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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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Papamitsiou, Zacharoula; Economides, Anastasios A. – Journal of Learning Analytics, 2014
Accurate and early predictions of student performance could significantly affect interventions during teaching and assessment, which gradually could lead to improved learning outcomes. In our research, we seek to identify and formalize temporal parameters as predictors of performance ("temporal learning analytics" or TLA) and examine…
Descriptors: Time Factors (Learning), Predictor Variables, Student Behavior, Academic Achievement
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Buerck, John P.; Mudigonda, Srikanth P. – Journal of Learning Analytics, 2014
Academic analytics and learning analytics have been increasingly adopted by academic institutions of higher learning for improving student performance and retention. While several studies have reported the implementation details and the successes of specific analytics initiatives, relatively fewer studies exist in literature that describe the…
Descriptors: Higher Education, Educational Research, Data Analysis, Data Collection
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Gray, Geraldine; McGuinness, Colm; Owende, Philip; Carthy, Aiden – Journal of Learning Analytics, 2014
Increasing college participation rates, and diversity in student population, is posing a challenge to colleges in their attempts to facilitate learners achieve their full academic potential. Learning analytics is an evolving discipline with capability for educational data analysis that could enable better understanding of learning process, and…
Descriptors: Psychometrics, Data Analysis, Academic Achievement, Postsecondary Education
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Lang, Charles – Journal of Learning Analytics, 2014
This article proposes a coherent framework for the use of Inverse Bayesian estimation to summarize and make predictions about student behaviour in adaptive educational settings. The Inverse Bayes Filter utilizes Bayes theorem to estimate the relative impact of contextual factors and internal student factors on student performance using time series…
Descriptors: Bayesian Statistics, Academic Achievement, Prediction, Student Behavior
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Méndez, Gonzalo; Ochoa, Xavier; Chiluiza, Katherine; de Wever, Bram – Journal of Learning Analytics, 2014
Learning analytics has been as used a tool to improve the learning process mainly at the micro-level (courses and activities). However, another of the key promises of learning analytics research is to create tools that could help educational institutions at the meso- and macro-level to gain better insight into the inner workings of their programs…
Descriptors: Data Analysis, Data Collection, Educational Research, Curriculum Design
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Ali, Liaqat; Hatala, Marek; Winne, Phil; Gaševic, Dragan – Journal of Learning Analytics, 2014
This study aims to investigate how the learning strategies and achievement goal orientations of students relate to their academic behaviours and performance in the context of an online learning system. The study also develops and validates a relational model between student learning strategies and achievement goals.
Descriptors: Goal Orientation, Academic Achievement, Learning Strategies, Questionnaires
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