Automated Learning Style Prediction using Weighted Neutrosophic Fuzzy Soft Rough Sets in E-learning Platform.

Albogami, Nasser Nammas

Neutrosophic fuzzy logic (NFL) is a prolongation of classical FL that integrates the neutrosophic conception that handles the indeterminacy concept. This method offers a more comprehensive and flexible architecture to handle inconsistent, uncertain, and indeterminate data, which makes it especially helpful in complicated reasoning and decision-making scenarios where classical FL might be defeated. A learning scheme, which is made from the internet and computer as the main components, is called as an e-learning platform. Although the training might happen on or off campuses, utilizing the internet is an integral part of online learning. In the meantime, to significantly augment the education standard, it is essential to forecast the learning style of the user through supervision and feedback. Nonetheless, it averts the intrinsic relationship amongst e-learning behaviors. There might be technological difficulty ranging from network connectivity issue to users memorizing their username and password while executing and developing an educational program. The learning style prediction in e-learning network is complex one and therefore we recommend a new methodology which employs web mining method for the feature extraction and log files of students from the e-learning network. This study develops an Automated Learning Style Prediction using Weighted Neutrosophic Fuzzy Soft Rough Sets (ALST-WNSFSRS) technique in E-learning Platform. The ALST-WNSFSRS technique mainly aims for the prediction of automated learning styles. Initially, the information is gathered from the Kaggle websites and utilizing a web mining method the feature from the web and log files are pre-processed. The preprocessed information is scrutinized to discover the pattern of approach to learning and later investigated the pattern. Then, the feature patterns are clustered by the fuzzy c-means (FCM) clustering technique and later utilizing the WNSFSRS method, the approach to students learning is anticipated. To improve the performance of the WNSFSRS technique, glowworm swarm optimization (GSO) algorithm is used. The performance of the ALST-WNSFSRS technique is compared with existing models and the results reported the supremacy of the ALST-WNSFSRS technique interms of different measures

PREDICTION models; DIGITAL learning; NEUTROSOPHIC logic; FUZZY systems; SET theory

International Journal of Neutrosophic Science (IJNS), 2025, Vol 25, Issue 1, p104

2692-6148

Academic Journal

10.54216/IJNS.250109