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In today’s highly competitive world, the purpose of this research is to emphasize the increasing significance of management education and advocate for the adoption of innovative teaching approaches, specifically focusing on artificial intelligence (AI)-driven personalized learning (PL). This study aims to explore the integration of self-determination theory (SDT) principles into management education, with a primary focus on enhancing student motivation, engagement and academic performance (AP).

This interdisciplinary research adopts a multifaceted approach, combining perspectives from AI, education and psychology. The design and methodology involve a thorough exploration of the theoretical foundations of both AI-driven education and SDT. The research demonstrates how these two elements can synergize to create a holistic educational experience. To substantiate the theoretical claims, empirical data-driven analyses are employed, showcasing the effectiveness of AI-enabled personalized learning (AIPL). The study integrates principles from SDT, such as autonomy, competence and relatedness, to create an environment where students are intrinsically motivated, receiving tailored instruction for optimal outcomes.

The study, rooted in SDT, demonstrates AIPL’s transformative impact on management education. It positively influences students’ autonomy, competence and relatedness, fostering engagement. Autonomy is a key driver, strongly linked to improved AP. The path analysis model validates these relationships, highlighting AI’s pivotal role in reshaping educational experiences and intrinsically motivating students.

This study holds substantial significance for educators, policymakers and researchers. The findings indicate that the AIPL model is effective in increasing student interest and improving AP. Furthermore, this study offers practical guidance for implementing AI in management education to empower students, enhance engagement and align with SDT principles.

Contribute original insights through an interdisciplinary lens. Synthesize AI and SDT principles, providing a roadmap for a more effective educational experience. Empirical data-driven analyses enhance credibility, offering valuable contributions for educators and policymakers in the technology-influenced education landscape.

This systematic literature review aims to identify the pattern of data mining (DM) research by looking at the levels and aspects of education.

This paper reviews 113 conference and research papers from well-known publishers of educational data mining (EDM) and learning analytics-related research using a recognized literature review in computer science by Carrera-Rivera et al. (2022a). Two major stages, planning and conducting the review, were used. The databases of Elsevier, Springer, IEEE, SAI, Hindawi, MDPI, Wiley, Emerald and Sage were searched to retrieve EDM papers from the period 2017 to 2023. The papers retrieved were then filtered based on the application of DM to the three educational levels – basic, pre-tertiary and tertiary education.

EDM is concentrated on higher education. Basic education is not given the needed attention in EDM. This does not enhance inclusivity and equity. Learner performance is given much attention. Resource availability and teaching and learning are not given the needed attention.

This review is limited to only EDM. Literature from the year 2017 to 2023 is covered. Other aspects of DM and other relevant literature published in EDM outside the research period are not considered.

As the current trend of EDM shows an increase in zeal, future research in EDM should concentrate on the lower levels of education to identify the challenges of basic education which serves as the core of education. This will enable addressing the challenges of education at an early stage and facilitate getting a quality education at all levels of education. Appropriate EDM techniques for mining the data at this level should be the focus of the research. Specifically, techniques that can cater for the variation in learner abilities and the appropriate identification of learner needs should be considered.

Content sequencing is necessary in facilitating an easy understanding of concepts. Curriculum design from basic to higher education dwells much on this. Identifying the challenge of learning at the early stages will facilitate efficient learning. At the basic level of learning, data on learning should be collected by educational institutions just as it is done at the tertiary level. This will enable EDM to accurately identify the challenges and appropriate solutions to educational problems. Resource availability is a catalyst for effective teaching and learning. The attributes of a learner will enable knowing the true nature of the learner to determine the prospects of the learner.

This research has not been published in any journal. The information presented is the original knowledge of the authors. However, a pre-print of the work is in Research Square.

This study aims to investigate the relationship between academic motivations and the risk of Not in Employment, Education or Training (NEET) among university undergraduates and Vocational Education and Training (VET) undergraduates.

The sample included 402 Vietnamese university undergraduates and 250 VET undergraduates in the southern region of Vietnam. Students took part in a survey, with all participants being informed about the study’s purpose and assured that their involvement was entirely voluntary. In addition to descriptive statistics, the study employed linear regression in SPSS to examine hypotheses.

The findings indicate that, for university students, intrinsic motivation and mastery approach motivation are associated with reduced NEET risk, while performance avoidance motivation is positively linked to this tendency. In contrast, for VET students, extrinsic motivation and performance approach motivation are negatively associated with NEET risk, but mastery approach motivation may exacerbate the risk.

