Intelligent Tutoring of Learners In E-learning Systems and Massive Open Online Courses (Mooc)

Abstract

In the last few years, many terms related to learning environments have emerged. Each one of these terms is distinguished by a set of criteria such as the target audience, the duration of learning, the type and nature of the educational content, the manner of dissemination of knowledge, etc. Among problems encountered in these environments, the lack of support for learners seems to be a serious problem that requires special attention. Tutoring can be seen as a good solution for this problem; it consists of giving assistance to learners who are in need for help. Regardless the nature of this assistance (pedagogical, social, etc.), it can be delivered in many forms: advice, guidance or even recommendation. If tutoring has been applied for a long time in traditional e-learning environments, its application in new systems such as Massive Open Online Courses (MOOC) is still under study. In fact, a considerable number of studies driven on MOOCs had reported the problem of learners' dropout. Several reasons can be listed as causes of such a problem. Among these reasons, we can find learners' isolation as well as learners' loss of motivation. This same problem has been reported by researchers working in the field of Computer-based Environments for Human Learning. In this article, we propose a new vision on how to apply an intelligent tutoring process in human learning systems in general and in MOOCs in particular. This new vision is based on the behaviors and skills of learners. This activity can take many forms and can be carried out by different types of actors (teachers, learners, etc.).

Delving into instructor‐led feedback interventions informed by learning analytics in massive open online courses

“…The textual support regarded text messages, hints and prompts, tips and personalized links or recommender systems and systems for automated corrections on provided solutions (Almeda et al, 2018; Caballe et al, 2014; Ferschke et al, 2015; Frick et al, 2022; Lafifi et al, 2020; Lan et al, 2015; Meku‐Fotso et al, 2020; Reza et al, 2021; Singelmann et al, 2019; Teusner et al, 2018; Wang et al, 2017; Yılmaz et al, 2021; Yu et al, 2021). Almeda et al (2018) proposed sending reminders with course‐related material and praising the top‐level discussion forum commenters.…”

“…Ferschke et al (2015) presented the Quick Helper, a help‐seeking tool that connects learners with peers to respond to unsolved questions. Lafifi et al (2020) proposed a tool, TutMOOC, to empower instructors' role in tutoring. According to the learners' problem, different feedback agents can be employed, such as computer agents for simple automated solutions or teacher agencies for pedagogical and learning problems.…”

“…The textual support regarded text messages, hints and prompts, tips and personalized links or recommender systems and systems for automated corrections on provided solutions (Almeda et al, 2018;Caballe et al, 2014;Ferschke et al, 2015;Frick et al, 2022;Lafifi et al, 2020;Lan et al, 2015;Meku-Fotso et al, 2020;Reza et al, 2021;Singelmann et al, 2019;Teusner et al, 2018…”