Malaysia and many other developing countries progressively adopting massively open online course (MOOC) in their national higher education approach. We have observed an increasing need for facilitating MOOC monitoring that is associated with the rising adoption of MOOCs. Our observation suggests that recent adoption cases led analyst and instructors to focus on monitoring enrolment and learning activities. Visual analytics in MOOC support education analysts in analyzing MOOC data via interactive visualization. Existing literature on MOOC visualization focuses on enabling visual analysis on MOOC data from forum and course material. We found limited studies that investigate and characterize domain problems or design requirements of visual analytics for MOOC. This paper aims to present the empirical problem characterization and abstraction for visual analytics in MOOC learner's support monitoring. Detailed characterization and abstraction of the domain problem help visualization designer to derive design requirements in generating appropriate visualization solution. We examined the literature and conducted a case study to elicit a problem abstraction based on data, users, and tasks. We interviewed five Malaysian MOOC experts from three higher education institutes using semi-structured questions. Our case study reveals the priority of enabling MOOC analysis on learner's progression and course completion. There is an association between design and analysis priority with the pedagogical type of implemented MOOC and users. The characterized domain problems and requirements offer a design foundation for visual analytics in MOOC monitoring analysis.
Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a popular multi-criteria decision-making method that ranks the available alternatives by examining the ideal-positive and ideal-negative solutions for each decision criterion. The first step of using TOPSIS is to normalize the presence of incommensurable data in the decision matrix. There are several normalization methods, and the choice of these methods does affect TOPSIS results. As such, some efforts were made in the past to compare and recommend suitable normalization methods for TOPSIS. However, such studies merely compared a limited collection of normalization methods or used a noncomprehensive procedure to evaluate each method's suitability, leading to equivocal recommendations. This study, therefore, employed an alternate, comprehensive procedure to evaluate and recommend suitable benefit/cost criteria-based normalization methods for TOPSIS (out of ten methods extracted from past literature). The procedure was devised based on three evaluation metrics: the average Spearman's rank correlation, average Pearson correlation, and standard deviation metrics, combined with the Borda count technique.•The first study examined the suitability of ten benefit/cost criteria-based normalization methods over TOPSIS.•Users should combine the sum-based method and vector method into the TOPSIS application for safer decision-making.•The maximum method (version I) or Jüttler's-Körth's method has an identical effect on TOPSIS results.