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Dongkwang Shin; Suh Keong Kwon; Wonjun Izac Noh; Yohan Hwang – Journal of Computer Assisted Learning, 2025
Background: This study examines the evolution of English speaking proficiency test methods, which have traditionally relied on face-to-face interactions to assess communicative language competence. Recently, computer-based language tests have also been used on a larger scale, albeit with concerns about their impact on measurement. Objectives: This…
Descriptors: Computer Simulation, Technology Uses in Education, English (Second Language), Second Language Learning
Angxuan Chen; Yuyue Zhang; Jiyou Jia; Min Liang; Yingying Cha; Cher Ping Lim – Journal of Computer Assisted Learning, 2025
Background: Language assessment plays a pivotal role in language education, serving as a bridge between students' understanding and educators' instructional approaches. Recently, advancements in Artificial Intelligence (AI) technologies have introduced transformative possibilities for automating and personalising language assessments. Objectives:…
Descriptors: Artificial Intelligence, Technology Uses in Education, Computer Assisted Testing, Language Tests
Bacca-Acosta, Jorge; Fabregat, Ramon; Baldiris, Silvia; Kinshuk; Guevara, Juan – Journal of Computer Assisted Learning, 2022
Background: Mobile-based assessment has been an active area of research in the field of mobile learning. Prior research has demonstrated that mobile-based assessment systems positively affect student performance. However, it is still unclear why and how these systems positively affect student performance. Objectives: This study aims to identify…
Descriptors: Academic Achievement, Electronic Learning, Handheld Devices, Computer Assisted Testing
Bacca-Acosta, Jorge; Avila-Garzon, Cecilia – Journal of Computer Assisted Learning, 2021
Research on mobile-based assessment systems is still an emerging topic in the mobile learning field. Current research has demonstrated that the use of mobile-based assessment systems seems to have a positive impact on students' learning outcomes and motivation. The paper identifies some factors that influence student engagement with mobile-based…
Descriptors: Learner Engagement, Handheld Devices, Computer Assisted Testing, Electronic Learning
Michinov, Nicolas; Anquetil, Éric; Michinov, Estelle – Journal of Computer Assisted Learning, 2020
Peer Instruction is an active learning method widely used in higher education, whereby students answer a series of questions twice, once before and once after peer discussion. There is an ongoing debate as to whether a collective feedback should be given after the students' initial answer, and if so, how the frequently observed group conformity…
Descriptors: Peer Teaching, Feedback (Response), Discussion, Active Learning
Wen Xin Zhang; John J. H. Lin; Ying-Shao Hsu – Journal of Computer Assisted Learning, 2025
Background Study: Assessing learners' inquiry-based skills is challenging as social, political, and technological dimensions must be considered. The advanced development of artificial intelligence (AI) makes it possible to address these challenges and shape the next generation of science education. Objectives: The present study evaluated the SSI…
Descriptors: Artificial Intelligence, Computer Assisted Testing, Inquiry, Active Learning
Gruss, Richard; Clemons, Josh – Journal of Computer Assisted Learning, 2023
Background: The sudden growth in online instruction due to COVID-19 restrictions has given renewed urgency to questions about remote learning that have remained unresolved. Web-based assessment software provides instructors an array of options for varying testing parameters, but the pedagogical impacts of some of these variations has yet to be…
Descriptors: Test Items, Test Format, Computer Assisted Testing, Mathematics Tests
Duncan, Alex; Joyner, David – Journal of Computer Assisted Learning, 2022
Background: It is important for institutions of higher education to maintain academic integrity, both for students and the institutions themselves. Proctoring is one way of accomplishing this, and with the increasing popularity of online courses--along with the sudden shift to online education sparked by the COVID-19 pandemic--digital proctoring…
Descriptors: Computer Assisted Testing, Supervision, Integrity, COVID-19
Candel, Carmen; Máñez, Ignacio; Cerdán, Raquel; Vidal-Abarca, Eduardo – Journal of Computer Assisted Learning, 2021
Elaborative feedback (EF) containing explanations on students' responses benefits learning. Computer-based environments provide learners with EF in different ways, for example, on an immediate question-by-question basis or after answering a set of questions. Recent findings also suggest that delaying EF enhances learning. However, it is unclear to…
Descriptors: Secondary School Students, Computer Assisted Testing, Multiple Choice Tests, Feedback (Response)
Nikola Ebenbeck; Morten Bastian; Andreas Mühling; Markus Gebhardt – Journal of Computer Assisted Learning, 2024
Background: Computerised adaptive tests (CATs) are tests that provide personalised, efficient and accurate measurement while reducing testing time, depending on the desired level of precision. Schools have different types of assessments that can benefit from a significant reduction in testing time to varying degrees, depending on the area of…
Descriptors: Computer Assisted Testing, Elementary Secondary Education, Public Schools, Special Schools
Veerbeek, Jochanan; Vogelaar, Bart; Verhaegh, Janneke; Resing, Wilma C. M. – Journal of Computer Assisted Learning, 2019
Task solving processes and changes in these processes have long been expected to provide valuable information about children's performance in school. This article used electronic tangibles (concrete materials that can be physically manipulated) and a dynamic testing format (pretest, training, and posttest) to investigate children's task solving…
Descriptors: Young Children, Pretests Posttests, Problem Solving, Outcomes of Education
Ifenthaler, Dirk; Schumacher, Clara; Kuzilek, Jakub – Journal of Computer Assisted Learning, 2023
Background: Formative assessments are vital for supporting learning and performance but are also considered to increase the workload of teachers. As self-assessments in higher education are increasingly facilitated via digital learning environments allowing to offer direct feedback and tracking students' digital learning behaviour these…
Descriptors: Self Evaluation (Individuals), Economics Education, Business Administration Education, Faculty Workload
Froehlich, Laura; Sassenberg, Kai; Jonkmann, Kathrin; Scheiter, Katharina; Stürmer, Stefan – Journal of Computer Assisted Learning, 2023
Background: The use of e-exams in higher education is increasing. However, the role of student diversity in the acceptance of e-exams is an under-researched topic. In the current study, we considered student diversity in terms of three sociodemographic characteristics (age, gender, and second language) and three dispositional student…
Descriptors: Student Diversity, Student Attitudes, Computer Assisted Testing, Student Characteristics
Patael, Smadar; Shamir, Julia; Soffer, Tal; Livne, Eynat; Fogel-Grinvald, Haya; Kishon-Rabin, Liat – Journal of Computer Assisted Learning, 2022
Background: The global COVID-19 pandemic turned the adoption of on-line assessment in the institutions for higher education from possibility to necessity. Thus, in the end of Fall 20/21 semester Tel Aviv University (TAU)--the largest university in Israel--designed and implemented a scalable procedure for administering proctored remote…
Descriptors: COVID-19, Pandemics, Computer Assisted Testing, Foreign Countries
Hsiung, C .M.; Luo, L. F.; Chung, H. C. – Journal of Computer Assisted Learning, 2014
Cooperative learning has many pedagogical benefits. However, if the cooperative learning teams become ineffective, these benefits are lost. Accordingly, this study developed a computer-aided assessment method for identifying ineffective teams at their early stage of dysfunction by using the Mahalanobis distance metric to examine the difference…
Descriptors: Cooperative Learning, Teamwork, Identification, Instructional Effectiveness
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