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Aylin Ozturk; Robin Schmucker; Tom Mitchell; Alper Tolga Kumtepe – International Educational Data Mining Society, 2025
This study investigates the heterogeneity in the effects of a Learning Analytics Dashboard (LAD) intervention, which provides personalized feedback messages, across a diverse population of learners. Specifically, it evaluates the impact of the LAD on learners' total material usage and final grades, considering variables such as age, sex, prior…
Descriptors: Learning Analytics, Learning Management Systems, Feedback (Response), Grades (Scholastic)
Barragán, Sandra; González, Leandro; Calderón, Gloria – Interchange: A Quarterly Review of Education, 2022
A combination of mathematical and statistical modelling techniques may be used to analyse student dropout behaviour. The aim of this study is to combine Survival Analysis and Analytic Hierarchy Process methodologies when identifying students at-risk of dropping out. This combination favours the institutional understanding of dropout as a dynamic…
Descriptors: Undergraduate Students, Gender Differences, Age Differences, Decision Making
Cohausz, Lea – Journal of Educational Data Mining, 2022
Student success and drop-out predictions have gained increased attention in recent years, connected to the hope that by identifying struggling students, it is possible to intervene and provide early help and design programs based on patterns discovered by the models. Though by now many models exist achieving remarkable accuracy-values, models…
Descriptors: Guidelines, Academic Achievement, Dropouts, Prediction
Tatel, Corey E.; Lyndgaard, Sibley F.; Kanfer, Ruth; Melkers, Julia E. – Journal of Learning Analytics, 2022
As the demand for lifelong learning increases, many working adults have turned to online graduate education in order to update their skillsets and pursue advanced credentials. Simultaneously, the volume of data available to educators and scholars interested in online learning continues to rise. This study seeks to extend learning analytics…
Descriptors: Course Selection (Students), Enrollment Trends, Academic Achievement, Learning Analytics
Peter Organisciak; Michele Newman; David Eby; Selcuk Acar; Denis Dumas – Grantee Submission, 2023
Purpose: Most educational assessments tend to be constructed in a close-ended format, which is easier to score consistently and more affordable. However, recent work has leveraged computation text methods from the information sciences to make open-ended measurement more effective and reliable for older students. This study asks whether such text…
Descriptors: Learning Analytics, Child Language, Semantics, Age Differences

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