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The purpose of this study was to analyze importance of artificial intelligence (AI) in education and its emphasis on assessment and adversity quotient (AQ).

The study utilizes a systematic literature review of over 141 journal papers and psychometric tests to evaluate AQ. Thematic analysis of quantitative and qualitative studies explores domains of AI in education.

Results suggest that assessing the AQ of students with the help of AI techniques is necessary. Education is a vital tool to develop and improve natural intelligence, and this survey presents the discourse use of AI techniques and behavioral strategies in the education sector of the recent era. The study proposes a conceptual framework of AQ with the help of assessment style for higher education undergraduates.

Research on AQ evaluation in the Indian context is still emerging, presenting a potential avenue for future research. Investigating the relationship between AQ and academic performance among Indian students is a crucial area of research. This can provide insights into the role of AQ in academic motivation, persistence and success in different academic disciplines and levels of education. AQ evaluation offers valuable insights into how individuals deal with and overcome challenges. The findings of this study have implications for higher education institutions to prepare for future challenges and better equip students with necessary skills for success. The papers reviewed related to AI for education opens research opportunities in the field of psychometrics, educational assessment and the evaluation of AQ.

This study aimed to use enrollment information including demographic, family background and financial status, which can be gathered before the first semester starts, to construct early prediction models (EPMs) and extract crucial factors associated with first-year student dropout probability.

The real-world samples comprised the enrolled records of 2,412 first-year students of a private university (UNI) in Taiwan. This work utilized decision trees (DT), multilayer perceptron (MLP) and logistic regression (LR) algorithms for constructing EPMs; under-sampling, random oversampling and synthetic minority over sampling technique (SMOTE) methods for solving data imbalance problems; accuracy, precision, recall, F1-score, receiver operator characteristic (ROC) curve and area under ROC curve (AUC) for evaluating constructed EPMs.

DT outperformed MLP and LR with accuracy (97.59%), precision (98%), recall (97%), F1_score (97%), and ROC-AUC (98%). The top-ranking factors comprised “student loan,” “dad occupations,” “mom educational level,” “department,” “mom occupations,” “admission type,” “school fee waiver” and “main sources of living.”

This work only used enrollment information to identify dropout students and crucial factors associated with dropout probability as soon as students enter universities. The extracted rules could be utilized to enhance student retention.

Although first-year student dropouts have gained non-stop attention from researchers in educational practices and theories worldwide, diverse previous studies utilized while-and/or post-semester factors, and/or questionnaires for predicting. These methods failed to offer universities early warning systems (EWS) and/or assist them in providing in-time assistance to dropouts, who face economic difficulties. This work provided universities with an EWS and extracted rules for early dropout prevention and intervention.

As global issues such as climate change, economic growth, social equality and the wealth gap are widely discussed, education for sustainable development (ESD) allows every human being to acquire the knowledge, skills, attitudes and values necessary to shape a sustainable future. It also requires participatory teaching and learning methods that motivate and empower learners to change their behavior and take action for sustainable development. Teachers have begun rating pupils based on peer assessment for open evaluation. Peer assessment enables students to transition from passive to active feedback recipients. The assessors improve critical thinking and encourage introspection, resulting in more significant recommendations. However, the quality of peer assessment is variable, resulting in reviewers not recognizing the remarks of other reviewers, therefore the benefits of peer assessment cannot be fulfilled. In the past, researchers frequently employed post-event questionnaires to examine the effects of peer assessment on learning effectiveness, which did not accurately reflect the quality of peer assessment in real time.

This study employs a multi-label model and develops a self-feedback system in order to use the AIOLPA system in the classroom to enhance students' learning efficacy and the validity of peer assessment.

The research findings indicate that the better peer assessment through the rapid feedback system, for the evaluator, encourages more self-reflection and attempts to provide more ideas, so bringing the peer rating closer to the instructor rating and assisting the evaluator. Improve self-evaluation and critical thinking for the evaluator, peers make suggestions and comments to help improve the work and support the growth of students' learning effectiveness, which can lead to more suggestions and an increase in the work’s quality.

