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Mikko Kokkoniemi; Ville Isomöttönen – Informatics in Education, 2025
This study builds on a recent systematic mapping of computing education literature by conducting an in-depth qualitative analysis of selected studies on group work in Project-Based Learning (PjBL), published between 2010 and 2021. We examined how prominent theoretical frameworks are used in this context. We found that frameworks were often applied…
Descriptors: Computer Science Education, Instructional Design, Teaching Methods, Student Projects
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Rohit Murali; Cristina Conati; David Poole – International Educational Data Mining Society, 2025
When tutoring students it is useful to be able to predict whether they are succeeding as early as possible. This paper compares multiple methods for predicting from sequential interaction data whether a student is on a successful path. Predicting students' future performance and intervening has shown promise in improving learner outcomes and…
Descriptors: Classification, Prediction, Markov Processes, Artificial Intelligence
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Yangyang Luo; Xibin Han; Chaoyang Zhang – Asia Pacific Education Review, 2024
Learning outcomes can be predicted with machine learning algorithms that assess students' online behavior data. However, there have been few generalized predictive models for a large number of blended courses in different disciplines and in different cohorts. In this study, we examined learning outcomes in terms of learning data in all of the…
Descriptors: Prediction, Learning Management Systems, Blended Learning, Classification
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Roman Abel; Anique de Bruin; Erdem Onan; Julian Roelle – Educational Psychology Review, 2024
Distinguishing easily confusable categories requires learners to detect their predictive differences. Interleaved sequences -- switching between categories -- help learners to detect such differences. Nonetheless, learners prefer to block -- switching within a category -- to detect commonalities. Across two 2 × 2-factorial experiments, we…
Descriptors: Sequential Learning, Learning Strategies, Interference (Learning), Classification
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Tim M. Steininger; Jörg Wittwer; Thamar Voss – Psychology Learning and Teaching, 2025
In order to make informed instructional decisions, teachers need psychological knowledge about relational categories. We conducted two 2 x 2 experiments to examine effective designs for learning relational categories in the context of teacher education. In both experiments, a blocked compared to an interleaved example format was more beneficial…
Descriptors: Classification, Learning Processes, Student Teachers, Psychology
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Seth Wiener; Timothy K. Murphy; Lori L. Holt – Language Learning, 2025
There is considerable lab-based evidence for successful incidental learning, in which a learner's attention is directed away from the to-be-learned stimulus and towards another stimulus. In this study, we extend incidental learning research into the language learning classroom. Three groups of adult second language (L2) learners (N = 52) engaged…
Descriptors: Incidental Learning, Auditory Perception, Acoustics, Phonetics
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Gülfem Gürses; Aysenur I?nceelli – Turkish Online Journal of Educational Technology - TOJET, 2024
ICAP is a framework that classifies learning processes based on students' explicit behaviors. The framework is developed for testing the hypothesis that interactive exercises are better than constructive exercises, and active exercises are better than the passive exercises for higher cognitive engagement and better learning outcomes. The ICAP…
Descriptors: Learning Processes, Learning Theories, Classification, Active Learning
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Verena Dornauer; Michael Netzer; Éva Kaczkó; Lisa-Maria Norz; Elske Ammenwerth – International Journal of Artificial Intelligence in Education, 2024
Cognitive presence is a core construct of the Community of Inquiry (CoI) framework. It is considered crucial for deep and meaningful online-based learning. CoI-based real-time dashboards visualizing students' cognitive presence may help instructors to monitor and support students' learning progress. Such real-time classifiers are often based on…
Descriptors: Electronic Learning, Discussion, Classification, Automation
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Han Zhang; Yilang Peng – Sociological Methods & Research, 2024
Automated image analysis has received increasing attention in social scientific research, yet existing scholarship has mostly covered the application of supervised learning to classify images into predefined categories. This study focuses on the task of unsupervised image clustering, which aims to automatically discover categories from unlabelled…
Descriptors: Social Science Research, Visual Aids, Visual Learning, Cluster Grouping
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Daniel Suuk; Dennis Wilmot; Peter T. Birteeb – Journal of Educational Research, 2024
This study was conducted to compare the efficacy of Lecture-Based-Learning (LBL) and Cooperative-Learning (CL) in teaching 'classification of living things' to Senior High School (SHS) students in Tamale, Northern Region, Ghana. The study was conducted in two SHSs using two science classes offering Biology during the 2020-2021 academic year. A…
Descriptors: Foreign Countries, Cooperative Learning, Lecture Method, High School Students
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Marcela Pessoa; Marcia Lima; Fernanda Pires; Gabriel Haydar; Rafaela Melo; Luiz Rodrigues; David Oliveira; Elaine Oliveira; Leandro Galvao; Bruno Gadelha; Seiji Isotani; Isabela Gasparini; Tayana Conte – IEEE Transactions on Learning Technologies, 2024
Game designers and researchers have sought to create gameful environments that consider user preferences to increase engagement and motivation. In this sense, it is essential to identify the most suitable game elements for users' profiles. Designers and researchers must choose strategies to classify users into predefined profiles and select the…
Descriptors: Educational Environment, Game Based Learning, Classification, Learner Engagement
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Zia Tajeddin; Ali Malmir – Language Teaching Research Quarterly, 2024
Learners' acquisition of pragmatic competence in additional languages has received mounting attention since the 1990s. However, although studies on general learning strategies have proliferated since Oxford's (1990) influential inventory was published, studies on pragmatic-specific learning strategies contributing to the acquisition of this…
Descriptors: Pragmatics, Second Language Learning, Second Language Instruction, Learning Strategies
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Halim Acosta; Seung Lee; Haesol Bae; Chen Feng; Jonathan Rowe; Krista Glazewski; Cindy Hmelo-Silver; Bradford Mott; James C. Lester – International Journal of Artificial Intelligence in Education, 2025
Understanding students' multi-party epistemic and topic based-dialogue contributions, or how students present knowledge in group-based chat interactions during collaborative game-based learning, offers valuable insights into group dynamics and learning processes. However, manually annotating these contributions is labor-intensive and challenging.…
Descriptors: Game Based Learning, Artificial Intelligence, Technology Uses in Education, Cooperative Learning
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Joseph B. Quinto; Manilyn R. Cacanindin – Advanced Education, 2024
Despite numerous studies about language learning strategies (LLSs), many learners still misunderstand their effectiveness, thinking they require too much effort for minimal gain. Additionally, students have varied and conflicting preferences for LLSs, and factors like cultural background influence their choices, indicating a need for more research…
Descriptors: Classification, Second Language Learning, Learning Strategies, Undergraduate Students
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Michelle A. Sveistrup; Jean Langlois; Timothy D. Wilson – Anatomical Sciences Education, 2025
The Cognitive Theory of Multimedia Learning (CTML) suggests humans learn through visual and auditory sensory channels. Haptics represent a third channel within CTML and a missing component for experiential learning. The objective was to measure visual and haptic behaviors during spatial tasks. The haptic abilities test (HAT) quantifies results in…
Descriptors: Learning Theories, Multimedia Instruction, Sensory Integration, Experiential Learning
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