Bayesian Student Modeling in the AC&NL Tutor (CROSBI ID 695478)
Prilog sa skupa u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Šarić-Grgić, Ines ; Grubišić, Ani ; Žitko, Branko ; Stankov, Slavomir ; Gašpar, Angelina ; Tomaš, Suzana ; Vasić, Daniel
engleski
Bayesian Student Modeling in the AC&NL Tutor
The reasoning process about the level of student’s knowledge can be challenging even for experienced human tutors. The Bayesian networks are a formalism for reasoning under uncertainty, which has been successfully used for various artificial intelligence applications, including student modeling. While Bayesian networks are a highly flexible graphical and probabilistic modeling framework, its main challenges are related to the structural design and the definition of “a priori” and conditional probabilities. Since the AC&NL Tutor’s authoring tool automatically generates tutoring elements of different linguistic complexity, the generated sentences and questions fall into three difficulty levels. Based on these levels, the probability- based Bayesian student model is proposed for mastery-based learning in intelligent tutoring system. The Bayesian network structure is defined by generated questions related to the node representing knowledge in a sentence. Also, there are relations between inverse questions at the same difficulty level. After the structure is defined, the process of assigning “a priori” and conditional probabilities is automated using several heuristic expert-based rules.
Intelligent tutoring systems ; Student modeling, Bayesian networks
Book Series: Lecture Notes in Computer Science, Volume: 12214
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Podaci o prilogu
245-257.
2020.
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objavljeno
10.1007/978-3-030-50788-6_18
Podaci o matičnoj publikaciji
Lecture notes in computer science
Sottilare, Robert A. ; Schwarz, Jessica
Cham: Springer
978-3-030-50787-9
0302-9743
1611-3349
Podaci o skupu
2nd Adaptive Instructional Systems (AIS 2020) ; 22nd International Conference on Human-Computer Interaction (HCII 2020)
predavanje
19.07.2020-24.07.2020
Kopenhagen, Danska