Abstract
The swelling use of computerized learning, accompanied by the rapid growth of information technology has become a surge of interest in the research community. Consequently, several technologies have been developed to maintain and promote computerized learning. In this study, we provided an in-depth analysis of two of the prominent computerized learning systems i.e., Intelligent Tutoring System (ITS) and Affective Tutoring System (ATS). An ITS is one of the training software systems, which use intelligent technologies to provide personalized learning content to students based on their learning needs with the aim of enhancing the individualized learning experience. Recently, researchers have demonstrated that the affect or emotional states of a student have an impact on the overall performance of his/her learning, which introduces a new trend of ITS development termed as ATS, which is the extended research of the previous one. Although there have been several studies on these tutoring systems, however, none of them has comprehensively analyzed both systems, particularly the transition from ITS to ATS. Therefore, this study examines these two tutoring systems more inclusively with regards to their architectures, models, and techniques and approaches used by taking into consideration the related researches conducted between 2014 to 2019. A crucial finding from the study is that ATS can be a promising tutoring system for the next generation learning environment by affiliating proper emotion recognition channels, along with computational intelligence approaches. Finally, this study concludes with research challenges and possible future directions and trends.
Highlights
The day-to-day use of computers and the Internet have created endless opportunities for the online education community
The non-parametric U test from MannWhitney was utilized for evaluation purposes on the final grades of students’ data on biotechnology, bioinformatics and, biochemistry test, and the results showed improvement in the tutoring strategy by reducing the number of errors committing by students
We suggest that further systematic literature review (SLR) and meta-analysis should be done to provide a detailed review of each category of approaches to these systems presented in this study
Summary
The day-to-day use of computers and the Internet have created endless opportunities for the online education community. This study’s aim is to assist novice researchers by providing them with a better understanding of the concept of Intelligent and Affective tutoring systems, motivating them to undertake research on these by helping them to choose appropriate and efficient techniques to implement the systems It provides experts with a broader perspective for further exploration to mitigate the current research challenges. To the best of our knowledge, this is the first study to showcase the in-depth analysis of these two promising computerized learning systems in terms of their architectures, models, techniques, and approaches together with the challenges and future directions For this task, 487 articles were taken in this study from various sources of the Internet and digital libraries. Step 3 – Filtering procedure In this stage, articles were clustered using the subject clustering process based on the results of the keyword search They were clustered into two groups: Intelligent and Affective tutoring systems. The following criteria were mainly considered for the selection: 1) publication time (issued from 2014-2019), 2) the articles should be either high-quality journals or WoS
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