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Farrow, Elaine; Moore, Johanna D.; Gaševic, Dragan – Journal of Learning Analytics, 2022
By participating in asynchronous course discussion forums, students can work together to refine their ideas and construct knowledge collaboratively. Typically, some messages simply repeat or paraphrase course content, while others bring in new material, demonstrate reasoning, integrate concepts, and develop solutions. Through the messages they…
Descriptors: Asynchronous Communication, Computer Mediated Communication, Group Discussion, Learning Analytics
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Siebert-Evenstone, Amanda L.; Irgens, Golnaz Arastoopour; Collier, Wesley; Swiecki, Zachari; Ruis, Andrew R.; Shaffer, David Williamson – Journal of Learning Analytics, 2017
Analyses of learning based on student discourse need to account not only for the content of the utterances but also for the ways in which students make connections across turns of talk. This requires segmentation of discourse data to define when connections are likely to be meaningful. In this paper, we present an approach to segmenting data for…
Descriptors: Semantics, Discourse Analysis, Models, Epistemology
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Knight, David B.; Brozina, Cory; Novoselich, Brian – Journal of Learning Analytics, 2016
This paper investigates how first-year engineering undergraduates and their instructors describe the potential for learning analytics approaches to contribute to student success. Results of qualitative data collection in a first-year engineering course indicated that both students and instructors emphasized a preference for learning analytics…
Descriptors: Undergraduate Students, Engineering Education, College Faculty, Attitude Measures
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Gaševic, Dragan; Jovanovic, Jelena; Pardo, Abelardo; Dawson, Shane – Journal of Learning Analytics, 2017
The use of analytic methods for extracting learning strategies from trace data has attracted considerable attention in the literature. However, there is a paucity of research examining any association between learning strategies extracted from trace data and responses to well-established self-report instruments and performance scores. This paper…
Descriptors: Foreign Countries, Undergraduate Students, Engineering Education, Educational Research
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Crick, Ruth Deakin; Knight, Simon; Barr, Steven – Journal of Learning Analytics, 2017
Central to the mission of most educational institutions is the task of preparing the next generation of citizens to contribute to society. Schools, colleges, and universities value a range of outcomes--e.g., problem solving, creativity, collaboration, citizenship, service to community--as well as academic outcomes in traditional subjects. Often…
Descriptors: Educational Improvement, Holistic Approach, Data Collection, Data Analysis
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Aguiar, Everaldo; Ambrose, G. Alex; Chawla, Nitesh V.; Goodrich, Victoria; Brockman, Jay – Journal of Learning Analytics, 2014
As providers of higher education begin to harness the power of big data analytics, one very fitting application for these new techniques is that of predicting student attrition. The ability to pinpoint students who might soon decide to drop out, or who may be following a suboptimal path to success, allows those in charge not only to understand the…
Descriptors: Academic Persistence, Engineering Education, Portfolios (Background Materials), Dropouts
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Gilmore, Dawn – Journal of Learning Analytics, 2014
This research applies Goffman's Presentation of Self in Everyday Life to analyze online and offline student participation in two online subjects. Mixed-methods will be used to produce a fuller account of student experiences.
Descriptors: Online Courses, Student Participation, Mixed Methods Research, Student Experience
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Worsley, Marcelo; Blikstein, Paulo – Journal of Learning Analytics, 2014
Learning analytics and educational data mining are introducing a number of new techniques and frameworks for studying learning. The scalability and complexity of these novel techniques has afforded new ways for enacting education research and has helped scholars gain new insights into human cognition and learning. Nonetheless, there remain some…
Descriptors: Data Analysis, Data Collection, Engineering, Design