Human body-language is one of the richest and most obscure sources of information in inter-personal communication which we aim to re-introduce into the classroom's ecosystem. In this paper we present our observations of student-to-student influence and measurements. We show parallels with previous theories and formulate a new concept for measuring the level of attention based on synchronization of student actions. We observed that the students with lower levels of attention are slower to react then focused students, a phenomenon we named "sleepers' lag".
{"title":"Sleepers' lag - study on motion and attention","authors":"Mirko Raca, R. Tormey, P. Dillenbourg","doi":"10.1145/2567574.2567581","DOIUrl":"https://doi.org/10.1145/2567574.2567581","url":null,"abstract":"Human body-language is one of the richest and most obscure sources of information in inter-personal communication which we aim to re-introduce into the classroom's ecosystem. In this paper we present our observations of student-to-student influence and measurements. We show parallels with previous theories and formulate a new concept for measuring the level of attention based on synchronization of student actions. We observed that the students with lower levels of attention are slower to react then focused students, a phenomenon we named \"sleepers' lag\".","PeriodicalId":178564,"journal":{"name":"Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123136548","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's performance is a challenging, yet complicated task for institutions, instructors and learners. Accurate predictions of performance could lead to improved learning outcomes and increased goal achievement. In this paper we explore the predictive capabilities of student's time-spent on answering (in-)correctly each question of a multiple-choice assessment quiz, along with student's final quiz-score, in the context of computer-based testing. We also explore the correlation between the time-spent factor (as defined here) and goal-expectancy. We present a case study and investigate the value of using this parameter as a learning analytics factor for improving prediction of performance during computer-based testing. Our initial results are encouraging and indicate that the temporal dimension of learning analytics should be further explored.
{"title":"Temporal learning analytics for computer based testing","authors":"Z. Papamitsiou, V. Terzis, A. Economides","doi":"10.1145/2567574.2567609","DOIUrl":"https://doi.org/10.1145/2567574.2567609","url":null,"abstract":"Predicting student's performance is a challenging, yet complicated task for institutions, instructors and learners. Accurate predictions of performance could lead to improved learning outcomes and increased goal achievement. In this paper we explore the predictive capabilities of student's time-spent on answering (in-)correctly each question of a multiple-choice assessment quiz, along with student's final quiz-score, in the context of computer-based testing. We also explore the correlation between the time-spent factor (as defined here) and goal-expectancy. We present a case study and investigate the value of using this parameter as a learning analytics factor for improving prediction of performance during computer-based testing. Our initial results are encouraging and indicate that the temporal dimension of learning analytics should be further explored.","PeriodicalId":178564,"journal":{"name":"Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114240025","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}
Rebecca Ferguson, A. Liddo, Denise Whitelock, M. Laat, S. B. Shum
The first international workshop on discourse-centric learning analytics (DCLA) took place at LAK13 in Leuven, Belgium. That workshop succeeded in its aim of catalysing ideas and building community connections between those working in this field of social learning analytics. It also proposed a mission statement for DCLA: to devise and validate analytics that look beyond surface measures in order to quantify linguistic proxies for deeper learning. This year, the focus of the second international DCLA workshop, like that of LAK14, is on the intersection of learning analytics research, theory and practice. Once researchers have developed and validated discourse-centric analytics, how can these be successfully deployed at scale to support learning?
{"title":"DCLA14: second international workshop on discourse-centric learning analytics","authors":"Rebecca Ferguson, A. Liddo, Denise Whitelock, M. Laat, S. B. Shum","doi":"10.1145/2567574.2567631","DOIUrl":"https://doi.org/10.1145/2567574.2567631","url":null,"abstract":"The first international workshop on discourse-centric learning analytics (DCLA) took place at LAK13 in Leuven, Belgium. That workshop succeeded in its aim of catalysing ideas and building community connections between those working in this field of social learning analytics. It also proposed a mission statement for DCLA: to devise and validate analytics that look beyond surface measures in order to quantify linguistic proxies for deeper learning. This year, the focus of the second international DCLA workshop, like that of LAK14, is on the intersection of learning analytics research, theory and practice. Once researchers have developed and validated discourse-centric analytics, how can these be successfully deployed at scale to support learning?","PeriodicalId":178564,"journal":{"name":"Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","volume":"455 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123875890","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}
Real-world learning in a field is an important educational area for experience-based activities. Formative assessment by constant monitoring of the intellectual achievement of real-world learners is essential for adaptive learning support, but no assessment methodology has yet been developed. We consider a method to systematically integrate heterogeneous factors of real-world learning: learners' internal situations, their external situations, and their learning field. Then, we propose a method for formatively assessing the situation of real-world learning. The method enables us to recognize the sequence of characteristic stay behavior and the associated body posture of a learner, and to estimate the 3D location of his/her interest. The method enables the estimation of not only the learning topic that a learner is currently examining in a field but also the prospective topics that he/she should learn. Our assessment method is the basis for context-aware support to promote the emergence of new knowledge from intellectual collaboration in the world.
