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Few resources exist to incorporate principles of modular approach to course design. This research aimed to help instructors by presenting principles for practical and empirically informed modular course design in engineering education.

In the first phase, a systematic literature review was completed to identify categories addressing a modular course design. Search and screening procedures resulted in 33 qualifying articles describing the development of a modular course. In the second phase, 6 expert interviews were conducted to elaborate on the identified categories.

Guided by the interview results and the ADDIE (Analyze, Design, Develop, Implement, and Evaluate) course design model, the categories were compiled into six design principles. To present the design principles in relation to the guiding principles of modular approach, an overarching conceptual model was developed.

Here, we present our innovation; a foundation for an evidence-based systematic approach to modular course design. Implications have value for supporting flexibility and autonomy in learning.

The study integrated understanding by design-Internet of Things (UbD-IoT) education with design thinking and computational thinking to plan and design an IoT course. Cross-domain application examples were employed to train students in problem-understanding, deep thinking and logical design for IoT applications.

In this study, the UbD model was integrated with design thinking and computational thinking in the planning and design of an IoT course. The examples of cross-domain applications were used to train students to understand a problem by engaging themselves in deep thinking and helping them think and design logically for an IoT application.

The UbD-IoT learning design greatly decreased students' overall cognitive load. UbD-IoT learning has a significant impact on the performance of computational thinking in problem-solving and problem-understanding. The impact of UbD-IoT learning on logical thinking and program learning cognition in students needs to be verified.

The results of this study have shown that the UbD model is effective in reducing the cognitive load of a learning course and also strengthens T-competencies in the lateral skills of computational thinking, critical problem-solving, logical thinking and creative thinking.

Preservice teacher (PST) professional development programs are crucial for cultivating high-quality STEAM teachers of the future, significantly impacting the quality of regional STEAM education. The Guangdong-Hong Kong-Macao Greater Bay Area, as a region of cross-border cooperation, integrates the resources and advantages of Guangdong, Hong Kong, and Macao, possessing rich cultural heritage and innovative capabilities. Transdisciplinary Education for Cultural Inheritance (C-STEAM) is an effective approach to promoting educational collaboration within the Greater Bay Area, facilitating the integration of both technological and humanities education. This study aims to develop a Technology-Enabled University-School-Enterprise (T-USE) collaborative education model and implement it in the Greater Bay Area, to explore its role as a support mechanism in professional development and its impact on C-STEAM PSTs' professional capital.

Adopting a qualitative methodology, the study interviewed PSTs who participated in a C-STEAM teacher education course under the T-USE model. Thematic coding is used to analyze their knowledge acquisition, interaction benefits with community members, and autonomous thinking and decision-making in theoretical learning and teaching practice.

The findings show that the T-USE model significantly enhanced the PSTs' human capital, including teaching beliefs, knowledge, and skills. In terms of social capital, PSTs benefited from collaboration with PST groups, university teaching teams, in-service teachers, and enterprises, though challenges such as varying levels of expertise among in-service teachers and occasional technical instability emerged. For decisional capital, the T-USE model provided opportunities for autonomous thinking and promoted teaching judgment skills through real teaching challenges and scenarios. Reflective practice activities also supported PSTs' professional growth.

This study reveals the effectiveness and internal mechanism of the T-USE model in C-STEAM PST training, offering significant theoretical and practical references for future PST education.

The aim of this study is to understand the effect of class configurations in an active learning classroom (ALC) on students' self-perception of experiences and learning outcomes, namely participation, performance, motivation and creativity.

A self-administered survey (N = 131) was conducted in seven classes from the varied disciplines of communication, interior design and architecture. During the first half of the semester, all selected courses were conducted in traditional classrooms, whereas those in the second half were conducted in an ALC. ALC was designed to be used with several furniture configurations which could be easily set up by members of the learning community themselves. The survey was conducted at the end of semester before final exams, when students have a clear idea of the experiences in both the traditional and the new (ALC) classrooms, having spent equal time in each of these learning environments.

The main finding of the study is that students were eager to have future classes in the ALC rather than in traditional settings since the students experienced better learning outcomes in the ALC. During the second half of the semester, students who were in classes conducted following active learning (AL) pedagogies, with its supportive spatial configurations, were more aware of the learning outcomes facilitated by the physical environment. Further, the authors found that the increase in the number of furniture configurations has a statistically significant positive impact on learning outcomes.

