Telecom (Feb 2023)
Machine Learning Based Recommendation System for Web-Search Learning
Abstract
Nowadays, e-learning and web-based learning are the most integrated new learning methods in schools, colleges, and higher educational institutions. The recent web-search-based learning methodological approach has helped online users (learners) to search for the required topics from the available online resources. The learners extracted knowledge from textual, video, and image formats through web searching. This research analyzes the learner’s significant attention to searching for the required information online and develops a new recommendation system using machine learning (ML) to perform the web searching. The learner’s navigation and eye movements are recorded using sensors. The proposed model automatically analyzes the learners’ interests while performing online searches and the origin of the acquired and learned information. The ML model maps the text and video contents and obtains a better recommendation. The proposed model analyzes and tracks online resource usage and comprises the following steps: information logging, information processing, and word mapping operations. The learner’s knowledge of the captured online resources using the sensors is analyzed to enhance the response time, selectivity, and sensitivity. On average, the learners spent more hours accessing the video and the textual information and fewer hours accessing the images. The percentage of participants addressing the two different subject quizzes, Q1 and Q2, increased when the learners attempted the quiz after the web search; 43.67% of the learners addressed the quiz Q1 before completing the web search, and 75.92% addressed the quiz Q2 after the web search. The average word counts analysis corresponding to text, videos, overlapping text or video, and comprehensive resources indicates that the proposed model can also apply for a continuous multi sessions online search learning environment. The experimental analysis indicates that better measures are obtained for the proposed recommender using sensors and ML compared with other methods in terms of recall, ranking score, and precision. The proposed model achieves a precision of 27% when the recommendation size becomes 100. The root mean square error (RMSE) lies between 8% and 16% when the number of learners < 500, and the maximum value of RMSE is 21% when the number of learners reaches 1500. The proposed recommendation model achieves better results than the state-of-the-art methods.
Keywords