{"title":"Formative assessment method of real-world learning by integrating heterogeneous elements of behavior, knowledge, and the environment","authors":"Masaya Okada, Masahiro Tada","doi":"10.1145/2567574.2567579","DOIUrl":"https://doi.org/10.1145/2567574.2567579","url":null,"abstract":"Real-world learning in a field is an important educational area for experience-based activities. Formative assessment by constant monitoring of the intellectual achievement of real-world learners is essential for adaptive learning support, but no assessment methodology has yet been developed. We consider a method to systematically integrate heterogeneous factors of real-world learning: learners' internal situations, their external situations, and their learning field. Then, we propose a method for formatively assessing the situation of real-world learning. The method enables us to recognize the sequence of characteristic stay behavior and the associated body posture of a learner, and to estimate the 3D location of his/her interest. The method enables the estimation of not only the learning topic that a learner is currently examining in a field but also the prospective topics that he/she should learn. Our assessment method is the basis for context-aware support to promote the emergence of new knowledge from intellectual collaboration in the world.","PeriodicalId":178564,"journal":{"name":"Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128058082","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 is an exploratory effort to find a place for learning analytics in humane education. After distinguishing humane education from training on the basis of the Aristotelian model of intellectual capabilities, and arguing that humane education is distinct by virtue of its interest in cultivating prudence, which is unteachable, an account of three key characteristics of humane education is provided. Appealing to thinkers of the Italian Renaissance, it is argued that ingenium, eloquence, and self-knowledge constitute the what, how, and why of humane education. Lastly, looking to several examples from recent learning analytics literature, it is demonstrated that learning analytics is not only helpful as set of aids for ensuring success in scientific and technical disciplines, but in the humanities as well. In order to function effectively as an aid to humane education, however, learning analytics must be embedded within a context that encourages continuous reflection, responsiveness, and personal responsibility for learning.
{"title":"Teaching the unteachable: on the compatibility of learning analytics and humane education","authors":"Timothy D. Harfield","doi":"10.1145/2567574.2567607","DOIUrl":"https://doi.org/10.1145/2567574.2567607","url":null,"abstract":"This paper is an exploratory effort to find a place for learning analytics in humane education. After distinguishing humane education from training on the basis of the Aristotelian model of intellectual capabilities, and arguing that humane education is distinct by virtue of its interest in cultivating prudence, which is unteachable, an account of three key characteristics of humane education is provided. Appealing to thinkers of the Italian Renaissance, it is argued that ingenium, eloquence, and self-knowledge constitute the what, how, and why of humane education. Lastly, looking to several examples from recent learning analytics literature, it is demonstrated that learning analytics is not only helpful as set of aids for ensuring success in scientific and technical disciplines, but in the humanities as well. In order to function effectively as an aid to humane education, however, learning analytics must be embedded within a context that encourages continuous reflection, responsiveness, and personal responsibility for learning.","PeriodicalId":178564,"journal":{"name":"Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","volume":"2290 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127471922","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}
Incorporating a learner's level of cognitive processing into Learning Analytics presents opportunities for obtaining rich data on the learning process. We propose a framework called COPA that provides a basis for mapping levels of cognitive operation into a learning analytics system. We utilise Bloom's taxonomy, a theoretically respected conceptualisation of cognitive processing, and apply it in a flexible structure that can be implemented incrementally and with varying degree of complexity within an educational organisation. We outline how the framework is applied, and its key benefits and limitations. Finally, we apply COPA to a University undergraduate unit, and demonstrate its utility in identifying key missing elements in the structure of the course.
{"title":"A cognitive processing framework for learning analytics","authors":"Andrew Gibson, Kirsty Kitto, Jill Willis","doi":"10.1145/2567574.2567610","DOIUrl":"https://doi.org/10.1145/2567574.2567610","url":null,"abstract":"Incorporating a learner's level of cognitive processing into Learning Analytics presents opportunities for obtaining rich data on the learning process. We propose a framework called COPA that provides a basis for mapping levels of cognitive operation into a learning analytics system. We utilise Bloom's taxonomy, a theoretically respected conceptualisation of cognitive processing, and apply it in a flexible structure that can be implemented incrementally and with varying degree of complexity within an educational organisation. We outline how the framework is applied, and its key benefits and limitations. Finally, we apply COPA to a University undergraduate unit, and demonstrate its utility in identifying key missing elements in the structure of the course.","PeriodicalId":178564,"journal":{"name":"Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133624770","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}
Kimberly E. Arnold, G. Lynch, Daniel Huston, L. Wong, Linda Jorn, C. Olsen
The last five years have brought an explosion of research in the learning analytics field. However, much of what has emerged has been small scale or tool-centric. While these efforts are vitally important to the development of the field, in order to truly transform education, learning analytics must scale and become institutionalized at multiple levels throughout an educational system. Many institutions are currently undertaking this grand challenge and this panel will highlight cases from: the University of Wisconsin System, the Society for Learning Analytics Research, the University of New England, and Rio Salado College.
