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This study presents an in-depth exploration of the impact of online learning interactions on student learning outcomes. Drawing from the Stimulus-Organism-Response (SOR) paradigm, our study focuses on the effects of online learning interactions on learners' perception usefulness and ease of use, subsequently impacting their learning outcomes. The study employs a quantitative research methodology, gathering data from a sample of 397 students enrolled in various higher education institutions across China. Data collection involved administering structured questionnaires that were designed to quantitatively assess the three components of the SOR model: stimulus (online learning interactions), organism (students' perceptions), and response (learning outcomes). The measurement model assessment and structural model assessment were conducted. Our findings reveal that online learning interactions can effectively enhance learners' perception of online learning (usefulness and ease of use), thereby influencing their learning outcomes. Notably, perceived usefulness negatively mediates the relationship between online learning interactions and learning outcomes, while perceived ease of use positively mediates this relationship. These findings offer both theoretical and practical implications.
RESUMO
Educational institutions need to respond to global competitive problems, and branding has become a method for higher education institutions to differentiate themselves. Thus, this study attempted to investigate predictors of employee brand-based equity. A cross-sectional research design has been used to record the perception of the teachers, and data are collected using a convenience sampling technique. Before administrating the study on large scale, a pilot testing was conducted, and reliability of the scale and their items was ensured. Pilot testing results indicated a satisfactory reliability level, and constructs correlations were in the assumed directions, which allowed to conduct the study on a large scale. A sample size of 400 was set, and questionnaires were distributed among the participants, out of which, 376 were received back, while 351 were left at the end after discarding incomplete responses. The left over and completed questionnaires indicate 88% response rate. Data have been analyzed through the Smart PLS software by applying the structural equation modeling technique. After establishment of the measurement model through reliability and validity, the structural model was used to test study hypotheses. All the study hypotheses were found statistically significant on the basis of t and p statistics. Results indicate that teacher's emotional intelligence enhances teachers' self-efficacy, which further improves their brand-based equity. Similarly, emotional intelligence increases teacher's performance, which also increases their brand-based equity. Limitations and future directions of the study are also reported.
RESUMO
Several primary school students in Fujian Province have perceived studying mathematics as challenging. To deal with this issue, computer technology advancements, specifically artificial intelligence (AI), present an opportunity to evaluate individual students' learning challenges and give individualized support to optimize their success in mathematics classes. It is also possible to use virtual reality (VR) to assist learners in acquiring complex mathematical and logical ideas and to lessen learners' mistakes. As a result, researchers, particularly beginners, are missing out on a complete perspective of the study of AI in teaching mathematics. That is why we are exploring the role of AI in math education by developing a "fuzzy-based tweakable convolution neural network with a long short-term memory (FT-CNN-LSTM-AM)" method. For this investigation, the students' datasets are taken and educated by mathematical teaching via the application of AI. The proposed method is utilized to predict the students' performance in mathematical education. A grey wolf optimizer is employed to boost the effectiveness of the proposed method. Furthermore, the performance of the proposed method is analyzed and compared with existing approaches to gain the highest reliability.