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Gao, Ming; Zhang, Jingjing; Lu, Yu; Kahn, Ken; Winters, Niall – Journal of Computer Assisted Learning, 2023
Background: As a non-cognitive trait, grit plays an important role in human learning. Although students higher in grit are more likely to perform well on tests, how they learn in the process has been underexamined. Objectives: This study attempted to explore how students with different levels of grit behave and learn in an exploratory learning…
Descriptors: Resilience (Psychology), Academic Persistence, Personality Traits, Usability
Flor, Michael; Andrews-Todd, Jessica – Journal of Computer Assisted Learning, 2022
Background: Collaborative problem solving (CPS) is important for success in the 21st century, especially for teamwork and communication in technology-enhanced environments. Measurement of CPS skills has emerged as an essential aspect in educational assessment. Modern research in CPS relies on theory-driven measurements that are usually carried out…
Descriptors: Automation, Documentation, Cooperative Learning, Teamwork
Spikol, Daniel; Ruffaldi, Emanuele; Dabisias, Giacomo; Cukurova, Mutlu – Journal of Computer Assisted Learning, 2018
Multimodal learning analytics provides researchers new tools and techniques to capture different types of data from complex learning activities in dynamic learning environments. This paper investigates the use of diverse sensors, including computer vision, user-generated content, and data from the learning objects (physical computing components),…
Descriptors: Student Projects, Active Learning, Teaching Methods, Group Dynamics
Wang, Cixiao; Fang, Ting; Miao, Rong – Journal of Computer Assisted Learning, 2018
In the increasing pervasiveness of today's digital society, mobile devices are changing the face of education. Students can interact with mobile devices in context-aware environment. This paper presents a mobile application based on expert system (Plant-E) for students to acquire knowledge about plant classification by answering decision-making…
Descriptors: Cognitive Processes, Difficulty Level, Electronic Learning, Handheld Devices
Jagušt, T.; Boticki, I.; So, H. -J. – Journal of Computer Assisted Learning, 2018
This study reviews empirical research articles published in the field of technology-enhanced learning in the out-of-class contexts in primary schools between the years 2007 and 2016 and explores how the body of research has connected formal and informal learning experiences, referred to in the paper as bridging the gap. The review focuses on 43…
Descriptors: Elementary School Students, Informal Education, Student Interests, Student Motivation
Cress, U.; Held, C. – Journal of Computer Assisted Learning, 2013
Tagging systems represent the conceptual knowledge of a community. We experimentally tested whether people harness this collective knowledge when navigating through the Web. As a within-factor we manipulated people's prior knowledge (no knowledge vs. prior knowledge that was congruent/incongruent to the collective knowledge inherent in the tags).…
Descriptors: Classification, Internet, Prior Learning, Navigation (Information Systems)
Hui, W.; Hu, P. J.-H.; Clark, T. H. K.; Tam, K. Y.; Milton, J. – Journal of Computer Assisted Learning, 2008
A field experiment compares the effectiveness and satisfaction associated with technology-assisted learning with that of face-to-face learning. The empirical evidence suggests that technology-assisted learning effectiveness depends on the target knowledge category. Building on Kolb's experiential learning model, we show that technology-assisted…
Descriptors: Listening Comprehension, Web Based Instruction, Experiential Learning, Vocabulary Development
Arnseth, H. C.; Saljo, R. – Journal of Computer Assisted Learning, 2007
The topic of this article concerns how students make sense of categories of progressive inquiry made available to them through a discussion and inquiry type of software called Future Learning Environments 2 (FLE2). The idea behind tools of this kind is to induce approaches to school-work that build on the metaphor of learning as research. By…
Descriptors: Learning Activities, Educational Practices, Classification, Computer Software

Heywood, G. – Journal of Computer Assisted Learning, 1986
Form and function of reinforcement is discussed with attention to the focus for feedback. Case studies of five junior high schools provide data and field notes for a tentative analysis of the determinants of reinforcement including organizational factors and types of computer assisted instruction applications. (Author/MBR)
Descriptors: Case Studies, Classification, Computer Assisted Instruction, Feedback