Educational Stakeholders’ Independent Evaluation of an Artificial Intelligence-Enabled Adaptive Learning System Using Bayesian Network Predictive Simulations

Artificial intelligence-enabled adaptive learning systems (AI-ALS) are increasingly being deployed in education to enhance the learning needs of students. However, educational stakeholders are required by policy-makers to conduct an independent evaluation of the AI-ALS using a small sample size in a...

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Vydané v:Education sciences Ročník 9; číslo 2; s. 110 - 141
Hlavní autori: HOW, Meng-Leong, HUNG, Wei Loong David
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.06.2019
Predmet:
Adaptive learning
Artificial Intelligence
Bayesian
Bayesian analysis
Bayesian Statistics
Cognition & reasoning
Cognitive ability
Control Groups
Education policy
Educational Environment
Educational Policy
Educational technology
Evaluation Methods
evaluation of artificial intelligence educational systems
Hypotheses
Hypothesis Testing
intelligent adaptive learning
Intelligent Tutoring Systems
Interactive learning
Mathematical problems
Mathematics education
Mathematics teachers
nonparametric data
Nonparametric statistics
Pedagogy
Prediction
Pretests Posttests
Probability
Problem Solving
Researchers
Simulation
Stakeholders
Students
Teaching
Teaching Methods
United States > US
Germany
Singapore
ISSN:2227-7102, 2227-7102
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Shrnutí:Artificial intelligence-enabled adaptive learning systems (AI-ALS) are increasingly being deployed in education to enhance the learning needs of students. However, educational stakeholders are required by policy-makers to conduct an independent evaluation of the AI-ALS using a small sample size in a pilot study, before that AI-ALS can be approved for large-scale deployment. Beyond simply believing in the information provided by the AI-ALS supplier, there arises a need for educational stakeholders to independently understand the motif of the pedagogical characteristics that underlie the AI-ALS. Laudable efforts were made by researchers to engender frameworks for the evaluation of AI-ALS. Nevertheless, those highly technical techniques often require advanced mathematical knowledge or computer programming skills. There remains a dearth in the extant literature for a more intuitive way for educational stakeholders—rather than computer scientists—to carry out the independent evaluation of an AI-ALS to understand how it could provide opportunities to educe the problem-solving abilities of the students so that they can successfully learn the subject matter. This paper proffers an approach for educational stakeholders to employ Bayesian networks to simulate predictive hypothetical scenarios with controllable parameters to better inform them about the suitability of the AI-ALS for the students.
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ISSN:2227-7102
2227-7102
DOI:10.3390/educsci9020110