Grounded in the principles of Self-Determination Theory (SDT) and Achievement Goal Theory (AGT), the study proposes that university students may prioritize competence improvement, knowledge acquisition and the satisfaction of their learning interests, which they believe will help them acquire valuable knowledge beneficial for their future careers. Conversely, VET students emphasize performance and external achievement, which may enhance their outcome and reduce NEET risk. These findings offer significant theoretical and practical insights into the adoption of SDT and AGT and also provide educators or policymakers with more detailed information regarding university and VET students’ learning and development.

Large class sizes are becoming the norm in higher education against concerns of dropping learning qualities. To maintain the standard of learning and add value, one of the common strategies is for the course convenor to proactively monitor student engagement with learning activities against their assessment outcomes and intervene timely. Learning analytics has been increasingly adopted to provide these insights into student engagement and their performance. This case study explores how learning analytics can be used to meet the convenor’s requirements and help reduce administrative workload in a large health science class at the University of New South Wales.

This case-based study adopts an “action learning research approach” in assessing ways of using learning analytics for reducing workload in the educator’s own context and critically reflecting on experiences for improvements. This approach emphasises reflexive methodology, where the educator constantly assesses the context, implements an intervention and reflects on the process for in-time adjustments, improvements and future development.

The results highlighted ease for the teacher towards the early “flagging” of students who may not be active within the learning management system or who have performed poorly on assessment tasks. Coupled with the ability to send emails to the “flagged” students, this has led to a more personal approach while reducing the number of steps normally required. An unanticipated outcome was the potential for additional time saving through improving the scaffolding mechanisms if the learning analytics were customisable for individual courses.

The results provide further benefits for learning analytics to assist the educator in a growing blended learning environment. They also reveal the potential for learning analytics to be an effective adjunct towards promoting personal learning design.

This paper provides novel evidence on the role of gender in the performance of university students, which is particularly relevant to the debate on the performance of female students in science, technology, engineering and mathematics (STEM) subjects.

Our approach relies on the metafrontier approach proposed by Huang et al. (2014), which measures students' efficiency within a given faculty and the impact of the faculty’s technology on students’ efficiency. We use a sample of 53,159 first-year students in 8 faculties from a large university in southern Italy from 2002–2003 to 2010–2011.

Students’ efficiency is relatively low, reflecting an essential role of unobserved heterogeneity. The different technologies of somewhat similar faculties have minimal impact on efficiency. There is a performance gap against women in five faculties, which on average is strongest for the faculties in the pure and applied science area. This gap increases with the proportion of female students and decreases with female lecturers.

The metafrontier has the benefit of providing relevant policy information on the drivers of student success by relying on data that universities routinely generate and preserve.

The stochastic metafrontier approach allows us to separate the group-specific frontiers from the metafrontier, yielding a decomposition of the efficiency scores of various faculties into technical efficiency scores and technological gaps.

Our study was designed to investigate the longitudinal trajectories of student leader development capacities in a sample of students enrolled in multiple leadership-focused courses across several semesters. Our goal was to assess the degree to which course enrollment was associated with growth over the time that students engage as undergraduates in academic leadership programs, and if so, to assess the shape and speed of capacity change.

We utilized a multilevel intra-individual modeling approach assessing students’ motivation to lead, leader self-efficacy, and leadership skills across multiple data collection points for students in a campus major or minor focused on leadership studies. We compared an unconditional model, a fixed effect model, a random intercept model, a random slope model, and a random slope and intercept model to determine the shape of score trajectories. Our approach was not to collect traditional pre-test and post-test data – choosing to collect data only at the beginning of each semester – to reduce time cues typically inherent within pre-test and post-test collections.

Our results strongly suggested that individual students differ greatly in the degree to which they report the capacity to lead when initially enrolling in their first class. Surprisingly, the various models were unable to predict a pattern of longitudinal leader development through repeated course enrollment in our sample.

Our investigation employed statistical methods that are not often utilized in leadership education quantitative research, and also included a data collection effort designed to avoid a linear pre-test/post-test score comparison.

Design an innovative Flipped classroom’s Delivery through virtual laboratory.

The fundamental framework of the present investigation is a Participatory Action Research (PAR) design. By merging the impetus of “action.” with the inclusiveness of “participation,” PAR establishes a “network” for collaborative teaching or research. PAR is pertinent to our research because it facilitates the participation of infrastructures and individuals in formulating a critical community. This community encourages critical self-reflection, promotes accountability, redistributes authority, and cultivates confidence in research. PAR fosters constructive transformation in educational environments by utilizing participants' combined insights and experiences to establish a structure for substantive dialogue and proactive measures.

