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Student engagement evaluation is considered to be connected to many aspects of the management of higher education, but outside Western higher education, research and evaluation on student engagement and experiences has been limited so far. Our study focuses on the underexplored aspects of Palestinian higher education with the aim of gaining an actionable understanding from the overall student engagement situation to enhance the management and development of local teaching and learning practices.

A quantitatively oriented, sequential mixed-methods design was adopted. With the applied and validated engagement measurement we collected 946 engagement questionnaire responses from Palestinian university students. Quantitative data were analysed using structural equation modelling, K-means cluster analysis and chi-squared tests. Inductive and deductive thematic analysis was employed for the open answers.

With the three validated student engagement dimensions, the applied cluster analysis allowed three different engagement profile groups to be distinguished: strongly, moderately and loosely engaged. In the subsequent statistical and qualitative thematic analyses, these three engagement clusters differ in the degree to which they had a clear vision of a future profession or in their academic engagement with their studies. Moreover, qualitative analysis brought up many shared concerns regarding theoretically oriented studies and uncertain professional and career prospects in the Palestinian higher education context.

This study is one of the first attempts to develop tools for student engagement management in Palestinian higher education. The study findings are particularly significant for developing micro- and meso-level management practices in Palestinian higher education institutions.

Student engagement is a multifaceted concept that encompasses various dimensions that significantly influence students and their learning journey. This study aims to explore the extent of engagement among management students outside the confines of the classroom, specifically focusing on their engagement with the campus and community and assessing the outcomes resulting from this engagement.

The literature review provided the basis for developing a student engagement framework, focusing on campus and community engagement and their outcomes. Established scales measured these variables through surveys administered to 386 UG and PG management students. In-depth analysis using PLS-SEM technique revealed the interplay of variables, beyond demographic and descriptive examinations.

The study found that campus and community engagement fosters a sense of responsibility. Campus engagement also cultivates long-term loyalty to the institution, while community engagement enhances social consciousness. Additionally, no significant differences in engagement levels were observed based on gender or educational level among management students.

The study faces limitations that need addressing for balanced understanding and future research guidance. Firstly, varying definitions across studies lead to inconsistent outcomes and comparability challenges. Secondly, accurate measurement is difficult due to reliance on self-reporting tools, which are prone to biases. Cultural and contextual differences also limit generalizability, and quantitative data alone may not capture the full picture. In India, identifying specific skills and competencies as engagement outcomes in outcome-based education is challenging, requiring precise variable identification.

The study would contribute to improving the efficacy of efforts beyond the classroom engagement activities as it tests, validates, and projects them as outcome-driven by showcasing learning both as generic competencies in a broad sense and higher-order competencies in particular.

The literature indicates higher education institutes' activities beyond classroom teaching enhance students' campus and community relationships. This study measures these engagements' outcomes and suggests new research dimensions in student engagement.

Student engagement has become increasingly significant in sustainability education for engineers because it enables future engineers to develop competencies, knowledge and values relevant to acting for sustainability. Therefore, this paper aims to examine characteristics of student engineer engagement with sustainability and to discuss the meanings of this concept.

To build a more holistic picture of student engineer engagement with sustainability, this study followed a literature review approach to search, screen and appraise relevant journal articles on this topic. As a result of this research, 30 articles were identified as eligible.

Based on the theoretical framework for student engagement with sustainability, newly synthesized here, and the content analysis of the 30 papers included in this study, four patterns of engagement were identified: intrapersonal engagement, inter-relational engagement, engagement as connection and disconnection and situated engagement.

This review provides practical recommendations about how to support the engagement of student engineers with sustainability at the levels of the individual, staff, educational programmes and associated curricula. Future research directions are also discussed.

This study contributes a theoretical framework synthesizing student engagement theory with sustainability education. It also describes current characteristics of student engineers’ engagement with sustainability.

The COVID-19 pandemic has dramatically changed educational practices due to the intense use of information technology for teaching and learning. That phenomenon presents challenges for lecturers in higher education establishments because student engagement is threatened during online interactions. This study aims to analyze the effectiveness of the SME project for maintaining student engagement during online learning.

The research used a quasi-experimental method involving experimental and control groups. Researchers collect quantitative and qualitative data to obtain comprehensive information. The data were collected using an electronic questionnaire. Descriptive statistics, one-way ANOVA, and open coding were used to analyze the data.

The research used a quasi-experimental method involving experimental and control groups. Researchers collected quantitative and qualitative data using an electronic questionnaire to obtain comprehensive information. Descriptive statistics, one-way ANOVA, and open coding were used to analyze the data.

This study provides the educational community with a new insight into optimizing PBL in the online learning environment. Qualified PBL, as practiced by educators, will lead to student engagement, which leads to meaningful learning.

Digital technology's integration into education has transformed learning frameworks, necessitating the exploration of factors influencing students’ engagement in digital informal settings. This study, grounded in self-determination theory (SDT), proposes a model comprising artificial intelligence (AI) competence, chatbot usage, perceived autonomy (PA), digital informal learning (DIL) and students’ engagement.

The study collected survey data from 409 participants at Saudi Arabian universities, ultimately using 387 valid responses for analysis. This dataset was subjected to a thorough examination to confirm the validity of our proposed model. To decipher the complex interactions within our model, we utilized partial least squares structural equation modeling (PLS-SEM). The study adopted a disjoint two-stage method to formulate a reflective-formative higher-order construct (HOC).

The study's findings showed that cognitive learning (CL), metacognitive learning (MCL) and social and motivational learning (SML) are the essential components of DIL. Significantly, the study determined that AI competence, chatbot usage, PA and DIL markedly affect students’ engagement. Moreover, the R² value of 0.592 for student engagement indicates the model's robustness in explaining 59.2% of the variance, highlighting its effectiveness in identifying key drivers of student engagement in DIL contexts.

