RESUMEN
This mixed-method study explores the acceptance of ChatGPT as a tool for Metacognitive Self-Regulated Learning (MSRL) among academics. Despite the growing attention towards ChatGPT as a metacognitive learning tool, there is a need for a comprehensive understanding of the factors influencing its acceptance in academic settings. Engaging 300 preservice teachers through a ChatGPT-based scenario learning activity and utilizing convenience sampling, this study administered a questionnaire based on the proposed Technology Acceptance Model at UTM University's School of Education. Structural equation modelling was applied to analyze participants' perspectives on ChatGPT, considering factors like MSRL's impact on usage intention. Post-reflection sessions, semi-structured interviews, and record analysis were conducted to gather results. Findings indicate a high acceptance of ChatGPT, significantly influenced by personal competency, social influence, perceived AI usefulness, enjoyment, trust, AI intelligence, positive attitude, and metacognitive self-regulated learning. Interviews and record analysis suggest that academics view ChatGPT positively as an educational tool, seeing it as a solution to challenges in teaching and learning processes. The study highlights ChatGPT's potential to enhance MSRL and holds implications for teacher education and AI integration in educational settings.
RESUMEN
Investigation of task-technology fit and intention to use social media tools needs to focus specifically on higher education for teaching and learning, and its impact on students' academic performance. This article aims to develop a model that would identify essential aspects that are predicted to continue to play a large role in TTF for learning in BI, which could be used to improve academic performance in higher education. The purpose of this study was to investigate the characteristics and aspects of SM and the relationship between their use in the TTF and UTAUT theory to determine how they affect research students' satisfaction and AP in HE institutions. Data for the unified theory of acceptance and use of technology (UTAUT) and task-technology fit (TTF) theories were collected using a questionnaire survey. This research hypothesizes that behavioral intention to utilize social media and task-technology fit for learning will influence social characteristics, technology characteristics, performance expectancy, and effort expectancy, all of which will improve academic performance. As a test bed for this research, a structural equation model (SEM) was constructed examining the relationships between factors that affect students' academic performance. A stratified random sample strategy was used to disseminate the main tool of data collection, a questionnaire, to 383 students. A quantitative method was used to examine the results. The obtained outcomes showed that there was a correlation among social characteristics, technological characteristics, behavioral intention to use social media, and task-technology fit for academic performance, which aided student performance and results. The study indicates that PEX and EEX also demonstrated a strong relation to task-technology fit and behavioral intent to use social media for academic purposes, both of which positively impacted academic performance. As a result, the study found that behavioral intention to utilize and task-technology-fit social media promote students' active learning and enable them to discuss and exchange knowledge and information more efficiently. In conclusion, we encourage students to use social media for educational purposes in their studies and teaching through lectures in HE institutions.