{"title":"Building institutional capacities and competencies for systemic learning analytics initiatives","authors":"Kimberly E. Arnold, G. Lynch, Daniel Huston, L. Wong, Linda Jorn, C. Olsen","doi":"10.1145/2567574.2567593","DOIUrl":"https://doi.org/10.1145/2567574.2567593","url":null,"abstract":"The last five years have brought an explosion of research in the learning analytics field. However, much of what has emerged has been small scale or tool-centric. While these efforts are vitally important to the development of the field, in order to truly transform education, learning analytics must scale and become institutionalized at multiple levels throughout an educational system. Many institutions are currently undertaking this grand challenge and this panel will highlight cases from: the University of Wisconsin System, the Society for Learning Analytics Research, the University of New England, and Rio Salado College.","PeriodicalId":178564,"journal":{"name":"Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129182802","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}
Rebeca Cerezo, N. Suarez, J. C. Núñez, Miguel Sánchez-Santillán
eGraph is a virtual tool developed with the aim of make easier to track the students' learning process in Learning Management Systems like Moodle. It is based in the log files that the learning platform records when the students are interacting with and allows teachers, students, and researchers to track the learning route that learners have followed during a particular time span.
{"title":"eGraph tool: graphing the learning process in LMSs","authors":"Rebeca Cerezo, N. Suarez, J. C. Núñez, Miguel Sánchez-Santillán","doi":"10.1145/2567574.2567596","DOIUrl":"https://doi.org/10.1145/2567574.2567596","url":null,"abstract":"eGraph is a virtual tool developed with the aim of make easier to track the students' learning process in Learning Management Systems like Moodle. It is based in the log files that the learning platform records when the students are interacting with and allows teachers, students, and researchers to track the learning route that learners have followed during a particular time span.","PeriodicalId":178564,"journal":{"name":"Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","volume":"362 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115942882","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 a method we have developed to convert statistical predictive models into visual narratives which explain student classifications. Building off of the work done within the user experience community, we apply the concept of personas to predictive models. These personas provide familiar and memorable descriptions of the learners identified by data mining activities, and bridge the gap between the data scientist and the learning specialist.
{"title":"Explaining predictive models to learning specialists using personas","authors":"Christopher A. Brooks, J. Greer","doi":"10.1145/2567574.2567612","DOIUrl":"https://doi.org/10.1145/2567574.2567612","url":null,"abstract":"This paper describes a method we have developed to convert statistical predictive models into visual narratives which explain student classifications. Building off of the work done within the user experience community, we apply the concept of personas to predictive models. These personas provide familiar and memorable descriptions of the learners identified by data mining activities, and bridge the gap between the data scientist and the learning specialist.","PeriodicalId":178564,"journal":{"name":"Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","volume":"111 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116635729","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}
Temporality matters for analysis of collaborative learning. The present study attempts to uncover temporal patterns that distinguish "productive" threads of knowledge building inquiry. Using a rich knowledge building discourse dataset, in which notes' contribution types and threads' productivity have been coded, a secondary temporal analysis was conducted. In particular, Lag-sequential Analysis was conducted to identify transitional patterns among different contribution types that distinguish productive threads from "improvable" ones. Results indicated that productive inquiry threads involved significantly more transitions among questioning, theorizing, obtaining information, and working with information; in contrast, responding to questions and theories by merely giving opinions was not sufficient to achieve knowledge progress. This study highlights the importance of investigating temporality in collaborative learning and calls for attention to developing and testing temporal analysis methods in learning analytics research.
{"title":"Uncovering what matters: analyzing transitional relations among contribution types in knowledge-building discourse","authors":"Bodong Chen, M. Resendes","doi":"10.1145/2567574.2567606","DOIUrl":"https://doi.org/10.1145/2567574.2567606","url":null,"abstract":"Temporality matters for analysis of collaborative learning. The present study attempts to uncover temporal patterns that distinguish \"productive\" threads of knowledge building inquiry. Using a rich knowledge building discourse dataset, in which notes' contribution types and threads' productivity have been coded, a secondary temporal analysis was conducted. In particular, Lag-sequential Analysis was conducted to identify transitional patterns among different contribution types that distinguish productive threads from \"improvable\" ones. Results indicated that productive inquiry threads involved significantly more transitions among questioning, theorizing, obtaining information, and working with information; in contrast, responding to questions and theories by merely giving opinions was not sufficient to achieve knowledge progress. This study highlights the importance of investigating temporality in collaborative learning and calls for attention to developing and testing temporal analysis methods in learning analytics research.","PeriodicalId":178564,"journal":{"name":"Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115303334","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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