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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
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Xiong, Yao; Schunn, Christian D.; Wu, Yong – Journal of Computer Assisted Learning, 2023
Background: For peer assessment, reliability (i.e., consistency in ratings across peers) and validity (i.e., consistency of peer ratings with instructors or experts) are frequently examined in the research literature to address a central concern of instructors and students. Although the average levels are generally promising, both reliability and…
Descriptors: Peer Evaluation, Computer Assisted Testing, Test Reliability, Test Validity
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Botelho, Anthony; Baral, Sami; Erickson, John A.; Benachamardi, Priyanka; Heffernan, Neil T. – Journal of Computer Assisted Learning, 2023
Background: Teachers often rely on the use of open-ended questions to assess students' conceptual understanding of assigned content. Particularly in the context of mathematics; teachers use these types of questions to gain insight into the processes and strategies adopted by students in solving mathematical problems beyond what is possible through…
Descriptors: Natural Language Processing, Artificial Intelligence, Computer Assisted Testing, Mathematics Tests
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Jewoong Moon; Sheunghyun Yeo; Seyyed Kazem Banihashem; Omid Noroozi – Journal of Computer Assisted Learning, 2024
Background: Traditionally, understanding students' learning dynamics, collaboration, emotions, and their impact on performance has posed challenges in formative assessment. The complexity of monitoring and assessing these factors have often limited the depth and breadth of insights. Objectives: This study aims to explore the potential of…
Descriptors: Formative Evaluation, Nonverbal Communication, Outcomes of Education, Learning Analytics
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Wang, Tingting; Zheng, Juan; Tan, Chengyi; Lajoie, Susanne P. – Journal of Computer Assisted Learning, 2023
Background: Computer-based scaffolding has been intensively used to facilitate students' self-regulated learning (SRL). However, most previous studies investigated how computer-based scaffoldings affected the cognitive aspect of SRL, such as knowledge gains and understanding levels. In contrast, more evidence is needed to examine the effects of…
Descriptors: Metacognition, Scaffolding (Teaching Technique), Computer Assisted Instruction, Intelligent Tutoring Systems
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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
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Slof, Bert; van Leeuwen, Anouschka; Janssen, Jeroen; Kirschner, Paul A. – Journal of Computer Assisted Learning, 2021
In computer-supported collaborative learning research, studies examining the combined effects of individual level, group level and within-group differences level measures on individual achievement are scarce. The current study addressed this by examining whether individual, group and within-group differences regarding engagement and prior…
Descriptors: Cooperative Learning, Prior Learning, Secondary School Students, Academic Achievement
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Yu, L. C.; Lee, C. W.; Pan, H. I.; Chou, C. Y.; Chao, P. Y.; Chen, Z. H.; Tseng, S. F.; Chan, C. L.; Lai, K. R. – Journal of Computer Assisted Learning, 2018
This study presents a model for the early identification of students who are likely to fail in an academic course. To enhance predictive accuracy, sentiment analysis is used to identify affective information from text-based self-evaluated comments written by students. Experimental results demonstrated that adding extracted sentiment information…
Descriptors: Prediction, Academic Failure, Models, Identification
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Alonso-Fernández, Cristina; Martínez-Ortiz, Iván; Caballero, Rafael; Freire, Manuel; Fernández-Manjón, Baltasar – Journal of Computer Assisted Learning, 2020
Serious games have proven to be a powerful tool in education to engage, motivate, and help students learn. However, the change in student knowledge after playing games is usually measured with traditional (paper) prequestionnaires-postquestionnaires. We propose a combination of game learning analytics and data mining techniques to predict…
Descriptors: Case Studies, Teaching Methods, Game Based Learning, Student Motivation
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Leeuwestein, Hanneke; Barking, Marie; Sodaci, Hande; Oudgenoeg-Paz, Ora; Verhagen, Josje; Vogt, Paul; Aarts, Rian; Spit, Sybren; de Haas, Mirjam; de Wit, Jan; Leseman, Paul – Journal of Computer Assisted Learning, 2021
Providing first language (L1) translations in L2 vocabulary interventions may be beneficial for L2 vocabulary learning. However, in linguistically diverse L2 classrooms, teachers cannot provide L1 translations to all children. Social robots do offer such opportunities, as they can be programmed to speak any combination of languages. This study…
Descriptors: Native Language, Translation, Second Language Learning, Vocabulary Development
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Zhang, Yining; Lin, Chin-Hsi – Journal of Computer Assisted Learning, 2021
This study extends the community of inquiry (CoI) framework and self-regulated learning (SRL) theory through an exploration of the structural relationships among existing CoI variables, learning presence (i.e., self-efficacy and online SRL strategy) and learning outcomes in the context of K-12 online learning. To help understand the influence of…
Descriptors: Mentors, Communities of Practice, Metacognition, Self Efficacy
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Blieck, Yves; Kauwenberghs, Kurt; Zhu, Chang; Struyven, Katrien; Pynoo, Bram; DePryck, Koen – Journal of Computer Assisted Learning, 2019
Online and blended learning (OBL) is valued, but it also offers challenges. Literature indicates that OBL can enhance access to education and increase flexibility for students. However, the reported dropout rates indicate that student participation in OBL programmes is a concern. Scientifically valid knowledge about how factors that help students…
Descriptors: Online Courses, Blended Learning, Educational Technology, Technology Uses in Education
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Rodríguez-Aflecht, G.; Jaakkola, T.; Pongsakdi, N.; Hannula-Sormunen, M.; Brezovszky, B.; Lehtinen, E. – Journal of Computer Assisted Learning, 2018
The present study focused on 212 fifth graders' situational interest trajectories during an intervention with a digital mathematics game called Number Navigation. Our aims were to explore the development of situational interest whilst playing the game and to investigate the relationship between situational interest and individual math interest.…
Descriptors: Computer Games, Mathematics Instruction, Intervention, Teaching Methods
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Lin, Feng; Chan, Carol K. K. – Journal of Computer Assisted Learning, 2018
This study characterized students' online collaborative discourse from a theory-building perspective and examined its relation to epistemic and conceptual understanding. Fifty-two fifth graders' Knowledge Forum discussions on electricity were analysed. Discourse moves were coded within the inquiry threads, and two key epistemic patterns were…
Descriptors: Computer Mediated Communication, Energy, Discourse Analysis, Cooperative Learning
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Drachsler, H.; Kalz, M. – Journal of Computer Assisted Learning, 2016
The article deals with the interplay between learning analytics and massive open online courses (MOOCs) and provides a conceptual framework to situate ongoing research in the MOOC and learning analytics innovation cycle (MOLAC framework). The MOLAC framework is organized on three levels: On the micro-level, the data collection and analytics…
Descriptors: Online Courses, Data Collection, Data Analysis, Reflection
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