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Richard Hall – International Journal of Educational Technology in Higher Education, 2024
This article situates the potential for intellectual work to be renewed through an enriched engagement with the relationship between indigenous protocols and artificial intelligence (AI). It situates this through a dialectical storytelling of the contradictions that emerge from the relationships between humans and capitalist technologies, played…
Descriptors: Artificial Intelligence, Social Systems, Protocol Analysis, Technological Advancement
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Shan Li; Xiaoshan Huang; Tingting Wang; Juan Zheng; Susanne P. Lajoie – Journal of Computing in Higher Education, 2025
Coding think-aloud transcripts is time-consuming and labor-intensive. In this study, we examined the feasibility of predicting students' reasoning activities based on their think-aloud transcripts by leveraging the affordances of text mining and machine learning techniques. We collected the think-aloud data of 34 medical students as they diagnosed…
Descriptors: Information Retrieval, Artificial Intelligence, Prediction, Abstract Reasoning
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Fan, Yizhou; van der Graaf, Joep; Lim, Lyn; Rakovic, Mladen; Singh, Shaveen; Kilgour, Jonathan; Moore, Johanna; Molenaar, Inge; Bannert, Maria; Gaševic, Dragan – Metacognition and Learning, 2022
Contemporary research that looks at self-regulated learning (SRL) as processes of learning events derived from trace data has attracted increasing interest over the past decade. However, limited research has been conducted that looks into the validity of trace-based measurement protocols. In order to fill this gap in the literature, we propose a…
Descriptors: Validity, Metacognition, Learning Strategies, Artificial Intelligence
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Yunjiu, Luo; Wei, Wei; Zheng, Ying – SAGE Open, 2022
Artificial intelligence (AI) technologies have the potential to reduce the workload for the second language (L2) teachers and test developers. We propose two AI distractor-generating methods for creating Chinese vocabulary items: semantic similarity and visual similarity. Semantic similarity refers to antonyms and synonyms, while visual similarity…
Descriptors: Chinese, Vocabulary Development, Artificial Intelligence, Undergraduate Students
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Kinnebrew, John S.; Loretz, Kirk M.; Biswas, Gautam – Journal of Educational Data Mining, 2013
Computer-based learning environments can produce a wealth of data on student learning interactions. This paper presents an exploratory data mining methodology for assessing and comparing students' learning behaviors from these interaction traces. The core algorithm employs a novel combination of sequence mining techniques to identify deferentially…
Descriptors: Data Analysis, Middle School Students, Information Retrieval, Student Behavior
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Easterday, Matthew W.; Aleven, Vincent; Scheines, Richard; Carver, Sharon M. – International Journal of Artificial Intelligence in Education, 2009
Policy problems like "What should we do about global warming?" are ill-defined in large part because we do not agree on a system to represent them the way we agree Algebra problems should be represented by equations. As a first step toward building a policy deliberation tutor, we investigated: (a) whether causal diagrams help students learn to…
Descriptors: Causal Models, Protocol Analysis, Tutors, Inferences
Greene, Richard Tabor – Online Submission, 2008
Purpose: To get beyond religious, philosophic, and political definitions of educatedness by going empirical. To redo Plato, in effect, by defining "the good" empirically. Background: This research was part of the Excellence Science (orthogonal disciplines) Research Project at the University of Chicago. That project redid Plato by…
Descriptors: Foreign Countries, Educational Attainment, High Achievement, Surveys