Beat Schwendimann, M. Rodríguez-Triana, A. Vozniuk, L. Prieto, Mina Shirvani Boroujeni, A. Holzer, D. Gillet, P. Dillenbourg
Research on learning dashboards aims to identify what data is meaningful to different stakeholders in education, and how data can be presented to support sense-making processes. This paper summarizes the main outcomes of a systematic literature review on learning dashboards, in the fields of Learning Analytics and Educational Data Mining. The query was run in five main academic databases and enriched with papers coming from GScholar, resulting in 346 papers out of which 55 were included in the final analysis. Our review distinguishes different kinds of research studies as well as different aspects of learning dashboards and their maturity in terms of evaluation. As the research field is still relatively young, many of the studies are exploratory and proof-of-concept. Among the main open issues and future lines of work in the area of learning dashboards, we identify the need for longitudinal research in authentic settings, as well as studies that systematically compare different dashboard design options.
{"title":"Understanding learning at a glance: an overview of learning dashboard studies","authors":"Beat Schwendimann, M. Rodríguez-Triana, A. Vozniuk, L. Prieto, Mina Shirvani Boroujeni, A. Holzer, D. Gillet, P. Dillenbourg","doi":"10.1145/2883851.2883930","DOIUrl":"https://doi.org/10.1145/2883851.2883930","url":null,"abstract":"Research on learning dashboards aims to identify what data is meaningful to different stakeholders in education, and how data can be presented to support sense-making processes. This paper summarizes the main outcomes of a systematic literature review on learning dashboards, in the fields of Learning Analytics and Educational Data Mining. The query was run in five main academic databases and enriched with papers coming from GScholar, resulting in 346 papers out of which 55 were included in the final analysis. Our review distinguishes different kinds of research studies as well as different aspects of learning dashboards and their maturity in terms of evaluation. As the research field is still relatively young, many of the studies are exploratory and proof-of-concept. Among the main open issues and future lines of work in the area of learning dashboards, we identify the need for longitudinal research in authentic settings, as well as studies that systematically compare different dashboard design options.","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":"133751581","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}
Educational institutions are evolving away from the one-application-fits-all learning management system to a loosely connected digital learning ecosystem comprising diverse services that increasingly leverage data analytics to drive pedagogical innovation. Yet an ecosystem rich in services but lacking a common approach to measuring learning activity will find data collection, aggregation and analysis time-consuming and costly. The IMS Caliper Analytics™ specification addresses the need for data and semantic interoperability by providing an extensible information model, controlled vocabularies and an API for instrumenting learning applications and systems that log learning events. However, many learning activities have yet to be modeled by the Caliper working group. Engaging the SoLAR community directly in this effort will help ensure that the needs of researchers and other consumers of learning analytics data will inform future versions of the specification. The LAK16 Caliper workshop is being offered with this goal in mind. The half-day session, facilitated by members of Team Caliper, will provide LAK16 participants with an opportunity to extend the Caliper specification by modeling new learning activity profiles. New profiles, new connections and new friendships are expected outcomes.
{"title":"LAK16 workshop: extending IMS caliper analytics™ with learning activity profiles","authors":"Anthony Whyte, Prashant Nayak, John Johnston","doi":"10.1145/2883851.2883858","DOIUrl":"https://doi.org/10.1145/2883851.2883858","url":null,"abstract":"Educational institutions are evolving away from the one-application-fits-all learning management system to a loosely connected digital learning ecosystem comprising diverse services that increasingly leverage data analytics to drive pedagogical innovation. Yet an ecosystem rich in services but lacking a common approach to measuring learning activity will find data collection, aggregation and analysis time-consuming and costly. The IMS Caliper Analytics™ specification addresses the need for data and semantic interoperability by providing an extensible information model, controlled vocabularies and an API for instrumenting learning applications and systems that log learning events. However, many learning activities have yet to be modeled by the Caliper working group. Engaging the SoLAR community directly in this effort will help ensure that the needs of researchers and other consumers of learning analytics data will inform future versions of the specification. The LAK16 Caliper workshop is being offered with this goal in mind. The half-day session, facilitated by members of Team Caliper, will provide LAK16 participants with an opportunity to extend the Caliper specification by modeling new learning activity profiles. New profiles, new connections and new friendships are expected outcomes.","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":"130227753","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. Pardo, Negin Mirriahi, Roberto Martínez Maldonado, J. Jovanović, S. Dawson, D. Gašević
The pervasive collection of data has opened the possibility for educational institutions to use analytics methods to improve the quality of the student experience. However, the adoption of these methods faces multiple challenges particularly at the course level where instructors and students would derive the most benefit from the use of analytics and predictive models. The challenge lies in the knowledge gap between how the data is captured, processed and used to derive models of student behavior, and the subsequent interpretation and the decision to deploy pedagogical actions and interventions by instructors. Simply put, the provision of learning analytics alone has not necessarily led to changing teaching practices. In order to support pedagogical change and aid interpretation, this paper proposes a model that can enable instructors to readily identify subpopulations of students to provide specific support actions. The approach was applied to a first year course with a large number of students. The resulting model classifies students according to their predicted exam scores, based on indicators directly derived from the learning design.
