AN INTELLIGENT TUTORING SYSTEM TO MAINTAIN THE STUDENTS’ MOTIVATION

Recently, many educational institutions around the world has transformed to online education specially during the COVID-19 pandemic. This fast and in many cases unplanned transformation leads to the needs for more researches to find solutions for the problems of this rapid transformation. As it's the best economic options during this pandemic, this study focused on creating a web-based (asynchronous system) intelligent tutoring system (ITS) to support the teachers in the C programming language course. Nonetheless, the suggested system takes into consideration one of the biggest challenges for asynchronous system which is how to maintain the students' motivation for the entire learning process. Therefore, the current study suggested the use of an interactive ITS as a solution for this challenge. The created system C-ITS used a set of motivational state rules and tactics to assess and maintain the motivation of the students. Finally, after using the system by the students and the teachers for two weeks, we conducted an evaluation study to evaluate the quality of the system design, the usability, the functionality, the compatibility. The result of the evaluation study showed that C-ITS system acceptable from both the students and the teachers.

TOWARDS WEBCAM-BASED FACE DIRECTION TRACKING TO DETECT LEARNERS' ATTENTION WITHIN ASYNCHRONOUS E-LEARNING ENVIRONMENT

Recently, as a consequence of COVID-19 pandemic, the delivery of education at most of the educational institutions depended mainly on e-learning. So, the researchers give more attention for both synchronous and asynchronous e-learning. Although from an economical perspective, asynchronous e-learning seems to be the best e-learning option for institutions, still one of the biggest challenges is how to keep learners motivated for the entire learning process. One of important motivational factors that drives the success of the learning process is the learner attention. Therefore, to retain the learners' attention during the asynchronous e-learning process, we need first to detect their loss of attention. Accordingly, more studies started to focus on detecting learners’ attention. However, those studies can't be widely used for attention detection within asynchronous e-learning environments, as the used approaches tend to be inaccurate, and complex for the design and maintain. In contrast, in this study, we explore the possibility to find a simple way that can be widely used to detect learners' attention within the asynchronous e-learning environments. Therefore, we used webcams which are available in almost every laptop, and computer vision tools to detect learners' attention by tracking their faces. Thereafter, we evaluated the accuracy of our suggested method, the result of this evaluation showed that our method is efficient.