As virtual laboratories are becoming essential in 21st-century science education, we found groundbreaking evidence that can support our novel approach to enhance the quality and equity in education. Our results show that virtual labs engage scientific goals and practices, develop scientific literacy, foster scientific inquiry and problem-solving, and promote metacognition. The effects of the virtual laboratory can develop high self-efficacy and positive attitudes among students. It improves students' laboratory performance, which we noted from laboratory activities, simulations, and long exam results.

The study offers groundbreaking account to depict epistemic fluency aided by virtual laboratory.

This study examines how perceived institutional support, parental role models, and entrepreneurial self-efficacy, representing both macro-level and personal-level factors, collectively influence students' intentions to pursue entrepreneurship in Mexico and Uruguay.

This research utilized quantitative methodology, specifically survey techniques, to collect data from students attending private universities. The study achieved a valid sample size of 419 respondents. Various reliability and validity tests were conducted before structural equation modeling was employed to test the hypothesized relationships between variables.

The analysis revealed that perceived institutional support does not directly impact students' entrepreneurial intentions (EI). Instead, its effect is mediated through entrepreneurial self-efficacy and the presence of parental role models, both of which are strong predictors of EI. Additionally, the study identified a direct correlation between students' nationality, their academic programs, and their EI. Age and gender, however, did not significantly influence EI.

This study provides theoretical insights into understanding EI by combining macro-level and personal factors. This integrative method contributes to a more comprehensive approach of predicting EI within the context of Latin America.

The study suggests boosting investment to improve the quality of institutions, fostering an environment that supports entrepreneurship, and offering students opportunities to learn from successful role models.

This study was conducted in the context of two economies in Latin America. The novelty lies in combining perceived institutional factors and individual motivators to understand EI in Latin America. It uniquely emphasizes the significance of familial influences, particularly parental role models, in its analysis.

The present study explores five Iranian English language teachers’ hopes by drawing on an ecological approach as its conceptual underpinning.

Data were collected from narrative frames and semi-structured interviews.

Analyses of our data highlighted how teacher- and student-related factors caused fluctuations in teachers’ hopes. In addition, our findings indicated that while teachers’ past teaching experiences increased their hope, teachers’ lack of agency and economic inflation were among the most significant factors that decreased teachers’ hope in their profession.

This study is one of the first studies in the field of language studies with an ecological perspective on language teachers' hope.

Our study focuses on providing empirical evidence regarding the optimization of podcasting in asynchronous learning. This action research aimed to innovate the delivery of asynchronous classes using differentiated podcasts.

We utilized action research as the research design for the study. Participating in action research entails developing practical knowledge to improve educational practices through specific methods and critical perspectives (Sáez Bondía and Cortés Gracia, 2022). According to Burns (2007), action research involves deliberate interventions usually prompted by identified issues, mysteries or inquiries that individuals in the social setting seek to improve or change. Implementing changes to enhance individuals' actions and understanding within their context is the focus of action research (Kemmis, 2010). The study’s approach is ideal for examining new practices and gaining enhanced theoretical insights (Altrichter et al., 2002). Engaging in action research helps enhance understanding and empowers us to impact and enhance practices through continuous reflection, exploration and action. Through this iterative process, we can continuously enhance our comprehension and make substantial strides toward fostering positive transformation.

The study findings showed an apparent rise in student regulation and engagement and remarkable enhancements in learning outcomes, as demonstrated by differences in pre-test and final exam scores. These results highlight the actual effect of specialized podcasts on self-paced inducing students' self-efficacy in learning. Our research provides valuable insights on effectively incorporating podcasts into education, offering innovations and improvement of practice among educators and institutions adapting to the ever-changing landscape of the educational environment while catering to the diverse needs of the learners. This research is pioneering research catering to the various learning styles of asynchronous learning environments.

Although our current sample offered valuable insights, upcoming studies could gain from more extensive and more diverse participant groups to strengthen the reliability of our results and guarantee broader applicability across various demographics and contexts. Moreover, the length of our intervention may have been relatively brief, which could have limited our ability to evaluate the long-term impact of customized podcasts on learning results. Continued investigation into the long-term effects of these interventions could provide valuable insights into their effectiveness over time and help shape the creation of more lasting educational approaches.

Innovation in asynchronous learning differentiated teaching attuned to the diverse learning styles of the students.

The study promotes equitable education, which eventually promotes learning outcomes of the students.

We created differentiated podcasts tailored to the learning styles of the students. This research is pioneering research catering to the various learning styles of asynchronous learning environments.