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Leonie Brummer; Hester de Boer; Jolien M. Mouw; Jan-Willem Strijbos – Learning Environments Research, 2024
Even though context, content, and task factors are considered essential parts of digitally delivered instructional feedback, their effects on learning performance are most often studied separately. A meta-analysis was carried out to address the effects of context, content, and task factors of digitally delivered instructional feedback on learning…
Descriptors: Feedback (Response), Teacher Response, Computer Mediated Communication, Computer Assisted Instruction
Shujun Liu; Azzeddine Boudouaia; Xinya Chen; Yan Li – Asia-Pacific Education Researcher, 2025
The application of Automated Writing Evaluation (AWE) has recently gained researchers' attention worldwide. However, the impact of AWE feedback on student writing, particularly in languages other than English, remains controversial. This study aimed to compare the impacts of Chinese AWE feedback and teacher feedback on Chinese writing revision,…
Descriptors: Foreign Countries, Middle School Students, Grade 7, Writing Evaluation
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
Kirk Vanacore; Ashish Gurung; Adam Sales; Neil Heffernan – Society for Research on Educational Effectiveness, 2024
Background: Gaming the system -- attempting to progress through a learning activity without learning (R. Baker et al., 2008) -- is an enduring problem that reduces the efficacy of Computer Based Learning Platforms (CBLPs). Researchers made substantial progress in identifying instances when students are gaming the system (Baker et al., 2006; Dang…
Descriptors: Gamification, Program Effectiveness, Computer Assisted Instruction, Feedback (Response)
Abdou L. J. Jammeh; Claude Karegeya; Savita Ladage – Education and Information Technologies, 2025
Clicker-integrated instruction is the current innovation in teaching and learning. Several studies used this technology to investigate learning processes, while others mainly used it to asses for learning, facilitation of group discussion and students' participation. All applications require creativity and analytical thinking and very much…
Descriptors: Chemistry, Science Instruction, Audience Response Systems, Computer Assisted Instruction
Learners' Use of Audio/Video Playback Controls in Technology-Enhanced Listening: A Systematic Review
Natalia Andrea Roldán-Mora; Mónica Stella Cárdenas-Claros – JALT CALL Journal, 2024
This systematic review investigates learner use of audio/video playback (AVP) controls in technology-enhanced listening environments. To this aim, 61 academic works produced from 2000-2021 underwent inclusion/exclusion criteria and were analyzed. The resulting corpus was made up of 16 peer-reviewed articles. We first situate the studies examined…
Descriptors: Technology Uses in Education, Audio Equipment, Video Technology, Feedback (Response)
Yang Jiang; Beata Beigman Klebanov; Jiangang Hao; Paul Deane; Oren E. Livne – Journal of Computer Assisted Learning, 2025
Background: Writing is integral to educational success at all levels and to success in the workplace. However, low literacy is a global challenge, and many students lack sufficient skills to be good writers. With the rapid advance of technology, computer-based tools that provide automated feedback are being increasingly developed. However, mixed…
Descriptors: Feedback (Response), Writing Evaluation, Middle School Students, High School Students
Ignacio Villagran; Rocio Hernandez; Gregory Schuit; Andres Neyem; Javiera Fuentes-Cimma; Constanza Miranda; Isabel Hilliger; Valentina Duran; Gabriel Escalona; Julian Varas – IEEE Transactions on Learning Technologies, 2024
This article presents a controlled case study focused on implementing and using generative artificial intelligence, specifically large language models (LLMs), in physiotherapy education to assist instructors with formulating effective technology-mediated feedback for students. It outlines how these advanced technologies have been integrated into…
Descriptors: Artificial Intelligence, Physical Therapy, Technology Uses in Education, Case Studies
Enhancing Procedural Writing through Personalized Example Retrieval: A Case Study on Cooking Recipes
Paola Mejia-Domenzain; Jibril Frej; Seyed Parsa Neshaei; Luca Mouchel; Tanya Nazaretsky; Thiemo Wambsganss; Antoine Bosselut; Tanja Käser – International Journal of Artificial Intelligence in Education, 2025
Writing high-quality procedural texts is a challenging task for many learners. While example-based learning has shown promise as a feedback approach, a limitation arises when all learners receive the same content without considering their individual input or prior knowledge. Consequently, some learners struggle to grasp or relate to the feedback,…
Descriptors: Writing Instruction, Academic Language, Content Area Writing, Cooking Instruction
Kaur Kiran; Rohaida Mohd Saat; Lieven Demeester; Magdeleine Duan Ning Lew; Wei Leng Neo; Nopphol Pausawasdi; Thasaneeya Ratanaroutai Nopparatjamjomras – Contemporary Educational Technology, 2025
Online teaching during the COVID-19 pandemic compelled many instructors to seek efficient and effective ways to stay connected with their students and improve the learning experience by using a wide range of available technologies. This multiple-case study, in three South-East Asian universities, investigated whether the use of technology in…
Descriptors: Technology Uses in Education, Individualized Instruction, Computer Assisted Instruction, Web Based Instruction
Chen, Binbin; Bao, Lina; Zhang, Rui; Zhang, Jingyu; Liu, Feng; Wang, Shuai; Li, Mingjiang – Journal of Educational Computing Research, 2024
Language learning has increasingly benefited from Computer-Assisted Language Learning (CALL) technologies, especially with Artificial Intelligence involved in recent years. CALL in writing learning acknowledged as the core of language learning is being realized by technologies like Automated Writing Evaluation (AWE), and Automated Essay Scoring…
Descriptors: Computer Assisted Instruction, English (Second Language), Second Language Learning, Writing Instruction
Yen-Jung Chen; Liwei Hsu; Shao-wei Lu – Computer Assisted Language Learning, 2024
It is well known that teachers' feedback plays an important role in students' learning, as it enhances learners' cognitive development; yet there has been little research on how positive feedback given in the form of emojis works in computer-assisted language learning (CALL) courses. In this study, an experiment was designed to clarify how English…
Descriptors: Visual Aids, English (Second Language), Second Language Learning, Feedback (Response)
Wenli Chen; Hua Hu; Qianru Lyu; Lishan Zheng – Journal of Computer Assisted Learning, 2024
Background: Critical thinking is one of the 21st Century competencies for students. While previous research acknowledges the potential of peer feedback to enhance critical thinking skills, particularly within computer-supported collaborative learning (CSCL) environments, there is limited understanding of which specific aspects of critical thinking…
Descriptors: Critical Thinking, Peer Evaluation, Feedback (Response), Cooperative Learning
Mimi Li – International Journal of Computer-Assisted Language Learning and Teaching, 2024
This paper discusses the increasingly prominent role of ChatGPT in providing feedback and assessment for L2 writing in the digital age. It reviews representative studies that address five research strands about the use of ChatGPT in L2 writing contexts. After a critical evaluation of the existing literature, the author extensively explains four…
Descriptors: Second Language Learning, Feedback (Response), Evaluation, Artificial Intelligence
Quantifying the Impact of ASR-Based Instruction: What Does the "iSpraak" Platform Learner Data Show?
Dan Nickolai – The EUROCALL Review, 2024
Computer-assisted Pronunciation Training (CAPT) tools have become increasingly dependent on Automatic Speech Recognition (ASR) technology to provide automated corrective pronunciation feedback to learners. The extent to which ASR-based tools measurably improve second language (L2) pronunciation is of great interest to language educators globally,…
Descriptors: Computer Assisted Instruction, Pronunciation, Technology Uses in Education, Second Language Learning