Instructor Clarity and Student Interest: The Mediating Role of Students’ Academic Satisfaction and State Motivation in Spanish Higher Education

Generative Artificial Intelligence (GenAI) tools hold significant promises for enhancing teaching and learning outcomes in higher education. However, continues usage behavior and satisfaction of educators with GenAI systems are still less explored. Therefore, this study aims to identify factors influencing academic staff satisfaction and continuous GenAI usage in higher education, employing a survey method and analyzing data using Partial Least Squares Structural Equation Modeling (PLS-SEM). This research utilized the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Expectation Confirmation Model (ECM) as its theoretical foundations, while also integrating ethical concerns as a significant factor. Data was collected from a sample of 127 university academic staff through an online survey questionnaire. The study found a positive correlation between effort expectancy, ethical consideration, expectation confirmation, and academic staff satisfaction. However, performance expectancy did not show a positive correlation with satisfaction. Performance expectancy was positively related to the intention to use GenAI tools, while academic staff satisfaction positively influenced the intention to use GenAI. The social influence did not correlate positively with the use of GenAI. Security and privacy were positively associated with staff satisfaction. Facilitation conditions also positively influenced the intention to use GenAI. The findings of this study provide valuable insights for academia and policymakers, guiding the responsible integration of GenAI tools in education while emphasizing factors for policy considerations and developers of GenAI tools.

This study examines the influence of generative Artificial Intelligence (AI) tools on student learning outcomes in higher education. A questionnaire developed using two validated instruments (perceived usefulness of generative AI tools among students and the level of interest of students’ interaction with study materials after using generative AI tools) was used to collect primary data from 208 students across four institutions. The collected data were analyzed using descriptive statistics, inferential statistical methods, and Partial Least Squares Structural Equation Modeling (PLS-SEM). The outcome of the analysis highlights the ability of generative AI to enhance academic engagement, intellectual curiosity, and personalized learning experiences. Key findings include the high perceived usefulness of generative AI in understanding complex topics and connecting coursework to real-world applications. Also, generative AI has the potential to support active knowledge construction and cognitive development, offering actionable insights for educators and policymakers. Challenges such as limited technical training for academics and data privacy concerns are identified as factors that reduce the positive impact of generative AI in student learning outcomes. Conclusively, generative AI could be used to enhance learning outcomes and streamline educational processes.

This systematic review evaluates the impact of educational technology on learning outcomes in the higher education sector. With the rapid integration of digital tools in educational settings, understanding their effectiveness has become crucial. The paper's objective is to synthesize existing research findings to assess how educational technologies influence learning outcomes. We employed a comprehensive search strategy across multiple databases, including peer-reviewed journals and conference proceedings, to collect studies published in the last decade. The inclusion criteria focused on empirical studies that measured the impact of technologies such as learning management systems, online simulations, and digital collaborative tools on student learning outcomes in higher education. Our methodology involved a rigorous screening process, quality assessment, and data extraction, followed by a thematic synthesis of the findings. The review included a total of 47 studies, which were analyzed to identify patterns, themes, and gaps in the current literature. Key findings suggest that educational technology, when effectively integrated into teaching and learning processes, can enhance student engagement, improve knowledge retention, and foster higher-order thinking skills. However, the impact varies significantly depending on the type of technology used, pedagogical approach, and context of implementation. The review also highlights the importance of faculty training and support in maximizing the potential benefits of educational technologies. Educational technology holds promise for improving learning outcomes in higher education, but its success is contingent upon thoughtful implementation, pedagogical alignment, and ongoing support for instructors. Future research should focus on longitudinal studies to better understand the long-term effects of educational technologies on learning outcomes. Keywords: Educational Technology, Learning Outcomes, Higher Education, Digital Divide, Accessibility, Equity, Blended Learning, Virtual Reality, Artificial Intelligence, Mobile Learning, Pedagogical Approaches, Professional Development, Strategic Integration.

