Abstract—Opinion mining has been widely used in recent online reviews or feedback due to its ability to analyse text-based data. The use of this technique for analysing data from students’ feedback needs to be addressed, since most educational institutions are focusing more on questionnaires based on the Likert-scale rather than on the open-review type. To this end, there is a lack of online assessment systems that could automatically analyse open-review questionnaires. Therefore, the main aim of this study is to analyse students’ feedback in an online assessment system through the opinion mining technique, by focusing on textual form data derived from the open-review questionnaires. To achieve this aim, an opinion mining feedback system, known as the OMFeedback, was developed. The Vader Sentiment Intensity Analyser was adapted for processing students’ feedback and the lexicon based approach was used for analysing the words. In addition, the OMFeedback incorporates the capitalisation of words and emoji features to enrich the capability of the system. This newly developed system could lead to new paradigms in educational institutions for enhancing students’ learning process and for guiding them through their learning journey.
Index Terms—Lexicon based approach, online assessment system, opinion mining, students’ feedback.
The authors are with the Department of Computer Science, Faculty of Defence Science and Technology, National Defence University of Malaysia (e-mail: email@example.com, firstname.lastname@example.org, email@example.com, firstname.lastname@example.org, email@example.com, firstname.lastname@example.org).
Cite:Muslihah Wook, Sharmelen Vasanthan, Suzaimah Ramli, Noor Afiza Mat Razali, Nor Asiakin Hasbullah, and Norulzahrah Mohd Zainudin, "Exploring Students’ Feedback in Online Assessment System Using Opinion Mining Technique," International Journal of Information and Education Technology vol. 10, no. 9, pp. 664-668, 2020.Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).