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Esnaashari, Shadi; Gardner, Lesley A.; Arthanari, Tiru S.; Rehm, Michael – Journal of Computer Assisted Learning, 2023
Background: It is vital to understand students' Self-Regulatory Learning (SRL) processes, especially in Blended Learning (BL), when students need to be more autonomous in their learning process. In studying SRL, most researchers have followed a variable-oriented approach. Moreover, little has been known about the unfolding process of students' SRL…
Descriptors: Metacognition, Student Attitudes, Learning Strategies, Questionnaires
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
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)
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
Bhagya Maheshi; Wei Dai; Roberto Martinez-Maldonado; Yi-Shan Tsai – Journal of Computer Assisted Learning, 2024
Background: Feedback is central to formative assessments but aligns with a one-way information transmission perspective obstructing students' effective engagement with feedback. Previous research has shown that a responsive, dialogic feedback process that requires educators and students to engage in ongoing conversations can encourage student…
Descriptors: Feedback (Response), Learning Analytics, Dialogs (Language), Learner Engagement
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
Yueh-Min Huang; Wei-Sheng Wang; Hsin-Yu Lee; Chia-Ju Lin; Ting-Ting Wu – Journal of Computer Assisted Learning, 2024
Background: Virtual reality (VR) offers significant potential for hands-on learning environments by providing immersive and visually stimulating experiences. Interacting with such environments can bring numerous benefits to learning, including enhanced engagement, knowledge construction, and higher-order thinking. However, many current VR studies…
Descriptors: Computer Simulation, Feedback (Response), Reflection, Experiential Learning
Ahmad Uzir, Nora'ayu; Gaševic, Dragan; Matcha, Wannisa; Jovanovic, Jelena; Pardo, Abelardo – Journal of Computer Assisted Learning, 2020
This paper aims to explore time management strategies followed by students in a flipped classroom through the analysis of trace data. Specifically, an exploratory study was conducted on the dataset collected in three consecutive offerings of an undergraduate computer engineering course (N = 1,134). Trace data about activities were initially coded…
Descriptors: Time Management, Blended Learning, Learning Analytics, Undergraduate Students
Exploring Learner Motivation and Mobile-Assisted Peer Feedback in a Business English Speaking Course
Xu, Qi; Peng, Hongying – Journal of Computer Assisted Learning, 2022
Background: Peer-to-peer feedback exchanges have been recognized as crucial to language learning. While studies on peer feedback proliferate, little is known about whether and how peer feedback is affected by learners' motivational levels. Objectives: Situated in a mobile collaborative learning context, the current study examined how English as a…
Descriptors: Student Motivation, Electronic Learning, Handheld Devices, Peer Evaluation
Suping Yi; Wayan Sintawati; Yibing Zhang – Journal of Computer Assisted Learning, 2025
Background: Natural language processing (NLP) and machine learning technologies offer significant advantages, such as facilitating the delivery of reflective feedback in collaborative learning environments while minimising technical constraints for educators related to time and location. Recently, scholars' interest in reflective feedback has…
Descriptors: Reflection, Feedback (Response), Cooperative Learning, Natural Language Processing
Olga Viberg; Martine Baars; Rafael Ferreira Mello; Niels Weerheim; Daniel Spikol; Cristian Bogdan; Dragan Gasevic; Fred Paas – Journal of Computer Assisted Learning, 2024
Background Study: Peer feedback has been used as an effective instructional strategy to enhance students' learning in higher education. Objectives: This paper reports on the findings of an explorative study that aimed to increase our understanding of the nature and role of peer feedback in the students' learning process in a computer-supported…
Descriptors: Feedback (Response), Peer Evaluation, Computer Assisted Instruction, Cooperative Learning
Arguedas, Marta; Daradoumis, Thanasis – Journal of Computer Assisted Learning, 2021
There is a lack of studies that examine the role of a pedagogical agent on student development in a specific learning situation that involves psychological and cognitive preparatory activities in high school settings. We examined the effectiveness of pedagogical agent (APT) cognitive and affective feedback on learner motivation and well-being. We…
Descriptors: High School Students, Feedback (Response), Learning Motivation, Well Being
Tacoma, Sietske; Drijvers, Paul; Jeuring, Johan – Journal of Computer Assisted Learning, 2021
Intelligent tutoring systems (ITSs) can provide inner loop feedback about steps within tasks, and outer loop feedback about performance on multiple tasks. While research typically addresses these feedback types separately, many ITSs offer them simultaneously. This study evaluates the effects of providing combined inner and outer loop feedback on…
Descriptors: Feedback (Response), Intelligent Tutoring Systems, Statistics Education, Higher Education
Topali, Paraskevi; Chounta, Irene-Angelica; Martínez-Monés, Alejandra; Dimitriadis, Yannis – Journal of Computer Assisted Learning, 2023
Background: Providing feedback in massive open online courses (MOOCs) is challenging due to the massiveness and heterogeneity of learners' population. Learning analytics (LA) solutions aim at scaling up feedback interventions and supporting instructors in this endeavour. Paper Objectives: This paper focuses on instructor-led feedback mediated by…
Descriptors: Teaching Methods, Learning Analytics, Feedback (Response), MOOCs
Wang, Dongqing; Han, Hou – Journal of Computer Assisted Learning, 2021
With the development of a technology-supported environment, it is plausible to provide rich process-oriented feedback in a timely manner. In this paper, we developed a learning analytics dashboard (LAD) based on process-oriented feedback in iTutor to offer learners their final scores, sub-scale reports, and corresponding suggestions on further…
Descriptors: Learning Analytics, Educational Technology, Feedback (Response), Intelligent Tutoring Systems