This study analyzes the effectiveness of adding educational gaming elements into the online lecture system of the flipped classroom as a method to increase participation and interest in online preparation before class. This study held an “understanding sequence” for 20 classes during the 7‐week automation equipment class with 30 s year high school students in Incheon's specialized technical high school as target. After the class, the learning attitude of learners was measured through surveys and in‐depth interviews. As a result, first, it was found that the degree of preparation participation in flip learning using gaming elements had a statistically significant increase from 65.56% to 78.89%, compared with the traditional flip learning using YouTube. Second, comparing the academic achievements showed a diagnostic assessment of 57.44 before applying the gaming elements and 20.17 after the application. However, for summative assessment, the degree after applying gaming elements was statistically higher at 84.52 than the degree before application, which was 78.86. Third, comparing academic achievement with word game results showed no significant correlation, and it is judged that all students were able to enjoy word games, regardless of their grades. Also, when the average word game scores were compared based on students’ grades, mid‐upper level students had statistically significant higher scores than upper level students. Lastly, analysis on the correlation between attitude and word game showed that there is a quantitatively high correlation between the ranking system's competitive spirit and interest. The ranking system increased competitive spirit, as well as interest.

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The most commonly used algorithm in recommendation systems is collaborative filtering. However, despite its wide use, the prediction accuracy of this algorithm is unexceptional. Furthermore, whether quantitative data such as product rating or purchase history reflect users’ actual taste is questionable. In this article, we propose a method to utilise user review data extracted with opinion mining for product recommendation systems. To evaluate the proposed method, we perform product recommendation test on Amazon product data, with and without the additional opinion mining result on Amazon purchase review data. The performances of these two variants are compared by means of precision, recall, true positive recommendation (TPR) and false positive recommendation (FPR). In this comparison, a large improvement in prediction accuracy was observed when the opinion mining data were taken into account. Based on these results, we answer two main questions: ‘Why is collaborative filtering algorithm not effective?’ and ‘Do quantitative data such as product rating or purchase history reflect users’ actual tastes?’

As Information & Communication Technology(ICT) is rapidly evolved, educational paradigms have been changing. The ultimate goal of education with the aid of ICT is to provide customized training for learners to improve the effectiveness of their learning at anytime and anywhere. In the online learning environment where the Internet, mobile devices, peer-to-peer (P2P) and the cloud technology are leveraged, all the information in learning activities is converted into digital data and stored in the Computer Supported Collaborative Learning (CSCL) system. The data in the CSCL system contains various learners' information including the learning objectives, learning preferences, competences and achievements. Thus, by analyzing the activity information of learners in an online CSCL system, meaningful and useful information can be extracted and provided for learners, teachers and administrators as feedback. In this paper, we propose a learner activity model that represents the learner's activity information stored in a CSCL system. As for the proposed learner activity model, we classified the learning activities in a CSCL system into three categories: vivacity, learning and relationship; then we created quotients to represent them accordingly. In addition, we developed a CSCL System, which we termed as COLLA, applied the proposed learner activity model and analyzed the results.

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