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Priti Oli; Rabin Banjade; Jeevan Chapagain; Vasile Rus – Grantee Submission, 2023
This paper systematically explores how Large Language Models (LLMs) generate explanations of code examples of the type used in intro-to-programming courses. As we show, the nature of code explanations generated by LLMs varies considerably based on the wording of the prompt, the target code examples being explained, the programming language, the…
Descriptors: Computational Linguistics, Programming, Computer Science Education, Programming Languages
Ching-Huei Chen; Ching-Ling Chang – Education and Information Technologies, 2024
This study aimed to investigate the effectiveness of using AI-assisted game-based learning on science learning outcomes, intrinsic motivation, cognitive load, and learning behavior. A total of 202 seventh graders were recruited and randomly assigned to the following three groups: (1) Game only (N = 70), (2) GameGPT (N = 63), and (3)…
Descriptors: Artificial Intelligence, Game Based Learning, Technology Uses in Education, Science Instruction
Michelle P. Banawan; Jinnie Shin; Tracy Arner; Renu Balyan; Walter L. Leite; Danielle S. McNamara – Grantee Submission, 2023
Academic discourse communities and learning circles are characterized by collaboration, sharing commonalities in terms of social interactions and language. The discourse of these communities is composed of jargon, common terminologies, and similarities in how they construe and communicate meaning. This study examines the extent to which discourse…
Descriptors: Algebra, Discourse Analysis, Semantics, Syntax
Aisha Abdulmohsin Al Abdulqader; Amenah Ahmed Al Mulla; Gaida Abdalaziz Al Moheish; Michael Jovellanos Pinero; Conrado Vizcarra; Abdulelah Al Gosaibi; Abdulaziz Saad Albarrak – International Association for Development of the Information Society, 2022
The COVID-19 epidemic had caused one of the most significant disruptions to the global education system. Many educational institutions faced sudden pressure to switch from face-to-face to online delivery of courses. The conventional classes are no longer the primary means of delivery; instead, online education and resources have become the…
Descriptors: COVID-19, Pandemics, Teaching Methods, Online Courses
Rahimi, Zahra; Litman, Diane; Correnti, Richard; Wang, Elaine; Matsumura, Lindsay Clare – International Journal of Artificial Intelligence in Education, 2017
This paper presents an investigation of score prediction based on natural language processing for two targeted constructs within analytic text-based writing: 1) students' effective use of evidence and, 2) their organization of ideas and evidence in support of their claim. With the long-term goal of producing feedback for students and teachers, we…
Descriptors: Scoring, Automation, Scoring Rubrics, Natural Language Processing
Kelly, Sean; Olney, Andrew M.; Donnelly, Patrick; Nystrand, Martin; D'Mello, Sidney K. – Educational Researcher, 2018
Analyzing the quality of classroom talk is central to educational research and improvement efforts. In particular, the presence of authentic teacher questions, where answers are not predetermined by the teacher, helps constitute and serves as a marker of productive classroom discourse. Further, authentic questions can be cultivated to improve…
Descriptors: Middle School Students, Natural Language Processing, Artificial Intelligence, Teaching Methods
Jacovina, Matthew E.; McNamara, Danielle S. – Grantee Submission, 2017
In this chapter, we describe several intelligent tutoring systems (ITSs) designed to support student literacy through reading comprehension and writing instruction and practice. Although adaptive instruction can be a powerful tool in the literacy domain, developing these technologies poses significant challenges. For example, evaluating the…
Descriptors: Intelligent Tutoring Systems, Literacy Education, Educational Technology, Technology Uses in Education
Deane, Paul; Lawless, René R.; Li, Chen; Sabatini, John; Bejar, Isaac I.; O'Reilly, Tenaha – ETS Research Report Series, 2014
We expect that word knowledge accumulates gradually. This article draws on earlier approaches to assessing depth, but focuses on one dimension: richness of semantic knowledge. We present results from a study in which three distinct item types were developed at three levels of depth: knowledge of common usage patterns, knowledge of broad topical…
Descriptors: Vocabulary, Test Items, Language Tests, Semantics