ESD consequently promotes competencies like critical thinking, imagining future scenarios and making decisions in a collaborative way. This study builds an online peer assessment system with a self-feedback mechanism capable of classifying peer comments, comparing them with scores in a consistent manner and providing prompt feedback to critics.

This article investigates the application of generative artificial intelligence (GenAI) in experiential learning for authentic assessment in higher education. Recognized for its human-like content generation, GenAI has garnered widespread interest, raising concerns regarding its reliability, ethical considerations and overall impact. The purpose of this study is to explore the transformative capabilities and limitations of GenAI for experiential learning.

The study uses “thing ethnography” and “incremental prompting” to delve into the perspectives of ChatGPT 3.5, a prominent GenAI model. Through semi-structured interviews, the research prompts ChatGPT 3.5 on critical aspects such as conceptual clarity, integration of GenAI in educational settings and practical applications within the context of authentic assessment. The design examines GenAI’s potential contributions to reflective thinking, hands-on learning and genuine assessments, emphasizing the importance of responsible use.

The findings underscore GenAI’s potential to enhance experiential learning in higher education. Specifically, the research highlights GenAI’s capacity to contribute to reflective thinking, hands-on learning experiences and the facilitation of genuine assessments. Notably, the study emphasizes the significance of responsible use in harnessing the capabilities of GenAI for educational purposes.

This research showcases the application of GenAI in operations management education, specifically within lean health care. The study offers insights into its capabilities by exploring the practical implications of GenAI in a specific educational domain through thing ethnography and incremental prompting. Additionally, the article proposes future research directions, contributing to the originality of the work and opening avenues for further exploration in the integration of GenAI in education.

Key to transnational higher education (HE) cooperation is building trust to allow for seamless recognition of studies. Building on the Tuning Educational Structures initiative (2001) and lessons learnt from the Organisation for Economic Cooperation and Development (OECD)-Assessment of Learning Outcomes in Higher Education (AHELO) feasibility study, this paper offers a sophisticated approach developed by the European Union (EU)-co-financed project Measuring and Comparing Achievements of Learning Outcomes in Europe (CALOHEE). These evidence the quality and relevance of learning by applying transparent and reliable indicators at the overarching and disciplinary levels. The model results allow for transnational diagnostic assessments to identify the strength and weaknesses of degree programmes.

The materials presented have been developed from 2016 to 2023, applying a bottom-up approach involving approximately 150 academics from 20+ European countries, reflecting the full spectrum of academic fields. Based on intensive face-to-face debate and consultation of stakeholders and anchored in academic literature and wide experience.

As a result, general (overarching) state-of-the-art reference frameworks have been prepared for the associated degree, bachelor, master and doctorate, as well as aligned qualifications reference frameworks and more detailed learning outcomes/assessment frameworks for 11 subject areas, offering a sound basis for quality assurance. As a follow-up, actual assessment formats for five academic fields have been developed to allow for measuring the actual level of learning at the institutional level from a comparative perspective.

Frameworks as well as assessment models and items are highly innovative, content-wise as in the strategy of development, involving renown academics finding common ground. Its value is not limited to Europe but has global significance. The model developed, is also relevant for micro-credentials in defining levels of mastery.

With so many placement opportunities around the students in their final or prefinal year, they start to feel the strain of the season. The students feel the need to be aware of their position and how to increase their chances of being hired. Hence, a system to guide their career is one of the needs of the day.

The job role prediction system utilizes machine learning techniques such as Naïve Bayes, K-Nearest Neighbor, Support Vector machines (SVM) and Artificial Neural Networks (ANN) to suggest a student’s job role based on their academic performance and course outcomes (CO), out of which ANN performs better. The system uses the Mepco Schlenk Engineering College curriculum, placement and students’ Assessment data sets, in which the CO and syllabus are used to determine the skills that the student has gained from their courses. The necessary skills for a job position are then extracted from the job advertisements. The system compares the student’s skills with the required skills for the job role based on the placement prediction result.

