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Bin Tan; Hao-Yue Jin; Maria Cutumisu – Computer Science Education, 2024
Background and Context: Computational thinking (CT) has been increasingly added to K-12 curricula, prompting teachers to grade more and more CT artifacts. This has led to a rise in automated CT assessment tools. Objective: This study examines the scope and characteristics of publications that use machine learning (ML) approaches to assess…
Descriptors: Computation, Thinking Skills, Artificial Intelligence, Student Evaluation
Jin, Hao-Yue; Cutumisu, Maria – Education and Information Technologies, 2023
Computational thinking (CT) skills of pre-service teachers have been explored extensively, but the effectiveness of CT training has yielded mixed results in previous studies. Thus, it is necessary to identify patterns in the relationships between predictors of CT and CT skills to further support CT development. This study developed an online CT…
Descriptors: Preservice Teachers, Computation, Thinking Skills, Predictor Variables
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
Ruth Irwin – Educational Philosophy and Theory, 2025
Education is concerned with the production of intelligence. Is AI intelligent? and what are the implications for educating humanity? Samuel Butler makes the case that machinery emerges in co-relation with the evolution of humanity. In other words, the evolution of machines relies on the human intervention for reproduction, and the evolution of…
Descriptors: Computer Software, Artificial Intelligence, Educational Philosophy, Humanism
Siu-Cheung Kong; Wei Shen – Interactive Learning Environments, 2024
Logistic regression models have traditionally been used to identify the factors contributing to students' conceptual understanding. With the advancement of the machine learning-based research approach, there are reports that some machine learning algorithms outperform logistic regression models in terms of prediction. In this study, we collected…
Descriptors: Student Characteristics, Predictor Variables, Comprehension, Computation
Zhai, Xiaoming; Haudek, Kevin C.; Ma, Wenchao – Research in Science Education, 2023
In this study, we developed machine learning algorithms to automatically score students' written arguments and then applied the cognitive diagnostic modeling (CDM) approach to examine students' cognitive patterns of scientific argumentation. We abstracted three types of skills (i.e., attributes) critical for successful argumentation practice:…
Descriptors: Persuasive Discourse, Artificial Intelligence, Cognitive Measurement, Diagnostic Tests
Mena-Guacas, Andres F.; Urueña Rodríguez, Jairo Alonso; Santana Trujillo, David Mauricio; Gómez-Galán, José; López-Meneses, Eloy – Contemporary Educational Technology, 2023
The diversity of topics in education makes it difficult for artificial intelligence (AI) to address them all in depth. Therefore, guiding to focus efforts on specific issues is essential. The analysis of competency development by fostering collaboration should be one of them because competencies are the way to validate that the educational…
Descriptors: Cooperative Learning, Skill Development, Artificial Intelligence, Educational Development
Jiangyue Liu; Jing Ma; Siran Li – Education and Information Technologies, 2025
This study developed a school-based AI curriculum suitable for senior high school students, with the primary objective of enhancing their mastery of knowledge and proficiency in AI, as well as their computational thinking abilities. The curriculum was designed with an awareness of the practical challenges encountered by the subject school in their…
Descriptors: Artificial Intelligence, Technology Uses in Education, Curriculum Design, Computation
Educational Technology, 1993
Provides the transcript of an interview with Dr. Lev Landa that addressed issues related to his algorithmico-heuristic theories of learning and instruction, called Landamatics. Highlights include teaching thinking versus knowledge; algorithms; instructional design; improving training and performance in industry, business, and government;…
Descriptors: Algorithms, Artificial Intelligence, Heuristics, Instructional Design