Automatic Content Searching Model During Blended Learning Class Sessions

Abstract

Blended Learning is one of the emerging methods of teaching and learning these days. Its fame is due to the integration of both electronic as well as traditional teaching methods that has proven remarkable outcomes. It is noted that the students require an access of additional resources while they attend their lectures. In e-learning they have this provision of searching related reference material either after their lecture class or they may do so in advance, if guided. However, it may be noted that, having the reference material right at the time of ongoing lecture may help the student not to miss any important concept but also improves their learning experience. The main objective of this research is to enhance the literature search by applying it during active lecture sessions. The research adapted the voice recognition system to acquire the keywords from instructor's voice during lecture. Stemming and TF -IDF is used to acquire relevant keywords that results in the formulation of desired key strings. The process has worked in background and produced significant results in comparison to traditional search methods.