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

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.

Download Full-text

Data-Driven Intelligent Tutoring System for Accelerating Practical Skills Development. A Deep Learning Approach

Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education - Smart Innovation, Systems and Technologies ◽

10.1007/978-981-15-7383-5_17 ◽

2020 ◽

pp. 197-209

Author(s):

Robert Marinescu-Muster ◽

Sjoerd de Vries ◽

Wouter Vollenbroek

Keyword(s):

Deep Learning ◽

Intelligent Tutoring ◽

Intelligent Tutoring System ◽

Data Driven ◽

Skills Development ◽

Learning Approach ◽

Practical Skills ◽

Tutoring System

Download Full-text

Online Pedagogical Tutorial Tactics Optimization Using Genetic-Based Reinforcement Learning

The Scientific World JOURNAL ◽

10.1155/2015/352895 ◽

2015 ◽

Vol 2015 ◽

pp. 1-11 ◽

Cited By ~ 2

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.

Download Full-text