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Fu Chen; Chang Lu; Ying Cui – Education and Information Technologies, 2024
Successful computer-based assessments for learning greatly rely on an effective learner modeling approach to analyze learner data and evaluate learner behaviors. In addition to explicit learning performance (i.e., product data), the process data logged by computer-based assessments provide a treasure trove of information about how learners solve…
Descriptors: Computer Assisted Testing, Problem Solving, Learning Analytics, Learning Processes
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Matt Bower; Jodie Torrington; Jennifer W. M. Lai; Peter Petocz; Mark Alfano – Education and Information Technologies, 2024
There has been widespread media commentary about the potential impact of generative Artificial Intelligence (AI) such as ChatGPT on the Education field, but little examination at scale of how educators believe teaching and assessment should change as a result of generative AI. This mixed methods study examines the views of educators (n = 318) from…
Descriptors: Artificial Intelligence, Teacher Evaluation, Surveys, Computer Assisted Testing
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Mehri Izadi; Maliheh Izadi; Farrokhlagha Heidari – Education and Information Technologies, 2024
In today's environment of growing class sizes due to the prevalence of online and e-learning systems, providing one-to-one instruction and feedback has become a challenging task for teachers. Anyhow, the dialectical integration of instruction and assessment into a seamless and dynamic activity can provide a continuous flow of assessment…
Descriptors: Adaptive Testing, Computer Assisted Testing, English (Second Language), Second Language Learning