Over the past decade, international interest has grown in the assessment of student learning outcomes (SLOs) in higher education. Policy-driven, outcomes-oriented reform programs have introduced long-term changes to the higher education sector, particularly in member countries of the Organisation for Economic Co-operation and Development (OECD). International assessments of education first gained importance in primary and secondary education with the First International Mathematics Study (FIMS), carried out in 1964 by the International Association for the Evaluation of Educational Achievement (IEA). FIMS was followed by a second study (SIMS), and a third that also included science, the Third International Mathematics and Science Study (TIMSS). Higher education institutions are expected to be accountable for the effectiveness of their courses and study programs; such accountability can be enhanced through information gathered from formative and summative assessments of students' basic generic and domain-specific knowledge and skills, and relevant determinants thereof, such as aptitudes and competencies they bring with them upon entering higher education.

This systematic review evaluates the impact of educational technology on learning outcomes in the higher education sector. With the rapid integration of digital tools in educational settings, understanding their effectiveness has become crucial. The paper's objective is to synthesize existing research findings to assess how educational technologies influence learning outcomes. We employed a comprehensive search strategy across multiple databases, including peer-reviewed journals and conference proceedings, to collect studies published in the last decade. The inclusion criteria focused on empirical studies that measured the impact of technologies such as learning management systems, online simulations, and digital collaborative tools on student learning outcomes in higher education. Our methodology involved a rigorous screening process, quality assessment, and data extraction, followed by a thematic synthesis of the findings. The review included a total of 47 studies, which were analyzed to identify patterns, themes, and gaps in the current literature. Key findings suggest that educational technology, when effectively integrated into teaching and learning processes, can enhance student engagement, improve knowledge retention, and foster higher-order thinking skills. However, the impact varies significantly depending on the type of technology used, pedagogical approach, and context of implementation. The review also highlights the importance of faculty training and support in maximizing the potential benefits of educational technologies. Educational technology holds promise for improving learning outcomes in higher education, but its success is contingent upon thoughtful implementation, pedagogical alignment, and ongoing support for instructors. Future research should focus on longitudinal studies to better understand the long-term effects of educational technologies on learning outcomes.

One of the most significant European higher education reform initiatives of the last decade is the introduction of a European Qualification Framework (EQF) emphasizing Learning Outcomes (LOs) in higher education. The EQF is offered as a reform to contribute to increased transparency and mobility, and also implies a certain degree of standardization and comparability as to how these initiatives are implemented in European countries. The current article considers these changes in light of institutional perspectives that highlight how common HE reforms, in practice, often vary considerably. It investigates how factors of national policy-making contexts, reform traditions and broader reform agendas contribute to variations in contemporary interpretations and applications of LOs, here in the cases of Norwegian and English HE. It argues that (1) the characteristics of English and Norwegian higher education provided contexts where the perceptions of LOs evolved in very different ways, (2) the different political–administrative structures in the two countries were linked to different governance logics at the national level and institutional levels, and (3) despite these variations, some common mechanisms driving reform can be identified, in the role of intermediary and quality assurance bodies.

Motivation is a core determinant of students’ academic engagement, persistence, and learning outcomes in higher education. This study investigates the effects of classroom teachers’ motivational practices on students’ learning outcomes in selected higher education institutions of South Punjab. Using a quantitative descriptive correlational design, the research sampled 50 teachers and 120 students through cluster sampling. Two structured Likert-scale questionnaires measured motivational practices, motivational climate, and student learning outcomes. Data analysis through SPSS included frequencies, mean scores, standard deviations, t-tests, and Pearson correlation coefficients. Descriptive results showed that teachers frequently use motivational practices such as praise, constructive feedback, fairness, reinforcement, and encouragement. Students agreed that these practices significantly enhanced their academic performance, classroom participation, and overall motivation. Inferential statistics revealed a strong positive correlation (r = .71, p < .01) between motivational practices and learning outcomes. Gender-based differences in perceptions were statistically insignificant. The study concludes that motivational practices have a substantial positive effect on students’ learning outcomes. It recommends institutional support for motivational training, reflective teaching, student-centered pedagogy, and motivational assessments in higher education.

