Analysing the learning style of an individual and suggesting field of study using Machine Learning techniques

Kuttattu, Ananthu S; Gokul, G; Prasad, Hari; Murali, Jijith; Nair, Lekshmi S

doi:10.1109/icces45898.2019.9002051

Cited by 13 publications

(10 citation statements)

References 6 publications

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“…The supervised learning models were mostly trained with learning style ground truths that were manually collected by questionnaires, which required laborious work. In other studies, the ground truths were generated by using clustering methods [64]- [68] instead of a manual collection. Although viable, the validity of ground truths generated by the clustering algorithm is questionable.…”

Section: Discussionmentioning

confidence: 99%

AI-Based Learning Style Prediction in Online Learning for Primary Education

et al. 2022

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Online learning has been widely applied due to developments in information technology. However, there are fewer relevant evaluations and applications for primary school students. All innovation efforts in learning are directed at improving the quality of education by creating an active learning atmosphere for students. Students' participation in the teaching-learning process can be improved by selecting appropriate learning materials suitable to the student's learning style. The research aims to develop and measure the impact of an Artificial-Intelligence (AI)-based learning style prediction model in an online learning portal for primary school students. The subjects were recruited from Indonesian primary school students in grades 4 to 6. To fulfill the principle of personalized learning, the AI model in the online learning portal was designed to recommend learning materials that suit students' learning styles. We formulated a new AI approach that enables collaborative filtering-based AI models to be driven by learning style prediction. With this AI algorithm, the online learning portal can provide material recommendations tailored specifically to the learning style of each student. The AI model performance test achieved satisfactory results, with an average RMSE (Root Mean Squared Error) of 0.9035 from a rating scale of 1 to 5. Moreover, students' learning performance was improved based on the results of t-test analysis on 269 subjects between the pre-test and post-test scores.

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Section: Discussionmentioning

confidence: 99%

AI-Based Learning Style Prediction in Online Learning for Primary Education

et al. 2022

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“…Data that is erroneous is perplexing because there is no solution. Pre-processing is important for delivering higher-quality analysis outcomes for data researchers, data scientists, and enterprise users to avoid this [6,28,29].…”

Section: Pre-processingmentioning

confidence: 99%

A data mining approach to forecast students’ career placement probabilities and recommendations in the programming field

Mahboob¹,

Asif²,

Haider³

2023

Mehran Univ. res. j. eng. technol.

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The career opportunities in computer programming are vast and rapidly increasing. Skilled software engineers, programmers, and developers are vigorously in demand worldwide. The capability to forecast a student's future career can be helpful in a wide variety of pedagogical practices. Data mining is becoming a more robust tool for analysis and forecasting. Therefore, to forecast career placement probabilities in the programming field, data mining classification and forecast techniques are used in this study to facilitate prospective students to make sensible career decisions. To achieve this objective, passed-out graduates' data is utilized, which comprises features like graduates' educational attainments in pre-university grades, i.e. grades of matriculation and intermediate, programming courses taught in early semesters along with the Cumulative Grade Point Average (CGPA) with the internship experience, gender, and family demographic information. Various multi-way Classification Trees are generated, which could help students to choose a branch with high career placement probabilities. From historical data, the Classification Trees have determined whether the branch is 'Good', 'Satisfactory', or 'Poor' based on the given information. The experimental findings indicate that all the features significantly influence the career placement probabilities in the programming field.

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“…We can divide the dataset into test and train sets once the cluster number has been added to it. In this example, we'll divide the data set in half, with one serving as the test set and the other as the train set (Kuttattu et al, 2019).…”

Section: Data Clusteringmentioning

confidence: 99%

“…As a consequence of various investigations, cloud computing has been presented as a solution for higher resource utilization than traditional servers. Because of its flexibility, speed, scalability, and flexibility, cloud computing has been offered as a unique way for enabling IT solutions (Kuttattu et al, 2019). When using an e-learning system on a cloud platform, a variety of benefits may be acquired; the following list summarizes a few of these advantages: the main advantage is that the e-learning system's performance will be improved since the system's services will be put on the cloud environment.…”

Section: Introductionmentioning

confidence: 99%

“…When using an e-learning system on a cloud platform, a variety of benefits may be acquired; the following list summarizes a few of these advantages: the main advantage is that the e-learning system's performance will be improved since the system's services will be put on the cloud environment. By deploying the system's services on a cloud computing platform, you may save money on things like maintenance and management (Kuttattu et al, 2019). Another advantage is that the system's services will always be available to the system's users.…”

Section: Introductionmentioning

confidence: 99%

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The Factor Affecting Student’s Performance of E-Learning Environment Using Machine Learning Algorithm

Haque

Kumar

Mishra

et al. 2022

Proceedings of MOL2NET'22, Conference on Molecular, Biomedical &Amp; Computational Sciences and Engineering, 8th Ed. - MOL2NET:

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The Covid-19 pandemic is affecting many aspects of society, especially university educational programs worldwide. As a result, online learning is an effective strategy that is adopted by many educational institutions nowadays. However, not all training institutions have the necessary environment, assets, and ability to conduct effective online learning, particularly in poor nations with resource constraints. As a result, many institutions are struggling to build traditional courses or E-Learning in limited condition while still meeting students' demands. To overcome this limitation, we present a technique for assessing the impact of these elements on the e-learning system. Then, utilizing data from students who have participated in the program, this is an issue of explaining the significance and prioritizing construction investments for every component based on the K-means clustering algorithm. The purpose of this paper is to investigate the relationship between the students' responses to e-learning platforms and their performance in terms of various skill levels with the help of K-Means clustering algorithms. The clustering findings demonstrate that individuals with greater levels of involvement outperform those with intermediate or lower levels of engagement.

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Analysing the learning style of an individual and suggesting field of study using Machine Learning techniques

Cited by 13 publications

References 6 publications

AI-Based Learning Style Prediction in Online Learning for Primary Education

AI-Based Learning Style Prediction in Online Learning for Primary Education

A data mining approach to forecast students’ career placement probabilities and recommendations in the programming field

The Factor Affecting Student’s Performance of E-Learning Environment Using Machine Learning Algorithm

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