{"title":"Generating actionable predictive models of academic performance","authors":"A. Pardo, Negin Mirriahi, Roberto Martínez Maldonado, J. Jovanović, S. Dawson, D. Gašević","doi":"10.1145/2883851.2883870","DOIUrl":"https://doi.org/10.1145/2883851.2883870","url":null,"abstract":"The pervasive collection of data has opened the possibility for educational institutions to use analytics methods to improve the quality of the student experience. However, the adoption of these methods faces multiple challenges particularly at the course level where instructors and students would derive the most benefit from the use of analytics and predictive models. The challenge lies in the knowledge gap between how the data is captured, processed and used to derive models of student behavior, and the subsequent interpretation and the decision to deploy pedagogical actions and interventions by instructors. Simply put, the provision of learning analytics alone has not necessarily led to changing teaching practices. In order to support pedagogical change and aid interpretation, this paper proposes a model that can enable instructors to readily identify subpopulations of students to provide specific support actions. The approach was applied to a first year course with a large number of students. The resulting model classifies students according to their predicted exam scores, based on indicators directly derived from the learning design.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"66 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":"116725676","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}
Aneesha Bakharia, Kirsty Kitto, A. Pardo, D. Gašević, S. Dawson
An ongoing challenge for Learning Analytics research has been the scalable derivation of user interaction data from multiple technologies. The complexities associated with this challenge are increasing as educators embrace an ever growing number of social and content-related technologies. The Experience API (xAPI) alongside the development of user specific record stores has been touted as a means to address this challenge, but a number of subtle considerations must be made when using xAPI in Learning Analytics. This paper provides a general overview to the complexities and challenges of using xAPI in a general systemic analytics solution - called the Connected Learning Analytics (CLA) toolkit. The importance of design is emphasised, as is the notion of common vocabularies and xAPI Recipes. Early decisions about vocabularies and structural relationships between statements can serve to either facilitate or handicap later analytics solutions. The CLA toolkit case study provides us with a way of examining both the strengths and the weaknesses of the current xAPI specification, and we conclude with a proposal for how xAPI might be improved by using JSON-LD to formalise Recipes in a machine readable form.
{"title":"Recipe for success: lessons learnt from using xAPI within the connected learning analytics toolkit","authors":"Aneesha Bakharia, Kirsty Kitto, A. Pardo, D. Gašević, S. Dawson","doi":"10.1145/2883851.2883882","DOIUrl":"https://doi.org/10.1145/2883851.2883882","url":null,"abstract":"An ongoing challenge for Learning Analytics research has been the scalable derivation of user interaction data from multiple technologies. The complexities associated with this challenge are increasing as educators embrace an ever growing number of social and content-related technologies. The Experience API (xAPI) alongside the development of user specific record stores has been touted as a means to address this challenge, but a number of subtle considerations must be made when using xAPI in Learning Analytics. This paper provides a general overview to the complexities and challenges of using xAPI in a general systemic analytics solution - called the Connected Learning Analytics (CLA) toolkit. The importance of design is emphasised, as is the notion of common vocabularies and xAPI Recipes. Early decisions about vocabularies and structural relationships between statements can serve to either facilitate or handicap later analytics solutions. The CLA toolkit case study provides us with a way of examining both the strengths and the weaknesses of the current xAPI specification, and we conclude with a proposal for how xAPI might be improved by using JSON-LD to formalise Recipes in a machine readable form.","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":"128658089","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}
M. Cukurova, K. Avramides, Daniel Spikol, R. Luckin, M. Mavrikis
Systematic investigation of the collaborative problem solving process in open-ended, hands-on, physical computing design tasks requires a framework that highlights the main process features, stages and actions that then can be used to provide 'meaningful' learning analytics data. This paper presents an analysis framework that can be used to identify crucial aspects of the collaborative problem solving process in practice-based learning activities. We deployed a mixed-methods approach that allowed us to generate an analysis framework that is theoretically robust, and generalizable. Additionally, the framework is grounded in data and hence applicable to real-life learning contexts. This paper presents how our framework was developed and how it can be used to analyse data. We argue for the value of effective analysis frameworks in the generation and presentation of learning analytics for practice-based learning activities.
