First-year students’ learning performance has received much attention in educational practice and theory. Previous works used some variables, which should be obtained during the course or in the progress of the semester through questionnaire surveys and interviews, to build prediction models. These models cannot provide enough timely support for the poor performance students, caused by economic factors. Therefore, other variables are needed that allow us to reach prediction results earlier. This study attempts to use family background variables that can be obtained prior to the start of the semester to build learning performance prediction models of freshmen using random forest (RF), C5.0, CART, and multilayer perceptron (MLP) algorithms. The real sample of 2407 freshmen who enrolled in 12 departments of a Taiwan vocational university will be employed. The experimental results showed that CART outperforms C5.0, RF, and MLP algorithms. The most important features were mother’s occupations, department, father’s occupations, main source of living expenses, and admission status. The extracted knowledge rules are expected to be indicators for students’ early performance prediction so that strategic intervention can be planned before students begin the semester.
Due to advances in information and communication technology, e-teaching has become increasingly popular and is in high demand by educational organizations. During the lockdown period of COVID-19 especially, e-teaching provided prior solutions to address the pressing need for monitoring students’ learning progress. However, in many developing countries, it is apparent that a wide variety of issues are related to e-teaching adoption. Although the implementation issues associated with e-teaching have been addressed in the existing research literature and in practice for many years, from the available research, the evaluation of e-teaching adoption criteria and ranking using fuzzy theory has been ignored. Therefore, the present research aims to evaluate and rank the criteria for e-teaching adoption through Fuzzy Delphi and Fuzzy TOPSIS. A total of four criteria and twelve sub-criteria for e-teaching adoption were determined based on a systematic literature review and professors’ opinions in India. In addition, the Fuzzy Delphi method was employed to finalize the criteria, and the Fuzzy TOPSIS method was employed for ranking the alternatives. The assessment results showed that among the identified alternatives, the “share the technology with other organizations” and “course integration with technology” were the top-ranked alternatives for improving e-teaching adoption. An understanding of these conceptual alternatives can encourage the adoption of e-teaching in educational organizations.
Traditional data-driven feature selection techniques for extracting important attributes are often based on the assumption of maximizing the overall classification accuracy. However, the selected attributes are not always meaningful for practical problems. So, we need additional confirmation from the experts in the domain knowledge to determine whether these extracted features are meaningful knowledge. Moreover, due to advances in mobile devices and wireless environments, programmatic buying (PB) has become one of the critical consumer behaviors in e-commerce. However, it is extremely difficult for PB service providers to build customers’ loyalty, since PB customers require a high level of service quality and can quickly shift the purchases from one website to another. Previous studies developed various dimensions/models to measure the service quality of PB; nevertheless, they did not identify the key factors for increasing customers’ loyalty and satisfaction. Consequently, this study used an importance–satisfaction (IS) model as domain knowledge and proposed a new IS-DT feature selection method. This new IS-DT method combined the IS model and the decision tree (DT) algorithm to extract useful service quality factors for enhancing customer satisfaction and loyalty in PB. An actual case was also provided to illustrate the effectiveness of our proposed method. The results showed that for increasing customer satisfaction, the highest impact factors included “problem solving”, “punctuality”, “valence”, and “ease of use”; for building customer loyalty, the most important factors were “expertise”, “problem solving”, “information”, “single column”, “voice guidance”, “QR code”, “situation”, “tangibles”, “assurance”, “entertainment”, and “safety”. Our IS-DT method can effectively determine important service quality factors in programmatic buying.
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