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Mengcun Gao; Brandon M. Turner; Vladimir M. Sloutsky – Cognitive Science, 2024
Numerous studies have found that selective attention affects category learning. However, previous research did not distinguish between the contribution of focusing and filtering components of selective attention. This study addresses this issue by examining how components of selective attention affect category representation. Participants first…
Descriptors: Attention, Classification, Memory, Knowledge Representation
Susan C. Mirabal; Darcy A. Reed; Yvonne Steinert; Cynthia R. Whitehead; Scott M. Wright; Sean Tackett – Advances in Health Sciences Education, 2024
While explicit conceptual models help to inform research, they are left out of much of the health professions education (HPE) literature. One reason may be the limited understanding about how to develop conceptual models with intention and rigor. Group concept mapping (GCM) is a mixed methods conceptualization approach that has been used to…
Descriptors: Allied Health Occupations Education, Medical Education, Concept Mapping, Learning Strategies
Any Fatmawati; Siti Zubaidah; Susriyati Mahanal; Sutopo Sutopo; Muhammad Roil Bilad; Masitah Shahrill – Pegem Journal of Education and Instruction, 2024
One of the essential goals of science learning is to lead students to master scientific concepts or ideas and apply them to explain relevant everyday phenomena. Such mastery should help students to work with various representations. The objective of this study was to determine the effectiveness of the Learning Cycle Multiple Representation (LCMR)…
Descriptors: Preservice Teacher Education, Preservice Teachers, Plants (Botany), Scientific Concepts
Guoqian Luo; Hengnian Gu; Xiaoxiao Dong; Dongdai Zhou – Education and Information Technologies, 2025
In the realm of e-learning, supporting personalized learning effectively necessitates recommending sequences of learning items that maximize learning efficiency while minimizing cognitive load, all tailored to the learner's goals. These recommendations must account for the prerequisite relationships among learning items and the learner's…
Descriptors: Electronic Learning, Individualized Instruction, Sequential Learning, Learning Processes
Lixiang Yan; Lele Sha; Linxuan Zhao; Yuheng Li; Roberto Martinez-Maldonado; Guanliang Chen; Xinyu Li; Yueqiao Jin; Dragan Gaševic – British Journal of Educational Technology, 2024
Educational technology innovations leveraging large language models (LLMs) have shown the potential to automate the laborious process of generating and analysing textual content. While various innovations have been developed to automate a range of educational tasks (eg, question generation, feedback provision, and essay grading), there are…
Descriptors: Educational Technology, Artificial Intelligence, Natural Language Processing, Educational Innovation
Bin Meng; Fan Yang – International Journal of Web-Based Learning and Teaching Technologies, 2025
This paper proposes a computer-aided teaching model using knowledge graph construction and learning path recommendation. It first creates a multimodal knowledge graph to illustrate complex relationships among knowledge. Learning elements and sequences are then used to form time sequences stored as directed graphs, supporting flexible path…
Descriptors: Students, Teachers, Computer Assisted Instruction, Knowledge Representation