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Purpose – The purpose of this paper is to propose a game-based learning (GBL) content design model that replicates the two-dimensional Bloom cognitive process in GBL units. The proposed model, called the knowledge and cognitive-process representation (KCR) model, enables a game player to access three types of Bloom knowledge by allowing the learner to experience-related cognitive processes that can be replicated in the GBL units via appropriate representation approaches. Design/methodology/approach – To validate the feasibility of the proposed KCR model, 14 GBL units for a Cisco-certified network associate (CCNA) certification training program were designed and installed on several servers. Players played the GBL units via internet browsers. According to the problem-solving theory, three game components, including a tool, feedback, and goal, are necessary for game playing and should be adopted to implement three sub-cognitive processes. A three-phase experiment was performed for one year. Subjects were university sophomores and a randomized block experiment design was implemented. Findings – The experimental results show that, compared with a traditional web-based learning platform, the GBL platform is more efficient and it enables learners to achieve improved learning performance. In addition, most hypotheses support the fact that particular cognizance processes should be implemented by a specific representation approach in GBL. Finally, a KCR model for GBL content design is inferred to represent a cognitive process appropriately that can be referenced for both the digital content instructor and the game developer. Research limitations/implications – Because the CCNA training material does not include meta-knowledge of Bloom knowledge type and the creation of the Bloom cognitive process, the KCR model should be further extended. In addition, others certification training materials (such as Oracle DBA, Java programmer) can be implemented on the basis of the KCR model for general validation as further research. Practical implications – Players can acquire specific types of knowledge, such as factual knowledge, by experiencing a particular cognitive process, such as the “remembering & understanding” processes, which can be represented with a computer tool. The KCR model can provide both the instructor and the game developer with design recommendations and accelerate GBL content implementation. Originality/value – GBL is a learning platform that can stimulate a learner by improving the motivation to learn and the learning experience. To ensure high-learning performance, the learner should perform specific cognitive processes and acquire knowledge. This research proposes a content design model for GBL units that appropriately replicate the Bloom framework in a computer game.

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In practice, single item support cannot comprehensively address the complexity of items in large datasets. In this study, we propose a big data analytics framework (named Multiple Item Support Frequent Patterns, MISFP-growth algorithm) that uses Hadoop-based parallel computing to achieve high-efficiency mining of itemsets with multiple item supports (MIS). The proposed architecture consists of two phases. First, in the counting support phase, a Hadoop MapReduce architecture is employed to determine the support for each item. Next, in the analytics phase, sub-transaction blocks are generated according to MIS and the MISFP-growth algorithm identifies the frequency of patterns. To facilitate decision makers in setting MIS, we also propose the concept of classification of item (COI), which classifies items of higher homogeneity into the same class, by which the items inherit class support as their item support. Three experiments were implemented to validate the proposed Hadoop-based MISFP-growth algorithm. The experimental results show approximately 38% reduction in the execution time on parallel architectures. The proposed MISFP-growth algorithm can be implemented on the distributed computing framework. Furthermore, according to the experimental results, the enhanced performance of the proposed algorithm indicates that it could have big data analytics applications.

Decision making is a recursive process and usually involves multiple decision criteria. However, such multiple criteria decision making may have a problem in which partial decision criteria may conflict with each other. An information technology, such asthe decision support system(DSS) and group DSS (GDSS), emerges to assist decision maker for decision-making process. Both the DSS and GDSS should integrate with a symmetrical approach to assist decision maker to take all decision criteria into consideration simultaneously. This study proposes a GDSS architecture named hybrid decision-making support model (HDMSM) and integrated four decision approaches (Delphi, DEMATEL, ANP, and MDS) to help decision maker to rank and select appropriate alternatives. The HDMSM consists of five steps, namely, criteria identification, criteria correlation calculation, criteria evaluation, critical criteria selection, and alternative rank and comparison. Finally, to validate the proposed feasibility of the proposed model, this study also conducts a case study to find out the important indexes of corporate social responsibility (CSR) from multiple perspectives. As the case study demonstrates the proposed HDMSM enables a group of decision makers to implement the MCDM effectively and help them to analyze the relation and degree of mutual influence among different evaluation factors.