Adverse childhood experiences and student engagement in higher education: evidence from Indonesian university students

This special issue of Student Success celebrates the work of Professor Ella Kahu and her substantial contribution to the advancement of our collective understanding of student engagement. In June 2024 Professors Karen Nelson and Chi Baik, and Dr Cat Picton sat down with Ella for a reflective conversation about her work, and additional insights in her theorising and research on student experience and engagement. In this special issue, we present the interview transcript in three parts and showcase Ella’s key publications. Understanding student engagement has become essential for retention and success in higher education. This first part of the issue brings together three influential articles that have contributed to advancing our conceptualisation of student engagement. The first article, Framing Student Engagement in Higher Education (Kahu, 2013), offers a foundational review of prevailing perspectives on student engagement and proposing a comprehensive framework to address existing definitional and conceptual gaps. Building on this foundation, the second article by Ella and Karen Nelson, Student Engagement in the Educational Interface: Understanding the Mechanisms of Student Success (Kahu & Nelson, 2018) introduces the “educational interface”—a metaphor for the space where student and institutional factors interact, affecting engagement. This article refines our understanding of engagement by identifying key psychosocial constructs that mediate this interaction. The third article, Pathways to Engagement: A Longitudinal Study of the First-Year Student Experience in the Educational Interface, a collaborative study by Ella Kahu, Karen Nelson, and Catherine Picton, (Kahu et al., 2020) employs longitudinal research to trace the first-year student experience within this educational interface, providing empirical support for the framework and underscoring the importance of self-efficacy, belonging, emotions, and wellbeing in the engagement process. Together, these articles deepen our understanding of the theoretical and practical aspects of student engagement, providing educators and policymakers with insights to shape more effective engagement strategies. Journal articles: Kahu, E. R. (2013). Framing student engagement in higher education. Studies in Higher Education, 38(5), 758-773. https://doi.org/10.1080/03075079.2011.598505 Kahu, E. R., & Nelson, K. (2018) Student engagement in the educational interface: Understanding the mechanisms of student success. Higher Education Research & Development, 37(1), 58-71. https://doi.org/10.1080/07294360.2017.1344197 Kahu, E. R., & Picton, C., & Nelson, K. (2020). Pathways to engagement: A longitudinal study of the first-year student experience in the educational interface. Higher Education, 79(4), 657-673. https://doi.org/10.1007/s10734-019-00429-w

Purpose This study aims to explore the influential ability of artificial intelligence (AI)-teacher collaboration in higher education, examining its impact on academic performance and engagement of students. By leveraging AI, educators can deliver tailored learning experiences, enhance student participation and streamline administrative processes, addressing key limitations of traditional teaching methods. Design/methodology/approach A mixed-methods research design was employed to comprehensively examine the impact of AI-teacher collaboration on student engagement. Quantitative data were collected through a structured survey targeting students in higher education to capture their perspectives on AI-supported learning environments and related outcomes. Qualitative data were obtained via in-depth interviews with students, providing deeper insights into the interaction between AI tools and teaching methodologies. The data collection spanned diverse higher education institutions, encompassing various disciplines, institutional types and geographical contexts. This comprehensive approach ensures that the findings are robust and representative of diverse educational settings. Findings The findings reveal that AI-teacher collaboration significantly enhances student engagement and improves academic outcomes. AI integration enables personalized learning, fostering greater student involvement, while educators benefit from reduced administrative workloads and improved teaching efficiency. However, challenges persist, including barriers to AI adoption and the need for adequate educator training to effectively utilize AI tools. Research limitations/implications The major limitation of this research is that all student participants were self-selected and voluntarily chose to engage in the process. The study was confined to participants from higher education and their motivation to enhance their knowledge and skills in AI-integrated teaching likely influenced their responses. It remains unclear how students might respond if participation were mandated rather than voluntary. Practical implications This study assists educators and policymakers in creating AI-integrated curricula that improve student engagement while guaranteeing ethical AI utilization. It underscores AI’s function in customizing education, alleviating administrative responsibilities and stressing the necessity for equal access and educator training. Furthermore, it delineates obstacles in AI implementation, directing subsequent research toward optimal methods for sustainable and inclusive education. Originality/value This study presents empirical evidence about the influence of AI-teacher collaboration, elucidating its significance in improving student engagement and academic performance in higher education. This work integrates AI into several educational contexts, contributing to the discourse on sustainable, inclusive and high-quality teaching techniques, in alignment with Sustainable Development Goal 4 (Quality Education).

