Little is known about the processes institutions use when discerning their readiness to implement learning analytics. This study aims to address this gap in the literature by using survey data from the beta version of the Learning Analytics Readiness Instrument (LARI) [1]. Twenty-four institutions were surveyed and 560 respondents participated. Five distinct factors were identified from a factor analysis of the results: Culture; Data Management Expertise; Data Analysis Expertise; Communication and Policy Application; and, Training. Data were analyzed using both the role of those completing the survey and the Carnegie classification of the institutions as lenses. Generally, information technology professionals and institutions classified as Research Universities--Very High research activity had significantly different scores on the identified factors. Working within a framework of organizational learning, this paper details the concept of readiness as a reflective process, as well as how the implementation and application of analytics should be done so with ethical considerations in mind. Limitations of the study, as well as next steps for research in this area, are also discussed.
{"title":"The learning analytics readiness instrument","authors":"M. Oster, Steven Lonn, M. Pistilli, M. Brown","doi":"10.1145/2883851.2883925","DOIUrl":"https://doi.org/10.1145/2883851.2883925","url":null,"abstract":"Little is known about the processes institutions use when discerning their readiness to implement learning analytics. This study aims to address this gap in the literature by using survey data from the beta version of the Learning Analytics Readiness Instrument (LARI) [1]. Twenty-four institutions were surveyed and 560 respondents participated. Five distinct factors were identified from a factor analysis of the results: Culture; Data Management Expertise; Data Analysis Expertise; Communication and Policy Application; and, Training. Data were analyzed using both the role of those completing the survey and the Carnegie classification of the institutions as lenses. Generally, information technology professionals and institutions classified as Research Universities--Very High research activity had significantly different scores on the identified factors. Working within a framework of organizational learning, this paper details the concept of readiness as a reflective process, as well as how the implementation and application of analytics should be done so with ethical considerations in mind. Limitations of the study, as well as next steps for research in this area, are also discussed.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"79 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":"128924327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents an analytics dashboard that has been developed for designers of interactive e-books. This is part of the EU-funded MC Squared project that is developing a platform for authoring interactive educational e-books. The primary objective is to develop technologies and resources that enhance creative thinking for both designers (authors) and learners. The learning material is expected to offer learners opportunities to engage creatively with mathematical problems and develop creative mathematical thinking. The analytics dashboard is designed to increase authors' awareness so that they can make informed decisions on how to redesign and improve the e-books. This paper presents architectural and design decisions on key features of the dashboard and discusses future steps with respect to the potential for exploratory data analysis.
{"title":"Towards analytics for educational interactive e-books: the case of the reflective designer analytics platform (RDAP)","authors":"Sokratis Karkalas, M. Mavrikis, Oliver Labs","doi":"10.1145/2883851.2883943","DOIUrl":"https://doi.org/10.1145/2883851.2883943","url":null,"abstract":"This paper presents an analytics dashboard that has been developed for designers of interactive e-books. This is part of the EU-funded MC Squared project that is developing a platform for authoring interactive educational e-books. The primary objective is to develop technologies and resources that enhance creative thinking for both designers (authors) and learners. The learning material is expected to offer learners opportunities to engage creatively with mathematical problems and develop creative mathematical thinking. The analytics dashboard is designed to increase authors' awareness so that they can make informed decisions on how to redesign and improve the e-books. This paper presents architectural and design decisions on key features of the dashboard and discusses future steps with respect to the potential for exploratory data analysis.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"55 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":"129343512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper describes the analysis of the social and semantic structure of discussion forums in massive open online courses (MOOCs) in terms of information exchange and user roles. To that end, we analyse a network of forum users based on information-giving relations extracted from the forum data. Connection patterns that appear in the information exchange network of forum users are used to define specific user roles in a social context. Semantic roles are derived by identifying thematic areas in which an actor seeks for information (problem areas) and the areas of interest in which an actor provides information to others (expertise). The interplay of social and semantic roles is analysed using a socio-semantic blockmodelling approach. The results show that social and semantic roles are not strongly interdependent. This indicates that communication patterns and interests of users develop simultaneously only to a moderate extent. In addition to the case study, the methodological contribution is in combining traditional blockmodelling with semantic information to characterise participant roles.
