Time Spent Online as an Online Learning Behavior Variable in a Blended Learning Environment with an Ontology-Based Intelligent Tutoring System

2018 ◽  
Author(s):  
Ines Saric ◽  
Ljiljana Seric
Keyword(s):  
Online Learning ◽  
Learning Environment ◽  
Blended Learning ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Learning Behavior ◽  
Tutoring System

Student Clustering Based on Learning Behavior Data in the Intelligent Tutoring System

2020 ◽  
Vol 18 (2) ◽  
pp. 73-89
Author(s):  
Ines Šarić-Grgić ◽  
Ani Grubišić ◽  
Ljiljana Šerić ◽  
Timothy J. Robinson
Keyword(s):  
Online Learning ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Learning Behavior ◽  
Tutoring System ◽  
Student Groups ◽  
Posttest Performance ◽  
Adaptive Scaffolding ◽  
The Mean ◽  
Human Teacher

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.

scholarly journals Online Pedagogical Tutorial Tactics Optimization Using Genetic-Based Reinforcement Learning

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Hsuan-Ta Lin ◽  
Po-Ming Lee ◽  
Tzu-Chien Hsiao
Keyword(s):  
Online Learning ◽  
Reinforcement Learning ◽  
Real World ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Rule Discovery ◽  
Learning Gains ◽  
Tutoring System

Tutorial tactics are policies for an Intelligent Tutoring System (ITS) to decide the next action when there are multiple actions available. Recent research has demonstrated that when the learning contents were controlled so as to be the same, different tutorial tactics would make difference in students’ learning gains. However, the Reinforcement Learning (RL) techniques that were used in previous studies to induce tutorial tactics are insufficient when encountering large problems and hence were used in offline manners. Therefore, we introduced a Genetic-Based Reinforcement Learning (GBML) approach to induce tutorial tactics in an online-learning manner without basing on any preexisting dataset. The introduced method can learn a set of rules from the environment in a manner similar to RL. It includes a genetic-based optimizer for rule discovery task by generating new rules from the old ones. This increases the scalability of a RL learner for larger problems. The results support our hypothesis about the capability of the GBML method to induce tutorial tactics. This suggests that the GBML method should be favorable in developing real-world ITS applications in the domain of tutorial tactics induction.

The ‘all-inclusive’ tutor – excluding learner autonomy?

ReCALL ◽  
1999 ◽  
Vol 11 (1) ◽  
pp. 111-116 ◽  
Author(s):  
Markus Ritter ◽  
Christiane Kallenbach ◽  
James Pankhurst
Keyword(s):  
Learning Environment ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Learner Autonomy ◽  
The Other ◽  
Tutoring System ◽  
The Road ◽  
On The Road ◽  
Sufficient Space ◽  
Multimedia Learning Environment

A multimedia learning environment would appear to benefit from an intelligent tutoring system that draws on didactic expertise, knowledge of the program structure, and knowledge of the learner's previous activities. On the other hand, one may argue against a tutor because of the damaging effects on learner autonomy: the tutor may hamper genuine learning by taking the learner by the hand, whereas what the learner needs is to have sufficient space to move freely through material in an explorative rather than an executive mode, generating her own queries and finding her own solutions. It is argued that tutoring may be a necessary stage on the road to autonomy.

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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