Aim/Purpose: The purpose of this study is to review and categorize current trends in student engagement and performance prediction using machine learning techniques during online learning in higher education. The goal is to gain a better understanding of student engagement prediction research that is important for current educational planning and development. However, implementing machine learning approaches in student engagement studies is still very limited. Background: The rise of online learning during and after COVID-19 has created new difficulties for students’ engagement and academic achievements. Lecturers’ manual monitoring and supporting of students are inadequate online, leading to disengagement and performance challenges that may be very difficult to notice. Machine learning has great potential to provide an accurate prognosis of students’ engagement and outcomes to make early interventions possible. Nevertheless, the current knowledge deficit is in the systematic presentation of trends and insights concerning the utilization of these approaches in higher education online learning, especially with a focus on student engagement research. This research fills a crucial void by explaining and analyzing current trends in machine learning-based prediction models to enhance the quality and efficiency of an online learning system. Methodology: This research examines the existing literature on the application of machine learning, which allows computers to learn from data and improve their performance for early identification of student engagement and academic performance in higher education during online learning. The PICOC protocol was implemented to guide the search process and define the relevant keywords aligned with the research questions. Based on the PRISMA framework, a structured approach is adopted to identify and select studies to screen and extract the relevant papers from the database. Meta-analysis was adopted in data analysis whereby studies are combined and evaluated to provide insights into machine learning techniques’ effectiveness in student engagement and academic performance research. Contribution: This paper aims to present the current trends in predicting student engagement and academic achievement by applying machine learning approaches with a focus on their relevance in the context of online learning. It defines challenges that emerge with an interpretation of the extent of student engagement, which include the absence of consensus on levels of student engagement that hampers the use of explainable artificial intelligence – approaches that make training of machine learning models more logical, understandable and easily interpretable by lecturers. The finding points to the fact that through the prediction models, lecturers are enabled to recognize disengaged students early and foster their needs towards learning, providing direction toward more customized and effective online learning. Findings: A total of 96 primary studies have been identified and included in this systematic review. It is important to highlight the relevance of classification machine learning methods that are implemented in 88.60% of papers, while clustering methods are only employed in 15.19% of studies. Furthermore, the review shows that most research focuses on student performance prediction (82.28%) compared to student engagement level prediction (12.66%). Besides, student engagement datasets are used in 92.14% of studies, emphasizing student engagement’s popularity in educational prediction research. Moreover, classification machine learning methods are more prevalent in educational prediction research. In contrast, classification methods for student engagement research are still limited due to challenges in constructing consistent engagement levels. Recommendations for Practitioners: Lecturers need to occasionally assess student engagement levels during online learning to identify students who are left out and take immediate planning and action to encourage the student to engage during online learning. The syllabus designer should observe the students’ engagement level during online learning to plan the course content that can attract and engage the students. Students’ engagement during online learning can ensure their academic success and prevent them from dropping out. Recommendation for Researchers: Researchers should focus on defining the consensus on differentiating student engagement levels and implementing more explainable AI to enhance the interpretability and transparency of student engagement level predictive models. Researchers should enhance educational predictive models’ explainability, transparency, and accuracy by addressing issues brought about by feature selection, resampling techniques, and hyperparameter tuning. Impact on Society: The study highlights the growing importance of understanding student engagement through digital footprints, which can support personalized learning experiences and provide better educational outcomes. The efficient predictive models on student engagement can improve the effectiveness of higher education systems, benefiting students and institutions. Future Research: The challenges of current computational methods need to be overcome, including the need for more consistent approaches in differentiating engagement levels and enhancing the explainability and accuracy of educational predictive models through better feature selection, resampling techniques, and hyperparameter tuning.

Often, student engagement in higher education (HE) is viewed from a structural perspective, which reduces the role of student agency. This mixed methods study thus focuses on investigating what engages students in HE through the lens of self-determination theory (SDT), in order to better understand the internal processes of engagement in individual students. In Phase 1, through a survey distributed to 838 students in four higher education institutions in Malaysia, it was revealed that perceived sense of autonomy, competence and belonging provided a crucial framework for understanding what is important for student engagement in HE. Further analysis revealed significant differences between what students believed as important for engagement, and what they actually do (actions), affirming how engagement is invariably a joint product of student motivation and the availability of support in the HE context. Conclusions from this study highlighted the importance of establishing the basic psychological needs of SDT, namely; competency, autonomy and relatedness in HE in order to create a supportive and conducive learning environment in HE for enhancing and sustaining student engagement. Implications and limitations of the findings are discussed.

