Abstract
Feedback may be an effective interaction provided by the intelligent tutoring system. Nevertheless, the learning feedback is not easily definable, especially in front of learners with their characteristics and preferences. In this work, the authors propose to predict personalized feedback in a programming language learning context that promotes the feedback of the ITS according to the learner preferences and learner style. The recommended method uses a combination of machine learning techniques to suggest the best appropriate feedback according to learner’s preferences and characteristics. 
 For that purpose, the predictive personalized feedback method will respect the following phases: collect the learning experience from the learning resources (LR) and learner preferences (LP), generate groups of clusters that contain the common characteristics using the k-means algorithm, and define the association rules between the four categories and their corresponding activity. Finally, generate the personalized feedback and propose the recommendation through the intervention of an expert in the field.
Highlights
Introduction & BackgroundFeedback is an essential form of an intelligent tutoring system intervention
Numerous researchers were investigated and studied the problems and improvement of the feedback in the intelligent tutoring system; some researchers apply the dialogical techniques to know the level of understanding the pedagogical sequence [4]
Some researchers proposed a new intelligent tutoring system named Smart Tutoring System (STS) which combined both tutors: explicit and implicit [8] or to convert tacit knowledge into explicit knowledge [9] to improve the feedback of the tutor
Summary
Feedback is an essential form of an intelligent tutoring system intervention. The feedback can help students to identify and correct their mistakes, support learners to correct their knowledge and acquire new knowledge in a learning sequence [1]. Numerous researchers were investigated and studied the problems and improvement of the feedback in the intelligent tutoring system; some researchers apply the dialogical techniques to know the level of understanding the pedagogical sequence [4]. Some researchers proposed a new intelligent tutoring system named Smart Tutoring System (STS) which combined both tutors: explicit and implicit [8] or to convert tacit knowledge into explicit knowledge [9] to improve the feedback of the tutor. These feedbacks are note personalized according to the learner preferences and style.
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