The system predicts placement possibilities with an accuracy of 93.33 and 98% precision. Also, the skill analysis for students gives the students information about their skill-set strengths and weaknesses.

For skill-set analysis, only the direct assessment of the students is considered. Indirect assessment shall also be considered for future scope.

The model is adaptable and flexible (customizable) to any type of academic institute or universities.

The research will be very much useful for the students community to bridge the gap between the academic and industrial needs.

Several works are done for career guidance for the students. However, these career guidance methodologies are designed only using the curriculum and students’ basic personal information. The proposed system will consider the students’ academic performance through direct assessment, along with their curriculum and basic personal information.

In the Netherlands, thesis assessment quality is a growing concern for the national accreditation organization due to increasing student numbers and supervisor workload. However, the accreditation framework lacks guidance on how to meet quality standards. This study aims to address these issues by sharing our experience, identifying problems and proposing guidelines for quality assurance for a thesis assessment system.

This study has two parts. The first part is a narrative literature review conducted to derive guidelines for thesis assessment based on observations made at four Dutch universities. The second part is a case study conducted in one bachelor’s psychology-related program, where the assessment practitioners and the vice program director analyzed the assessment documents based on the guidelines developed from the literature review.

The findings of this study include a list of guidelines based on the four standards. The case study results showed that the program meets most of the guidelines, as it has a comprehensive set of thesis learning outcomes, peer coaching for novice supervisors, clear and complete assessment information and procedures for both examiners and students, and a concise assessment form.

This study is original in that it demonstrates how to holistically ensure the quality of thesis assessments by considering the context of the program and paying more attention to validity (e.g. program curriculum and assessment design), transparency (e.g. integrating assessment into the supervision process) and the assessment expertise of teaching staff.

Prosperity and growth of emerging economies rely heavily on the innovativeness of higher education institutions (HEIs) and their ability to provide employable graduates with entrepreneurial competencies and flexibility in the assessment of their progress in gaining knowledge. The innovativeness of the higher education system is not always reflected through optimal educational content, innovative study programs, skills improvement, assessment methods, etc. The study aims to analyze and evaluate how selected internal factors, reflected in organizational support for innovativeness of HEIs, and external factors reflected in government support for innovativeness of HEIs determine the innovativeness of HEIs, as one of the key determinants of economic prosperity.

The authors analyzed three facets of higher education innovativeness, i.e. graduates' employability, entrepreneurial competencies and new ways of assessment. The sample included 664 students from the University of Kragujevac, Serbia, and the University of Maribor, Slovenia. The authors applied structural equation modeling (SEM) to examine relations.

The results clearly show that in order to become the driver of development in emerging economies, universities must act in different directions, focusing on different organizational drivers of innovativeness, such as academic autonomy of teachers, student participation in curricula design, enhancing new ideas and compensation system, provide resources etc. Organizational support and government for innovativeness of HEIs affect students' entrepreneurial and social skills, creative thinking, leadership, interactive competencies and knowledge-skill-attitude. Assessment tasks and associated learning should be redesigned to enable students to be involved in the evaluation of their work.

The study strives to reduce the research gap identified in the field of researching the drivers of innovativeness in higher education and offers implications for emerging markets regarding various factors that determine the innovativeness of HEIs and consequently contribute to fostering innovation and entrepreneurship in emerging markets. Originality derives from the fact that even though the authors have explored HEIs in Serbia and Slovenia, the results are fully transferrable to other former socialist states, considering their similar socio-economic and educational background. Results of this research complement the understanding and provide new knowledge for further development of innovativeness in HE.

This research aims to investigate the leadership strategies employed by two higher education institutions in Malaysia as they navigated the shift to online delivery of their computer science programs in response to the demands of Education 4.0.

