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Pregled bibliografske jedinice broj: 1087586

Bayesian Student Modeling in the AC&NL Tutor

Šarić-Grgić, Ines; Grubišić, Ani; Žitko, Branko; Stankov, Slavomir; Gašpar, Angelina; Tomaš, Suzana; Vasić, Daniel
Bayesian Student Modeling in the AC&NL Tutor // Adaptive Instructional Systems: 2nd International Conference, AIS 2020, held as part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020: Proceedings / Sottilare, Robert A. ; Schwarz, Jessica (ur.).
Cham: Springer, 2020. str. 245-257 doi:10.1007/978-3-030-50788-6_18 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)

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Naslov
Bayesian Student Modeling in the AC&NL Tutor

Autori
Šarić-Grgić, Ines ; Grubišić, Ani ; Žitko, Branko ; Stankov, Slavomir ; Gašpar, Angelina ; Tomaš, Suzana ; Vasić, Daniel

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Adaptive Instructional Systems: 2nd International Conference, AIS 2020, held as part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020: Proceedings / Sottilare, Robert A. ; Schwarz, Jessica - Cham : Springer, 2020, 245-257

ISBN
978-3-030-50787-9

Skup
2nd Adaptive Instructional Systems (AIS 2020) ; 22nd International Conference on Human-Computer Interaction (HCII 2020)

Mjesto i datum
Kopenhagen, Danska, 19.07.2020. - 24.07.2020

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Intelligent tutoring systems ; Student modeling, Bayesian networks

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

Napomena
Book Series: Lecture Notes in Computer Science,
Volume: 12214



POVEZANOST RADA

Ustanove:
Prirodoslovno-matematički fakultet, Split,
Filozofski fakultet u Splitu

Profili:

Avatar Url Slavomir Stankov (autor)

Avatar Url Suzana Tomaš (autor)

Avatar Url Angelina Gašpar (autor)

Avatar Url Branko Žitko (autor)

Avatar Url Ani Grubišić (autor)

Avatar Url Ines Šarić-Grgić (autor)

Poveznice na cjeloviti tekst rada:

doi link.springer.com

Citiraj ovu publikaciju:

Šarić-Grgić, Ines; Grubišić, Ani; Žitko, Branko; Stankov, Slavomir; Gašpar, Angelina; Tomaš, Suzana; Vasić, Daniel
Bayesian Student Modeling in the AC&NL Tutor // Adaptive Instructional Systems: 2nd International Conference, AIS 2020, held as part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020: Proceedings / Sottilare, Robert A. ; Schwarz, Jessica (ur.).
Cham: Springer, 2020. str. 245-257 doi:10.1007/978-3-030-50788-6_18 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Šarić-Grgić, I., Grubišić, A., Žitko, B., Stankov, S., Gašpar, A., Tomaš, S. & Vasić, D. (2020) Bayesian Student Modeling in the AC&NL Tutor. U: Sottilare, R. & Schwarz, J. (ur.)Adaptive Instructional Systems: 2nd International Conference, AIS 2020, held as part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020: Proceedings doi:10.1007/978-3-030-50788-6_18.
@article{article, author = {\v{S}ari\'{c}-Grgi\'{c}, Ines and Grubi\v{s}i\'{c}, Ani and \v{Z}itko, Branko and Stankov, Slavomir and Ga\v{s}par, Angelina and Toma\v{s}, Suzana and Vasi\'{c}, Daniel}, year = {2020}, pages = {245-257}, DOI = {10.1007/978-3-030-50788-6\_18}, keywords = {Intelligent tutoring systems, Student modeling, Bayesian networks}, doi = {10.1007/978-3-030-50788-6\_18}, isbn = {978-3-030-50787-9}, title = {Bayesian Student Modeling in the AC and NL Tutor}, keyword = {Intelligent tutoring systems, Student modeling, Bayesian networks}, publisher = {Springer}, publisherplace = {Kopenhagen, Danska} }
@article{article, author = {\v{S}ari\'{c}-Grgi\'{c}, Ines and Grubi\v{s}i\'{c}, Ani and \v{Z}itko, Branko and Stankov, Slavomir and Ga\v{s}par, Angelina and Toma\v{s}, Suzana and Vasi\'{c}, Daniel}, year = {2020}, pages = {245-257}, DOI = {10.1007/978-3-030-50788-6\_18}, keywords = {Intelligent tutoring systems, Student modeling, Bayesian networks}, doi = {10.1007/978-3-030-50788-6\_18}, isbn = {978-3-030-50787-9}, title = {Bayesian Student Modeling in the AC and NL Tutor}, keyword = {Intelligent tutoring systems, Student modeling, Bayesian networks}, publisher = {Springer}, publisherplace = {Kopenhagen, Danska} }

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