Adaptive learning in the educational e-LORS system: an approach based on preference categories

Abstract

In the field of electronic education, the recommendation of contents with higher levels of relevance may potentially attract the students' attention. In this context, this work considers students' learning styles, delineated with structured questionnaires, as a means of selecting the best content as for the learning-teaching process. The goal is to present a complete systematisationthe e-LORS system, which is able to recommend electronic educational content based on the relationship between detected learning styles and stored learning objects. Our contributions include the e-LORS system-its multiple-criteria architecture and study case, the methodology based on the Felder-Silverman learning style model and on the IEEE learning object metadata (LOM), and the reporting of experiments conducted in an actual educational context.

An architecture for multidimensional computer adaptive test with educational purposes

“…MADEPT presented the following characteristics: (1) the test time is shorter due to the adaptability of MCAT, reducing substantially the examinees' fatigue, when compared to long tests performed using pencil and paper; (2) it is a system adequate for assessment in Distance Education [28] and in teaching and learning activities held on E-learning environments [38]; (3) once it is guaranteed by the Item Response Theory, the test does no require that all the examinees perform the examination simultaneously [2], (4) the elaboration and multidimensional calibration of the item bank requires an specialized know-how; (5) it may require a server based on distributed applications and parallel processing, due to the high operational cost of multidimensional numerical integration and matrix calculus; (6) the implementation of the MCAT Module is very expensive and labor-intensive because it involves statistical and mathematical theories in a multidimensional field. The algorithms of this nature have to be projected to ensure a correct, secure and fast processing, enabling trustable and reliable results.…”

Museum Education Using XR Technologies: A Survey of Metadata Models

“…In addition to describing the educational resources, LOM supports the reusability and adaptability [29] of learning objects as it enables educators to identify appropriate materials for their specific teaching contexts and adapt these for their instruction by providing detailed information about the content, format, and pedagogical characteristics of a resource. Furthermore, LOM, when combined with adaptive learning systems, can enhance the effectiveness of educational content delivery and personalisation [30]. The LOM metadata features valuable information about learning objects, helping thus the adaptive system identify and recommend appropriate resources based on learners' preferences and learning styles.…”

A Personalized Learning Recommendation System Architecture for Learning Management System

“…This filtering technique mark the items for recommendations based on historical data information, which is followed by the development of an information recommender by using logical reasoning technology (Burke, 2002). Zaina et al, 2011 andZapata et al, 2013) highlighted the need of RSs for TEL based on a literature review which focuses on the availability and ever increasing quantity of digital learning resource repositories and from the outcomes of Social Information Retrieval for Technology Enhanced Learning (SIRTEL) annual workshop series and a Special Issue on Social Information Retrieval for TEL and proposed a DELPHOS and e-LORS (e-learning object recommender system) which are integral and intelligent solution for the recommendation of learning objects (LO) stored in a repository in which the recommendation are provided in an ordered list of LOs'.…”