{"title":"An analysis framework for collaborative problem solving in practice-based learning activities: a mixed-method approach","authors":"M. Cukurova, K. Avramides, Daniel Spikol, R. Luckin, M. Mavrikis","doi":"10.1145/2883851.2883900","DOIUrl":"https://doi.org/10.1145/2883851.2883900","url":null,"abstract":"Systematic investigation of the collaborative problem solving process in open-ended, hands-on, physical computing design tasks requires a framework that highlights the main process features, stages and actions that then can be used to provide 'meaningful' learning analytics data. This paper presents an analysis framework that can be used to identify crucial aspects of the collaborative problem solving process in practice-based learning activities. We deployed a mixed-methods approach that allowed us to generate an analysis framework that is theoretically robust, and generalizable. Additionally, the framework is grounded in data and hence applicable to real-life learning contexts. This paper presents how our framework was developed and how it can be used to analyse data. We argue for the value of effective analysis frameworks in the generation and presentation of learning analytics for practice-based learning activities.","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":"134325464","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}
Carly D. Robinson, M. Yeomans, J. Reich, Chris Hulleman, Hunter Gehlbach
Student intention and motivation are among the strongest predictors of persistence and completion in Massive Open Online Courses (MOOCs), but these factors are typically measured through fixed-response items that constrain student expression. We use natural language processing techniques to evaluate whether text analysis of open responses questions about motivation and utility value can offer additional capacity to predict persistence and completion over and above information obtained from fixed-response items. Compared to simple benchmarks based on demographics, we find that a machine learning prediction model can learn from unstructured text to predict which students will complete an online course. We show that the model performs well out-of-sample, compared to a standard array of demographics. These results demonstrate the potential for natural language processing to contribute to predicting student success in MOOCs and other forms of open online learning.
{"title":"Forecasting student achievement in MOOCs with natural language processing","authors":"Carly D. Robinson, M. Yeomans, J. Reich, Chris Hulleman, Hunter Gehlbach","doi":"10.1145/2883851.2883932","DOIUrl":"https://doi.org/10.1145/2883851.2883932","url":null,"abstract":"Student intention and motivation are among the strongest predictors of persistence and completion in Massive Open Online Courses (MOOCs), but these factors are typically measured through fixed-response items that constrain student expression. We use natural language processing techniques to evaluate whether text analysis of open responses questions about motivation and utility value can offer additional capacity to predict persistence and completion over and above information obtained from fixed-response items. Compared to simple benchmarks based on demographics, we find that a machine learning prediction model can learn from unstructured text to predict which students will complete an online course. We show that the model performs well out-of-sample, compared to a standard array of demographics. These results demonstrate the potential for natural language processing to contribute to predicting student success in MOOCs and other forms of open online learning.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"28 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":"133091443","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}
Design for learning in scaled courses is shifting away from replication of traditional on-campus or online teaching towards exploiting the distinctive characteristic and potentials of scale to transform both teaching and learning. Scaled learning environments such as MOOCs may represent a new paradigm for teaching. This workshop involves consideration of the how learning occurs in scaled environments, and how learning designers and analysts can assist. It will explore questions at the heart of effective learning design, using expert panelists and collaborative knowledge-building techniques to arrive at a stocktake of thinking.
