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Rebolledo-Mendez, Genaro; Huerta-Pacheco, N. Sofia; Baker, Ryan S.; du Boulay, Benedict – International Journal of Artificial Intelligence in Education, 2022
Many previous studies have highlighted the influence of learners' affective states on learning with tutoring systems. However, the associations between learning and learners' meta-affective capability are still unclear. The goal of this paper is to analyse meta-affective capability and its influence on learning outcomes as well as the dynamics of…
Descriptors: Affective Behavior, Intelligent Tutoring Systems, Mathematics Education, Secondary School Students
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Karumbaiah, Shamya; Ocumpaugh, Jaclyn; Baker, Ryan S. – International Journal of Artificial Intelligence in Education, 2022
Educational technology (EdTech) designers need to ensure population validity as they attempt to meet the individual needs of all students. EdTech researchers often have access to larger and more diverse samples of student data to test replication across broad demographic contexts as compared to either the small-scale experiments or the larger…
Descriptors: Educational Technology, Student Diversity, Student Needs, Educational Research
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Baker, Ryan S.; Hawn, Aaron – International Journal of Artificial Intelligence in Education, 2022
In this paper, we review algorithmic bias in education, discussing the causes of that bias and reviewing the empirical literature on the specific ways that algorithmic bias is known to have manifested in education. While other recent work has reviewed mathematical definitions of fairness and expanded algorithmic approaches to reducing bias, our…
Descriptors: Mathematics, Bias, Education, Race
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DeFalco, Jeanine A.; Rowe, Jonathan P.; Paquette, Luc; Georgoulas-Sherry, Vasiliki; Brawner, Keith; Mott, Bradford W.; Baker, Ryan S.; Lester, James C. – International Journal of Artificial Intelligence in Education, 2018
Tutoring systems that are sensitive to affect show considerable promise for enhancing student learning experiences. Creating successful affective responses requires considerable effort both to detect student affect and to design appropriate responses to affect. Recent work has suggested that affect detection is more effective when both physical…
Descriptors: Psychological Patterns, Stress Variables, Educational Games, Intelligent Tutoring Systems
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Baker, Ryan S. – International Journal of Artificial Intelligence in Education, 2016
The initial vision for intelligent tutoring systems involved powerful, multi-faceted systems that would leverage rich models of students and pedagogies to create complex learning interactions. But the intelligent tutoring systems used at scale today are much simpler. In this article, I present hypotheses on the factors underlying this development,…
Descriptors: Artificial Intelligence, Intelligent Tutoring Systems, Hypothesis Testing, Data Collection
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Gowda, Sujith M.; Baker, Ryan S.; Corbett, Albert T.; Rossi, Lisa M. – International Journal of Artificial Intelligence in Education, 2013
Recent research has extended student modeling to infer not just whether a student knows a skill or set of skills, but also whether the student has achieved robust learning--learning that enables the student to transfer their knowledge and prepares them for future learning (PFL). However, a student may fail to have robust learning in two fashions:…
Descriptors: Learning Processes, Transfer of Training, Outcomes of Education, Intelligent Tutoring Systems