Student engagement in higher education has been a topic of discussion for decades, as student engagement directly indexes student retention, achievement, and career development. While previous research emphasizes the importance of effective teaching practices to increase student engagement in higher education, faculty and staff report institutional and professional challenges to increase interactions with students. This study highlights cases of successful teacher-student relationships that engendered positive student emotions and advanced student engagement in higher education settings. Using the thank-you note messages provided by students in a Thank-a-Teacher initiative, data were analyzed qualitatively through the theoretical principles of humanizing pedagogy (del Carmen Salazar, 2013). The findings indicate that the enactment of humanizing pedagogy through conveying emotions and forming positive teacher-student relationships made a meaningful impact on student motivation, engagement, and growth. Implications for the transformation and liberation of higher education through affect-driven pedagogy are discussed.

ABSTRACTThis article reports on a systematic review of research into student retention and student engagement in higher education (HE). It discusses the origins and meaning of these terms, their relation to each other, their application and practice, and the issues and critiques that have arisen. The two concepts are seen as alternative ways of seeing and researching the same underlying issue. While student engagement is a more recent focus for research, it has now overtaken student retention in importance. As the responsibility for the financing of HE has shifted from the state to the student, so the understanding of student retention and engagement has shifted from being the student’s responsibility to that of the higher education institution (HEI).

Student engagement in higher education is a complex construct that encompasses the active participation, involvement, and investment of students in their learning experiences. However, foreign students, who bring unique cultural, linguistic, and social backgrounds, face distinct challenges and experiences in adapting to higher education environments. This research aimed to fill a practical research gap by exploring how foreign undergraduate students in Finland perceive student engagement and motivation in higher education. Moreover, it sought to advance the ongoing discussion about student engagement in academia. This inductive research adopted a qualitative research framework and gathered interview data with foreign degree students in Finland. The semi-structured interviews with foreign undergraduate students were analyzed thematically and yielded four primary themes. First, the theme of peer interaction and cultural stereotypes, followed by the second theme, which revolves around integration challenges. The third theme highlights challenges viewed as opportunities, and finally, the theme of institutional support. Moreover, the study contributes to the body of knowledge by providing valuable insights about a group of students that is often marginalized. The article concludes by providing implications for research and practice. Received: 3 December 2023 | Revised: 8 January 2024 | Accepted: 18 January 2024 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. Author Contribution Statement Kevin Fuchs: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing - original draft, Writing - review & editing, Visualization, Supervision, Project administration, Funding acquisition.

This report describes a participatory evaluation of a postgraduate course delivered at the University of Winchester in the UK. The PGCert/MA in Student Engagement in Higher Education utilized sector-leading practices and research to explore student engagement in contemporary higher education. As co-creation, co-design, and staff-student partnerships are integral concepts within student engagement scholarship and practice, a congruent participatory evaluation of the PGCert/MA was conducted. This approach utilized an Advisory Group of current students and graduates to co-construct a Theory of Change, design data-gathering methods, and support the synthesis of findings and associated recommendations. This report provides details of the stages of this evaluation and the participatory activities which were designed and delivered. In addition to providing a robust conclusion on the process, impact, and outcomes of the course, this report discusses the benefits and challenges of participatory evaluation.

