Utilizing Text Mining and Feature-Sentiment-Pairs to Support Data-Driven Design Automation Massive Open Online Course

Abstract

This study aimed to develop a case-based design framework to analyze online us-er reviews and understanding the user preferences in a Massive Open Online Course (MOOC) content-related design. Another purpose was to identify the fu-ture trends of MOOC content-related design. Thus, it was an effort to achieve da-ta-driven design automation. This research extracts pairs of keywords which are later called Feature-Sentiment-Pairs (FSPs) using text mining to identify user preferences. Then the user preferences were used as features of an MOOC content-related design. An MOOC case study is used to implement the proposed framework. The online reviews are collected from www.coursera.org as the MOOC case study. The framework aims to use these large scale online review data as qualitative data and converts them into quantitative meaningful infor-mation, especially on content-related design so that the MOOC designer can de-cide better content based on the data. The framework combines the online re-views, text mining, and data analytics to reveal new information about users’ preference of MOOC content-related design. This study has applied text mining and specifically utilizes FSPs to identify user preferences in the MOOC content-related design. This framework can avoid the unwanted features on the MOOC content-related design and also speed up the identification of user preference.

Sentiment Analysis of Students’ Feedback in MOOCs: A Systematic Literature Review

“…( Dina et al, 2021 ) explored the performance of a quantitative (SA based) model to measure the user preferences regarding the course content. The sentiment analysis classification has been done using Support Vector Machine.…”

“…Here, the positive features included "interesting," "easy," and "duration of video is appropriate" (Au et al, 2016). (Dina et al, 2021) explored the performance of a quantitative (SA based) model to measure the user preferences regarding the course content. The sentiment analysis classification has been done using Support Vector Machine.…”

E-learning Text Sentiment Classification Using Hierarchical Attention Network (HAN)

“…Researches in [9] proposed a full systematic overview of the current status of the educational text mining field, by analysing multiple text mining methods and their projection on different educational sources and resources. The study in [10] applied text mining and Feature-Sentiment-Pair (FSP) to identify user preferences in the MOOC. Similar studies conducted text data classification in the exam questions automatically into its learning levels using Bloom's taxonomy and Natural Language Processing (NLP) [11].…”

Measuring User Satisfaction of Educational Service Applications using Text Mining and Multicriteria Decision-Making Approach

“…The text-mining process in the current study consists text segmentation, summary extraction, keyword identification, topic detection, term clustering and document categorization. The aim is to compile dictionaries that consist of two types of words: a dictionary of attributes and a dictionary of sentiment words, which is different from the method proposed by [19]. They considered attribute and sentiment words as pairs.…”