Abstract:

Providing feedback from students about techniques, tools and teaching methods at the end of semesters can be a valuable input for enhancing and improving educational institutions in higher education. Using appropriate tools for analyzing and processing data from students feedback to extract valuable information is important. This paper presents various tools and techniques for analyzing students feedback on teaching methods using Semantic and Sentiment analyses. The essential task in Sentiment analysis is to identify how sentiments are expressed in students feedback comments in terms of positive, negative or neutral feelings, while semantic analysis aim to identify the context of sentiments within teaching methods and techniques. Finally, the output from Semantic and Sentiment analyses are cross validated with the average grade of teachers' assessment obtained from the students. The provided results define the relationship between teaching methods and their appliance towards students performance.

Keywords:

Learning Analytics, Educational Data Mining, Natural Language Processing, Machine Learning, Text Mining, Sentiment Analyses.