The experimental effect size is a difference in posttest means for experimental and control populations, divided by the within-group population standard deviation.
Published in Chapter:
Principles of Natural Language Processing and Adaptive Courseware in E-Assessments: Empirical Evaluations
Ani Grubišić (University of Split, Croatia), Slavomir Stankov (Independent Researcher, Croatia), Branko Žitko (University of Split, Croatia), Suzana Tomaš (University of Split, Croatia), Emil Brajković (University of Mostar, Bosnia and Herzegovina), Tomislav Volarić (University of Mostar, Bosnia and Herzegovina), Daniel Vasić (University of Mostar, Bosnia and Herzegovina), Ines Šarić (University of Split, Croatia), and Arta Dodaj (University of Zadar, Croatia)
Copyright: © 2019
|Pages: 32
DOI: 10.4018/978-1-5225-5936-8.ch014
Abstract
Over the last few decades, researchers put efforts to improve intelligent tutoring systems' abilities with the aim to get them as close as possible to the ultimate goal of one-to-one tutoring. CoLaB Tutor and AC-ware Tutor are intelligent tutoring systems based on conceptual knowledge learning and are notable due to the fact they are relatively easy to generalize to multiple knowledge domains. CoLaB Tutor's forte lies in teacher-learner communication in controlled natural language, while AC-ware Tutor focuses on the automatic and dynamic generation of adaptive courseware. In order to compare various intelligent tutoring system supported education environments, in this chapter, the authors summarize several empirical evaluations of CoLaB Tutor and AC-ware Tutor. The results of intelligent tutoring systems' effectiveness in these environments offer the possibility to observe the specific intelligent tutoring system across various education levels, as well as to compare the intelligent tutoring systems' supported education environments.