AL pedagogy is often studied as a way of teaching and rarely with its associated classroom environments. In most of the existing studies, the configuration of furniture has often been overlooked within an AL environment by a learning community. This study fills the gap in emphasising the spatial aspects of the ALC by focusing on the specific pedagogy being followed.

This study aims to focus on the Artificial Intelligence of Things (AIoT) course. As AIoT has many theoretical theories and students usually have little interest in learning the protocols, the experiments can help stimulate their curiosity. Due to the environmental factor, the teaching requires assistive videos and Problem-Based Learning (PBL) to understand students' learning conditions.

The experimental design generally follows the course theories going from easy to complex, and students can extend the acquired concepts to other project development, yet, without in-depth knowledge about the experiment, resulting in limited creativity.

The assessment analysis can reveal whether students have grown from the teaching. The final analysis at the end of the term can show learners' conditions; meanwhile, students can deliver their level of satisfaction. The click-and-mortar teaching environment provided in this research can improve learning setting and quality, solidifying learners' proficiency.

The research result has proved the feasibility of the proposed method. Apart from showing the experimental steps, the video also explains the corresponding theories, helping students reinforce experimental knowledge and boost learning willingness.

Although many universities have begun to provide artificial intelligence (AI)-related courses for students, the influence of the course on students' intention to participate in the development of AI-related products/services needs to be verified. In order to explore the factors that influence students' participation in AI services and system development, this study uses self-efficacy, AI literacy, and the theory of planned behaviour (TPB) to investigate students' intention to engage in AI software development.

The questionnaire was distributed online to collect university students' responses in central Taiwan. The research model and eleven hypotheses are tested using 151 responses. The testing process adopted SmartPLS 3.3 and SPSS 26 software.

AI programming self-efficacy, AI literacy, and course satisfaction directly affected the intention to participate in AI software development. Moreover, course playfulness significantly affected course satisfaction and AI literacy. However, course usefulness positively affected course satisfaction but did not significantly affect AI literacy and AI programming self-efficacy.

The model improves our comprehension of the influence of AI literacy and AI programming self-efficacy on the intention. Moreover, the effects of AI course usefulness and playfulness on literacy and self-efficacy were verified. The findings and insights can help design the AI-related course and encourage university students to participate in AI software development. The study concludes with suggestions for course design for AI course instructors or related educators.

Higher education plays an essential role in achieving the United Nations sustainable development goals (SDGs). However, there are only scattered studies on monitoring how universities promote SDGs through their curriculum. The purpose of this study is to investigate the connection of existing common core courses in a university to SDG education. In particular, this study wanted to know how common core courses can be classified by machine-learning approach according to SDGs.

In this report, the authors used machine learning techniques to tag the 166 common core courses in a university with SDGs and then analyzed the results based on visualizations. The training data set comes from the OSDG public community data set which the community had verified. Meanwhile, key descriptions of common core courses had been used for the classification. The study used the multinomial logistic regression algorithm for the classification. Descriptive analysis at course-level, theme-level and curriculum-level had been included to illustrate the proposed approach’s functions.

The results indicate that the machine-learning classification approach can significantly accelerate the SDG classification of courses. However, currently, it cannot replace human classification due to the complexity of the problem and the lack of relevant training data.

The study can achieve a more accurate model training through adopting advanced machine learning algorithms (e.g. deep learning, multioutput multiclass machine learning algorithms); developing a more effective test data set by extracting more relevant information from syllabus and learning materials; expanding the training data set of SDGs that currently have insufficient records (e.g. SDG 12); and replacing the existing training data set from OSDG by authentic education-related documents (such as course syllabus) with SDG classifications. The performance of the algorithm should also be compared to other computer-based and human-based SDG classification approaches for cross-checking the results, with a systematic evaluation framework. Furthermore, the study can be analyzed by circulating results to students and understanding how they would interpret and use the results for choosing courses for studying. Furthermore, the study mainly focused on the classification of topics that are taught in courses but cannot measure the effectiveness of adopted pedagogies, assessment strategies and competency development strategies in courses. The study can also conduct analysis based on assessment tasks and rubrics of courses to see whether the assessment tasks can help students understand and take action on SDGs.