A phenomenological, comparative case study approach was used to delve into the leadership and management practices of these institutions during the transition to online learning. Data were collected through interviews and document analysis.

This study explores the leadership strategies employed by two higher education institutions in Malaysia during their transition to online learning due to the COVID-19 pandemic. Five key themes emerged from the data: leadership and team coordination, training and skill development, adaptation to new assessment methods, resource management and work culture and environment. Both institutions demonstrated effective leadership, continuous training and adaptability in assessment methods. However, differences were noted in resource management and work culture. Institution A's leader had to liaise with various departments and personally invest in equipment, while Institution B was already well-equipped. The work culture at Institution A demonstrated flexibility and mutual understanding, while Institution B used key performance indicators to measure progress. Despite these differences, both leaders successfully managed the shift to online teaching, underscoring the importance of effective leadership, continuous training, flexibility, resource management and a supportive work culture in managing change. The study also highlighted the distinct roles of curriculum leaders in both institutions, with Institution A's leader focusing on multiple activities, while Institution B's leader was able to focus solely on curriculum change due to their institution's preparedness.

This study provides a rich, qualitative exploration of the strategies and challenges faced by program leaders in managing the shift to online teaching during the COVID-19 pandemic. Future research could build on these findings by conducting similar studies in other educational contexts or countries to compare and contrast the strategies and challenges faced by program leaders. Additionally, future research could also employ quantitative methods to measure the effectiveness of different strategies in managing the shift to online teaching. This could provide a more comprehensive understanding of the factors that contribute to successful change management in educational institutions.

This study provides valuable insights for program leaders, educators and policymakers in managing change in educational institutions. The themes identified in this study – effective leadership, continuous training and skill development, flexibility in adapting to new assessment methods, effective resource management and a supportive work culture and environment – can serve as a guide for program leaders in managing future changes in their institutions. Moreover, the strategies employed by the program leaders in this study, such as forming a powerful coalition, providing training on online tools and prioritizing student welfare, can be adopted or adapted by other program leaders in managing change.

This study presents a unique contribution to the existing literature by offering a comparative analysis of change management strategies in two distinct educational institutions during the shift to online teaching due to the COVID-19 pandemic. It uncovers the nuanced differences in leadership styles, resource management and pedagogical adaptations, providing a rich, context-specific understanding of the change process. The study fills a research gap by examining the practical application of Kotter's 8-Step Change Model and the McKinsey 7S Model in real-world educational settings. The findings offer valuable insights for other institutions navigating similar changes, thereby extending the practical and theoretical understanding of change management in higher education.

Digitalisation is reshaping the learning process, shifting it towards personalisation and online learning, and fuelling the growth of massive open online courses (MOOCs) and micro-credentials. Despite being a popular global trend, limited studies have looked at micro-credentials and their impact on learners' behavioural outcomes. The purpose of this study is to investigate the impact of programme design factors on learning experience, as well as the inter-relationships between programme design, learning experience and behavioural responses (e.g. engagement and willingness to pay more (WTPM)) towards micro-credentials. In addition, the study aims to investigate learning enjoyment as a moderator.

A survey questionnaire was used to collect data from 354 respondents who are working adults living in the major economic states in Malaysia. Data analysis was performed using the analysis of a moment structures (AMOS) statistical software and SPSS (Statistical Package for the Social Sciences) PROCESS macro.

The results show the significance of programme design factors (i.e., flexibility, system quality and content quality) in determining the learning experience. The learning experience is found as a mediator in the relationship between programme design factors and learner engagement and WTPM. In addition, the moderation assessment confirms that enjoyment during learning strengthens the relationship between experience and behavioural responses.

This study is amongst a few selected studies that focus on engagement in and WTPM for micro-credentials. In addition, it emphasises the mediating role of the learning experience and the moderating role of enjoyment in understanding the impact of programme design on learners' experiences and behavioural responses.