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MOOC Student Dropout Prediction Model Based on Learning Behavior Features and Parameter Optimization
Jin, Cong – Interactive Learning Environments, 2023
Since the advent of massive open online courses (MOOC), it has been the focus of educators and learners around the world, however the high dropout rate of MOOC has had a serious negative impact on its popularity and promotion. How to effectively predict students' dropout status in MOOC for early intervention has become a hot topic in MOOC…
Descriptors: MOOCs, Potential Dropouts, Prediction, Models
Cristina Vladescu – International Electronic Journal of Mathematics Education, 2023
This study aims at highlighting the relationship between mastery learning models and academic performance in mathematics, moderated by the number of hours allotted to studying mathematics. There are 305 first to eighth-grade students who learn at "Nae A. Ghica Middle School" in Romania. Students in sixth, seventh, and eighth grades…
Descriptors: Mastery Learning, Models, Mathematics Achievement, Study Habits
Adrienne M. Pesce; Daniel B. King – Journal of Chemical Education, 2023
Novice chemists often struggle with the highly visual nature of some chemistry topics. To make visually demanding concepts, such as isomerism and stereochemistry, more accessible to students, chemistry instructors have long recommended the use of molecular model kits as visual aids. However, studies pertaining to student model usage have shown…
Descriptors: Student Attitudes, Molecular Structure, Science Teachers, Science Instruction
Sonja Kleter; Uwe Matzat; Rianne Conijn – IEEE Transactions on Learning Technologies, 2024
Much of learning analytics research has focused on factors influencing model generalizability of predictive models for academic performance. The degree of model generalizability across courses may depend on aspects, such as the similarity of the course setup, course material, the student cohort, or the teacher. Which of these contextual factors…
Descriptors: Prediction, Models, Academic Achievement, Learning Analytics
Lea Dickhäuser; Christine Koddebusch; Christiane Hermann – Journal of College Student Mental Health, 2024
As stress in students has increased in the last years, factors predicting stress need to be investigated. The aim of the present study was to replicate previous findings using the demand-control model and to examine the role of emotional distress in a transactional model (inspired by Lazarus' transactional stress model). "Stress, mental…
Descriptors: Prediction, Stress Variables, Validity, Models
Sun, Meng – ProQuest LLC, 2022
This qualitative, non-experimental meta-synthesis explored the antecedents, consequences, and interventions of both active and passive procrastination among university students. Based on the academic procrastination paradigm proposed by Schraw, Wadkins, and Olafson in 2007, the study synthesized and interpreted the findings of twelve purposefully…
Descriptors: College Students, Study Habits, Time Management, Influences
Hecht, Cameron A.; Latham, Anita G.; Buskirk, Ruth E.; Hansen, Debra R.; Yeager, David S. – CBE - Life Sciences Education, 2022
Mindset interventions, which shift students' beliefs about classroom experiences, have shown promise for promoting diversity in science, technology, engineering, and mathematics (STEM). Psychologists have emphasized the importance of customizing these interventions to specific courses, but there is not yet a protocol for doing so. We developed a…
Descriptors: Biological Sciences, STEM Education, Intervention, Attitude Change
Felker, Zachary; Chen, Zhongzhou – Physical Review Physics Education Research, 2023
We examine the effectiveness of a planning prompt intervention to reduce procrastination on online homework for college students. The intervention asked students to indicate their intention to earn small amounts of extra credit for completing assignments earlier and form a plan to realize their intentions. Students' learning behavior is measured…
Descriptors: Physics, Science Instruction, Introductory Courses, Homework
D'Eon, Marcel; Yasinian, Maryam – Higher Education Research and Development, 2022
In this article, we propose a new model of student workload. We conducted an extensive literature review of student workload, its impact on students' lives, factors influencing student workload, objective and subjective measurements. The previous conceptualizations of student workload conflate student work and course workload, two related but…
Descriptors: Physics, Science Instruction, Learning Processes, Barriers
Stahl, Norman A.; Armstrong, Sonya L.; Hewett, Elizabeth – Community College Journal of Research and Practice, 2021
In recent years, "contextualization" has emerged as a buzzword in higher education, and, in community college developmental education contexts in particular. However, it is important to note that contextualization is more than an innovation that is specific to the current developmental education reform movement, as iterations of this…
Descriptors: Community Colleges, Reading Strategies, Study Habits, Instructional Design
Yao, Mengfan; Sahebi, Shaghayegh; Behnagh, Reza Feyzi – International Educational Data Mining Society, 2020
Student procrastination, as the voluntary delay of intended work despite expecting to be worse off for the delay, is an important factor with potentially negative consequences in student well-being and learning. In online educational settings such as Massive Open Online Courses (MOOCs), the effect of procrastination is considered to be even more…
Descriptors: Large Group Instruction, Online Courses, Student Behavior, Study Habits
Güngör, Cumhur – International Journal of Curriculum and Instruction, 2021
The transition of responsibility for learning to the student has been one of the current era's frequently debated expertise. In this sense, self-regulation skills, which are not limited to education and academic life, have become an essential infrastructure. This article's objective was to explore self-regulatory learning strategies (SRL) by high…
Descriptors: Learning Strategies, Metacognition, Student Attitudes, High School Students
Wolters, Christopher A.; Won, Sungjun; Hussain, Maryam – Metacognition and Learning, 2017
The primary goal of this study was to investigate whether college students' academic time management could be used to understand their engagement in traditional and active forms of procrastination within a model of self-regulated learning. College students (N = 446) completed a self-report survey that assessed motivational and strategic aspects of…
Descriptors: Time Management, Metacognition, Predictor Variables, College Students
Gitinabard, Niki; Xu, Yiqiao; Heckman, Sarah; Barnes, Tiffany; Lynch, Collin F. – IEEE Transactions on Learning Technologies, 2019
Blended courses that mix in-person instruction with online platforms are increasingly common in secondary education. These platforms record a rich amount of data on students' study habits and social interactions. Prior research has shown that these metrics are correlated with students performance in face-to-face classes. However, predictive models…
Descriptors: Blended Learning, Educational Technology, Technology Uses in Education, Prediction
Hill, Erin M.; Anderson, Laurie; Finley, Brandon; Hillyard, Cinnamon; Kochanski, Mark – Journal of STEM Education: Innovations and Research, 2019
What motivates and demotivates students in their engagement in at-home work for high-stakes assignments, such as test preparation and writing and revising papers? This paper outlines a student-centered method to identify learning strategies students actually use and obstacles students actually face compared to what is reported in the literature.…
Descriptors: Learning Processes, Undergraduate Students, Barriers, STEM Education