{"title":"Learning design and feedback processes at scale: stocktaking emergent theory and practice","authors":"Ulla Ringtved, Sandra Milligan, L. Corrin","doi":"10.1145/2883851.2883856","DOIUrl":"https://doi.org/10.1145/2883851.2883856","url":null,"abstract":"Design for learning in scaled courses is shifting away from replication of traditional on-campus or online teaching towards exploiting the distinctive characteristic and potentials of scale to transform both teaching and learning. Scaled learning environments such as MOOCs may represent a new paradigm for teaching. This workshop involves consideration of the how learning occurs in scaled environments, and how learning designers and analysts can assist. It will explore questions at the heart of effective learning design, using expert panelists and collaborative knowledge-building techniques to arrive at a stocktake of thinking.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"69 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":"130577658","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}
Roberto Martínez Maldonado, Davinia Hernández Leo, A. Pardo, D. Suthers, Kirsty Kitto, Sven Charleer, Naif R. Aljohani, H. Ogata
It is of high relevance to the LAK community to explore blended learning scenarios where students can interact at diverse digital and physical learning spaces. This workshop aims to gather the sub-community of LAK researchers, learning scientists and researchers from other communities, interested in ubiquitous, mobile and/or face-to-face learning analytics. An overarching concern is how to integrate and coordinate learning analytics to provide continued support to learning across digital and physical spaces. The goals of the workshop are to share approaches and identify a set of guidelines to design and connect Learning Analytics solutions according to the pedagogical needs and contextual constraints to provide support across digital and physical learning spaces.
{"title":"Cross-LAK: learning analytics across physical and digital spaces","authors":"Roberto Martínez Maldonado, Davinia Hernández Leo, A. Pardo, D. Suthers, Kirsty Kitto, Sven Charleer, Naif R. Aljohani, H. Ogata","doi":"10.1145/2883851.2883855","DOIUrl":"https://doi.org/10.1145/2883851.2883855","url":null,"abstract":"It is of high relevance to the LAK community to explore blended learning scenarios where students can interact at diverse digital and physical learning spaces. This workshop aims to gather the sub-community of LAK researchers, learning scientists and researchers from other communities, interested in ubiquitous, mobile and/or face-to-face learning analytics. An overarching concern is how to integrate and coordinate learning analytics to provide continued support to learning across digital and physical spaces. The goals of the workshop are to share approaches and identify a set of guidelines to design and connect Learning Analytics solutions according to the pedagogical needs and contextual constraints to provide support across digital and physical learning spaces.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"33 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":"130144916","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}
While much promise has been demonstrated in the learning analytics field with sentiment analysis, the analyses are typically post hoc. The Unizin Sentiment Visualizer demonstrates that the application of sentiment analysis in real-time provides a powerful new tool to support students in complex learning environments.
{"title":"Demonstration of the Unizin sentiment visualizer","authors":"J. Freeman","doi":"10.1145/2883851.2883903","DOIUrl":"https://doi.org/10.1145/2883851.2883903","url":null,"abstract":"While much promise has been demonstrated in the learning analytics field with sentiment analysis, the analyses are typically post hoc. The Unizin Sentiment Visualizer demonstrates that the application of sentiment analysis in real-time provides a powerful new tool to support students in complex learning environments.","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":"129821688","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}
Journal writing is an important and common reflective practice in education. Students' reflection journals also offer a rich source of data for formative assessment. However, the analysis of the textual reflections in class of large size presents challenges. Automatic analysis of students' reflective writing holds great promise for providing adaptive real time support for students. This paper proposes a method based on topic modeling techniques for the task of themes exploration and reflection grade prediction. We evaluated this method on a sample of journal writings from pre-service teachers. The topic modeling method was able to discover the important themes and patterns emerged in students' reflection journals. Weekly topic relevance and word count were identified as important indicators of their journal grades. Based on the patterns discovered by topic modeling, prediction models were developed to automate the assessing and grading of reflection journals. The findings indicate the potential of topic modeling in serving as an analytic tool for teachers to explore and assess students' reflective thoughts in written journals.
{"title":"Topic modeling for evaluating students' reflective writing: a case study of pre-service teachers' journals","authors":"Ye Chen, Bei Yu, Xuewei Zhang, Yihan Yu","doi":"10.1145/2883851.2883951","DOIUrl":"https://doi.org/10.1145/2883851.2883951","url":null,"abstract":"Journal writing is an important and common reflective practice in education. Students' reflection journals also offer a rich source of data for formative assessment. However, the analysis of the textual reflections in class of large size presents challenges. Automatic analysis of students' reflective writing holds great promise for providing adaptive real time support for students. This paper proposes a method based on topic modeling techniques for the task of themes exploration and reflection grade prediction. We evaluated this method on a sample of journal writings from pre-service teachers. The topic modeling method was able to discover the important themes and patterns emerged in students' reflection journals. Weekly topic relevance and word count were identified as important indicators of their journal grades. Based on the patterns discovered by topic modeling, prediction models were developed to automate the assessing and grading of reflection journals. The findings indicate the potential of topic modeling in serving as an analytic tool for teachers to explore and assess students' reflective thoughts in written journals.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"116 1-2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114105967","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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