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Purpose: The concept of sustainable development (SD) is a popular response to society's need to preserve and extend the life span of natural resources. One of the 17 goals of the SD is "education quality" (Fourth Goal of Sustainable Development [SDG-4]). Education quality is an important goal because education is a powerful force that can influence social policies and social change. The SDG-4 must be measured in different contexts, and the tools to quantify its effects require exploration. So, this study aims to propose a statistical model to measure the impact of higher education online courses on SD and a structural equation model (SEM) to find constructs or factors that help us explain a sustainability benefits rate. These proposed models integrate the three areas of sustainability: social, economic and environmental. Design/methodology/approach: A beta regression model suggests features that include the academic and economic opportunities offered by the institution, the involvement in research activities and the quality of the online courses. A structural equation modelling (SEM) analysis allowed selecting the key variables and constructs that are strongly linked to the SD. Findings: One of the key findings showed that the benefit provided by online courses in terms of SD is 62.99% higher than that of offline courses in aspects such as transportation, photocopies, printouts, books, food, clothing, enrolment fees and connectivity. Research limitations/implications: The SEM model needs large sample sizes to have consistent estimations. Thus, despite the obtained estimations in the proposed SEM model being reliable, the authors consider that a limitation of this study was the required time to collect data corresponding to the estimated sample size. Originality/value: This study proposes two novel and different ways to estimate the sustainability benefits rate focused on SDG-4, and machine learning tools are implemented to validate and gain robustness in the estimations of the beta model. Additionally, the SEM model allows us to identify new constructs associated with SDG-4.

Creativity is now accepted as a core 21st-century competency and is increasingly an explicit part of school curricula around the world. Therefore, the ability to assess creativity for both formative and summative purposes is vital. However, the "fitness-for-purpose" of creativity tests has recently come under scrutiny. Current creativity assessments have up to five key weaknesses that create a barrier to their widespread use in educational settings. These are: (a) A lack of domain/subject specificity; (b) Inconsistency, leading to a lack of trust; (c) A lack of authenticity in classroom settings; (d) Slowness (in providing useful results); (e) High cost to administer. The aim of the present study is to explore the feasibility of the automated assessment of mathematical creativity, drawing on tools and techniques from the field of natural language processing, as a means to address these weaknesses. This paper describes the performance of a machine learning algorithm, relative to human judges, demonstrating the practicality of automated creativity assessment for large-scale, school-based assessments.

The integration of AI in education has the potential to significantly transform teaching and learning. However, the successful adoption of AI is heavily reliant on the actions and perspectives of school leaders. As schools increasingly incorporate AI into their classrooms, it is essential to understand how education leaders perceive this technology and the factors that influence their decision-making around its implementation. This study explores the 'attitudes, beliefs, and concerns' of Australian school leaders regarding AI integration and implementation. Drawing on qualitative data from 11 interviews with school leaders, this paper highlights the challenges and opportunities of using AI. The findings provide insights into how education leaders can effectively integrate this emerging technology into school systems. [ABSTRACT FROM AUTHOR]

One of the abiding challenges in creativity research is assessment. Objectively scored tests of creativity such as the Torrance Tests of Creativity and the test of Creative Thinking–Drawing Production (TCT-DP; Urban & Jellen, 1996) offer high levels of reliability and validity but are slow and expensive to administer and score. As a result, many creativity researchers default to simpler and faster self-report measures of creativity and related constructs (e.g., creative self-efficacy, openness). Recent research, however, has begun to explore the use of computational approaches to address these limitations. Examples include the Divergent Association Task (Olson et al., 2021) that uses computational methods to rapidly assess the semantic distance of words, as a proxy for divergent thinking. To date, however, no research appears to have emerged that uses methods drawn from the field of artificial intelligence to assess existing objective, figural (i.e., drawing) tests of creativity. This article describes the application of machine learning, in the form of a convolutional neural network, to the assessment of a figural creativity test—the TCT-DP. The approach shows excellent accuracy and speed, eliminating traditional barriers to the use of these objective, figural creativity tests and opening new avenues for automated creativity assessment. [ABSTRACT FROM AUTHOR]

While Artificial Intelligence (AI) is set to transform how we live, work, and play, competencies such as creativity remain fundamentally human. However, the way we learn, develop, and deploy creativity will be impacted by AI. This paper sets out four underpinning propositions that will help to guide the integration of AI and human creativity. These propositions explain not only why AI is not independently creative, but also how AI can support and augment human creativity in areas such as education. It will also highlight implications for the development of creativity in an educational context. [ABSTRACT FROM AUTHOR]

Technology is omnipresent in our rapidly changing digital world. Capitalising on these technologies is paramount for education systems to support a modern workforce. This study presents a review of 10 countries, highlighting how different public education systems have integrated digital technologies for teaching and learning. These education systems were selected based on their high performance and continuous improvement in international assessments. Four main themes emerged as key areas of focus from our analysis: improved and enhanced teaching and learning; digital maturation, including tackling the first and second digital divide; data-driven decision making and evidence-based education; and AI in education. Our findings will help benchmark current approaches and uses of digital teaching and learning, including perceived challenges and opportunities that can be used to inform the development and implementation of digital education policies. [ABSTRACT FROM AUTHOR]

Educational institutions worldwide are increasingly urged to recognize the significance of integrating creative thinking and other essential capabilities across different academic disciplines. Additionally, there is a growing focus on students’ non-cognitive factors. However, relationships between creativity and non-cognitive factors, such as self-concept in subjects such as science, are not well understood. Given the global attempts to increase diversity in STEM subjects, this study explores the relationships between year 8 female students’ non-cognitive factors in science and their creative thinking through a series of structural equation models (SEM). The findings identified a positive relationship between specific non-cognitive factors and creative thinking, which has significant implications for curriculum writers and teachers, who should consider these findings to potentially increase student creativity in STEM subjects. This is particularly pertinent in light of initiatives aimed toward fostering female students’ engagement in STEM fields throughout their academic journey and into their future careers. [ABSTRACT FROM AUTHOR]

In an increasingly data‐driven world, statistical literacy is a necessity yet statistical learning is often inhibited by statistics anxiety. Using the Auzmendi Scale to Measure Attitude toward Statistics (ASMAS), this study examines how statistics anxiety in university students is related to other dimensions of their attitudes toward statistics and how statistics anxiety and other dimensions change following introductory statistics instruction. Based on data collected from Spain, Canada, and Australia, this study finds that anxiety is negatively related to security–confidence, pleasantness, and motivation. The structure of these relationships is consistent across countries and disciplines and remains in place after statistics instruction. Further, by the end of an introductory statistics course, students report higher security–confidence and pleasantness but lower anxiety. Results thus suggest where efforts to improve students' experience with statistics might need to be directed, and the paper concludes with a discussion of the implications of these results for statistics instruction. [ABSTRACT FROM AUTHOR]