To context-aware learner modeling based on ontology

Akharraz, Laila; Mezouary, Ali El; Mahani, Zouhir

doi:10.1109/educon.2018.8363383

Cited by 10 publications

(6 citation statements)

References 21 publications

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“…LifeOn represents static (special needs, preferences, profile) and dynamic (learning history and study plan) attributes of learners with academic and non-academic scopes. In Akharraz et al (2018), an ontology for reasoning about learners and their needs is presented. The goal of the ontology is to help learners realize intended learning goals by allowing exchanging the learners' profiles between the components of the e-learning system.…”

Section: Ontologies In Educationmentioning

confidence: 99%

An Ontology-Based Framework for Psychological Monitoring in Education During the COVID-19 Pandemic

2021

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BackgroundEspecially in the current crisis of the COVID-19 pandemic and the lockdown it entailed, technology became crucial. Machines need to be able to interpret and represent human behavior, to improve human interaction with technology. This holds for all domains but even more so for the domain of student behavior in relation to education and psychological well-being.MethodsThis work presents the theoretical framework of a psychologically driven computing ontology, CCOnto, describing situation-based human behavior in relation to psychological states and traits. In this manuscript, we use and apply CCOnto as a theoretical and formal description system to categorize psychological factors that influence student behavior during the COVID-19 situation. By doing so, we show the added value of ontologies, i.e., their ability to automatically organize information from unstructured human data by identifying and categorizing relevant psychological concepts.ResultsThe already existing CCOnto was modified to automatically categorize university students’ state and trait markers related to different aspects of student behavior, including learning, worrying, health, and socially based on psychological theorizing and psychological data conceptualization.DiscussionThe paper discusses the potential advantages of using ontologies for describing and modeling psychological research questions. The handling of dataset completion, unification, and its explanation by means of Artificial Intelligence and Machine Learning models is also discussed.

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Section: Ontologies In Educationmentioning

confidence: 99%

An Ontology-Based Framework for Psychological Monitoring in Education During the COVID-19 Pandemic

2021

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“…The course completion rate is defined as the number of certified learners divided by the total number of learners enrolled in the course [4]. It is low in LMSs, varies between 2 and 11% and typically less than 14%.…”

Section: Introductionmentioning

confidence: 99%

“…For some scientists, the courses are not adjusted to the learners' orientations and preferences [6]. In that sense, the problem of detecting how learners, study and acquire new skills and knowledge has been raised [3], [4].…”

Section: Introductionmentioning

confidence: 99%

“…The proposed approach aims to identify automatically the learners' learning styles in relation to their understanding of training information through the learning system formed on the global/sequential dimension of FSLSM [5]. In that regard, we are proposing to use K-means clustering algorithm [4] implemented on learners' behaviors to group them according to their engagement level. Then the learners are classified using a decision tree classifier adjusted by a well-defined set of decision rules [6].…”

Section: Introductionmentioning

confidence: 99%

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New Automatic Hybrid Approach for Tracking Learner Comprehension Progress in the LMS

Benabbes¹,

Hmedna²,

Housni³

et al. 2022

Int. J. Interact. Mob. Technol.

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Learning style is a significant learner-difference factor. Each learner has a preferred learning style and a different way of processing and understanding the novelty. In this paper, a new approach that automatically identify learners learning styles based on their interaction with the Learning Management System (LMS) is introduced. To implement this approach, the traces of 920 enrolled learners in three agronomy courses were exploited using an unsupervised clustering method to group learners according to their degree of engagement. The decision tree classification algorithm relies on the decision rules construction, which is widely adopted to identify the accurate learning style. As missing good decision rules would lead to learning style misclassification, the Felder-Silverman Learning Style Model (FSLSM) is used as it is among the most adopted models in the technology of quality improvement process. The results of this research highlight that most learners prefer the global learning style.

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“…One distinctive feature of such systems is the user model, a representation of the user’s information (Micarelli et al, 2007). According to several researchers, the key constraint involved in user modeling is to collect the appropriate information and to have a complete description of all aspects of the user’s behavior and environment (Akharraz et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Enriching the learner’s model through the semantic analysis of learning traces

Ait-Adda

Bousbia

Balla

2022

E-Learning and Digital Media

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Our aim in this paper is to improve the efficiency of a learning process by using learners’ traces to detect particular needs. The analysis of the semantic path of a learner or group of learners during the learning process can allow detecting those students who are in needs of help as well as identify the insufficiently mastered concepts. We examine the possibility of using a student’s browsing path during a learning session, based on his navigation traces, to update the learner model. We assume that the domain concepts examined outside the learning platform but that are related to the course concepts are problematic to the learner. Knowing about these concepts may allow the course’s author to adapt the course to the learner’s needs regarding these concepts, as well as allow the tutor to help and assist the learner on these problematic concepts. We rely on Web data mining methods to filter, organize, and analyze the student’s browsing path. More precisely, we use a domain ontology of the course and the similarities that exist between external documents (visited pages) and the domain concepts (the course keywords). This analysis process makes it possible to detect students’ learning difficulties and to adapt the course based on the learner’s model.

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To context-aware learner modeling based on ontology

Cited by 10 publications

References 21 publications

An Ontology-Based Framework for Psychological Monitoring in Education During the COVID-19 Pandemic

An Ontology-Based Framework for Psychological Monitoring in Education During the COVID-19 Pandemic

New Automatic Hybrid Approach for Tracking Learner Comprehension Progress in the LMS

Enriching the learner’s model through the semantic analysis of learning traces

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