DME - Comunicações a Conferências / ConferenceItem
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Percorrer DME - Comunicações a Conferências / ConferenceItem por Domínios Científicos e Tecnológicos (FOS) "Engenharia e Tecnologia"
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- Collaborative learning and use of digital tools: Impacts on University students’ learning motivation and satisfactionPublication . Silva, Osvaldo; Sousa, ÁureaABSTRACT: Collaborative learning is widely recognised as a pedagogical approach that enhances students’ active engagement, supports the co-construction of knowledge, and improves learning outcomes in higher education. The growth of digital and hybrid teaching has expanded opportunities to integrate online collaborative tools, enabling more flexible interaction, communication, and resource sharing, even across geographical distances. However, the effectiveness of these tools depends not only on their technical affordances but also on how they are perceived and adopted by students. The Technology Acceptance Model (TAM) therefore offers a robust framework for understanding the factors that shape students’ acceptance, motivation, and satisfaction when using educational technologies. In the university context, collaborative learning has been associated with improved critical thinking, stronger cognitive and emotional engagement, shared responsibility, and increased intrinsic motivation. This study examines how collaborative learning supported by online tools influences university students’ learning motivation and satisfaction, integrating key TAM variables, namely perceived usefulness (PU) and perceived ease of use (PEOU), using a PLS-SEM approach. Data were collected from 752 Portuguese students through a validated questionnaire including nine constructs and sociodemographic variables. All nineteen hypotheses were supported (thirteen direct and six indirect effects). Findings show that attitude strongly predicts perceived enjoyment and satisfaction, and that learning motivation is a powerful determinant of learning satisfaction. Perceived ease of use also strongly enhances learning motivation, while collaborative learning exerts significant positive effects on multiple outcomes. The use of online collaborative tools mediates the relationships between collaborative learning, perceived usefulness, perceived ease of use, and both learning motivation and satisfaction. Overall, combining collaborative learning with user-friendly digital tools leads to higher levels of motivation and satisfaction among university students. Careful instructional design, robust technological support, appropriate training, and the provision of clear guidance materials are essential for higher education institutions seeking to maximise the benefits of digital collaborative learning.
- How students feel and behave in Statistics: Predicting procrastination and performancePublication . Silva, Osvaldo; Sousa, Áurea; IATEDABSTRACT: Statistics education plays a crucial role in the development of scientific literacy across diverse academic fields, yet many university students experience difficulties associated with low self-efficacy, anxiety, and negative attitudes towards statistics. These affective and motivational factors influence engagement, procrastination, and academic performance, forming a complex network of relationships that remains underexplored in integrated models. This study examined the effects of self-efficacy, statistics anxiety, attitudes towards statistics, engagement in statistics, procrastination, and perceived academic performance using a structural equation modelling approach (PLS-SEM). A sample of 668 Portuguese university students completed a questionnaire comprising these six validated constructs and some sociodemographic variables. Of the twelve hypotheses tested, seven were supported—five direct effects and two indirect effects. Statistics anxiety had a strong positive impact on procrastination, while more favourable attitudes towards statistics significantly reduced anxiety and enhanced engagement and perceived academic performance. Procrastination negatively affected academic performance. Additionally, attitudes influenced performance indirectly through sequential mediation by anxiety and procrastination, and procrastination mediated the impact of anxiety on performance. These findings highlight the central role of attitudes, the detrimental effects of anxiety, and the behavioural consequences of procrastination in shaping students’ learning outcomes. The study underscores the need for pedagogical strategies that foster positive attitudes, reduce anxiety, support engagement, and promote effective self-regulation in statistics education.
- Key factors influencing university students’ intention to use generative AI and its impact on satisfactionPublication . Silva, Osvaldo; Sousa, ÁureaABSTRACT: This study aims to explore the key determinants influencing university students’ behavioural intention to use Generative Artificial Intelligence (BI_GAI) tools in educational settings, as well as the impact of this intention on student satisfaction (SS). Grounded in the Technology Acceptance Model (TAM), the research incorporates the traditional constructs of Perceived Ease of Use (PE) and Perceived Usefulness (PU) and extends the model by integrating Perceived Intelligence (PI), Perceived Trust (PT), Perceived Risk (PR), Expected Benefits (EB), and Technology Self-Efficacy (TSE). Data were collected from 775 students at a Portuguese higher education institution through a questionnaire comprising 40 items across nine constructs (PE, PI, PU, PT, PR, BI_GAI, EB, TSE, and SS), alongside sociodemographic variables. The data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The results reveal that PE and PI have a significant positive effect on Behavioural Intention to Use GAI (BI_GAI), whereas PU does not have a statistically significant direct influence. Perceived Trust (PT) emerges as a key mediating variable in the relationship between PU and BI_GAI, while Perceived Risk (PR) does not act as a significant mediator between the TAM constructs and BI_GAI. Behavioural Intention to Use GAI has the strongest direct influence on Student Satisfaction (SS), highlighting its central role in understanding students’ engagement with GAI tools. Moreover, both EB and TSE significantly affect SS, both directly and indirectly through BI_GAI. These findings support the development of an expanded TAM-based model that provides a more holistic perspective on the technological, psychological, and educational factors shaping GAI adoption in higher education. The inclusion of constructs such as PI, PT, and TSE offers deeper insights into the mechanisms through which students evaluate and adopt GAI for learning purposes, ultimately contributing to enhanced academic satisfaction.
- Potential factors promoting university student motivation and satisfaction in a blended learning contextPublication . Silva, Osvaldo; Sousa, ÁureaABSTRACT: Blended learning has gained prominence in higher education in response to the growing integration of technology in society, aiming to enhance student engagement by combining face-to-face instruction with online components. This pedagogical approach supports autonomy, encourages active participation, and promotes critical thinking by enabling students to apply theoretical concepts to practical situations through digital tools and interactive platforms. This study investigates the key factors influencing student motivation and satisfaction in blended learning environments. It draws on two theoretical frameworks: Self-Determination Theory (SDT), which highlights the psychological needs of autonomy, relatedness, and competence as essential for intrinsic motivation; and the Technology Acceptance Model (TAM), which focuses on Perceived Usefulness (PU) and Perceived Ease of use (PEOU) as predictors of technology acceptance. The research was conducted at a Portuguese university with a sample of 444 students, who completed a questionnaire comprising 38 items distributed across seven constructs (namely, Autonomy, Relatedness, Competence, PU, PEOU, Learning Motivation (LM), and Learning Satisfaction (LS)), along with sociodemographic data. The analysis employed Partial Least Squares Structural Equation Modelling (PLS-SEM), evaluating both the measurement model and the structural model, using bootstrapping to assess the significance of the path coefficients. The results confirmed most of the formulated hypotheses. Perceived ease of use and perceived usefulness were the strongest predictors of learning motivation, which, in turn, significantly influenced learning satisfaction. Additionally, PU and PEOU mediated the relationship between the SDT factors and learning motivation. However, PEOU did not mediate the relationship between competence and PU. These findings offer valuable insights for university leaders, educators, and student organisations, highlighting the importance of aligning blended learning strategies with students’ psychological and technological needs to enhance their motivation and overall satisfaction.