NotesFAQContact Us
Collection
Advanced
Search Tips
Audience
Laws, Policies, & Programs
What Works Clearinghouse Rating
Showing 1 to 15 of 36 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
PDF on ERIC Download full text
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
Peer reviewed Peer reviewed
PDF on ERIC Download full text
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
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Ren, Zhiyun; Rangwala, Huzefa; Johri, Aditya – International Educational Data Mining Society, 2016
The past few years has seen the rapid growth of data mining approaches for the analysis of data obtained from Massive Open Online Courses (MOOCs). The objectives of this study are to develop approaches to predict the scores a student may achieve on a given grade-related assessment based on information, considered as prior performance or prior…
Descriptors: Large Group Instruction, Online Courses, Educational Technology, Technology Uses in Education
Peer reviewed Peer reviewed
Direct linkDirect link
Fernex, Alain; Lima, Laurent; de Vries, Erica – Higher Education: The International Journal of Higher Education and Educational Planning, 2015
The purpose of this article is to study how students allocate time to different university and extra-university activities and to identify factors that might explain variability both between and within fields of study. At the heart of this exercise is the question of the time students dedicate to academic activities in competition with a whole…
Descriptors: Time Management, Time Factors (Learning), Study Habits, Learning Activities
Previous Page | Next Page »
Pages: 1  |  2  |  3