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Balyan, Renu; McCarthy, Kathryn S.; McNamara, Danielle S. – International Journal of Artificial Intelligence in Education, 2020
For decades, educators have relied on readability metrics that tend to oversimplify dimensions of text difficulty. This study examines the potential of applying advanced artificial intelligence methods to the educational problem of assessing text difficulty. The combination of hierarchical machine learning and natural language processing (NLP) is…
Descriptors: Natural Language Processing, Artificial Intelligence, Man Machine Systems, Classification
Snow, Erica L.; Likens, Aaron D.; Allen, Laura K.; McNamara, Danielle S. – International Journal of Artificial Intelligence in Education, 2016
Game-based environments frequently afford students the opportunity to exert agency over their learning paths by making various choices within the environment. The combination of log data from these systems and dynamic methodologies may serve as a stealth means to assess how students behave (i.e., deterministic or random) within these learning…
Descriptors: Student Behavior, Pretests Posttests, High School Students, Teaching Methods
McCarthy, Kathryn S.; Likens, Aaron D.; Johnson, Amy M.; Guerrero, Tricia A.; McNamara, Danielle S. – International Journal of Artificial Intelligence in Education, 2018
Research suggests that promoting metacognitive awareness can increase performance in, and learning from, intelligent tutoring systems (ITSs). The current work examines the effects of two metacognitive prompts within iSTART, a reading comprehension strategy ITS in which students practice writing quality self-explanations. In addition to comparing…
Descriptors: Metacognition, Difficulty Level, Prompting, Intelligent Tutoring Systems
Snow, Erica L.; Allen, Laura K.; Jackson, G. Tanner; McNamara, Danielle S. – International Journal of Artificial Intelligence in Education, 2015
Using students' process data from the game-based Intelligent Tutoring System (ITS) iSTART-ME, the current study examines students' propensity to use system currency to unlock game-based features, (i.e., referred to here as "spendency"). This study examines how spendency relates to students' interaction preferences, in-system performance,…
Descriptors: Intelligent Tutoring Systems, Educational Games, High School Students, Preferences