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Marlene Steinbach; Johanna Fleckenstein; Livia Kuklick; Jennifer Meyer – Journal of Computer Assisted Learning, 2025
Background: Providing students with information on their current performance could help them improve by stimulating their reflection, but negative feedback that saliently mirrors task-related failure can harm motivation. In the context of automated scoring based on artificial intelligence, we explored how feedback on written texts might be…
Descriptors: Student Motivation, Academic Achievement, Low Achievement, Feedback (Response)
Ute Mertens; Marlit A. Lindner – Journal of Computer Assisted Learning, 2025
Background: Educational assessments increasingly shift towards computer-based formats. Many studies have explored how different types of automated feedback affect learning. However, few studies have investigated how digital performance feedback affects test takers' ratings of affective-motivational reactions during a testing session. Method: In…
Descriptors: Educational Assessment, Computer Assisted Testing, Automation, Feedback (Response)
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
Yun Zhang; Fangzheng Zhao; Richard E. Mayer – Journal of Computer Assisted Learning, 2025
Background and Objective: The positivity principle states that students learn better from instructors who display positive rather than negative or neutral emotions in multimedia lessons (Lawson et al. 2021a). This study extends this work by exploring the role of affective and social cues displayed by feedback providers, such as their emotional…
Descriptors: Multimedia Instruction, Psychological Patterns, Feedback (Response), Gender Differences
Febe Demedts; Sameh Said-Metwaly; Kristian Kiili; Manuel Ninaus; Antero Lindstedt; Bert Reynvoet; Delphine Sasanguie; Fien Depaepe – Journal of Computer Assisted Learning, 2025
Background: The potential of adaptive feedback in digital educational games remains largely unexplored. Fractions are a suitable topic for investigating the effectiveness of adaptive feedback, as the complexity of this domain highlights the need for adequate feedback. Objectives: This study examines the effectiveness of explanatory adaptive…
Descriptors: Grade 4, Educational Games, Video Games, Feedback (Response)
Papadopoulos, Pantelis M.; Obwegeser, Nikolaus; Weinberger, Armin – Journal of Computer Assisted Learning, 2022
Background: The feedback offered to students in audience response systems may enhance conformity bias, while asking closed-type questions alone does not allow students to externalize and elaborate on their knowledge. Objectives: The study explores how writing short justifications and accessing peer justifications as collective feedback could…
Descriptors: Written Language, Academic Achievement, Self Esteem, Student Attitudes
Flora Ji-Yoon Jin; Debarshi Nath; Rui Guan; Tongguang Li; Xinyu Li; Rafael Ferreira Mello; Luiz Rodrigues; Cleon Pereira Junior; Heba Abuzayyad-Nuseibeh; Mladen Rakovic; Roberto Martinez-Maldonado; Dragan Gaševic; Yi-Shan Tsai – Journal of Computer Assisted Learning, 2025
Background: A key skill for self-regulated learners is the ability to critically interpret and act on feedback--key components of feedback literacy. Yet, the connection between feedback literacy and self-regulated learning (SRL) remains underexplored, particularly in terms of how different levels of feedback literacy influence SRL processes in…
Descriptors: Independent Study, Learning Analytics, Feedback (Response), Literacy
Zunera Zahid; Sara Ali; Shehriyar Shariq; Yasar Ayaz; Noman Naseer; Irum Yaseen – Journal of Computer Assisted Learning, 2024
Background: This study presents a Robot-Inspired Computer-Assisted Adaptive Autism Therapy (RoboCA[supercript 3]T) focusing on improving joint attention and imitation skills of children with autism spectrum disorder (ASD). By harnessing the inherent affinity of children with ASD for robots and technology, RoboCA[superscript 3]T offers a…
Descriptors: Autism Spectrum Disorders, Children, Robotics, Assistive Technology
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
Wannapon Suraworachet; Qi Zhou; Mutlu Cukurova – Journal of Computer Assisted Learning, 2025
Background: Many researchers work on the design and development of multimodal collaboration support systems with AI, yet very few of these systems are mature enough to provide actionable feedback to students in real-world settings. Therefore, a notable gap exists in the literature regarding students' perceptions of such systems and the feedback…
Descriptors: Graduate Students, Student Attitudes, Artificial Intelligence, Cooperative Learning
Sophie Gruhn; Eliane Segers; Jos Keuning; Ludo Verhoeven – Journal of Computer Assisted Learning, 2024
Background: Reading comprehension is an interactive process. Yet, instructional needs are usually identified with isolated componential tests. This study examined whether a dynamic approach, in which componential abilities are measured within the same text and global text comprehension is facilitated via feedback, can help in understanding…
Descriptors: Elementary School Students, Reading Comprehension, Feedback (Response), Reading Tests
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
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
Seyma Çaglar-Özhan; Perihan Tekeli; Selay Arkün-Kocadere – Journal of Computer Assisted Learning, 2025
Background: Feedback is an essential part of the educational process as it enriches students' learning experiences, provides information about their current performance, shows them what is lacking in achieving goals, and provides guidance on the strategies needed to achieve those goals. Teachers, especially in crowded classrooms, often have…
Descriptors: Feedback (Response), Artificial Intelligence, Teacher Role, Technology Uses in Education
Tornike Giorgashvili; Ioana Jivet; Cordula Artelt; Daniel Biedermann; Daniel Bengs; Frank Goldhammer; Carolin Hahnel; Julia Mendzheritskaya; Julia Mordel; Monica Onofrei; Marc Winter; Ilka Wolter; Holger Horz; Hendrik Drachsler – Journal of Computer Assisted Learning, 2025
Background: Learning analytics dashboards (LAD) have been developed as feedback tools to help students self-regulate their learning (SRL) by using the large amounts of data generated by online learning platforms. Despite extensive research on LAD design, there remains a gap in understanding how learners make sense of information visualised on LADs…
Descriptors: Field Studies, Student Reaction, Feedback (Response), Learning Analytics

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