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Lottridge, Susan; Woolf, Sherri; Young, Mackenzie; Jafari, Amir; Ormerod, Chris – Journal of Computer Assisted Learning, 2023
Background: Deep learning methods, where models do not use explicit features and instead rely on implicit features estimated during model training, suffer from an explainability problem. In text classification, saliency maps that reflect the importance of words in prediction are one approach toward explainability. However, little is known about…
Descriptors: Documentation, Learning Strategies, Models, Prediction
Shan Tang; Chi-Un Lei; Hong Qiang Wei – Journal of Computer Assisted Learning, 2024
Background: Given students' lack of self-directed learning skills and the growing concern about implementing massive online open courses (MOOCs) in K12 education, learning strategies are needed to facilitate MOOC learning. Many studies have provided different strategies for effective learning in MOOCs. However, there is still limited research to…
Descriptors: MOOCs, Elementary Secondary Education, Learning Strategies, Academic Achievement
Yu Gao; Linjing Wu; Xiaotong Lv; Xinqian Ma; Qingtang Liu – Journal of Computer Assisted Learning, 2024
Background: Both socially regulated learning and cognitive quality are important factors affecting collaborative knowledge building, but the current research lacks a joint quantified evaluation method that combines these two aspects. Objectives: Based on the existing framework, we proposed a joint evaluation method for regulated learning and…
Descriptors: Self Management, Cooperative Learning, Learning Strategies, Evaluation Methods
Fan, Yizhou; Rakovic, Mladen; van der Graaf, Joep; Lim, Lyn; Singh, Shaveen; Moore, Johanna; Molenaar, Inge; Bannert, Maria; Gaševic, Dragan – Journal of Computer Assisted Learning, 2023
Background: Many learners struggle to productively self-regulate their learning. To support the learners' self-regulated learning (SRL) and boost their achievement, it is essential to understand the cognitive and metacognitive processes that underlie SRL. To measure these processes, contemporary SRL researchers have largely utilized think aloud or…
Descriptors: Learning Strategies, Self Management, Protocol Analysis, Data Analysis
Dominic Lohr; Hieke Keuning; Natalie Kiesler – Journal of Computer Assisted Learning, 2025
Background: Feedback as one of the most influential factors for learning has been subject to a great body of research. It plays a key role in the development of educational technology systems and is traditionally rooted in deterministic feedback defined by experts and their experience. However, with the rise of generative AI and especially large…
Descriptors: College Students, Programming, Artificial Intelligence, Feedback (Response)
Yuqin Yang; Xueqi Feng; Gaoxia Zhu; Kui Xie – Journal of Computer Assisted Learning, 2024
Background: Undergraduates' collective epistemic agency is critical for their productive collaborative inquiry and knowledge building (KB). However, fostering undergraduates' collective epistemic agency is challenging. Studies have demonstrated the potential of computer-supported collaborative inquiry approaches, such as KB--the focus of this…
Descriptors: Undergraduate Students, Cooperative Learning, Epistemology, Inquiry
Fan, Yizhou; Tan, Yuanru; Rakovic, Mladen; Wang, Yeyu; Cai, Zhiqiang; Shaffer, David Williamson; Gaševic, Dragan – Journal of Computer Assisted Learning, 2023
Background: Select and enact appropriate learning tactics that advance learning has been considered a critical set of skills to successfully complete highly flexible online courses, such as Massive open online courses (MOOCs). However, limited by analytic methods that have been used in the past, such as frequency distribution, sequence mining and…
Descriptors: MOOCs, Students, Learning Processes, Learning Strategies
Ren-Zhi Luo; Yue-Liang Zhou – Journal of Computer Assisted Learning, 2024
Background: The COVID-19 has accelerated the transition to blended learning (BL) in higher education, prompting a need for further investigation into the efficacy of self-regulated learning strategies (SRLS) in these new educational environments. Objective: The primary goal of this research is to assess the effectiveness of SRLS in BL in higher…
Descriptors: Learning Strategies, Blended Learning, Self Management, Higher Education
Jana Gonnermann-Müller; Jule M. Krüger – Journal of Computer Assisted Learning, 2025
Background: Despite the numerous positive effects of augmented reality (AR) on learning, previous research has shown ambiguous results regarding the cognitive demand on the learner arising from, for example, the overlay of virtual elements or novel interaction techniques. At the same time, the number of evidence-based guidelines on designing AR is…
Descriptors: Computer Simulation, Computer Assisted Design, Difficulty Level, Cognitive Processes
Lanqin Zheng; Yunchao Fan; Zichen Huang; Lei Gao – Journal of Computer Assisted Learning, 2024
Background: Online collaborative learning has been widely adopted in the field of education. However, learners often find it difficult to engage in collaboratively building knowledge and jointly regulating online collaborative learning. Objectives: The study compared the impacts of the three learning approaches on collaborative knowledge building,…
Descriptors: Cooperative Learning, Electronic Learning, College Students, Learning Strategies
Hatice Yildiz Durak – Journal of Computer Assisted Learning, 2024
Background: Collaboration is a crucial concept in learning and has the potential to foster learning. However, the fact that collaborative groups act with a common understanding in a common task brings many difficulties. Therefore, there is a need for group regulation and guidance to support effective group regulation in collaborative learning. On…
Descriptors: Feedback (Response), Groups, Group Guidance, Cooperation
Malmberg, Jonna; Fincham, Oliver; Pijeira-Díaz, Héctor J.; Järvelä, Sanna; Gaševic, Dragan – Journal of Computer Assisted Learning, 2021
Using hidden Markov models (HMM), the current study looked at how learners' metacognitive monitoring is related to their physiological reactivity in the context of collaborative learning. The participants (N = 12, age 16-17 years, three females and nine males) in the study were high school students enrolled in an advanced physics course. The…
Descriptors: Physiology, Metacognition, Cooperative Learning, High School Students
Araos, Andrés; Damsa, Crina; Gaševic, Dragan – Journal of Computer Assisted Learning, 2023
Background: The surge of online platforms has generated interest in how specialized platforms support formal and informal learning in various disciplinary domains. Knowledge is still limited regarding how undergraduate students navigate and use platforms to learn. Objectives: This study explores computer and software engineering students' learning…
Descriptors: Computer Science Education, Computer Software, Learning Activities, Undergraduate Students
Julius Moritz Meier; Peter Hesse; Stephan Abele; Alexander Renkl; Inga Glogger-Frey – Journal of Computer Assisted Learning, 2024
Background: In example-based learning, examples are often combined with generative activities, such as comparative self-explanations of example cases. Comparisons induce heavy demands on working memory, especially in complex domains. Hence, only stronger learners may benefit from comparative self-explanations. While static text-based examples can…
Descriptors: Video Technology, Models, Cues, Problem Solving
Cheng, Meixia; Wang, Fuxing; Mayer, Richard E. – Journal of Computer Assisted Learning, 2023
Background: Learning-by-teaching is a generative learning strategy in which students are told they will have to teach what they are learning to others. Although learning-by-teaching has been shown to be effective in some cases, few studies have established guidelines for how to optimize the benefits of learning-by-teaching as a generative learning…
Descriptors: Educational Benefits, Student Developed Materials, Film Production, Instructional Films