An Empirical Investigation of University Students’ Behavioural Intention to Adopt Online Learning: Evidence from China

Exploring the factors affecting elementary mathematics teachers' innovative behavior: an integration of social cognitive theory

Teacher innovative behavior is one of the vital factors, affecting student engagement, addresses diverse needs, promotes critical thinking, fosters lifelong learning, and contributes to educational research and development. By encouraging and supporting teacher innovation, we may can ensure that education remains relevant, effective, and impactful in preparing students for the future. Teacher innovative behavior is also needed to improve the mathematics skills of elementary school students, and it is important to determine the predictors that significantly affecting Teacher innovative behavior. Therefore, this study aimed to develop a model that predicted possible factors affecting mathematics teachers' innovative behavior based on Social Cognitive Theory (SCT). Data were collected from 132 elementary school teachers in China to verify the model, and the analysis was conducted using a structural equation modelling approach. Theoretically, 10 of the 15 hypotheses were found to be significant. The results showed that facilitating conditions and self-efficacy significantly affect mathematics teachers' innovative behavior. Meanwhile, Technological, Pedagogical and Content Knowledge (TPACK) knowledge, Social Influences, Rewards, Work engagement and anxiety did not show any effect. The contribution developed a model and provided new knowledge about the factors affecting elementary school teachers' innovative behavior. Practically, this could be used to improve teachers' innovative behavior.

Determinants of Continuous Usage Intention in Community Group Buying Platform in China: Based on the Information System Success Model and the Expanded Technology Acceptance Model

Community group buying is a new retail model with broad development prospects. The community group buying model in China has brought obvious social and economic benefits. However, the continuous usage rate on some community group buying platforms is relatively low. Consumers' continuous usage intentions are closely related to the sustainable development of community group buying platforms. Therefore, this study integrates the information system success model (D&M model) and the expanded technology acceptance model (TAM) to construct a research model that explores the factors influencing consumers' continuous usage intentions from both the platform's and consumers' perspectives. The survey data involving 418 respondents who used community group buying platforms were developed and analyzed for structural equation model (SEM) testing. The results show the following: (1) Perceived usefulness, perceived ease of use, service quality, perceived trust, expectation confirmation, and subjective norms significantly affect continuous usage intention. (2) Subjective norms are significantly related to continuous usage intention. Perceived usefulness, perceived ease of use, service quality, perceived trust, and expectation confirmation indirectly affect continuous usage intention through user satisfaction. This research provides a new perspective for the theoretical research of community group buying and helps to promote the sustainable development of community group buying platforms in management practice.

Predicting and Comparing Students’ Online and Offline Academic Performance Using Machine Learning Algorithms

Due to COVID-19, the researching of educational data and the improvement of related systems have become increasingly important in recent years. Educational institutions seek more information about their students to find ways to utilize their talents and address their weaknesses. With the emergence of e-learning, researchers and programmers aim to find ways to maintain students’ attention and improve their chances of achieving a higher grade point average (GPA) to gain admission to their desired colleges. In this paper, we predict, test, and provide reasons for declining student performance using various machine learning algorithms, including support vector machine with different kernels, decision tree, random forest, and k-nearest neighbors algorithms. Additionally, we compare two databases, one with data related to online learning and another with data on relevant offline learning properties, to compare predicted weaknesses with metrics such as F1 score and accuracy. However, before applying the algorithms, the databases need normalization to meet the prediction format. Ultimately, we find that success in school is related to habits such as sleep, study time, and screen time. More details regarding the results are provided in this paper.