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Conijn, Rianne; Kleingeld, Ad; Matzat, Uwe; Snijders, Chris – Journal of Computer Assisted Learning, 2022
Background: Online and blended learning need an appropriate assessment strategy which ensures academic integrity. During the pandemic, many universities have chosen for online proctoring. Although some earlier examples suggest that online proctoring may reduce cheating, the potential side-effects of proctoring are largely unknown. Objectives:…
Descriptors: Supervision, Computer Assisted Testing, Integrity, Cheating
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Gulnur Tyulepberdinova; Madina Mansurova; Talshyn Sarsembayeva; Sulu Issabayeva; Darazha Issabayeva – Journal of Computer Assisted Learning, 2024
Background: This study aims to assess how well several machine learning (ML) algorithms predict the physical, social, and mental health condition of university students. Objectives: The physical health measurements used in the study include BMI (Body Mass Index), %BF (percentage of Body Fat), BSC (Blood Serum Cholesterol), SBP (Systolic Blood…
Descriptors: Artificial Intelligence, Algorithms, Predictor Variables, Physical Health
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Khalil, Mohammad; Prinsloo, Paul; Slade, Sharon – Journal of Computer Assisted Learning, 2022
Background: The COVID-19 pandemic disrupted higher education in many ways, such as the move to Emergency Remote Online Teaching and Learning (EROTL), often including a move to online assessments and examinations. With evidence of increased academic dishonesty in unproctored online assessment, institutions sought ways to ensure academic and…
Descriptors: Integrity, Observation, Computer Assisted Testing, COVID-19
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Richard Say; Denis Visentin; Annette Saunders; Iain Atherton; Andrea Carr; Carolyn King – Journal of Computer Assisted Learning, 2024
Background: Formative online multiple-choice tests are ubiquitous in higher education and potentially powerful learning tools. However, commonly used feedback approaches in online multiple-choice tests can discourage meaningful engagement and enable strategies, such as trial-and-error, that circumvent intended learning outcomes. These strategies…
Descriptors: Feedback (Response), Self Management, Formative Evaluation, Multiple Choice Tests
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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)
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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
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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
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Hartnett, Maggie; Butler, Philippa; Rawlins, Peter – Journal of Computer Assisted Learning, 2023
Background: The emergence of the COVID-19 and the resulting global pandemic has ushered in far-reaching changes for countries across the world, not least of which are changes to their education systems. With traditional location-based exams no longer possible at universities, the uptake of online proctored exams (OPE) has occurred at a pace not…
Descriptors: COVID-19, Pandemics, Computer Assisted Testing, Supervision
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Marco Rüth; Maria Jansen; Kai Kaspar – Journal of Computer Assisted Learning, 2024
Background: Online exams have become a more common form of assessment at universities due to the COVID-19 pandemic. However, cheating behaviour in online exams is widespread and threatens exam validity as well as student learning and well-being. Objective: To better understand the role of university students' needs, conceptions and reasons…
Descriptors: Foreign Countries, College Students, Computer Assisted Testing, Cheating
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Maria Aristeidou; Simon Cross; Klaus-Dieter Rossade; Carlton Wood; Terri Rees; Patrizia Paci – Journal of Computer Assisted Learning, 2024
Background: Research into online exams in higher education has grown significantly, especially as they became common practice during the COVID-19 pandemic. However, previous studies focused on understanding individual factors that relate to students' dispositions towards online exams in 'traditional' universities. Moreover, there is little…
Descriptors: Higher Education, Computer Assisted Testing, COVID-19, Pandemics
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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
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Hewson, Claire; Charlton, John P. – Journal of Computer Assisted Learning, 2019
The use of e-assessment methods raises important concerns regarding the reliability and validity of these methods. Potential threats to validity include mode effects and the possible influence of computer-related attitudes. Although numerous studies have now investigated the validity of online assessments in noncourse-based contexts, few studies…
Descriptors: Computer Assisted Testing, Test Reliability, Test Validity, College Students
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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
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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
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Liao, C. H.; Yang, M. H.; Yang, B. C. – Journal of Computer Assisted Learning, 2013
A gap exists between students' employment needs and higher education offerings. Thus, developing the capability to meet the learning needs of students in supporting their future aspirations should be facilitated. To bridge this gap in practice, this study uses multiple methods (i.e., nominal group technique and instructional systems development)…
Descriptors: Computer Assisted Testing, Employment Qualifications, Job Skills, Human Resources
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