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 %.

scholarly journals Immersive Intelligent Tutoring System for Remedial Learning Using Virtual Learning Environment

2021 ◽  
Vol 6 (3) ◽  
pp. 507-522
Author(s):  
R. Rasim ◽  
Yusep Rosmansyah ◽  
Armein Z.R. Langi ◽  
M. Munir
Keyword(s):  
Learning Environment ◽  
Technology Acceptance ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Virtual Learning ◽  
Virtual Learning Environment ◽  
Tutoring System ◽  
Dynamic Learning ◽  
Side Component ◽  
The Impact

Intelligent Tutoring System (ITS) has been widely used in supporting personal learning.  However, there is an aspects that have not become focus in ITS, namely immersive. This research proposes an Immersive Intelligent Tutoring (IIT) model based on Bayesian Knowledge Tracing (BKT) for determining the learner’s characteristics and learning content delivery strategies using genetic algorithms. The model uses remedial learning with a faded worked-out example. This study uses a 3-Dimensional Virtual Learning Environment (3DMUVLE) that implements immersive features to increase intrinsic motivation. This model was built using a client / server architecture. The server side component uses the MOODLE, the client side component uses OpenSim and its viewers, and the middleware component uses the Simulation Linked Object Oriented Dynamic Learning Environment (SLOODLE). Model testing is performed on user acceptance using a combination of Technology Acceptance Model (TAM) and Hedonic-Motivation System Adoption Model (HMSAM) and the impact of the model in learning using statistical test. The results showed 83% of the learners felt happy with the learning. Meanwhile, the evaluation of the impact on learning outcomes shows that the use of this model is significantly different from traditional learning.

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