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Yongtian Cheng; K. V. Petrides – Educational and Psychological Measurement, 2025
Psychologists are emphasizing the importance of predictive conclusions. Machine learning methods, such as supervised neural networks, have been used in psychological studies as they naturally fit prediction tasks. However, we are concerned about whether neural networks fitted with random datasets (i.e., datasets where there is no relationship…
Descriptors: Psychological Studies, Artificial Intelligence, Cognitive Processes, Predictive Validity
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Jolan Hanssens; Carolien Van Soom; Greet Langie – European Journal of Psychology of Education, 2025
The societal demand for graduates with expertise in Science, Technology, Engineering, and Mathematics (STEM) stands in contrast with prevalent issues of diminished interest among high school students in STEM subjects and low completion rates in STEM academic programs. In the Flemish educational context, heterogeneity in STEM preparedness of…
Descriptors: Foreign Countries, STEM Education, Higher Education, At Risk Students
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Allyson F. Hadwin; Ramin Rostampour; Philip H. Winne – Educational Psychology Review, 2025
Self-report measures are essential sources of information about learners' studying perceptions. These perceptions also guide self-regulated learning (SRL) decisions and strategies in future studying. However, the development of self-report methods has not kept pace with other multi-modal methodological advancements, particularly in the field of…
Descriptors: Attitude Measures, Student Attitudes, Beliefs, Measurement Techniques
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Xiaoyu Tang; Yayun Gong; Yang Xiao; Jianwen Xiong; Lei Bao – Journal of Science Education and Technology, 2025
Student engagement in science classroom is an essential element for delivering effective instruction. However, the popular method for measuring students' emotional learning engagement (ELE) relies on self-reporting, which has been criticized for possible bias and lacking fine-grained time solution needed to track the effects of short-term learning…
Descriptors: Physics, Science Instruction, Nonverbal Communication, Science Achievement
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Katherine E. Castellano; Daniel F. McCaffrey; Joseph A. Martineau – Educational Measurement: Issues and Practice, 2025
Growth-to-standard models evaluate student growth against the growth needed to reach a future standard or target of interest, such as proficiency. A common growth-to-standard model involves comparing the popular Student Growth Percentile (SGP) to Adequate Growth Percentiles (AGPs). AGPs follow from an involved process based on fitting a series of…
Descriptors: Student Evaluation, Growth Models, Student Educational Objectives, Educational Indicators