BackgroundResearch on student engagement has garnered significant interest from educators and practitioners because of its direct impact on academic success and achievement. Engaged students tend to perform better academically and exhibit fewer undesirable study behaviors, thereby enhancing academic outcomes.ObjectiveThis systematic literature review consolidates research on the impact of perceived teacher support on student engagement in higher education. This study emphasizes the association between teacher support in improving students’ academic performance, motivation, and retention. Furthermore, the review explores key theoretical frameworks, such as self-determination theory and social cognitive theory, alongside methodological tools such as measurement instruments and statistical analyses. The goal is to equip psychologists and educational researchers with insights into the relevant frameworks, tools, and methods for advancing future studies within the context of higher education.MethodsThis study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. We conducted a comprehensive search for academic studies published in English within databases such as APA PsycNet, Scopus, ERIC, EBSCOHost, ProQuest, and PubMed to identify eligible studies published between 2014 and 2024.ResultsA review of 13 selected articles revealed that both students’ personal characteristics and school environment factors mediate and moderate the relationship between perceived teacher support and student engagement. The students’ personal characteristics factors include self-efficacy, the fulfillment of psychological needs, and motivation, whereas school environment factors involve the learning environment and the quality of teacher-student and peer relationships. Our findings show a lack of studies prior to 2020, with most research conducted in China and limited contributions from Malaysia and Vietnam. The reviewed articles predominantly used cross-sectional quantitative designs and self-report questionnaires, employing statistical methods like path analysis and structural equation modeling. Theoretical frameworks on student engagement mostly followed Fredricks et al.‘s model, while teacher support theories varied, with three main patterns identified: direct influence, mediation through basic psychological needs, and social cognitive perspectives. This review emphasizes the crucial role of teacher support in enhancing student engagement in higher education and urges further exploration in this under-researched area.ConclusionIn conclusion, this review underscores the significant role of teacher support in enhancing student engagement in higher education. It highlights key theoretical frameworks and research methodologies, offering valuable insights for future studies aimed at advancing teacher support and student engagement in this context.

The higher education world around us is changing fast. Mobile learning (M-learning) and apps in education are new and exciting. However, this does not mean that the faculty is less relevant. On the contrary, the faculty are an ever-important factor that is needed to increase student engagement. Disengaged students are in fact more problematic than low achieving students. This is because of the disruption disengaged student bring in the classroom. Therefore, increasing student engagement is a key challenge in higher education today. Research suggests that active learning, value and expectations are important factors that determine student engagement. Some of these factors can be indirectly influenced by the faculty and course designers. Against this background, the objective of this chapter is to examine if dwindling student engagement in higher education can be addressed with CURPA, i.e. a CURriculum and course planning (CURP) App. CURPA is the result of our efforts at Utrecht University to help design courses and curricula by using cards to account for different activities, with the strategic aim being to stimulate student engagement in higher education. Our expert interviews resulted in a MoSCoW-prioritised list of requirements that can be implemented over time, structured in line with Becker's student engagement model.

Student engagement in higher education has been studied from various perspectives, but few studies have compared teachers’ and students’ perspectives on the subject. Considering that student engagement may be reduced to a performativity scarcely related to learning outcomes and that a good deal of it may be invisible to teachers, further studies are needed to contrast teachers’ and students’ views of student engagement in higher education. This article presents the results of a quantitative study about definitions and indicators of student engagement carried out with 118 students and 45 teachers of Law, Education and Nursing from two Catalan public universities (Spain). The results confirm the performativity associated with the concept by both teachers and students, and reveal that students may appreciate silent and invisible engagement more than teachers. The results also show that teachers may conflate signs of student engagement with good academic results and learning, risking an oversimplification of both student engagement and learning.

This guide aims to support our colleagues to have comprehensive understanding of student engagement in health professions education. Despite the universal agreement about the significance of student engagement, there is lack of uniformity in conceptualizing and operationalizing this emerging construct. We review the theoretical basis explaining student engagement from three main perspectives: behavioral, psychological, and socio-cultural. In addition, we propose a contemporary and comprehensive framework for the student engagement in higher education, which is applicable to health professions education contexts. Drawing from this framework, we explain the conceptualization of the construct and its preceding factors, mediators, dimensions, spheres, and outcomes of student engagement. The proposed framework introduces student ‘engagement through partnerships’ as a novel component compared with the existing models of student engagement in higher education. This way, we are proposing a mixed model that not only considers the student as a ‘customer’ but also as a ‘partner’ in education. Engagement of students through partnerships include four areas: (1) provision of the education program, (2) scholarly research, (3) governance and quality assurance, and (4) community activities. This guide will provide practical applications on how to improve student engagement in health professions education. Finally, we highlight the current gaps in areas of research in the student engagement literature and suggested plans for future directions. Practice points Student engagement is student investment of time and energy in academic and non-academic experiences that include learning, teaching, research, governance, and community activities. Students are involved in these aspects at the cognitive, affective, behavioral, agentic, and socio-cultural dimensions. Student engagement could be explained by three theoretical perspectives: behavioral, psychological, and socio-cultural. The theoretical framework for student engagement in health professions education consists of five components: (1) antecedents of engagement, which refer to institutional and student factors as well as the interactions (student-student and student-staff), (2) mediators of engagement, which include self-efficacy, motivation, belonging, and reflectivity, (3) engagement dimensions, which refer to the five dimensions of engagement: cognitive, behavioral, emotional, agentic, and socio-cultural, (4) Spheres of engagement, which refer to engagement in own learning and engagement through partnerships, and (5) outcomes of engagement, which are short-term and long-term. Engagement through partnerships includes four main areas: (1) provision of the education program, (2) scholarly research, (3) governance and quality assurance, and (4) community activities. The following measures are necessary to promote student engagement in HPE: (1) promote a culture of community and psychological safety, (2) create a culture of ‘students as partners’, (3) promote the use of active, collaborative learning methods with relevance to future career, and (4) promote the use of technology-enhanced learning.

