Classification of social networking skills for promoting personalized learning of Thai seniors

摘要

Some research proposes that age changing effects on learning performance. Although, age-related degraded perception cannot be rolled-back, it can be improved by training and learning. Since seniors can reach new technology slowly, social media is a way to support their participation in long term learning. However, the level of learning is limited by each personal background knowledge, that each senior supposes to have the learning path which mostly match with their social media knowledge. Therefore, this study proposes the method to classify groups of senior by using useful data mining algorithms. In this case, Random Forest and K-nearest neighbors algorithm (k-NN) model are used. The study conducted with 60 senior between 60-75 years old from seniors school in chiangrai province. Upon participants have done the pretest, these algorithms classifies an senior test results, and assign them into 4 appropriate groups, including Professional, Medium, Less Knowledge, and No Experience. The results show that both algorithms, Random Forest and K-nearest neighbors algorithm (k-NN) provide 95.56% and 93.33% accuracy respectively.