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.
Highlights
Massive Open Online Course, often abbreviated to MOOC, is online courses held by a university with the option of free or open registration
In the negative features, the most frequently occurred FSPs produced by the Machine Model are nearly the same as those produced by the Human Model
The sentiment analysis classification had been done using Support Vector Machine, and the number of accuracy, precision, recall, and F1 score was above 80%
Summary
Massive Open Online Course, often abbreviated to MOOC, is online courses held by a university with the option of free or open registration. Classes are conducted by weekly lectures using videos, online assessments, and discussion forums. Some educators assume that the MOOC’s quality of learning is different from faceto-face class because it cannot replace face-to-face classroom engagement, laboratory, fieldwork, and any other aspect [1][2][3]. According to [5], the challenges of Data-Driven Design usage as a product evaluation process are data structure, data understanding, incomplete information, nature of the data, and individual cognitive limitation. The data are often unstructured and heterogeneous; it is mandatory to process the data into information. The main challenge of data-driven design is to transform the large-scale unstructured data into meaningful information so that people are able to utilize the knowledge
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