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Showing 1 to 15 of 45 results Save | Export
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Koskinen, Pekka; Lämsä, Joni; Maunuksela, Jussi; Hämäläinen, Raija; Viiri, Jouni – International Journal of STEM Education, 2018
Background: Productive learning processes and good learning outcomes can be attained by applying the basic elements of active learning. The basic elements include fostering discussions and disputations, facing alternative conceptions, and focusing on conceptual understanding. However, in the face of poor course retention and high dropout rates,…
Descriptors: Active Learning, Educational Strategies, Teaching Methods, Models
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Wan, Han; Liu, Kangxu; Yu, Qiaoye; Gao, Xiaopeng – IEEE Transactions on Learning Technologies, 2019
Most educational institutions adopted the hybrid teaching mode through learning management systems. The logging data/clickstream could describe learners' online behavior. Many researchers have used them to predict students' performance, which has led to a diverse set of findings, but how to use insights from captured data to enhance learning…
Descriptors: Educational Practices, Learner Engagement, Identification, Study Habits
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Caprotti, Olga – Journal of Learning Analytics, 2017
This paper describes investigations in visualizing logpaths of students in an online calculus course held at Florida State University in 2014. The clickstreams making up the logpaths can be used to visualize student progress in the information space of a course as a graph. We consider the graded activities as nodes of the graph, while information…
Descriptors: Online Courses, Calculus, Markov Processes, Graphs
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Lindblom-Ylänne, Sari; Saariaho, Emmi; Inkinen, Mikko; Haarala-Muhonen, Anne; Hailikari, Telle – Frontline Learning Research, 2015
The study explored university undergraduates' dilatory behaviour, more precisely, procrastination and strategic delaying. Using qualitative interview data, we applied a theory-driven and person-oriented approach to test the theoretical model of Klingsieck (2013). The sample consisted of 28 Bachelor students whose study pace had been slow during…
Descriptors: Time Management, Undergraduate Students, Interviews, Student Behavior
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