Naslov
Data-Driven Student Clusters Based on Online Learning Behavior in a Flipped Classroom with an Intelligent Tutoring System

Autori
Šarić, Ines ; Grubišić, Ani ; Šerić, Ljiljana ; Robinson, Timothy, J.

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

Izvornik
ITS 2019: 15th International Conference on Intelligent Tutoring Systems: Proceedings / Coy, Andre ; Hayashi, Yugo ; Chang, Maiga - Cham : Springer, 2019, 72-81

ISBN
978-3-030-22244-4

Skup
15th International Conference on Intelligent Tutoring Systems (ITS 2019)

Mjesto i datum
Kingston, Jamajka, 03.07.2019. - 07.07.2019

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Intelligent tutoring system ; Blended learning environment ; Clustering

Sažetak
The idea of clustering students according to their online learning behavior has the potential of providing more adaptive scaffolding by the intelligent tutoring system itself or by a human teacher. With the aim of identifying groups of students who would benefit from the same intervention, in this paper, we study a set of 104 weekly behaviors observed for 26 students in a blended learning environment with AC-ware Tutor, an ontology-based intelligent tutoring system. Online learning behavior in AC-ware Tutor is described using 8 tracking variables: (i) the total number of content pages seen in the learning process ; (ii) the total number of concepts seen in the learning process ; (iii) the total content proficiency score gained online ; (iv) the total time spent online ; (v) the total number of student logins to AC-ware Tutor ; (vi) the stereotype value after the initial test in AC-ware Tutor, (vii) the final stereotype value in the learning process, and (viii) the mean stereotype variability in the learning process. The previous measures are used in a four-step analysis process that includes the following elements: data preprocessing (Z-score normalization), dimensionality reduction (Principal component analysis), the clustering (K-means), and the analysis of a posttest performance on a content proficiency exam. By using the Euclidean distance in K-means clustering, we identified 4 distinct online learning behavior clusters, which we designate by the following names: Engaged Pre-knowers, Pre-knowers Non-finishers, Hard-workers, and Non-engagers. The posttest proficiency exam scores were compared among the aforementioned clusters using the Mann-Whitney U test.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

Napomena
Book Series: Lecture Notes in Computer Science, Volume: 11528

POVEZANOST RADA