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Schmucker, Robin; Wang, Jingbo; Hu, Shijia; Mitchell, Tom M. – Journal of Educational Data Mining, 2022
We consider the problem of assessing the changing performance levels of individual students as they go through online courses. This student performance modeling problem is a critical step for building adaptive online teaching systems. Specifically, we conduct a study of how to utilize various types and large amounts of log data from earlier…
Descriptors: Academic Achievement, Electronic Learning, Artificial Intelligence, Predictor Variables
Makhlouf, Jihed; Mine, Tsunenori – Journal of Educational Data Mining, 2020
In recent years, we have seen the continuous and rapid increase of job openings in Science, Technology, Engineering and Math (STEM)-related fields. Unfortunately, these positions are not met with an equal number of workers ready to fill them. Efforts are being made to find durable solutions for this phenomena, and they start by encouraging young…
Descriptors: Learning Analytics, STEM Education, Science Careers, Career Choice
Chiu, Mei-Shiu – Journal of Educational Data Mining, 2020
This study aims to identify effective affective states and behaviors of middle-school students' online mathematics learning in predicting their choices to study science, technology, engineering, and mathematics (STEM) in higher education based on a "positive-affect-to-success hypothesis." The dataset (591 students and 316,974 actions)…
Descriptors: Gender Differences, Predictor Variables, STEM Education, Course Selection (Students)
Almeda, Ma. Victoria; Baker, Ryan S. – Journal of Educational Data Mining, 2020
Given the increasing need for skilled workers in science, technology, engineering, and mathematics (STEM), there is a burgeoning interest to encourage young students to pursue a career in STEM fields. Middle school is an opportune time to guide students' interests towards STEM disciplines, as they begin to think about and plan for their career…
Descriptors: Student Participation, Predictor Variables, STEM Education, Science Careers
Pavlik, Philip I., Jr. – Journal of Educational Data Mining, 2013
This paper describes the development of a dynamical systems model of motivation and metacognition during learning, which explains some of the practically and theoretically important relationships among three student engagement constructs and performance metrics during learning. In order to better calibrate and understand the model, the model was…
Descriptors: Vocabulary Development, Learning Strategies, Predictor Variables, Scores

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