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Timothy Gallagher; Bert Slof; Marieke van der Schaaf; Michaela Arztmann; Sofia Garcia Fracaro; Liesbeth Kester – Journal of Computer Assisted Learning, 2024
Background: Learning analytics dashboards are increasingly being used to communicate feedback to learners. However, little is known about learner preferences for dashboard designs and how they differ depending on the self-regulated learning (SRL) phases the dashboards are presented (i.e., forethought, performance, and self-reflection phases) and…
Descriptors: Learning Analytics, Experiential Learning, Individualized Instruction, Computer System Design
Paraskevi Topali; Ruth Cobos; Unai Agirre-Uribarren; Alejandra Martínez-Monés; Sara Villagrá-Sobrino – Journal of Computer Assisted Learning, 2024
Background: Personalised and timely feedback in massive open online courses (MOOCs) is hindered due to the large scale and diverse needs of learners. Learning analytics (LA) can support scalable interventions, however they often lack pedagogical and contextual grounding. Previous research claimed that a human-centred approach in the design of LA…
Descriptors: Learning Analytics, MOOCs, Feedback (Response), Intervention
Peidi Gu; Zui Cheng; Cheng Miaoting; John Poggio; Yan Dong – Journal of Computer Assisted Learning, 2025
Background: Today, the importance of STEM (Science, Technology, Engineering and Mathematics) education and training is widely recognised and accepted. Computer programming courses have become essential in higher education to nurture students' programming, analysis and computational skills, which are vital for success in all STEM fields and areas.…
Descriptors: Active Learning, Student Projects, Individualized Instruction, Student Motivation
Meina Zhu – Journal of Computer Assisted Learning, 2025
Background: Computer programming learning and education play a critical role in preparing a workforce equipped with the necessary skills for diverse fields. ChatGPT and YouTube are technologies that support self-directed programming learning. Objectives: This study aims to examine the sentiments and primary topics discussed in YouTube comments…
Descriptors: Computer Science Education, Programming, Social Media, Video Technology
Rashmi Khazanchi; Daniele Di Mitri; Hendrik Drachsler – Journal of Computer Assisted Learning, 2025
Background: Despite educational advances, poor mathematics achievement persists among K-12 students, particularly in rural areas with limited resources and skilled teachers. Artificial Intelligence (AI) based systems have increasingly been adopted to support the diverse learning needs of students and have been shown to enhance mathematics…
Descriptors: Mathematics Achievement, Rural Areas, Artificial Intelligence, Individualized Instruction
Joshua Weidlich; Aron Fink; Ioana Jivet; Jane Yau; Tornike Giorgashvili; Hendrik Drachsler; Andreas Frey – Journal of Computer Assisted Learning, 2024
Background: Developments in educational technology and learning analytics make it possible to automatically formulate and deploy personalized formative feedback to learners at scale. However, to be effective, the motivational and emotional impacts of such automated and personalized feedback need to be considered. The literature on feedback…
Descriptors: Emotional Response, Student Motivation, Feedback (Response), Automation
Yi-Fan Li; Jue-Qi Guan; Xiao-Feng Wang; Qu Chen; Gwo-Jen Hwang – Journal of Computer Assisted Learning, 2024
Background: Self-regulated learning (SRL) is a predictive variable in students' academic performance, especially in virtual reality (VR) environments, which lack monitoring and control. However, current research on VR encounters challenges in effective interventions of cognitive and affective regulation, and visualising the SRL processes using…
Descriptors: Electronic Learning, Individualized Instruction, Learning Processes, Performance
Burin, Debora I.; González, Federico M.; Martínez, Magali; Marrujo, Jonathan G. – Journal of Computer Assisted Learning, 2021
Most of the studies establishing factors affecting digital text and multimedia comprehension have been conducted in controlled conditions. The present study sought to test and extend the modality and seductive details effects, and the role of verbal ability and working memory capacity, to a remote, self-paced, E-learning scenario. Two hundred and…
Descriptors: Electronic Learning, Multimedia Materials, Comprehension, College Freshmen
Aydin Bulut; Mustafa Yildiz – Journal of Computer Assisted Learning, 2024
Background: The use of computer-assisted reading comprehension is of critical importance in the context of promoting effective and engaging literacy education in the digital age. It provides students with the opportunity to work at their own pace and convenience, thereby facilitating self-directed learning and accommodating various learning…
Descriptors: Computer Assisted Instruction, Direct Instruction, Reading Comprehension, Technology Uses in Education
Burke, Paul F.; Kearney, Matthew; Schuck, Sandy; Aubusson, Peter – Journal of Computer Assisted Learning, 2022
Background: Mobile learning studies often focus on teachers' perspectives. This study instead considers students' experiences of learning with mobile devices (i.e., m-learning) in secondary school mathematics and science. Objectives: The research aims to describe the m-learning experiences of secondary mathematics and science students, and to…
Descriptors: Telecommunications, Handheld Devices, Educational Technology, Technology Uses in Education
Luciana Maria Cavichioli Gomes Almeida; Stefan Münzer; Tim Kühl – Journal of Computer Assisted Learning, 2024
Background: According to the personalization effect in multimedia learning, the use of personal and possessive pronouns in instructional materials (e.g., 'you' and 'your') is beneficial. However, current research suggests that the personalization effect is inverted for emotionally aversive content (e.g., illnesses). Objective: This study…
Descriptors: Foreign Countries, Health Education, Health Promotion, Information Sources
de Mooij, Susanne M. M.; Raijmakers, Maartje E. J.; Dumontheil, Iroise; Kirkham, Natasha Z.; van de Maas, Han L. J. – Journal of Computer Assisted Learning, 2021
While response time and accuracy indicate overall performance, their value in uncovering cognitive processes, underlying learning, is limited. A promising online measure, designed to track decision-making, is computer mouse tracking, where mouse attraction towards different locations may reflect the consideration of alternative response options.…
Descriptors: Error Patterns, Identification, Computer Peripherals, Computer Uses in Education
Ghysels, J.; Haelermans, C. – Journal of Computer Assisted Learning, 2018
This paper provides new evidence on the effect of computerized individualized practice and instruction on language skills, more specifically on spelling. An individually randomized experiment among 7th grade students in the Netherlands is developed to study the effect of an adaptive digital homework tool on spelling performance. Using an…
Descriptors: Teaching Methods, Computer Assisted Instruction, Grade 7, Language Skills
Artino, A. R. – Journal of Computer Assisted Learning, 2008
Many would agree that learning on the Web--a highly autonomous learning environment--may be difficult for individuals who lack motivation and self-regulated learning skills. Using a social cognitive view of academic motivation and self-regulation, the objective of the present study was to investigate the relations between students' motivational…
Descriptors: Self Efficacy, Online Courses, Student Motivation, Educational Environment

Robson, E. H.; And Others – Journal of Computer Assisted Learning, 1988
Describes a study that examined the choice of topics and problems made by 14- and 15-year-olds using a computerized tutorial mathematics program. The discussion covers the reasons for student choices, the effectiveness of the tutorial mode, and the need for teaching programs to present clear choices. (5 references) (CLB)
Descriptors: Adolescents, Computer Assisted Instruction, Decision Making Skills, Evaluation Methods