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Morsy, Sara; Karypis, George – International Educational Data Mining Society, 2019
Grade prediction for future courses not yet taken by students is important as it can help them and their advisers during the process of course selection as well as for designing personalized degree plans and modifying them based on their performance. One of the successful approaches for accurately predicting a student's grades in future courses is…
Descriptors: Grades (Scholastic), Models, Prediction, Predictor Variables
Marras, Mirko; Vignoud, Julien Tuân Tu; Käser, Tanja – International Educational Data Mining Society, 2021
Early predictors of student success are becoming a key tool in flipped and online courses to ensure that no student is left behind along course activities. However, with an increased interest in this area, it has become hard to keep track of what the state of the art in early success prediction is. Moreover, prior work on early success prediction…
Descriptors: Benchmarking, Predictor Variables, Academic Achievement, Flipped Classroom
Mandalapu, Varun; Chen, Lujie Karen; Chen, Zhiyuan; Gong, Jiaqi – International Educational Data Mining Society, 2021
With the increasing adoption of Learning Management Systems (LMS) in colleges and universities, research in exploring the interaction data captured by these systems is promising in developing a better learning environment and improving teaching practice. Most of these research efforts focused on course-level variables to predict student…
Descriptors: Integrated Learning Systems, Interaction, Undergraduate Students, Minority Group Students
Akpinar, Nil-Jana; Ramdas, Aaditya; Acar, Umut – International Educational Data Mining Society, 2020
Educational software data promises unique insights into students' study behaviors and drivers of success. While much work has been dedicated to performance prediction in massive open online courses, it is unclear if the same methods can be applied to blended courses and a deeper understanding of student strategies is often missing. We use pattern…
Descriptors: Learning Strategies, Blended Learning, Learning Analytics, Student Behavior
Hur, Paul; Bosch, Nigel; Paquette, Luc; Mercier, Emma – International Educational Data Mining Society, 2020
Collaborative problem solving behaviors are difficult to identify and foster due to their amorphous and dynamic nature. In this paper, we investigate the value of considering early class period behaviors, based on small group development theory, for building predictive machine learning models of collaborative behaviors during problem solving. Over…
Descriptors: Cooperative Learning, Interaction, Peer Relationship, Handheld Devices
Chu, Wei; Pavlik, Philip I., Jr. – International Educational Data Mining Society, 2023
In adaptive learning systems, various models are employed to obtain the optimal learning schedule and review for a specific learner. Models of learning are used to estimate the learner's current recall probability by incorporating features or predictors proposed by psychological theory or empirically relevant to learners' performance. Logistic…
Descriptors: Reaction Time, Accuracy, Models, Predictor Variables
Aulck, Lovenoor; Nambi, Dev; Velagapudi, Nishant; Blumenstock, Joshua; West, Jevin – International Educational Data Mining Society, 2019
Each year, roughly 30% of first-year students at US baccalaureate institutions do not return for their second year and billions of dollars are spent educating these students. Yet, little quantitative research has analyzed the causes and possible remedies for student attrition. What's more, most of the previous attempts to model attrition at…
Descriptors: Student Records, Registrars (School), Predictor Variables, Undergraduate Students