A proposal for an adaptive Recommender System based on competences and ontologies

Clemente, Julia; Yago, Hector; Pedro-Carracedo, Javier de; Bueno, F. Javier

doi:10.1016/j.eswa.2022.118171

Cited by 12 publications

(8 citation statements)

References 29 publications

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“…Similarly, content-based filtering techniques aid in recommending courses aligning with students' academic interests [35,36]. Moreover, AI-based CRSs extend their utility to suggest courses facilitating the acquisition of competencies relevant to students' future careers [37,38]. Conversely, some AI-based CRSs focus on optimizing academic outcomes by identifying courses with the highest probability of yielding favorable results and recommending them [39].…”

Section: Ai-based Course-recommender Systemsmentioning

confidence: 99%

The Impact of AI-Based Course-Recommender System on Students’ Course-Selection Decision-Making Process

Cha,

Loeser,

Seo

2024

Applied Sciences

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The course-recommender system (CRS), designed to aid students’ course-selection decision-making process by suggesting courses aligned with their interests and grades, plays a crucial role in fulfilling curricular requirements, enhancing career opportunities, and fostering intellectual growth. Recent advancements in artificial intelligence (AI) have empowered CRSs to deliver personalized recommendations by considering individual contexts. However, the impact of AI-based CRS on students’ course-selection decision-making process (inter alia, search and evaluation phases) is an open question. Understanding student perceptions and expectations of AI-based CRSs is key to optimizing their decision-making process in course selection. For this purpose, we employed speed dating with storyboards to gather insights from 24 students on five different types of AI-based CRS. The results revealed that students expected AI-based CRSs to play an assistive role in the search phase, helping them efficiently complete time-consuming search tasks in less time. Conversely, during the evaluation phase, students expected AI-based CRSs to play a leading role as a benchmark to address their uncertainty about course suitability, learning value, and serendipity. These findings underscore the adaptive nature of AI-based CRSs, which adjust according to the intricacies of students’ course-selection decision-making process, fostering fruitful collaboration between students and AI.

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Section: Ai-based Course-recommender Systemsmentioning

confidence: 99%

The Impact of AI-Based Course-Recommender System on Students’ Course-Selection Decision-Making Process

Cha,

Loeser,

Seo

2024

Applied Sciences

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“…attempted to provide a schematization of existing approaches utilising a multi-agent system (Behr et al ., 2022). LOs can be associated with content objects (Liu and Yu, 2022) in “Towards intelligent e-learning systems”, educational objects (Shemy et al ., 2021) in “Exploring the need of using Auto-Produced e-learning Objects (Generator) in Oman Schools”, information objects and knowledge objects (Clemente et al ., 2022) in “A proposal for an adaptive Recommender System based on competences and ontologies”. As (Nafea et al ., 2019) encodes them, LOs must possess the following characteristics to function adequately at an individual level and be easily and effectively transformed and used in different educational environments:They are small, self-contained units of learning that offer a concept, piece of information, or process.…”

Section: Related Work For Learning Object Classificationmentioning

confidence: 99%

New perspective of learning objects in e-learning system

Amane

Aissaoui

Berrada

2023

IJILT

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PurposeTogether, learning objects (LOs) and e-pedagogical practices have the potential to improve the performance of e-learning systems in several ways. They can make e-learning more personalised and adaptable, providing students with a more engaging and effective learning experience.Design/methodology/approachThe development of LOs and e-pedagogical practices have significantly influenced and changed the performance of e-learning systems. LOs are self-contained, reusable units of instructional content that create instructional materials, such as online courses, tutorials and assessments. They provide a flexible and modular approach to designing and delivering e-learning content, allowing educators to easily customise and adapt their materials to the needs of their students. e-pedagogical practices refer to the use of technology to enhance and support the teaching and learning process. They include strategies such as online collaboration, gamification and adaptive learning to improve student engagement, motivation and achievement.FindingsTo achieve this objective, this study consists of two main phases. First, the authors extract metadata from LOs using latent semantic analysis algorithms, which are considered a strong tool in web-mining exploration techniques. Second, they identify LOs according to a particular form of similarity using fuzzy c-means (FCM) algorithms. To improve classification accuracy, the FCM is used as a clustering algorithm.Originality/valueFinally, in order to assess the effectiveness of LOs with FCM, a series of experimental studies using a real-world dataset are conducted. The results of this study indicate that the proposed approach exceeds the traditional approach and produces good results.

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“…The adaptive engine faces two main challenges: designing and implementing effective techniques; and using adaptive learning for a broader spectrum of combined disciplines. Future research directions for adaptive engines will require more competencies and transdisciplinary adaptive learning, involving the integration of multidisciplinary resources and interdisciplinary systems (Clemente et al, 2022).…”

Section: Adaptive (Learning Recommendation) Enginementioning

confidence: 99%

Reflection on the Construction and Impact of an Adaptive Learning Ecosystem

Zhang,

Galaup

2023

RITPU

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Emerging technologies are enabling adaptive learning systems to develop. This specific system consists of several models, including a learner model, domain knowledge model, instructional model, learning analytics model, and adaptive engine model. This paper reviewed multiple studies and highlighted the importance of refining each model in the context of creating a conceptual framework. We also proposed a metacognitive auxiliary model and an adaptive assessment model. The objective is to advance research into logical transitions in the internal structure of an adaptive learning ecosystem through the interpretation of different approaches, technologies, and solutions that facilitate the decision-making processes.

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A proposal for an adaptive Recommender System based on competences and ontologies

Cited by 12 publications

References 29 publications

The Impact of AI-Based Course-Recommender System on Students’ Course-Selection Decision-Making Process

The Impact of AI-Based Course-Recommender System on Students’ Course-Selection Decision-Making Process

New perspective of learning objects in e-learning system

Reflection on the Construction and Impact of an Adaptive Learning Ecosystem

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