Co-Learning: Cognitive Load-Based Multilingual Learning Content Generation Model.

With the growth of internet usage, countless educational videos are now available online. However, it can be a significant challenge for learners to identify the videos they need, especially in their preferred language and within their available time. Additionally, not all videos are suitable for subject-specific learning due to variations in length and presentation components. According to Sweller’s Cognitive Load Theory, working memory during the learning process is highly limited. Learners must be selective about which information from sensory memory they choose to focus on. In our proposed Co-Learning model (a model of connective learning where all necessary knowledge is refined and interconnected to support effective learning within cognitive limitations), we leverage NLP approaches to enhance the learning experience. These approaches include video speech refinement, subtitle generation, dubbed video translation, summarization, classification, keyword extraction for word cloud indexing, and quiz generation, thereby creating a multilingual, learner-efficient environment. In our preliminary survey, the generated content was well-received and effectively utilized for class adjustments with an acceptance rate of 93%.