Purpose In recent years, the study of various forms of teacher leadership styles and behaviours, and student engagement has gained increasing attention because these constructs are critical for understanding student success and well-being. However, investigation of how these constructs interact to shape students' learning, particularly in the Ethiopian higher education contexts, remains underexplored. Addressing the gap, the present study examines how teacher leadership behaviours, as conceptualized by the Full-Range Leadership Model (which views leadership as a continuum that includes transformational, transactional and non-leadership behaviours), influence student engagement in Ethiopian public universities. Design/methodology/approach Employing a cross-sectional study design, the study involved 840 undergradute student participants selected through multistage stratified sampling. The data were collected using questionnaires and then analysed through structural equation modelling. Findings The study findings reveal that transformational teacher leadership is strongly and positively associated with student engagement, suggesting that a higher level of student engagement is related to teachers' demonstrating transformational leadership behaviours. Conversely, transactional teacher leadership shows a small negative association with student engagement, highlighting its limitation in promoting the motivation essential for sustained student engagement. Non-leadership teacher behaviours, on the other hand, yield a negligible positive link to student engagement, resulting in a fragile learning environment that is not conducive to sustained engagement. Originality/value Based on the findings, the study suggests practical implications for effective teacher leadership development programs and their associated influences in improving student engagement in higher education. Furthermore, the study's contextual specialization within the Ethiopian public universities setting provides novelty because the majority of earlier teacher leadership and related student outcomes investigations were carried out more in Western and to some extent in Asian higher education settings.

Student engagement has become problematic following the rise of mass and universal forms of higher education. Significant attention has been devoted to identifying factors that are associated with higher levels of engagement, but it remains the case that the underlying reasons for student engagement and, indeed, the notion itself of ‘student engagement’ remain weakly theorised. In this article, we seek to develop the theoretical basis for student engagement in a way that highlights the student's own contribution. We explore how learning involves students taking responsibility for action in the face of uncertainty, whether in pursuit of personal or communal concerns. Drawing on perspectives primarily from realist social theory, we suggest that student engagement may be shaped by extended, restricted and fractured modes of reflexivity and co‐reflexivity. In this way student engagement in higher education is theorised as a form of distributed agency, with the impact of a learning environment on this agency mediated by reflexivity. Reflexivity itself is further influenced by the tasks and social relations encountered by students in a given learning environment. The role that social relations play in students' responses to learning specifically offers a means to strengthen the moral basis for education. Our account provides an explanation as to why specific educational practices, such as those termed ‘high impact’, might lead to higher levels of student engagement within the wider context of a knowledge society. We thus offer insights towards new forms of educational practice and relations that have the potential to engage students more fully.

Student engagement is a crucial factor in the success of students enrolled in higher education. Despite the existence of numerous studies on the factors that influence student engagement, investigating the factors that affect the level of student engagement in higher education is important. In applying the behavioral reasoning theory (BRT), this research investigates factors that influence freshman students’ decisions to engage in academic activities. Data have been collected from a sample of students enrolled in management studies at the University of Federico II in Naples, Italy. The results highlight that reasons serve as important links between students’ values, global motives, and engagement behavior, confirming one of the main premises of BRT. In particular, the findings suggest that BRT is a good model to predict student engagement as global motives, intentions, and reasons for engagement have a significant direct effect on engagement. These findings support the importance of examining and reinforcing the reasons for engagement (facilitators) while minimizing the reasons against engagement (obstacles) in order to enhance students’ engagement in academic activities.