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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.

The purpose of this study is to generate knowledge about assessment methods in blended business education, which have become increasingly important to establish sustainable assessment practices that support knowledge acquisition for undergraduate students in business administration at a Colombian university.

For the analysis, a two groups comparison was performed using a nonequivalent control group design with a sample of 420 students. As this study wants to find insights to improve the knowledge on assessment topics in marketing research (MR) education, it was focused on the students from the business administration program. This study also uses individual scores from the state test as prior cognitive scores and the high school classification provided by the National Ministry of Education in Colombia (2012).

It was found that the variables that best predict performance on the MR course examinations were the mathematics skills and reading comprehension scores on the state test. The study also showed a better performance of female students on both assessment methods. There were no significant differences between the assessment methods or among the high school levels.

One of the limitations of this study is the limited number of items on the tests. Additionally, the authors recommend conducting an analysis of the differences between the testing items to provide a detailed explanation of students’ performance when comparing computer-based testing and paper-and-pencil testing.

Further design of teaching material and assessments online and offline, based on local and regional marketing problems, is suggested. As the current text and readings are more oriented to the English-speaking contexts, most of the problems presented are oriented to multinational companies and brands.

Insights into the skills required for future jobs provide valuable guidance (World Economic Forum, 2020). Essential skills for emerging roles, like data scientists, can find robust support within the MR course. To further enrich in-class and online exercises with Excel and SPSS, Colombian educators can leverage data sets obtained from sources like the national statistics office and international market intelligence databases available through the university’s library, including Passport and Statista. Engaging with authentic data sets provides students with a more profound understanding of practical applications in MR.

This approach facilitates the identification of key variables, such as assessment and cognitive abilities in math and reading, which predict students’ knowledge acquisition in MR. It not only offers insights into the relevant factors influencing learning in MR but also provides valuable feedback. Additionally, it suggests potential avenues for future research in this field.

Despite the dramatic increase in construction toward additive manufacturing, several challenges are faced using natural materials such as Earth and salt compared to the most market-useable materials in 3D printing as concrete which consumes high carbon emission.

Characterization and mechanical tests were conducted on 19 samples for three natural binders in dry and wet tests to mimic the additive manufacturing process in order to reach an efficient extrudable and printable mixture that fits the 3D printer.

Upon testing compressive strength against grain size, compaction, cohesion, shape, heat and water content, X-Salt was shown to record high compressive strength of 9.5 MPa. This is equivalent to old Karshif and fire bricks and surpasses both rammed Earth and new Karshif. Material flow analysis for X-Salt assessing energy usage showed that only 10% recycled waste was produced by the end of the life cycle compared to salt.

Findings are expected to upscale the use of 3D salt printing in on-site and off-site architectural applications.

Findings contribute to attempts to resolve challenges related to vernacular architecture using 3D salt printing with sufficient stability.

Benefits include recyclability and minimum environmental impact. Social aspects related to technology integration remain however for further research.

This paper expands the use of Karshif, a salt-based traditional building material in Egypt's desert by using X-Salt, a salt-base and natural adhesive, and investigating its printability by testing its mechanical properties to reach a cleaner and low-cost sustainable 3D printed mixture.

Following the recent rise in generative artificial intelligence (GenAI) tools, fundamental questions about their wider impacts have started to reverberate around various disciplines. This study aims to track the unfolding landscape of general issues surrounding GenAI tools and to elucidate the specific opportunities and limitations of these tools as part of the technology-assisted enhancement of mechanical engineering education and professional practices.

As part of the investigation, the authors conduct and present a brief scientometric analysis of recently published studies to unravel the emerging trend on the subject matter. Furthermore, experimentation was done with selected GenAI tools (Bard, ChatGPT, DALL.E and 3DGPT) for mechanical engineering-related tasks.

The study identified several pedagogical and professional opportunities and guidelines for deploying GenAI tools in mechanical engineering. Besides, the study highlights some pitfalls of GenAI tools for analytical reasoning tasks (e.g., subtle errors in computation involving unit conversions) and sketching/image generation tasks (e.g., poor demonstration of symmetry).

To the best of the authors’ knowledge, this study presents the first thorough assessment of the potential of GenAI from the lens of the mechanical engineering field. Combining scientometric analysis, experimentation and pedagogical insights, the study provides a unique focus on the implications of GenAI tools for material selection/discovery in product design, manufacturing troubleshooting, technical documentation and product positioning, among others.

A challenge in implementing Universal Design (UD) is the perceived additional cost, which acts as a barrier to its widespread adoption. The few studies that have examined the cost for UD apply a theoretical research approach, failing to account for the unique design context that influences construction costs. This article presents a research methodology to calculate the UD cost in a realistic, context-dependent manner.

To address this gap, “Research-by-Design” is applied in a case study approach involving twelve cases from three typologies: secondary schools, town halls and small retail shops. Two scenarios are compared to the existing situation: (1) “renovating into a 100% inclusive building” and (2) “building fully inclusive right away”.

Although the methodology is time-consuming, it allows for a fair, realistic and detailed comparison between costs in different scenarios and cases. Findings show how financial implications are strongly related to its scale. Both “renovation” and “new build” scenarios involve costs, but these are considerably lower for the latter. “Circulation” and “Exterior stairs and ramps” are among the most expensive to renovate, but have almost no additional cost in the examined cases for the “new build scenario”.

The research methodology presented in this study is time-consuming, which may limit its feasibility for large-scale cost assessments in diverse contexts.

