scholarly journals The Impact of Note-Taking on the Learning Process in Intelligent Tutoring System Tutomat

2021 ◽  
Vol 26 (1) ◽  
pp. 31-37
Author(s):  
Ines Šarić-Grgić ◽  
Ani Grubišić ◽  
Branko Žitko

Abstract The research investigates how note-taking practice affects the learning process in Tutomat, an intelligent tutoring system. The complete analysis includes (i) the identification of learning analytics variables to describe student-Tutomat interaction; (ii) the description of experimental student groups using learning analytics variables; (iii) data-driven clustering and (iv) the comparison of the experimental groups and revealed clusters. The results show that there is a difference in how a student interacts with Tutomat based on note-taking practice. It is revealed that the note-taking practice can be detected using the proposed learning analytics variables with the prediction accuracy of the clustering approach of 85 %.

2013 ◽  
pp. 69-78 ◽  
Author(s):  
Jeremiah Sullins ◽  
Rob Meister ◽  
Scotty D. Craig ◽  
William M. Wilson ◽  
Anna Bargagliotti ◽  
...  

2016 ◽  
Vol 22 (1) ◽  
pp. 1-22 ◽  
Author(s):  
Keith Millis ◽  
Carol Forsyth ◽  
Patricia Wallace ◽  
Arthur C. Graesser ◽  
Gary Timmins

2020 ◽  
Vol 18 (2) ◽  
pp. 73-89
Author(s):  
Ines Šarić-Grgić ◽  
Ani Grubišić ◽  
Ljiljana Šerić ◽  
Timothy J. Robinson

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 student groups who would benefit from the same intervention in AC-ware Tutor, this research examined online learning behavior using 8 tracking variables: the total number of content pages seen in the learning process; the total number of concepts; the total online score; the total time spent online; the total number of logins; the stereotype after the initial test, the final stereotype, and the mean stereotype variability. The previous measures were used in a four-step analysis that consisted of data preprocessing, dimensionality reduction, the clustering, and the analysis of a posttest performance on a content proficiency exam. The results were also used to construct the decision tree in order to get a human-readable description of student clusters.


2009 ◽  
Vol 36 (2) ◽  
pp. 1229-1239 ◽  
Author(s):  
Aytürk Keleş ◽  
Rahim Ocak ◽  
Ali Keleş ◽  
Aslan Gülcü

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