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Zexuan Pan; Maria Cutumisu – AERA Online Paper Repository, 2023
Computational thinking (CT) is a fundamental ability for learners in today's society. Although CT assessments and interventions have been studied widely, little is known about CT predictions. This study predicted students' CT achievement in the ICILS 2018 using five machine learning models. These models were trained on the data from five European…
Descriptors: Computation, Thinking Skills, Artificial Intelligence, Prediction
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Tom Bleckmann; Gunnar Friege – Knowledge Management & E-Learning, 2023
Formative assessment is about providing and using feedback and diagnostic information. On this basis, further learning or further teaching should be adaptive and, in the best case, optimized. However, this aspect is difficult to implement in reality, as teachers work with a large number of students and the whole process of formative assessment,…
Descriptors: Concept Mapping, Formative Evaluation, Automation, Feedback (Response)
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Yufeng Qian – Journal of Educational Computing Research, 2025
The effectiveness of generative AI tools in education depends largely on prompt engineering--the practice of designing inputs and interactions that guide AI systems to produce relevant, high-quality outputs. This systematic literature review examines empirical studies published since the release of ChatGPT in late 2022, identifying two broad…
Descriptors: Artificial Intelligence, Technology Uses in Education, Prompting, Engineering
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Pineda, Pedro; Steinhardt, Isabel – Teaching in Higher Education, 2023
Through co-occurrence analysis of 1139 documents (1964-2018) we identified discussions about the implementation of student teaching evaluation (SET). We found that: (1) Attention to SET originated in the US in the 1970s, spreading to German-speaking countries in the mid-1990s and continuing in China and Latin America in the early 2000s. (2) SET is…
Descriptors: Student Evaluation of Teacher Performance, Program Implementation, Higher Education, Educational History