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Hyomin Kim; Gyunam Park; Minsu Cho – Education and Information Technologies, 2024
Learning analytics, located at the intersection of learning science, data science, and computer science, aims to leverage educational data to enhance teaching and learning. However, as educational data increases, distilling meaningful insights presents challenges, particularly concerning individual learner differences. This work introduces a…
Descriptors: Learner Engagement, Academic Achievement, Learning Processes, Learning Analytics
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Allyson Holbrook; Erika Spray; Rachel Burke; Kylie M. Shaw; Jayne Carruthers – Studies in Graduate and Postdoctoral Education, 2024
Purpose: Highly developed and agile learners who can clearly convey and call on their skills are sought in all walks of life. Diverse demand for these capacities has called attention to how the skills and knowledge gained during doctoral study can be conveyed, translated and leveraged in non-academic settings; however, the complex learning reality…
Descriptors: Doctoral Students, Learning Processes, Transfer of Training, Student Development
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Kim, Kathy MinHye; Maie, Ryo; Suga, Kiyo; Miller, Zachary F.; Hui, Bronson – Language Learning, 2023
This study addresses the role of awareness in learning and the variables that may facilitate adult second language (L2) implicit learning. We replicated Williams's (2005) study with a similar group of academic learners enrolled at university as well as a group of non-college-educated adults in order to explore the generalizability of the findings…
Descriptors: Second Language Learning, Individual Differences, Intelligence, Generalizability Theory
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Alyssa P. Lawson; Richard E. Mayer – Journal of Educational Computing Research, 2024
In multimedia learning, there is a lot of new information that learners are exposed to, making it a cognitively intensive process. Poorly-designed multimedia lessons can introduce distractions that must be dealt with by the learner. However, learners do not all share the same skill at managing incoming information or holding capacity, which could…
Descriptors: Individual Differences, Executive Function, Multimedia Instruction, Attention Control
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Luke Strickland; Simon Farrell; Micah K. Wilson; Jack Hutchinson; Shayne Loft – Cognitive Research: Principles and Implications, 2024
In a range of settings, human operators make decisions with the assistance of automation, the reliability of which can vary depending upon context. Currently, the processes by which humans track the level of reliability of automation are unclear. In the current study, we test cognitive models of learning that could potentially explain how humans…
Descriptors: Automation, Reliability, Man Machine Systems, Learning Processes
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Berweger, Belinda; Kracke, Bärbel; Dietrich, Julia – Journal of Educational Psychology, 2023
Learning processes that involve cognitive incongruity are closely tied to emotional experiences such as curiosity or confusion. The present study examined how discovering that a confidently held misconception is incorrect influences emotions and in turn the motivation to seek additional information. We asked 275 preservice teachers to judge if…
Descriptors: Preservice Teachers, Epistemology, Psychological Patterns, Academic Achievement
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Phil Hiver; Ali H. Al-Hoorie; Akira Murakami – Language Learning, 2025
In this paper, we report a longitudinal study of the effects of procedural task repetition on learners' task performance (i.e., syntactic complexity in relation to lexical complexity). We investigated how task repetition results in differences at the group and individual level across each task interval (T = 7). Intermediate-level Saudi learners of…
Descriptors: Task Analysis, Second Language Learning, Writing (Composition), Longitudinal Studies
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Zhaojun Duo; Jianan Zhang; Yonggong Ren; Xiaolu Xu – Education and Information Technologies, 2025
"Self-regulated learning" (SRL) significantly impacts the process and outcome of "programming problem-solving." Studies on SRL behavioural patterns of programming students based on trace data are limited in number and lack of coverage. In this study, hence, the Hidden Markov Model (HMM) was employed to probabilistically mine…
Descriptors: Students, Programming, Problem Solving, Self Management
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Libor Juhanák; Vojtech Jurík; Nicol Dostálová; Zuzana Juríková – Australasian Journal of Educational Technology, 2025
The use of metacognitive prompting to support self-regulated learning is a well-established area of research in education. Despite receiving considerable attention, the precise mechanism of prompting and its effects on the learning process remain unclear, especially in the context of multimedia learning. This study employed a controlled laboratory…
Descriptors: Metacognition, Cues, Outcomes of Education, Undergraduate Students
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Zhang, Qian; Fiorella, Logan – Educational Psychologist, 2023
Errors are inevitable in most learning contexts, but under the right conditions, they can be beneficial for learning. Prior research indicates that generating and learning from errors can promote retention of knowledge, higher-level learning, and self-regulation. The present review proposes an integrated theoretical model to explain two major…
Descriptors: Models, Error Correction, Learning Processes, Feedback (Response)
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Montuori, Luke M.; Montefiori, Lara – Journal of Intelligence, 2022
For decades, the field of workplace selection has been dominated by evidence that cognitive ability is the most important factor in predicting performance. Meta-analyses detailing the contributions of a wide-range of factors to workplace performance show that cognitive ability's contribution is partly mediated by the learning of task-relevant…
Descriptors: Learning Processes, Cognitive Ability, Job Performance, Psychometrics
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Austin L. Boroshok; Anne T. Park; Panagiotis Fotiadis; Gerardo H. Velasquez; Ursula A. Tooley; Katrina R. Simon; Jasmine C. P. Forde; Lourdes M. Delgado Reyes; M. Dylan Tisdall; Dani S. Bassett; Emily A. Cooper; Allyson P. Mackey – npj Science of Learning, 2022
Neuroplasticity, defined as the brain's "potential" to change in response to its environment, has been extensively studied at the cellular and molecular levels. Work in animal models suggests that stimulation to the ventral tegmental area (VTA) enhances plasticity, and that myelination constrains plasticity. Little is known, however,…
Descriptors: Individual Differences, Brain Hemisphere Functions, Learning Processes, Correlation
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Eglington, Luke G.; Pavlik, Philip I., Jr. – International Journal of Artificial Intelligence in Education, 2023
An important component of many Adaptive Instructional Systems (AIS) is a 'Learner Model' intended to track student learning and predict future performance. Predictions from learner models are frequently used in combination with mastery criterion decision rules to make pedagogical decisions. Important aspects of learner models, such as learning…
Descriptors: Computer Assisted Instruction, Intelligent Tutoring Systems, Learning Processes, Individual Differences
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Phillip Hamrick; Christopher A. Was; Yin Zhang – Studies in Second Language Acquisition, 2024
A growing body of evidence demonstrates that individual differences in declarative memory may be an important predictor of second language (L2) abilities. However, the evidence comes from studies using different declarative memory tasks that vary in their reliance on verbal abilities and task demands, which preclude estimating the size of the…
Descriptors: Verbal Ability, Nonverbal Ability, Task Analysis, Second Language Instruction
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Goh, Adeline Yuen Sze – International Review of Education, 2022
Understanding how individuals learn at work throughout their lives is significant for discussions of lifelong learning in the current era where changes can be unpredictable and frequent, as illustrated by the COVID-19 pandemic. Despite a corpus of literature on the subject of "learning", there is little research or theoretical…
Descriptors: Learning Processes, Workplace Learning, Lifelong Learning, Individual Development
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