This study aims to systematically review the application of machine learning-based clustering algorithms in the evaluation of Graduate Learning Outcomes (CPL) in higher education. The review was conducted using the PRISMA approach on articles published in the Scopus database during the period 2020–2025. A total of 52 articles were analyzed to identify trends in the algorithms used, implementation challenges, and their contributions to curriculum development. The findings show that algorithms such as K-Means, Hierarchical Clustering, and Fuzzy C-Means are frequently used in mapping student competencies. However, their implementation in practice remains limited due to insufficient model validation, lack of justification for algorithm selection, and a disconnect between analytical results and academic decision-making. This situation reflects a broader issue in the integration of machine learning into educational contexts, where the technical potential of algorithms has not yet been fully translated into meaningful pedagogical impact. As a conceptual contribution, this study develops a machine learning-based computational model that includes the stages of CPL data collection, preprocessing, cluster modeling, result evaluation, and integration into curriculum policy. The proposed model is designed to enhance transparency, adaptability, and evidence-based decision-making in curriculum management systems. This study also highlights the need for the development of soft clustering techniques, integration with digital learning systems, and attention to the ethics and transparency of algorithms in data-based evaluation. Thus, this study emphasizes the importance of bridging the gap between algorithmic analysis and applicable educational strategies within higher education institutions.

This study aims to analyze the effect of the blended learning science and technology community approach on student learning outcomes in higher education. The research design used in this study was quasi-experimental with The Matching-Only Post-test-Only Control Group Design. A total of 120 students participated in this study. Subjects were divided into 2 groups, namely the control group (K1) and the treatment group (K2). The instrument of learning outcomes items will first be tested for validity and reliability, then the learning outcomes data that have been obtained will be analyzed using the independent t-test method. The results of this study indicate that the data from the validity test items of the learning outcomes test instrument, out of 40 question items, there are 38 items that are declared valid. reliability test results with an alpha coefficient of 0.880 so that the learning outcomes test instrument is said to be reliable and consistent in data collection. The results of the t-test obtained P <0.05, meaning that there is a significant effect of blended learning with the science technology approach of society on learning outcomes. It can be concluded that blended learning with a community science technology approach can improve student learning outcomes in higher education. It is hoped that blended learning can be used and developed again in learning in higher education.

Metacognitive instructional strategies aim at enhancing the awareness of learners to their cognitive strategies so that they can plan, observe, and assess their learning strategies. These practices can change the focus on just accepting knowledge to self-regulated learning, which is a crucial skill in higher education, where critical thinking and problem solving are the keys to academic achievement and life-long education. The researcher seeks to examine the application and the efficacy of metacognitive instruction methods including self-questioning, reflection journaling, and think-aloud schemes on learning outcomes in higher education. The aims will be to determine the typical use of metacognitive instructional practices, assess their effects on student performance, and student perceptions of metacognitive instructional practices. The research design used was mixed-methods research design, integrating both quantitative and qualitative approaches to provide a comprehensive understanding of metacognitive teaching strategies and their impact on learning outcomes in higher education. The quantitative component examines the relationship between exposure to metacognitive strategies and academic performance, while the qualitative component explores in-depth perceptions and experiences of students and instructors. This dual approach enhances the validity and richness of the findings. The research design included a survey of 200 undergraduate students and in-depth interview of 20 faculty members of the university, of various disciplines. Quantitative data were analyzed using statistical tools to assess performance outcomes, while qualitative data provided insights into student and teacher experiences. The findings reveal a positive correlation between the use of metacognitive strategies and improved academic performance, motivation, and self-efficacy. Students exposed to metacognitive instruction demonstrated better critical thinking, problem-solving, and knowledge retention. Faculty reported enhanced classroom engagement and deeper learning. The study highlights the need for integrating metacognitive training into teacher education programs and curriculum design to foster independent, reflective learners in higher education.

As education undergoes digital transformation, ChatGPT-4 has emerged as one of the most visible tools of generative artificial intelligence. While widely discussed, its impact on student satisfaction and learning outcomes in higher education remains underexplored. This study investigates the factors that shape art and design students’ satisfaction when using ChatGPT to support coursework. Unlike previous research focusing on ChatGPT adoption behavior, this study extends the Information Systems Success Model (ISSM) to the context of art and design education. Drawing on 435 valid survey responses, we employed a mixed-methods approach. Partial Least Squares Structural Equation Modeling (PLS-SEM) was first applied to examine how system quality, compatibility, personal innovativeness, and perceived usefulness influence satisfaction directly and through mediating mechanisms. To complement this, fuzzy-set Qualitative Comparative Analysis (fsQCA) was used to identify multiple combinations of conditions that lead to high satisfaction. The findings show that compatibility, perceived usefulness, and personal innovativeness significantly enhance satisfaction, with path coefficients of 0.378, 0.342, and 0.155, respectively. Importance–Performance Map Analysis (IPMA) further highlights personal innovativeness and system quality as critical drivers. By providing both theoretical and practical insights, this study contributes to the growing body of research on generative AI in art and design education and informs the design of courses and digital learning tools.