{"title":"Investigating social and semantic user roles in MOOC discussion forums","authors":"Tobias Hecking, Irene-Angelica Chounta, H. Hoppe","doi":"10.1145/2883851.2883924","DOIUrl":"https://doi.org/10.1145/2883851.2883924","url":null,"abstract":"This paper describes the analysis of the social and semantic structure of discussion forums in massive open online courses (MOOCs) in terms of information exchange and user roles. To that end, we analyse a network of forum users based on information-giving relations extracted from the forum data. Connection patterns that appear in the information exchange network of forum users are used to define specific user roles in a social context. Semantic roles are derived by identifying thematic areas in which an actor seeks for information (problem areas) and the areas of interest in which an actor provides information to others (expertise). The interplay of social and semantic roles is analysed using a socio-semantic blockmodelling approach. The results show that social and semantic roles are not strongly interdependent. This indicates that communication patterns and interests of users develop simultaneously only to a moderate extent. In addition to the case study, the methodological contribution is in combining traditional blockmodelling with semantic information to characterise participant roles.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"61 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":"131861933","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 on Learning Analytics for Curriculum and Program Quality Improvement investigates how LAK can drive improvements in teaching practices, instructional and curricular design, and academic program delivery. This workshop brings forward research and examples of how LAK can help build the case for instructional, curricular, or programmatic change and further how LAK can be used to foster acceptance of change processes by teachers, administrators, and other stakeholders in the educational enterprise.
{"title":"Learning analytics for curriculum and program quality improvement (PCLA 2016)","authors":"J. Greer, M. Molinaro, X. Ochoa, T. McKay","doi":"10.1145/2883851.2883899","DOIUrl":"https://doi.org/10.1145/2883851.2883899","url":null,"abstract":"This workshop on Learning Analytics for Curriculum and Program Quality Improvement investigates how LAK can drive improvements in teaching practices, instructional and curricular design, and academic program delivery. This workshop brings forward research and examples of how LAK can help build the case for instructional, curricular, or programmatic change and further how LAK can be used to foster acceptance of change processes by teachers, administrators, and other stakeholders in the educational enterprise.","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":"130859178","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. B. Shum, Simon Knight, D. McNamara, L. Allen, Duygu Bektik, S. Crossley
Writing Analytics focuses on the measurement and analysis of written texts for the purpose of understanding writing processes and products, in their educational contexts, and improving the teaching and learning of writing. This workshop adopts a critical, holistic perspective in which the definition of "the system" and "success" is not restricted to IR metrics such as precision and recall, but recognizes the many wider issues that aid or obstruct analytics adoption in educational settings, such as theoretical and pedagogical grounding, usability, user experience, stakeholder design engagement, practitioner development, organizational infrastructure, policy and ethics.
{"title":"Critical perspectives on writing analytics","authors":"S. B. Shum, Simon Knight, D. McNamara, L. Allen, Duygu Bektik, S. Crossley","doi":"10.1145/2883851.2883854","DOIUrl":"https://doi.org/10.1145/2883851.2883854","url":null,"abstract":"Writing Analytics focuses on the measurement and analysis of written texts for the purpose of understanding writing processes and products, in their educational contexts, and improving the teaching and learning of writing. This workshop adopts a critical, holistic perspective in which the definition of \"the system\" and \"success\" is not restricted to IR metrics such as precision and recall, but recognizes the many wider issues that aid or obstruct analytics adoption in educational settings, such as theoretical and pedagogical grounding, usability, user experience, stakeholder design engagement, practitioner development, organizational infrastructure, policy and ethics.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"14 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":"133688466","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. B. Shum, Ágnes Sándor, R. Goldsmith, Xiaolong Wang, Randall Bass, Mindy McWilliams
When used effectively, reflective writing tasks can deepen learners' understanding of key concepts, help them critically appraise their developing professional identity, and build qualities for lifelong learning. As such, reflecting writing is attracting substantial interest from universities concerned with experiential learning, reflective practice, and developing a holistic conception of the learner. However, reflective writing is for many students a novel genre to compose in, and tutors may be inexperienced in its assessment. While these conditions set a challenging context for automated solutions, natural language processing may also help address the challenge of providing real time, formative feedback on draft writing. This paper reports progress in designing a writing analytics application, detailing the methodology by which informally expressed rubrics are modelled as formal rhetorical patterns, a capability delivered by a novel web application. This has been through iterative evaluation on an independently human-annotated corpus, showing improvements from the first to second version. We conclude by discussing the reasons why classifying reflective writing has proven complex, and reflect on the design processes enabling work across disciplinary boundaries to develop the prototype to its current state.
{"title":"Reflecting on reflective writing analytics: assessment challenges and iterative evaluation of a prototype tool","authors":"S. B. Shum, Ágnes Sándor, R. Goldsmith, Xiaolong Wang, Randall Bass, Mindy McWilliams","doi":"10.1145/2883851.2883955","DOIUrl":"https://doi.org/10.1145/2883851.2883955","url":null,"abstract":"When used effectively, reflective writing tasks can deepen learners' understanding of key concepts, help them critically appraise their developing professional identity, and build qualities for lifelong learning. As such, reflecting writing is attracting substantial interest from universities concerned with experiential learning, reflective practice, and developing a holistic conception of the learner. However, reflective writing is for many students a novel genre to compose in, and tutors may be inexperienced in its assessment. While these conditions set a challenging context for automated solutions, natural language processing may also help address the challenge of providing real time, formative feedback on draft writing. This paper reports progress in designing a writing analytics application, detailing the methodology by which informally expressed rubrics are modelled as formal rhetorical patterns, a capability delivered by a novel web application. This has been through iterative evaluation on an independently human-annotated corpus, showing improvements from the first to second version. We conclude by discussing the reasons why classifying reflective writing has proven complex, and reflect on the design processes enabling work across disciplinary boundaries to develop the prototype to its current state.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"29 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":"114311884","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}
D. Clow, Rebecca Ferguson, Leah P. Macfadyen, P. Prinsloo, Sharon Slade
As in many fields, most papers in the learning analytics literature report success or, at least, read as if they are reporting success. This is almost certainly not because learning analytics research and activity are always successful. Generally, we report our successes widely, but keep our failures to ourselves. As Bismarck is alleged to have said: it is wise to learn from the mistakes of others. This workshop offers an opportunity for researchers and practitioners to share their failures in a lower-stakes environment, to help them learn from each other's mistakes.
