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Waight, Noemi; Liu, Xiufeng; Whitford, Melinda – Research in Science Education, 2023
This study examined high school chemistry students' understandings of big ideas--matter and energy, how these understandings are related to macro and submicro representations and fine-grained distinguishing characteristics of students' explanations. The study was conducted in the context of computer-based models and model-based assessments.…
Descriptors: Chemistry, Scientific Concepts, Computer Assisted Instruction, Models
Geetika Ail; Frances Freer; Chui Shan Chan; Melissa Jones; John Broad; Gian Paulo Canale; Pedro Elston; Jessica Leeney; Paula Vickerton – Anatomical Sciences Education, 2024
Immersive virtual reality (i-VR) is a powerful tool that can be used to explore virtual models in three dimensions. It could therefore be a valuable tool to supplement anatomical teaching by providing opportunities to explore spatial anatomical relationships in a virtual environment. However, there is a lack of consensus in the literature as to…
Descriptors: Computer Simulation, Models, Anatomy, Premedical Students
Karaismailoglu, Fatma; Yildirim, Mehtap – Biochemistry and Molecular Biology Education, 2023
This article presents the integration of Tinkercad, a free online modeling program that allows students to model molecular genetic concepts, into the distance learning process. The students had the opportunity to learn molecular genetics in a fun and more efficient way in spite of the limitations of the COVID-19 lockdown, and, in this respect, it…
Descriptors: Models, Genetics, Molecular Structure, Scientific Concepts
Troussas, Christos; Giannakas, Filippos; Sgouropoulou, Cleo; Voyiatzis, Ioannis – Interactive Learning Environments, 2023
Computer-Supported Collaborative Learning is a promising innovation that ameliorates tutoring through modern technologies. However, the way of recommending collaborative activities to learners, by taking into account their learning needs and preferences, is an important issue of increasing interest. In this context, this paper presents a framework…
Descriptors: Computer Assisted Instruction, Cognitive Style, Cooperative Learning, Models
Boussaha, Karima; Boussouf, Raouf Amir – International Journal of Virtual and Personal Learning Environments, 2022
Several researchers studied the impact of collaboration between the learners, but few studies have been carried out on the impact of collaboration between teachers. In the previous work, the authors have studied the impact of the collaboration among the learners with a specific collaborative CEHL(K. Boussaha et al.,2015). In this work, the authors…
Descriptors: Computer Assisted Instruction, Cooperative Learning, Coaching (Performance), Intelligent Tutoring Systems
Ignacio Villagran; Rocio Hernandez; Gregory Schuit; Andres Neyem; Javiera Fuentes-Cimma; Constanza Miranda; Isabel Hilliger; Valentina Duran; Gabriel Escalona; Julian Varas – IEEE Transactions on Learning Technologies, 2024
This article presents a controlled case study focused on implementing and using generative artificial intelligence, specifically large language models (LLMs), in physiotherapy education to assist instructors with formulating effective technology-mediated feedback for students. It outlines how these advanced technologies have been integrated into…
Descriptors: Artificial Intelligence, Physical Therapy, Technology Uses in Education, Case Studies
Enhancing Procedural Writing through Personalized Example Retrieval: A Case Study on Cooking Recipes
Paola Mejia-Domenzain; Jibril Frej; Seyed Parsa Neshaei; Luca Mouchel; Tanya Nazaretsky; Thiemo Wambsganss; Antoine Bosselut; Tanja Käser – International Journal of Artificial Intelligence in Education, 2025
Writing high-quality procedural texts is a challenging task for many learners. While example-based learning has shown promise as a feedback approach, a limitation arises when all learners receive the same content without considering their individual input or prior knowledge. Consequently, some learners struggle to grasp or relate to the feedback,…
Descriptors: Writing Instruction, Academic Language, Content Area Writing, Cooking Instruction
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
Zheng, Lanqin; Niu, Jiayu; Zhong, Lu – British Journal of Educational Technology, 2022
Learning analytics (LA) has been widely adopted in research on education. However, most studies in the area have conducted LA after computer-supported collaborative learning (CSCL) activities rather than during CSCL. To address this problem, this study proposed a LA-based real-time feedback approach based on a deep neural network model to improve…
Descriptors: Learning Analytics, Feedback (Response), Outcomes of Education, Cooperative Learning
Kim, Jinhee; Lee, Kate Sang-Soog – Asia Pacific Journal of Education, 2022
For scaling up pedagogical innovation with information and communications technology (ICT), governments around the world put a concerted effort into teachers' acceptance of ICTs and the actual use of ICTs for instruction, yet there is limited literature about the conceptual framework of teachers accepting the ICTs and their usage for instruction…
Descriptors: Foreign Countries, Computer Assisted Instruction, Secondary School Teachers, Adoption (Ideas)
Yang, Xigui – TechTrends: Linking Research and Practice to Improve Learning, 2023
Collaborative learning and cooperative learning are two separate approaches developed independently by two groups of scholars around the same period of time in the 1960 and 1970s. Due to their different origins and intertwined paths of development, they have their own distinct features while sharing many similarities. The relationship between…
Descriptors: Cooperative Learning, Educational History, Group Dynamics, Teaching Methods
Zhang, Shunan; Che, ShaoPeng; Nan, Dongyan; Li, Yincen; Kim, Jang Hyun – Education and Information Technologies, 2023
Considering the importance of group member familiarity in collaborative learning in classroom learning environments, this study examined the impact of group member familiarity on CSCL (computer-supported collaborative learning) in a networked setting. Also, the differences between CSCL in the online environments and FtF (face-to-face)…
Descriptors: Group Dynamics, Familiarity, Cooperative Learning, Computer Assisted Instruction
Jionghao Lin; Wei Tan; Lan Du; Wray Buntine; David Lang; Dragan Gasevic; Guanliang Chen – IEEE Transactions on Learning Technologies, 2024
Automating the classification of instructional strategies from a large-scale online tutorial dialogue corpus is indispensable to the design of dialogue-based intelligent tutoring systems. Despite many existing studies employing supervised machine learning (ML) models to automate the classification process, they concluded that building a…
Descriptors: Classification, Dialogs (Language), Teaching Methods, Computer Assisted Instruction
Wenli Chen; Si Zhang; Zhongling Pi; Jesmine S. H. Tan; Yun Wen; Chee-Kit Looi; Jennifer Yeo; Qingtang Liu – Technology, Pedagogy and Education, 2024
This study investigates the role of a collaboration script, the Funnel Model, in supporting students' computer-supported collaborative scientific argumentation, and how the students appropriated the collaboration script in scientific argumentation. In this exploratory case study, a class of 33 Secondary grade four students went through four phases…
Descriptors: Cooperative Learning, Scripts, Computer Assisted Instruction, Persuasive Discourse
Mohammed A. E. Suliman; Wenlan Zhang; Rehab A. I. Suluman; Kamal Abubker Abrahim Sleiman – Education and Information Technologies, 2025
This study contributes to the knowledge about mobile learning among medical students in the context of developing countries. This research used the Technology Acceptance Model (TAM) to study the preconditions for m-learning among medical students. A twenty-item self-reported survey was used to gather data from 387 medical students, and structural…
Descriptors: Medical Students, Student Attitudes, Information Technology, Technology Integration