Unraveling Views of Students Towards Computer Programming a Sentiment Analysis and Latent Semantic Indexing Application
Niel Francis Casillano
Niel Francis B. Casillano, Department of Information Technology, Eastern Samar State University, Philippines
Manuscript received on 11 April 2019 | Revised Manuscript received on 16 May 2019 | Manuscript published on 30 May 2019 | PP: 453-456 | Volume-8 Issue-1, May 2019 | Retrieval Number: A3440058119/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Programming is a fundamental skill a computing student must master. It requires an excellent and correct understanding of logical and abstract concepts. Due to this, students find a hard time dissecting and understanding programming problems. This paper focused on unraveling the views and sentiments of students towards computer programming. The researcher utilized a machine learning tool to analyse and visualize the corpus of documents containing the views and sentiments of students. VADER model was used to analyze the sentiments of the students and Latent semantic indexing topic modeling was utilized to generate topics from the corpus of documents. It was determined that most students have a negative sentiment towards programming subjects. The topic modeling showed that the underlying themes were generally talking about the difficulties and challenges students are experiencing when dealing with programming subjects. It was also observed that some students are using coping mechanisms and finding new learning methodologies to solve programming tasks given to them. The result of this research can be utilized as inputs in the development of a teaching model for programming.
Index Terms: LSI, Latent Themes, Topic Modelling, Programming, Sentiment Analysis
Scope of the Article: Advanced Computing Architectures and New Programming Models