The Impact of an Intelligent Tutoring System in Programming: A Case Study in Improving Academic Performance of Student in Tertiary Education

2019 ◽  
Author(s):  
Juliet V Menor ◽  
Melvin Ballera
Keyword(s):  
Academic Performance ◽  
Tertiary Education ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Tutoring System ◽  
The Impact

The Impact of a Mathematical Intelligent Tutoring System on Students’ Performance on Standardized High-Stake Tests

2013 ◽  
pp. 69-78 ◽  
Author(s):  
Jeremiah Sullins ◽  
Rob Meister ◽  
Scotty D. Craig ◽  
William M. Wilson ◽  
Anna Bargagliotti ◽  
...  
Keyword(s):  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Tutoring System ◽  
High Stake ◽  
The Impact

The Impact of Game-Like Features on Learning from an Intelligent Tutoring System

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

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
Keyword(s):  
Learning Process ◽  
Learning Analytics ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Complete Analysis ◽  
Note Taking ◽  
Tutoring System ◽  
Student Groups ◽  
Clustering Approach ◽  
The Impact

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 %.

A Novel Architecture for Learner-Centric Curriculum Sequencing in Adaptive Intelligent Tutoring System

2018 ◽  
Vol 20 (3) ◽  
pp. 1-20
Author(s):  
Ninni Singh ◽  
Neelu Jyothi Ahuja ◽  
Amit Kumar
Keyword(s):  
Learning Style ◽  
Seismic Data ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Data Interpretation ◽  
Tutoring System ◽  
Face To Face ◽  
Learning Path ◽  
Curriculum Sequencing

An ideal face-to-face tutor learner interaction aims to offer learning to the learner in a manner that best suits an individual learner's learning level and learning style. This ability of differentiated instruction has been built in Seis-Tutor Intelligent Tutoring system, developed to offer subject matter knowledge of ‘Seismic Data Interpretation,' a field of geo-physics. The detailed architecture of learner-centric curriculum sequencing module, built to this effect, with its components, sub-components, their interconnected functioning, to generate exclusive learning path, have been described. An algorithm for learner-centric curriculum sequencing, a mathematical model and proposed implementation using a case study has been elaborated.

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