This research enhances understanding by detailing the intricate relationships among AI competence, chatbot usage, and students’ engagement in informal digital learning. It extends SDT to emphasize intrinsic motivations and AI capabilities, introducing reflective-formative HOCs for comprehending educational intricacies. It provides practical strategies for enhancing AI abilities and chatbot use in education, promoting personalized, engaging and autonomous digital learning spaces, thereby advancing educational theory and practice.

The integration of digital technologies into education has brought about a profound transformation, fundamentally reshaping the learning landscape. The purpose of this study is to underscore the importance of investigating the factors influencing students’ engagement (SE) in this evolving digital era, particularly within formal digital learning environments. To address this need, the study is grounded in self-determination theory (SDT) and presents a comprehensive model comprising interconnected elements: digital competence (DC), smartphone use (SPU), perceived autonomy (PA), digital formal learning (DFL) and SE.

The research conducted an investigation within Saudi Arabian universities, collecting a robust data set of 392 cases. This data set underwent rigorous analysis to validate the proposed model. To untangle the intricate relationships within the framework, the study used partial least squares structural equation modelling. Given the distinct dimensions of the two constructs under study, the researcher used a disjoint two-stage approach to establish reflective-formative higher-order constructs (HOC).

The findings revealed that digital literacy and digital skills (DS) constitute the foundational constituents of DC. Simultaneously, the study identified facilitation, distraction and connectedness as integral components of SPU. Importantly, the study established that DC, SPU, PA and DFL significantly influence SE. Furthermore, the research illuminated the mediating roles played by SPU, PA and DFL in the complex relationship between DC and SE.

This study advances the literature by delineating the dynamic interplay between DC, SPU and SE in digital learning. It extends SDT within educational contexts, emphasizing the role of internal motivations and DS. Methodologically, it innovates through reflective-formative HOCs, deepening the analysis of complex educational constructs. Managerially, it guides institutions in enhancing DC and integrating smartphones effectively into learning, advocating for tailored strategies to foster engaging and autonomous digital learning environments, thereby enriching both theoretical understanding and practical application in education.

The principal aim of the research was to develop Interactive Mobile Learning Modules (IMLM) as an approach to innovative teaching practices in online distance education.

The study employed educational action research, using a quasi-experimental design catering to ninth-grade students (N = 104) attending a public high school in Manila.

Our research indicates that IMLM has a positive effect on student engagement. It provides students with a convenient, easily accessible, and engaging means of understanding Genetics concepts. The execution of this initiative has facilitated the emergence of novel ideas, heightened the ease of use, and advanced more equitable opportunities within the education domain. Thus, IMLM’s utilization has resulted in a favorable shift in conceptual understanding. It has been found to support learners' concept test performance, as evidenced by score gains and statistically significant improvement in understanding.

The study is limited with the development and utilization of mlearning strategy to accommodate learners in the public school system in a developing country.

The study addresses ongoing strategy and discussion to enrich online learning through the mlearning strategy.

The study accommodates inclusivity and equitable learning through personalize mlearning strategy.

The study is novel because it utilize mlearning as innovative approach to teaching genetics.

This paper is a case study of student response systems used in large-class teaching. It considers the benefits, including the engagement of students and academic gains such as reduced administration. The constraints and impacts in classroom teaching are noted, drawing upon the experience of two teachers with their learning captured as a means of dissemination of practice to support other teachers who may be considering adopting and later adapting such practices (Gribble and Beckmann, 2023).

An autoethnographical account was undertaken using an action-learning approach as a sense-making exercise. These accounts enabled a depth of insight beyond the anecdotal evidence experienced by an individual teacher alone.

The findings show that while student response systems have constraints, these can be addressed by putting pedagogical concerns in front of any technology deployment, reaping benefits for students and teachers. Once engaged in using the system, students become more willing to enter further discussions. However, the limitations of both systems indicate that there may be a need for multiple systems to be available based on the pedagogical needs of the class.

The exploration of student response systems and outcomes of positive engagement by students in classroom settings provides insight to those wishing to explore such systems for use in large-class teaching settings.

This work extends discussions surrounding interactivity using student response systems. Additionally, practical insights from the users into their experiences with their students in using such systems provide alternatives for engagement in delivering large-class learning at scale.

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.

The purpose of this study is to explore learner-centered emerging technologies, methods of learning with artificial intelligence (AI), student engagement and concerns related to the assessment process at public and private universities in Bangladesh.

The study employed a qualitative research approach to investigate innovations in learning, student engagement and assessment concerns. Respondents were categorized into three clusters: Cluster-A comprised students from various academic levels, Cluster-B included faculty members with online teaching experience and Cluster-C consisted of information and communication technology (ICT) experts from university information technology (IT) departments. Qualitative data were collected via in-depth interviews.

The research findings indicate the widespread acceptance and utilization of learner-centered technologies, with online and hybrid learning offering flexibility, accessibility, self-paced learning and better student engagement opportunities. While AI platforms like ChatGPT and Gemini are valued for clarifying theoretical concepts, concerns exist regarding their ethical use, particularly in completing assignments. The assessment process faces challenges related to online exams, with concerns over cheating prompting calls for AI-supported solutions. However, there is a gap between commitment and action in regulating unethical AI usage, highlighting the need for clearer guidance and investment in technological solutions for an effective assessment process.

Limitations of this study include the inability to quantify the satisfaction level with the emergence of learner-centered technologies, which could be better explained through a quantitative study.

This study’s originality and value stem from its thorough investigation of innovations in learner-centered education considering the emergence of AI and student engagement, uniquely focusing on the perspective of a developing country.