The research methodology proposed in this study provides valuable insights for architects, designers and stakeholders involved in the implementation of UD. It offers a realistic and context-dependent approach to assess the cost implications of UD, enabling informed decision-making during the design and construction phases.

By revealing the specific cost implications of UD in different building contexts, this study contributes to promoting greater accessibility and inclusion in the built environment.

The applied approach distinguishes this study for its ability to provide rich contextual information on actual cost implications for UD. By considering real cases and their unique design contexts, valuable insights are offered into the true costs of implementing UD in the built environment.

This study holistically and systematically consolidates the available research on digital reading to reveal the research trends of the past 20 years. Moreover, it explores the thematic evolution, hotspots and developmental characteristics of digital reading. This study, therefore, has the potential to serve as a research guide to researchers and educators in relevant fields.

The authors applied a bibliometric approach using Derwent Data Analyzer and VOSviewer to retrieve 2,456 publications for 2003–2022 from the Web of Science (WoS) database.

The results revealed that most studies' participants were university students and the experimental methods and questionnaires were preferred in digital reading researches. Among the influential countries or regions, institutions, journals and authors, the United States of America, University of London, Electronic Library and Chen, respectively, accounted for the greatest number of publications. Moreover, the authors identified the developmental characteristics and research trends in the field of digital reading by analyzing the evolution of keywords from 2003–2017 to 2018–2022 and the most frequently cited papers by year. “E-books,” “reading comprehension” and “literacy” were the primary research topics. In addition, “attention,” “motivation,” “cognitive load,” “dyslexia,” “engagement,” “eye-tracking,” “eye movement,” “systematic analysis,” “meta-analysis,” “smartphone” and “mobile reading/learning” were potential new research hotspots.

This study provides valuable insights into the current status, research direction, thematic evolution and developmental characteristics in the field of digital reading. Therefore, it has implications for publishers, researchers, librarians, educators and teachers in the digital reading field.

The purpose of this article is to posit an alternative learning design approach to the technology-led magnification and multiplication of learning and to the linearity of curricular design approaches such as a constructive alignment. Learning design ecosystem thinking creates complex and interactive networks of activity that engage the widest span of the community in addressing critical pedagogical challenges. They identify the pinch-points where negative engagements become structured into the student experience and design pathways for students to navigate their way through the uncertainty and transitions of higher education at-scale.

It is a conceptual paper drawing on a deep and critical engagement of literature, a reflexive approach to the dominant paradigms and informed by practice.

Learning design ecosystems create spaces within at-scale education for deep learning to occur. They are not easy to design or maintain. They are epistemically and pedagogically complex, especially when deployed within the structures of an institution. As Gough (2013) argues, complexity reduction should not be the sole purpose of designing an educational experience and the transitional journey into and through complexity that students studying in these ecosystems take can engender them with resonant, deeply human and transdisciplinary graduate capabilities that will shape their career journey.

The paper is theoretical in nature (although underpinned by rigorous evaluation of practice). There are limitations in scope in part defined by the amorphous definitions of scale. It is also limited to the contexts of higher education although it is not bound to them.

This paper challenges the dialectic that argues for a complexity reduction in higher education and posits the benefits of complexity, connection and transition in the design and delivery of education at-scale.

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.

The purpose of this study was to ascertain the relationship between EFL teachers' perception of the intended and unintended consequences of formative assessment (FA) decisions and their sense of self-efficacy and anxiety toward data-driven decision-making (DDDM).

A correlational research design and correlational/regression analysis was utilized to conduct this study. In addition, a thematic analysis was conducted of participants' responses to the open-ended questions.

Descriptive statistics suggest that most EFL teachers perceived both intended and unintended consequential validity of their FA decisions with a moderate level of DDDM self-efficacy and a low level of anxiety. The results of the correlational analysis indicated a strong positive relationship indicating that those with higher teachers' perceptions of consequential validity of formative assessment (TPCVFA) scores tended to report higher DDDM efficacy. The results of the correlational analysis also indicated that a significant relationship did not exist between TPCVFA scores and DDDM anxiety. Finally, multiple regression analyses revealed that TPCVFA was a significant predictor of DDDM efficacy; however, TPCVFA was not a significant predictor of DDDM anxiety.

Data collected from 114 Iranian EFL teachers using the Data-Driven Decision-Making Efficacy and Anxiety Inventory (3D-MEA, Dunn et al. 2013a) and the TPCVFA questionnaire, which was self-developed and validated for the current study.

The increasing urgency to address climate change in construction has made green construction (GC) and sustainability critical topics for academia and industry professionals. However, the volume of literature in this field has made it impractical to rely solely on traditional systematic evidence mapping methodologies.

This study employs machine learning (ML) techniques to analyze the extensive evidence-base on GC. Using both supervised and unsupervised ML, 5,462 relevant papers were filtered from 10,739 studies published from 2010 to 2022, retrieved from the Scopus and Web of Science databases.

Key themes in GC encompass green building materials, construction techniques, assessment methodologies and management practices. GC assessment and techniques were prominent, while management requires more research. The results from prevalence of topics and heatmaps revealed important patterns and interconnections, emphasizing the prominent role of materials as major contributors to the construction sector. Consistency of the results with VOSviewer analysis further validated the findings, demonstrating the robustness of the review approach.

Unlike other reviews focusing only on specific aspects of GC, use of ML techniques to review a large pool of literature provided a holistic understanding of the research landscape. It sets a precedent by demonstrating the effectiveness of ML techniques in addressing the challenge of analyzing a large body of literature. By showcasing the connections between various facets of GC and identifying research gaps, this research aids in guiding future initiatives in the field.