Educational Recommender Systems: An Overview and Guidelines for Further Research and Development / Obrazovni sustavi preporučivanja: pregled stanja sa smjernicama za daljnja istraživanja i razvoj

Abstract

Educational Recommender Systems (ERS) are increasingly used as tools to help students and teachers during the implementation of the learning process. The main difference between ERS and their commercial counterparts is in the pedagogical principles appropriate for the learning and teaching process. The differences in the educational methods used in a variety of educational situations, and their dependence on the field of study, set initial guidelines for ERS design. This paper reviews the evolution of ERS up to the currently achieved level of development and presents the basic techniques used in ERS design and the common problems they encounter in their work. Examples of classification of different ERS, according to their specific characteristics and basic approaches in their work, are presented. Based on this analysis, along with the training and upgrading of the existing algorithms, five specific areas in which future research and development can be expected are defined: construction of universal ERS, ERS intended primarily for teachers, ERS that links student achievements across different courses, ERS which take into account physical distance between students and use of ERS to motivate students to work continuously.

A Recommender System for Scientific Resources Based on Recurrent Neural Networks

“…It can be concluded that the rapid increase of the information volume, the limitation of search engines to search for information, and the increasing number of visitors to websites in recent years, are critical challenges in the recommender systems. A Student must find the required Educational Resources from the appropriate sources, which would be timeconsuming and costly without information filtering and recommender systems [4].…”

“…Artificial intelligence and machine learning have the potential to address many of the problems that have arisen in the transfer of teaching methods to online learning. Including students' resistance to changing their education, increasing curriculum planning, and addressing the loss of personal interaction between students and teachers [4]. This paper aims to design a time-based educational recommender system to suggest new resources to users based on the features that include a person's pre-clicked or downloaded educational resources and the rating that the user has given to each resource.…”