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Logan Sizemore; Brian Hutchinson; Emily Borda – Chemistry Education Research and Practice, 2024
Education researchers are deeply interested in understanding the way students organize their knowledge. Card sort tasks, which require students to group concepts, are one mechanism to infer a student's organizational strategy. However, the limited resolution of card sort tasks means they necessarily miss some of the nuance in a student's strategy.…
Descriptors: Artificial Intelligence, Chemistry, Cognitive Ability, Abstract Reasoning

Conrad Borchers; Jeroen Ooge; Cindy Peng; Vincent Aleven – Grantee Submission, 2025
Personalized problem selection enhances student practice in tutoring systems. Prior research has focused on transparent problem selection that supports learner control but rarely engages learners in selecting practice materials. We explored how different levels of control (i.e., full AI control, shared control, and full learner control), combined…
Descriptors: Intelligent Tutoring Systems, Artificial Intelligence, Learner Controlled Instruction, Learning Analytics
Zhao, Fuzheng; Liu, Gi-Zen; Zhou, Juan; Yin, Chengjiu – Educational Technology & Society, 2023
Big data in education promotes access to the analysis of learning behavior, yielding many valuable analysis results. However, with obscure and insufficient guidelines commonly followed when applying the analysis results, it is difficult to translate information knowledge into actionable strategies for educational practices. This study aimed to…
Descriptors: Learning Analytics, Man Machine Systems, Artificial Intelligence, Learning Strategies
Yang, Xi; Zhou, Guojing; Taub, Michelle; Azevedo, Roger; Chi, Min – International Educational Data Mining Society, 2020
In the learning sciences, heterogeneity among students usually leads to different learning strategies or patterns and may require different types of instructional interventions. Therefore, it is important to investigate student subtyping, which is to group students into subtypes based on their learning patterns. Subtyping from complex student…
Descriptors: Grouping (Instructional Purposes), Learning Strategies, Artificial Intelligence, Learning Analytics
Lahza, Hatim; Khosravi, Hassan; Demartini, Gianluca – Journal of Computer Assisted Learning, 2023
Background: The use of crowdsourcing in a pedagogically supported form to partner with learners in developing novel content is emerging as a viable approach for engaging students in higher-order learning at scale. However, how students behave in this form of crowdsourcing, referred to as learnersourcing, is still insufficiently explored.…
Descriptors: Learning Analytics, Learning Strategies, Electronic Learning, Independent Study
Xiaofang Liao; Xuedi Zhang; Zhifeng Wang; Heng Luo – British Journal of Educational Technology, 2024
Formative assessment is essential for improving teaching and learning, and AI and visualization techniques provide great potential for its design and delivery. Using NLP, cognitive diagnostic and visualization techniques designed to analyse and present students' monthly exam data, we developed an AI-enabled visual report tool comprising six…
Descriptors: Artificial Intelligence, Design, Program Implementation, Formative Evaluation
Yuxiao Xie; Ziyi Xie; Siyu Chen; Lei Shen; Zhizhuang Duan – Education and Information Technologies, 2025
The National College English Test Band 4 (CET-4) is a key test to assess the English language ability of Chinese university students, and the success rate of the test is important to improve the quality of their English learning. Artificial intelligence technology can be used to predict and explore the factors influencing the success rate. This…
Descriptors: Language Tests, English (Second Language), Second Language Learning, Second Language Instruction
Joel Weijia Lai; Wei Qiu; Maung Thway; Lei Zhang; Nurabidah Binti Jamil; Chit Lin Su; Samuel S. H. Ng; Fun Siong Lim – Journal of Learning Analytics, 2025
The growing use of generative AI (GenAI) has sparked discussions regarding integrating these tools into educational settings to enrich the learning experience of teachers and students. Self-regulated learning (SRL) research is pivotal in addressing this inquiry. One prevalent manifestation of GenAI is the large-language model (LLM) chatbot,…
Descriptors: Artificial Intelligence, Computer Software, Learning Analytics, Introductory Courses
Hilpert, Jonathan C.; Greene, Jeffrey A.; Bernacki, Matthew – British Journal of Educational Technology, 2023
Capturing evidence for dynamic changes in self-regulated learning (SRL) behaviours resulting from interventions is challenging for researchers. In the current study, we identified students who were likely to do poorly in a biology course and those who were likely to do well. Then, we randomly assigned a portion of the students predicted to perform…
Descriptors: Learning Theories, Independent Study, Artificial Intelligence, Biology
Viberg, Olga; Kukulska-Hulme, Agnes; Peeters, Ward – International Journal of Mobile and Blended Learning, 2023
Mobile-assisted language learning (MALL) research includes examination and development of second language learners' cognitive and metacognitive self-regulated learning skills, but the affective learning component of self-regulation in this context remains largely unexplored. Support for affective learning, which is defined by learners' beliefs,…
Descriptors: Metacognition, Computer Assisted Instruction, Second Language Learning, Second Language Instruction