{"title":"LAK failathon","authors":"D. Clow, Rebecca Ferguson, Leah P. Macfadyen, P. Prinsloo, Sharon Slade","doi":"10.1145/2883851.2883918","DOIUrl":"https://doi.org/10.1145/2883851.2883918","url":null,"abstract":"As in many fields, most papers in the learning analytics literature report success or, at least, read as if they are reporting success. This is almost certainly not because learning analytics research and activity are always successful. Generally, we report our successes widely, but keep our failures to ourselves. As Bismarck is alleged to have said: it is wise to learn from the mistakes of others. This workshop offers an opportunity for researchers and practitioners to share their failures in a lower-stakes environment, to help them learn from each other's mistakes.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"187 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":"121062869","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}
Kirsty Kitto, Aneesha Bakharia, Mandy Lupton, D. Mallet, J. Banks, P. Bruza, A. Pardo, S. B. Shum, S. Dawson, D. Gašević, George Siemens, G. Lynch
This demonstration introduces the Connected Learning Analytics (CLA) Toolkit. The CLA toolkit harvests data about student participation in specified learning activities across standard social media environments, and presents information about the nature and quality of the learning interactions.
{"title":"The connected learning analytics toolkit","authors":"Kirsty Kitto, Aneesha Bakharia, Mandy Lupton, D. Mallet, J. Banks, P. Bruza, A. Pardo, S. B. Shum, S. Dawson, D. Gašević, George Siemens, G. Lynch","doi":"10.1145/2883851.2883881","DOIUrl":"https://doi.org/10.1145/2883851.2883881","url":null,"abstract":"This demonstration introduces the Connected Learning Analytics (CLA) Toolkit. The CLA toolkit harvests data about student participation in specified learning activities across standard social media environments, and presents information about the nature and quality of the learning interactions.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"18 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":"115078029","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}
Data-driven methods have previously been used in intelligent tutoring systems to improve student learning outcomes and predict student learning methods. We have been incorporating data-driven methods for feedback and problem selection into Deep Thought, a logic tutor where students practice constructing deductive logic proofs. In this latest study we have implemented our data-driven proficiency profiler (DDPP) into Deep Thought as a proof of concept. The DDPP determines student proficiency without expert involvement by comparing relevant student rule scores to previous students who behaved similarly in the tutor and successfully completed it. The results show that the DDPP did improve in performance with additional data and proved to be an effective proof of concept.
{"title":"Data-driven proficiency profiling: proof of concept","authors":"B. Mostafavi, T. Barnes","doi":"10.1145/2883851.2883935","DOIUrl":"https://doi.org/10.1145/2883851.2883935","url":null,"abstract":"Data-driven methods have previously been used in intelligent tutoring systems to improve student learning outcomes and predict student learning methods. We have been incorporating data-driven methods for feedback and problem selection into Deep Thought, a logic tutor where students practice constructing deductive logic proofs. In this latest study we have implemented our data-driven proficiency profiler (DDPP) into Deep Thought as a proof of concept. The DDPP determines student proficiency without expert involvement by comparing relevant student rule scores to previous students who behaved similarly in the tutor and successfully completed it. The results show that the DDPP did improve in performance with additional data and proved to be an effective proof of concept.","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":"128662246","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 is a proposal for organizing a Multimodal Learning Analytics (MLA) data challenge as part of the workshop offering of the Learning Analytics and Knowledge (LAK) conference. It explains the motivation of the event, its objectives, target groups, expected format, organization, dissemination strategy and schedule.
{"title":"Multimodal learning analytics data challenges","authors":"X. Ochoa, M. Worsley, Nadir Weibel, S. Oviatt","doi":"10.1145/2883851.2883913","DOIUrl":"https://doi.org/10.1145/2883851.2883913","url":null,"abstract":"This is a proposal for organizing a Multimodal Learning Analytics (MLA) data challenge as part of the workshop offering of the Learning Analytics and Knowledge (LAK) conference. It explains the motivation of the event, its objectives, target groups, expected format, organization, dissemination strategy and schedule.","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":"128676663","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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