IMS Compliant Ontological Learner Model for Adaptive E-Learning Environments

Abstract

It has been proven that adopting the “one size fits one” approach has better learning outcomes than the “one size fits all” one. A customized learning experience is attainable with the use of learner models, the main source of variability, in adaptive educational hypermedia systems or any intelligent learning environment. While such a model includes a large number of characteristics which can be difficult to incorporate and use, several standards that were developed to overcome these complexities. 
 In this paper, the proposed work intents to improve learner’s model representation to meet the requirements and needs of adaptation. We took IMS-LIP, IMS-ACCLIP and IMS-RDCEO standards into consideration and incorporated their characteristics to our proposed learner model so that it conforms to international standards. Moreover, the suggested learner model takes advantage of the semantic web technologies that offer a better data organization, indexing and management and ensures the reusability, the interoperability and the extensibility of this model. Furthermore, due to the use of ontologies, the metadata about a learner can be used by a wide range of personalization techniques to provide more accurate customization.

Towards an Ontological Learner’s Modeling During and After the COVID-19 Pandemic

“…User models in adaptive hypermedia are generally compatible with learner standards. There are several standard learner models like IMS ACCLIP (Accessibility for Learning Information Package), IMS RDCEO (Reusable Definition of Competency or Educational Objective), and others but the most used are PAPI learner (Public and Private Information) and IMS-LIP [18,19] and they're the models that the next section presents, they organize a certain number of user data according to different structures.…”

“…The Hamilton depression scale (HAM-D) is among the most used scales to test depression clinically [29] with an optimized 17 item, it tests the existence and sorts the severity of depression: no depression (0-7); mild depression (8)(9)(10)(11)(12)(13)(14)(15)(16); moderate depression (17)(18)(19)(20)(21)(22)(23); to severe depression (≥24) [30]. The second one is The Beck Depression Inventory (BDI) which is a self-report scale designed to measure the severity of depressive symptoms; the first version contained 21 items associated to relative scores [31].…”

“…The third is the Quick Inventory of Depressive Symptomatology (QIDS), is a selfreport version of the IDS 28 items that should be administered clinically, QIDS is a short questionnaire that takes 5 to 7 minutes with 16 items that reveal the severity of symptoms, and symptomatic change. The severity of depression ranges from 0 to 27 as follows (0-5) None, (6-10) Mild, (11)(12)(13)(14)(15) Moderate, (16)(17)(18)(19)(20) Severe, (21-27) Very severe [32].…”

“…In addition to existing ontological modules such as Labib et al who have worked on learning styles [4]. Zine et al who regroup several standard models in order to realize its ontology [18]. Or [43] and [5] who have developed two ontologies in order to model lifelong learning with different relevant components.…”

An Ontology-based Decision Support System for Multi-objective Prediction Tasks

“…The ontology captures information about learner's personal information, prior knowledge, and learning styles. In [19], proposed an ontology to model learners enrolled in distance learning. The proposed ontology arranges learner model characteristics into facets.…”