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Arshad, Arooj; Ghazal, Saima; Saleem, Noshina; Hanan, Mian Ahmad; Arshad, Muhammad Haseeb – Journal of Computer Assisted Learning, 2022
Background: In this technologically advanced era, media literacy is necessary to effectively evaluate the information and understand various biases inherent in media messages. Several media literacy (ML) tools are available; however, we need generic and objective tools that can be applied to all forms of media messages. Objectives: The current…
Descriptors: Media Literacy, Foreign Countries, Measurement Techniques, Measures (Individuals)
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Héctor J. Pijeira-Díaz; Shashank Subramanya; Janneke van de Pol; Anique de Bruin – Journal of Computer Assisted Learning, 2024
Background: When learning causal relations, completing causal diagrams enhances students' comprehension judgements to some extent. To potentially boost this effect, advances in natural language processing (NLP) enable real-time formative feedback based on the automated assessment of students' diagrams, which can involve the correctness of both the…
Descriptors: Learning Analytics, Automation, Student Evaluation, Causal Models
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Mohammad Nayef Ayasrah; Mohamad Ahmad Saleem Khasawneh; Mazen Omar Almulla; Amoura Hassan Aboutaleb – Journal of Computer Assisted Learning, 2025
Background: One area that has been dramatically changed by artificial intelligence (AI) is educational environments. Chatbots, Recommender Systems, Adaptive Learning Systems and Large Language Models have been emerging as practical tools for facilitating learning. However, using such tools appropriately is challenging. In this regard, the…
Descriptors: Test Construction, Test Validity, Test Reliability, Rating Scales
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Ali Alqarni – Journal of Computer Assisted Learning, 2025
Background: Critical thinking is essential in modern education, and artificial intelligence (AI) offers new possibilities for enhancing it. However, the lack of validated tools to assess teachers' AI-integrated pedagogical skills remains a challenge. Objectives: The current study aimed to develop and validate the Artificial Intelligence-Critical…
Descriptors: Artificial Intelligence, Technology Uses in Education, Test Construction, Test Validity
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Jaramillo-Morillo, Daniel; Ruipérez-Valiente, José A.; Burbano Astaiza, Claudia Patricia; Solarte, Mario; Ramirez-Gonzalez, Gustavo; Alexandron, Giora – Journal of Computer Assisted Learning, 2022
Background: Small private online courses (SPOCs) are one of the strategies to introduce the massive open online courses (MOOCs) within the university environment and to have these courses validates for academic credit. However, numerous researchers have highlighted that academic dishonesty is greatly facilitated by the online context in which…
Descriptors: Learning Analytics, Cheating, Integrated Learning Systems, Intervention
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Wafa Mohammed Aldighrir; Fatima's Mohamed Asiri – Journal of Computer Assisted Learning, 2025
Background: As educational institutions increasingly operate as multicultural hubs, leaders must navigate the complexities of cultural differences, language barriers and diverse learning styles in digital environments. These challenges are amplified by the lack of non-verbal cues and the asynchronous nature of online communication, which can lead…
Descriptors: Foreign Countries, Test Construction, Measures (Individuals), Test Validity
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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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Chang, Wen-Hui; Liu, Yuan-Chen; Huang, Tzu-Hua – Journal of Computer Assisted Learning, 2017
The purpose of this study is to develop a multi-dimensional scale to measure students' awareness of key competencies for M-learning and to test its reliability and validity. The Key Competencies of Mobile Learning Scale (KCMLS) was determined via confirmatory factor analysis to have four dimensions: team collaboration, creative thinking, critical…
Descriptors: Test Construction, Multidimensional Scaling, Electronic Learning, Test Reliability
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Uzunboylu, H.; Ozdamli, F. – Journal of Computer Assisted Learning, 2011
Successful integration of mobile learning (m-learning) technologies in education primarily demands that teachers' perception of such technologies should be determined. Therefore, the perceptions of teachers are of great significance. There is no available instrument that assesses teachers' perceptions of m-learning. Our research provided the first…
Descriptors: Electronic Learning, Feedback (Response), Test Validity, Measures (Individuals)
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Wang, Y.; Harrington, M.; White, P. – Journal of Computer Assisted Learning, 2012
This paper introduces "CTutor", an automated writing evaluation (AWE) tool for detecting breakdowns in local coherence and reports on a study that applies it to the writing of Chinese L2 English learners. The program is based on Centering theory (CT), a theory of local coherence and salience. The principles of CT are first introduced and…
Descriptors: Foreign Countries, Educational Technology, Expertise, Feedback (Response)
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Jia, J.; Chen, Y.; Ding, Z.; Bai, Y.; Yang, B.; Li, M.; Qi, J. – Journal of Computer Assisted Learning, 2013
This research conducted quasi-experiments in four middle schools to evaluate the long-term effects of an intelligent web-based English instruction system, Computer Simulation in Educational Communication (CSIEC), on students' academic attainment. The analysis of regular examination scores and vocabulary test validates the positive impact of CSIEC,…
Descriptors: Web Based Instruction, English Instruction, Academic Achievement, Computer Simulation
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Tsai, P.-S.; Tsai, C.-C.; Hwang, G.-J. – Journal of Computer Assisted Learning, 2012
This study developed a survey to explore students' preferences in constructivist context-aware ubiquitous learning environments. A constructivist context-aware ubiquitous learning (u-learning) environment survey (CULES) was developed, consisting of eight scales, including ease of use, continuity, relevance, adaptive content, multiple sources,…
Descriptors: Constructivism (Learning), Student Attitudes, Learning Activities, Foreign Countries