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Showing 16 to 30 of 58 results Save | Export
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Nagy, Gabriel; Ulitzsch, Esther; Lindner, Marlit Annalena – Journal of Computer Assisted Learning, 2023
Background: Item response times in computerized assessments are frequently used to identify rapid guessing behaviour as a manifestation of response disengagement. However, non-rapid responses (i.e., with longer response times) are not necessarily engaged, which means that response-time-based procedures could overlook disengaged responses.…
Descriptors: Guessing (Tests), Academic Persistence, Learner Engagement, Computer Assisted Testing
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Hilliger, Isabel; Ruipérez-Valiente, José A.; Alexandron, Giora; Gaševic, Dragan – Journal of Computer Assisted Learning, 2022
Background: Online learning has grown significantly during the past two decades, and COVID-19 pandemic has expedited this process. However, previous research has shown how academic dishonesty is more prevalent under these modalities. Therefore, there is the challenge of performing trustworthy remote assessments, in order to obtain valid and…
Descriptors: Online Courses, Ethics, Student Evaluation, Evaluation Methods
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Surahman, Ence; Wang, Tzu-Hua – Journal of Computer Assisted Learning, 2022
Background: Academic dishonesty (AD) and trustworthy assessment (TA) are fundamental issues in the context of an online assessment. However, little systematic work currently exists on how researchers have explored AD and TA issues in online assessment practice. Objectives: Hence, this research aimed at investigating the latest findings regarding…
Descriptors: Ethics, Trust (Psychology), Computer Assisted Testing, Educational Technology
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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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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
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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
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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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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
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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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Gardner, John; O'Leary, Michael; Yuan, Li – Journal of Computer Assisted Learning, 2021
Artificial Intelligence is at the heart of modern society with computers now capable of making process decisions in many spheres of human activity. In education, there has been intensive growth in systems that make formal and informal learning an anytime, anywhere activity for billions of people through online open educational resources and…
Descriptors: Artificial Intelligence, Educational Assessment, Formative Evaluation, Summative Evaluation
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LaFlair, Geoffrey T.; Langenfeld, Thomas; Baig, Basim; Horie, André Kenji; Attali, Yigal; von Davier, Alina A. – Journal of Computer Assisted Learning, 2022
Background: Digital-first assessments leverage the affordances of technology in all elements of the assessment process--from design and development to score reporting and evaluation to create test taker-centric assessments. Objectives: The goal of this paper is to describe the engineering, machine learning, and psychometric processes and…
Descriptors: Computer Assisted Testing, Affordances, Scoring, Engineering
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Daniels, Lia M.; Bulut, Okan – Journal of Computer Assisted Learning, 2020
In computer-based testing (CBT) environments instructors can provide students with feedback immediately. Commonly, instructors give students their percentage correct without additional descriptive feedback. Our objectives were (a) to compare students' perceived usefulness of a percentage-only score report vs. a descriptive feedback report in a CBT…
Descriptors: Computer Assisted Testing, Feedback (Response), Value Judgment, Student Attitudes
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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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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
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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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