting the dynamic visualization better supported retention than revisiting static material, but only for distributed revisiting. Our findings suggest that revisiting can be a useful analytic when aligned to the framework guiding learning design.","PeriodicalId":36754,"journal":{"name":"Journal of Learning Analytics","volume":"2 1","pages":"75-101"},"PeriodicalIF":3.9,"publicationDate":"2015-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.18608/JLA.2015.22.7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67542804","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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