Early Alert of Academically At-Risk Students: An Open Source Analytics Initiative
TLDR
The process and challenges of collecting, organizing and mining student data to predict academic risk, and report results on the predictive performance of those models, their portability across pilot programs at partner institutions, and the results of interventions on at-risk students are depicted.Abstract:
The Open Academic Analytics Initiative (OAAI) is a collaborative, multi-year grant program aimed at researching issues related to the scaling up of learning analytics technologies and solutions across all of higher education. The paper describes the goals and objectives of the OAAI, depicts the process and challenges of collecting, organizing and mining student data to predict academic risk, and report results on the predictive performance of those models, their portability across pilot programs at partner institutions, and the results of interventions on at-risk students.read more
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Book ChapterDOI
Educational Data Mining and Learning Analytics
TL;DR: How these methods emerged in the early days of research in this area is discussed, which methods have seen particular interest in the EDM and learning analytics communities, and how this has changed as the field matures and has moved to making significant contributions to both educational research and practice.
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Let’s not forget: Learning analytics are about learning
TL;DR: The field of learning analytics is introduced and the lessons learned from well-known case studies in the research literature are outlined, including the critical topics that require immediate research attention for learning analytics to make a sustainable impact on the research and practice of learning and teaching.
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Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success
TL;DR: The results suggest that it is imperative for learning analytics research to account for the diverse ways technology is adopted and applied in course-specific contexts, and require consideration before the log-data can be merged to create a generalized model for predicting academic success.
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Identifying significant indicators using LMS data to predict course achievement in online learning
TL;DR: The results demonstrated that students' regular study, late submissions of assignments, number of sessions, and proof of reading the course information packets significantly predicted their course achievement.
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Predicting Student Performance from LMS Data: A Comparison of 17 Blended Courses Using Moodle LMS
TL;DR: This work analyzes 17 blended courses with 4,989 students in a single institution using Moodle LMS and predicts student performance from LMS predictor variables as used in the literature and from in-between assessment grades, using both multi-level and standard regressions.
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