Collaborative filtering recommendation system : a framework in massive open online courses

Massive open online courses (MOOCs) are growing relatively rapidly in the education environment. There is a need for MOOCs to move away from its one-size-fit-all mode. This framework will introduce an algorithm based recommendation system, which will use a collaborative filtering method (CFM). Collaborative filtering method (CFM) is the process of evaluating several items through the rating choices of the participants. Recommendation system is widely becoming popular in online study activities; we want to investigate its support to learning and encouragement to more effective participation. This research will be reviewing existing literature on recommender systems for online learning and its support to learners’ experiences. Our proposed recommendation system will be based on course components rating. The idea was for learners to rate the course and components they have studied in the platform between the scales of 1 – 5. After the rating, we then extract the values into a comma separated values (CSV) file then implement recommendation using Python programming based on learners with similar rating patterns. The aim was to recommend courses to different users in a text editor mode using Python programming. Collaborative filtering will act upon similar rating patterns to recommend courses to different learners, so as to enhance their learning experience and enthusiasm

Exploring Unconventional Sources in Big Data: A Data Lifecycle Approach for Social and Economic Analysis with Machine Learning

This study delves into the realm of leveraging unconventional sources within the domain of Big Data for conducting insightful social and economic analyses. Employing a Data Lifecycle Approach, the research focuses on harnessing the potential of linear regression, random forest, and XGBoost techniques to extract meaningful insights from unconventional data sources. The study encompasses a structured methodology involving data collection, preprocessing, feature engineering, model selection, and iterative refinement. By applying these techniques to diverse datasets, encompassing sources like social media content, sensor data, and satellite imagery, the study aims to provide a comprehensive understanding of social and economic trends. The results obtained through these methods contribute to an enhanced comprehension of the intricate relationships within societal and economic systems, further highlighting the importance of unconventional data sources in driving valuable insights for decision-makers and researchers alike