An Architecture for Recommendation of Courses in E-learning

Dwivedi, Sanjay; Rawat, Bhupesh

doi:10.5815/ijitcs.2017.04.06

Cited by 13 publications

(6 citation statements)

References 10 publications

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“…Integrating machine learning techniques in recommender system certainly will improve the recommendation accuracy. In this paper [13], authors proposed a courses recommender system to learners based on their profiles. This system used weka open source software which consists of a collection of machine learning algorithms and implemented k-means algorithm to create learners' profiles supported by their data on Moodle.…”

Section: Improving Recommender Systems With Machine Learningmentioning

confidence: 99%

Ontology-based Recommender System in Higher Education

Obeid

Lahoud

Khoury

et al. 2018

Companion of the the Web Conference 2018 on the Web Conference 2018 - WWW '18

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Academic advising is limited in its ability to assist students in identifying academic pathways. Selecting a major and a university is a challenging process rife with anxiety. Students at high school are not sure how to match their interests with their working future or major. Therefore, high school students need guidance and support. Moreover, students need to filter, prioritize and efficiently get appropriate information from the web in order to solve the problem of information overload. This paper represents an approach for developing ontology-based recommender system improved with machine learning techniques to orient students in higher education. The proposed recommender system is an assessment tool for students' vocational strengths and weaknesses, interests and capabilities. The main objective of our ontology-based recommender system is to identify the student requirements, interests, preferences and capabilities to recommend the appropriate major and university for each one.

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Section: Improving Recommender Systems With Machine Learningmentioning

confidence: 99%

Ontology-based Recommender System in Higher Education

Obeid

Lahoud

Khoury

et al. 2018

Companion of the the Web Conference 2018 on the Web Conference 2018 - WWW '18

View full text Add to dashboard Cite

show abstract

“…As asserted by authors [3]- [6] in their works, the emergence of the Internet over the last few years has substantially transformed the forms and content of traditional education, giving rise to various learning systems such as computer-assisted and web-based learning. These electronic learning systems enable educators to create meaningful content with diverse scenarios for practical sessions, prepare tasks for simulating situations, engage in discussions, and manage distance-learning activities.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence for Improving Situational Training of Cybersecurity Specialists

Shchavinsky,

Muzhanova,

Yakymenko

et al. 2023

ITLT

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The article identifies the problem of the need for continuous development and improvement of practical skills for cybersecurity professionals due to the constant growth and evolution of threats to information and cyber security for organizations, businesses, society, and the state. The relevance of implementing innovative technologies to improve the methods of developing technical and managerial competencies of cybersecurity specialists in higher education institutions is justified in accordance with the strategic direction of education reform in Ukraine. The relevance of developing the ability and skills for cybersecurity professionals to respond promptly to threats is associated with the use of artificial intelligence by cybercriminals. The analysis conducted in this work allowed us to conclude the need for improvement of the situational teaching method as one of the main ways to develop the competencies of students majoring in Cybersecurity and Information Security in higher education institutions. One of the ways to improve the method is to use artificial intelligence tools in creating various types of tasks for classes. To create educational situations and options for resolving conflicting situations in cybersecurity management and cyber incidents with the aim of developing skills in future cybersecurity managers to make timely, correct, and effective decisions, it proposed to use the artificial intelligence tool - the ChatGPT language model. Thanks to its excellent capabilities, which include summarizing and analyzing articles, encoding, debugging, and generating thematic blocks of situations, it represents significant progress in the field of artificial intelligence. The application of ChatGPT allowed the creation of the necessary number of situational tasks with options for correct solutions in a short time, covering all areas of activity for cybersecurity specialists. However, during the research, there was a need for critical evaluation and verification of the information provided by the model for compliance with the context and rules, laws, and ethical norms that apply in each specific situation. This issue addressed by refining and specifying the request to the ChatGPT language model to generate situations.

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“…E-Learning involves the use of electronic devices for learning, including the delivery of content via electronic media such as Internet/intranet/extranet, audio or video, satellite broadcast, interactive TV, and so on. E-Learning gives several advantages to students: cost effectiveness, timely content, and access flexibility [1,2]. E-Learning systems, such as virtual campus environments, have gradually established themselves as a plausible alternative to, and a complement of, traditional distance education models.…”

Section: Introductionmentioning

confidence: 99%

An e-Learning Toolbox Based on Rule-Based Fuzzy Approaches

2020

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In this paper, an e-Learning toolbox based on a set of fuzzy logic data mining techniques is presented. The toolbox is mainly based on the fuzzy inductive reasoning (FIR) methodology and two of its key extensions: (i) the linguistic rules extraction algorithm (LR-FIR), which extracts comprehensible and consistent sets of rules describing students’ learning behavior, and (ii) the causal relevance approach (CR-FIR), which allows to reduce uncertainty during a student’s performance prediction stage, and provides a relative weighting of the features involved in the evaluation process. In addition, the presented toolbox enables, in an incremental way, detecting and grouping students with respect to their learning behavior, with the main goal to timely detect failing students, and properly provide them with suitable and actionable feedback. The proposed toolbox has been applied to two different datasets gathered from two courses at the Latin American Institute for Educational Communication virtual campus. The introductory and didactic planning courses were analyzed using the proposed toolbox. The results obtained by the functionalities offered by the platform allow teachers to make decisions and carry out improvement actions in the current course, i.e., to monitor specific student clusters, to analyze possible changes in the different evaluable activities, or to reduce (to some extent) teacher workload.

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An Architecture for Recommendation of Courses in E-learning

Cited by 13 publications

References 10 publications

Ontology-based Recommender System in Higher Education

Ontology-based Recommender System in Higher Education

Application of Artificial Intelligence for Improving Situational Training of Cybersecurity Specialists

An e-Learning Toolbox Based on Rule-Based Fuzzy Approaches

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