Text analytics approach to extract course improvement suggestions from students’ feedback

Abstract

In academic institutions, it is normal practice that at the end of each term, students are required to complete a questionnaire that is designed to gather students’ perceptions of the instructor and their learning experience in the course. Students’ feedback includes numerical answers to Likert scale questions and textual comments to open-ended questions. Within the textual comments given by the students are embedded suggestions. A suggestion can be explicit or implicit. Any suggestion provides useful pointers on how the instructor can further enhance the student learning experience. However, it is tedious to manually go through all the qualitative comments and extract the suggestions. In this paper, we provide an automated solution for extracting the explicit suggestions from the students’ qualitative feedback comments. The implemented solution leverages existing text mining and data visualization techniques. It comprises three stages, namely data pre-processing, explicit suggestions extraction and visualization. We evaluated our solution using student feedback comments from seven undergraduate core courses taught at the School of Information Systems, Singapore Management University. We compared rule-based methods and statistical classifiers for extracting and summarizing the explicit suggestions. Based on our experiments, the decision tree (C5.0) works the best for extracting the suggestions from students’ qualitative feedback.

Maintaining reading experience continuity across e-book revisions

“…The second step is word normalization and term counting. Since several previous researches have mentioned and proved that lemmatization can help increase the accuracy of the classification task and opinion mining task in nature language processing for English documents and be able to perform better on clustering of text documents when comparing with stemming (Gottipati et al 2018; Korenius et al 2004); in this paper, we use lemmatization as our word normalization technique. We then count the term frequency to represent the occurrences of terms in each document in the corpus.…”

Proceedings of The 3rd Workshop on e-Commerce and NLP

“…Facet selection. Facet selection is linked to query classification on the research side (Lin et al, 2018;Skinner and Kallumadi, 2019) and query scoping on the product side, i.e. pre-selecting, say, the facet color with value black for a query such as "black basketball shoes" (Liberman and Lempel, 2014;Vandic et al, 2013).…”

“…Transportation and its related logistic services are critical aspects of every economy as they account for nearly 40% of the value of international trade (Rodrigue, 2007). As with most businesses (Gallaugher and Ransbotham, 2010;Gottipati et al, 2018), transportation also often relies on social media to ascertain feedback and initiate appropriate responses (Stelzer et al, 2016(Stelzer et al, , 2014. In our experimental work, we started with an annotated set of 326 samples of transportation complaints, and after four iterations of the approach, we collected 2,840 indicators and over 3,700 tweets.…”

NTUA-ISLab at SemEval-2019 Task 9: Mining Suggestions in the wild

“…The same line of research is followed in Negi and Buitelaar (2015) where, an SVM approach is employed to classify heuristically devised sequential features. In a follow-up work (Negi et al, 2016) Gottipati et al (2018) that exploits suggestions on student feedback comments utilizing a decision tree classification approach.…”