Knowledge discovery for course choice decision in Massive Open Online Courses using machine learning approaches

Nilashi, Mehrbakhsh; Minaei‐Bidgoli, Behrouz; Alghamdi, Abdullah A.; Alrizq, Mesfer; Alghamdi, Omar; Nayer, Fatima Khan; Aljehane, Nojood O.; Khosravi, Arash; Mohd, Saidatulakmal

doi:10.1016/j.eswa.2022.117092

Cited by 35 publications

(8 citation statements)

References 46 publications

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“…Some studies have also proposed to integrate parameters such as students' postclass grades, teachers' performance in class, and student's classroom performance into neural networks students to obtain trend lines of teaching effectiveness factors as an evaluation criterion. Researchers in the literature [14] on the development of online teaching systems point out that the quality assessment of online courses is a comprehensive evaluation system that requires researchers to reasonably control the course efciency indicators from an objective perspective, and the evaluation system needs to fully take into account the student side and the teacher side of the course patterns and preferences and cannot be generalized with a uniform specifcation [15][16][17][18]. Diferent courses should also set up independent evaluation systems, establish independent human-machine models, and learn two-way evaluation indicators between teachers and students.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Neural Network for the Online Course Quality Assessment System

Bai

Yong-guo

2022

Mathematical Problems in Engineering

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Under the influence of COVID-19, online office and online education has ushered in a golden period of development. The teaching quality of online education has been a controversial issue. Our study takes online course teaching quality assessment as the starting point, explores the influencing factors of online course quality assessment with online courses as the research object, and analyzes the latest research proposal for an online course quality index. To make the online course quality assessment more intelligent, we propose an online course quality assessment method based on a fuzzy neural network. The method uses fuzzy rules as the baseline and adds a TSK perception mechanism to expand the perception domain of the fuzzy neural network and improve the course quality index prediction accuracy. At the input side of the fuzzy neural network, we preclassify the online course data into four parts, and each part of the data represents a different assessment domain. Due to the large data cost, we expanded the collective amount of data using data augmentation methods. In addition, we parse the structure of the fuzzy neural network hierarchy and introduce the construction and role of the TSK perception mechanism in the fuzzy rules. An optimal learning strategy is proposed in the fuzzy neural network training. Finally, in the experimental session, we verify the effectiveness of data augmentation and explore the distribution of course quality assessment weights. In the comparison of the model prediction results with the actual assessment results, our method achieves an excellent matching rate, which proves the high efficiency of our method in the online course quality assessment system.

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Section: Introductionmentioning

confidence: 99%

Fuzzy Neural Network for the Online Course Quality Assessment System

Bai

Yong-guo

2022

Mathematical Problems in Engineering

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“…e recall and precision measures are provided in equation (21) and equation (22). In equation ( 23), we present F-measure [74].…”

Section: Methods Evaluation and Comparisonsmentioning

confidence: 99%

A Hybrid Method to Solve Data Sparsity in Travel Recommendation Agents Using Fuzzy Logic Approach

Nilashi

Abumalloh

Alrizq

et al. 2022

Mathematical Problems in Engineering

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Travel recommendation agents have been a helpful tool for travelers in their decision-making for destination choices. It has been shown that sparsity can significantly impact on the accuracy of recommendation agents. The COVID-19 outbreak has affected the tourism and hospitality industry of almost all countries in the world. Tourists who have planned to travel are canceling or postponing trips due to this pandemic. Accordingly, this will impact the rate of travelers’ online reviews on tourism products. Hence, the lack of data, in terms of ratings and textual reviews on hotels, will be a major issue for travel recommendation agents during the COVID-19 outbreak in the context of tourism and hospitality. This will be a new challenge for the researchers in the development of travel recommendation agents. Machine learning has been found to be effective in dealing with the data sparsity in recommendation agents. Therefore, developing new algorithms would be helpful to overcome the sparsity issue in travel recommendation agents. This research provides a new method through neurofuzzy, dimensionality reduction, and clustering techniques and evaluates it on the TripAdvisor dataset to see its effectiveness in solving the sparsity issue. The results showed that the method which used the fuzzy logic technique with the aid of clustering, dimensionality reduction, and fuzzy logic is more efficient in addressing the sparsity problem and presenting more accurate results. The results of the method evaluation are presented and discussed, and several suggestions are provided for future studies.

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“…These outcomes demonstrate that the strategy helps students close their knowledge gaps. Through the application of machine learning techniques, Nilashi et al 34 suggested an RS to promote courses in MOOCs based on the preferences and behavior of students. The approach was created employing multi-criteria ratings taken from learners' reviews posted online.…”

Section: Literature Reviewmentioning

confidence: 99%

An adaptable and personalized framework for top-N course recommendations in online learning

Amin,

Uddin,

Alarood

et al. 2024

Sci Rep

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In recent years, the proliferation of Massive Open Online Courses (MOOC) platforms on a global scale has been remarkable. Learners can now meet their learning demands with the help of MOOC. However, learners might not understand the course material well if they have access to a lot of information due to their inadequate expertise and cognitive ability. Personalized Recommender Systems (RSs), a cutting-edge technology, can assist in addressing this issue. It greatly increases resource acquisition through personalized availability for various people of all ages. Intelligent learning methods, such as machine learning and Reinforcement Learning (RL) can be used in RS challenges. However, machine learning needs supervised data and classical RL is not suitable for multi-task recommendations in online learning platforms. To address these challenges, the proposed framework integrates a Deep Reinforcement Learning (DRL) and multi-agent approach. This adaptive system personalizes the learning experience by considering key factors such as learner sentiments, learning style, preferences, competency, and adaptive difficulty levels. We formulate the interactive RS problem using a DRL-based Actor-Critic model named DRR, treating recommendations as a sequential decision-making process. The DRR enables the system to provide top-N course recommendations and personalized learning paths, enriching the student's experience. Extensive experiments on a MOOC dataset such as the 100 K Coursera course review validate the proposed DRR model, demonstrating its superiority over baseline models in major evaluation metrics for long-term recommendations. The outcomes of this research contribute to the field of e-learning technology, guiding the design and implementation of course RSs, to facilitate personalized and relevant recommendations for online learning students.

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Knowledge discovery for course choice decision in Massive Open Online Courses using machine learning approaches

Cited by 35 publications

References 46 publications

Fuzzy Neural Network for the Online Course Quality Assessment System

Fuzzy Neural Network for the Online Course Quality Assessment System

A Hybrid Method to Solve Data Sparsity in Travel Recommendation Agents Using Fuzzy Logic Approach

An adaptable and personalized framework for top-N course recommendations in online learning

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