NotesFAQContact Us
Collection
Advanced
Search Tips
Publication Date
In 20260
Since 202525
Source
Journal of Computer Assisted…25
Audience
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Showing 1 to 15 of 25 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
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)
Peer reviewed Peer reviewed
Direct linkDirect link
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)
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
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)
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
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)
Peer reviewed Peer reviewed
Direct linkDirect link
Byunghoon Ahn; Negar Matin; Myriam Johnson; So Yeon Lee; Ning-Zi Sun; Jason M. Harley – Journal of Computer Assisted Learning, 2025
Background: High fidelity simulations can be an effective tool for anti-harassment education. While emotions have been identified as crucial in simulation-based education, their role in anti-harassment education within medical training remains underexplored. Objectives: We aimed to investigate emotional profiles of medical residents during…
Descriptors: Medical Students, Psychological Patterns, Bullying, Prevention
Peer reviewed Peer reviewed
Direct linkDirect link
Pei-Ching Ngu; Chih-Chung Chien; Huei-Tse Hou – Journal of Computer Assisted Learning, 2025
Background: Situated simulation is a pedagogical method used for on-the-job training in many occupations. Establishing a framework that uses mobile devices to provide both simulation elements and incorporates instant feedback as reasoning scaffolding is a promising and relatively unexplored research topic. Objective: In this study, we designed a…
Descriptors: Decision Making, Educational Games, Handheld Devices, Simulation
Peer reviewed Peer reviewed
Direct linkDirect link
Farzaneh Sheikhzadeh; Rasool Abedanzadeh; Eliot Hazeltine – Journal of Computer Assisted Learning, 2025
Background: Today, active video games, in which players' own body movements are used to control the avatar, can be used to teach students motor skills by providing concurrent feedback. The purpose of this study is to investigate the effectiveness of concurrent and delayed feedback on basketball free throw learning. Methods: Thirty female students…
Descriptors: Team Sports, Student Athletes, Females, Athletics
Peer reviewed Peer reviewed
Direct linkDirect link
Zhongling Pi; Xuran Li; Mengjie Tong; Xin Zhao; Jiayu Wang; Xiying Li; Xiangchao Guo – Journal of Computer Assisted Learning, 2025
Background: Instructors' facial expressions in instructional videos can greatly influence how learners perceive their emotions, thereby affecting students' attention to the learning content and their overall performance. While the short-term effects of instructors' specific facial expressions in instructional videos have been well documented, less…
Descriptors: Video Technology, Technology Uses in Education, Teacher Behavior, Nonverbal Communication
Peer reviewed Peer reviewed
Direct linkDirect link
Elisabeth Bauer; Michael Sailer; Frank Niklas; Samuel Greiff; Sven Sarbu-Rothsching; Jan M. Zottmann; Jan Kiesewetter; Matthias Stadler; Martin R. Fischer; Tina Seidel; Detlef Urhahne; Maximilian Sailer; Frank Fischer – Journal of Computer Assisted Learning, 2025
Background: Artificial intelligence, particularly natural language processing (NLP), enables automating the formative assessment of written task solutions to provide adaptive feedback automatically. A laboratory study found that, compared with static feedback (an expert solution), adaptive feedback automated through artificial neural networks…
Descriptors: Artificial Intelligence, Feedback (Response), Computer Simulation, Natural Language Processing
Previous Page | Next Page »
Pages: 1  |  2