Early Alert of Academically At-Risk Students: An Open Source Analytics Initiative

By Sandeep M. Jayaprakash, Erik W. Moody, Eitel J. M. Lauría, James R. Regan, Joshua D. Baron. Journal of Learning Analytics, 2014

411 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Science Applications

View Paper: https://www.semanticscholar.org/paper/4f66d276bce0719ca830b91c1f3a2d1e9719bc6b

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Citations in the corpus

Predicting Student Performance from LMS Data: A Comparison of 17 Blended Courses Using Moodle LMS (2017, methodology): “The objective of the OAAI is to advance the field of learning analytics by exploring the challenges in scaling learning analytics across all higher education institutions [35].”
Stupid Tutoring Systems, Intelligent Humans (2016, background): “…ecosystem of companies (and non-profits, and university projects) offering predictive analytics on student success and early warnings for instructors when a student is at risk, including ZogoTech (Wood and Williams 2013), and the Open Academic Analytics Initiative (Jayaprakash et al. 2014).”
Mining Big Data in Education: Affordances and Challenges (2020, background): “…assessed real-world deployments of early-warning systems; however, a challenge remains of selecting the appropriate institutional response and types of information to convey to students in order to effectively increase their chances of success (Chaturapruek et al., 2018; Jayaprakash et al., 2014).”
Let’s not forget: Learning analytics are about learning (2014, background): “light on the inconsistent results of the trace data‐based predictors of academic success that are often reported in the literature (Jayaprakash et al., 2014; Macfadyen & Dawson, 2012).”
Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success (2016, methodology): “Regardless of the data source, the prediction of student grades is generally determined by applying logistic regression (Barber & Sharkey, 2012; Campbell, DeBlois, & Oblinger, 2007; Lauría, Baron, Devireddy, Sundararaju, & Jayaprakash, 2012; Palmer, 2013).”
Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores (2018, background): “Such communication is important because it assesses student workloads and addresses issues at an early stage of the course [23].”