In order to define and accomplish learning outcomes in higher education, this study investigates the idea of transdisciplinary learning and its consequences. It explores how transdisciplinary techniques might improve students critical thinking, creativity, and problem-solving abilities by drawing on theoretical frameworks and practical data. It also discusses challenges and opportunities associated with implementing transdisciplinary approaches in higher education, including issues related to curriculum design, assessment methods, and institutional support structures. It highlights examples of successful transdisciplinary initiatives from various educational. This proposed book chapter seeks to explore the significance of transdisciplinary approaches in shaping learning outcomes in higher education settings. By offering insights into the theoretical foundations and practical implications of transdisciplinary approaches to learning outcomes, it aims to contribute ongoing discussions and debates surrounding curriculum development and educational innovation in higher education.

Modern higher education faces challenges for both learners and lecturers. While digital resources, Web 2.0 technologies, and online connectivity offer significant learning opportunities, many Virtual Learning Environments (VLEs) remain basic and fragmented. This study investigated technology-enhanced learning and teaching in Kenyan higher education institutions. Utilizing a descriptive survey design, the study involved 15 ICT staff and 1467 students from various institutions, employing stratified and purposive sampling methods. Data was collected through questionnaires and interview schedules, with quantitative analysis conducted using SPSS and qualitative data analyzed thematically. The findings reveal that technology enhances learning by providing access to information, promoting interactive and immersive experiences, fostering collaboration, and offering cost-effective resources. It also supports adaptive learning, critical thinking, and skills development. However, challenges such as limited access to technology, digital literacy gaps, high costs, data privacy concerns, and resistance to technology adoption were identified. Despite these obstacles, universities are successfully using technology to promote active learning, collaboration, and academic support, contributing to improved teaching and learning outcomes in higher education.

The main objective of this study was to work toward the development of a number of measures of student learning outcomes (SLOs) in higher education. Specifically, we used data from Exame Nacional de Desempenho dos Estudantes (ENADE), a college-exit examination developed and used in Brazil. The fact that Brazil administered the ENADE to both freshmen and senior students provided a unique opportunity to get a first approximation of the general and subject area knowledge gained in different programs. The results suggested that, on average, students in the three different categories of programs were gaining valuable general and subject area knowledge. The gains in the subject area were of a larger magnitude than those in the general knowledge component of the test. This study contributes to the field by providing empirical and visually compelling evidence related to SLOs gains in higher education.

Bacground. The traditional lecture-based model of teaching in higher education has faced increasing criticism due to its limited engagement and ability to foster active learning. In response, the flipped classroom model has emerged as a promising alternative, where students engage with instructional content outside the classroom and use class time for collaborative and interactive activities. This pedagogical shift aims to enhance student learning outcomes, promote critical thinking, and increase overall engagement. Purpose. This study aims to investigate the effectiveness of flipped classroom models in higher education, focusing on their impact on student engagement, academic performance, and learning outcomes. Method: A mixed-methods approach was employed, combining quantitative analysis of student performance data with qualitative insights from surveys and interviews. A sample of 200 students from five different universities participated in flipped classroom courses, and their learning outcomes were compared to those of students in traditional lecture-based courses. Data were collected at the beginning and end of the semester to assess changes in engagement and academic performance. Results: The study found that students in flipped classrooms showed significantly higher levels of engagement and academic performance compared to their peers in traditional settings. Students reported increased satisfaction with the learning process, particularly in terms of collaborative learning and self-paced study. Conclusion: The flipped classroom model proves to be an effective strategy for enhancing student engagement and improving learning outcomes in higher education. This approach fosters a more active, student-centered learning environment that better prepares students for real-world challenges.