The proposed approach explores the possibility of using machine learning for SDG classifications in scale.

Education is widely recognized as a key domain for the promotion of the sustainable development goals (SDGs), prompting an increased focus on sustainable development in foreign language education. Despite increased attention, guidelines about SDGs are often primarily policy-based without concrete guidance, and the integration of education for sustainable development (ESD) within higher education curricula has been slow. This paper aims to mitigate this gap by providing an outline for the curriculum development for three elective English courses integrating SDG themes.

The paper begins by introducing the SDGs, ESD and its application to language education. From there, this paper outlines three content and language integrated learning courses that integrate global issues into their curriculum. Finally, there is a discussion and consideration of various factors to consider when implementing global issues into an English language classroom.

Following practical examples of how to integrate global issues into an English language classroom, considerations such as socio-cultural context, teaching context and the expertise of the instructor are discussed.

This paper covers a variety of social topics related to sustainable development in addition to the often addressed environmental topics. Many guidelines about integrating SDGs into education are policy-based without concrete guidance, so this paper aims to provide practical examples and considerations.

E-learning has become a polarizing issue. Some say that it enhances accessibility to education and some say that it hinders it. While the literature on the subject underscores the effectiveness of the pedagogical frameworks, strategies and distance learning technologies, the firsthand accounts of students, parents and practitioners challenge the validity of experts’ assessments. There is a gap between theory and practice and between the perceptions of providers and consumers of online learning. Following a period of lockdowns and a transition to online learning during the recent pandemic, the prevailing sentiment toward a distance mode of instruction became one of strong skepticism and negative bias. The aim of the study was to examine why e-learning has struggled to meet stakeholder expectations. Specifically, the study posed two research questions: 1. What are the reasons for dissatisfaction with online learning? 2. What are the implications for future research and practice?

The study used a mixed methods approach to examine the reasons behind negative perceptions of online learning by comparing the firsthand accounts posted on social media with the literature. To this end, n = 62,874 social media comments of secondary and postsecondary students, as well as parents, teachings staff and working professionals, covering the span of over 14 years (2008–2022), were collected and analyzed.

The study identified 28 themes that explain the stakeholder’s discontent with the online learning process and highlighted the importance of user-centric design. The analysis revealed that the perceived ineffectiveness of distance education stems from the failure to identify and address stakeholders’ needs and, more particularly, from the incongruence of instructional strategies, blindness to the cost of decisions related to instructional design, technology selection and insufficient levels of support. The findings also highlight the importance of user-centric design.

To address dissatisfaction with e-learning, it is imperative to remove barriers to learning and ensure alignment between technology and learners’ needs. In other words, the learning experience should be personalized to account for individual differences. Despite its cost-effectiveness, the one-size-fits-all approach hinders the learning process and experience and is likely to be met with resistance.

Drawing from the extensive literature, the study offers an explanation for stakeholders’ discontent with e-learning. Unlike survey research that is prone to social desirability bias, the sample provides a rare opportunity to observe and measure the visceral reactions that provide a more authentic sense of stakeholders’ perceptions toward online learning. The authors offer recommendations and identify areas for future research.

Prior research suggests that collaboration is key to sustainable community development and environmental management, and peer-to-peer learning (P2PL) may facilitate community building and collaborative learning skills. This study aims to examine the effect of P2PL on the enhancement of environmental management and sustainable development skills, community building and social capital (i.e. connectedness) and understanding of course learning objectives.

Quantitative and qualitative longitudinal survey data was collected in a sustainable development focused course offered at a large American public university that uses P2PL to explicitly facilitate community building and collaborative skills. Safety precautions and changing locational course offerings due to the COVID-19 pandemic in years 2020, 2021 and 2022 provided an opportunity to evaluate the impact of P2PL on these skills during both virtual and in-person formats. Additionally, this study compared in-course student evaluations with students taking other sustainable development-related courses with collaborative learning aspects to understand the wider effectiveness of this course structure.

This study finds that course format (virtual vs in-person) overall made no difference in either connectedness or conceptual understandings, and that students in both formats felt more connected to others than students taking other courses with P2PL. Scaffolding P2PL and supplemental peer support can yield improved connectedness and learning among students taking environmental coursework.

Sustainable development requires group collaboration and partnership building skills. Issues are consistently raised about the challenges to teaching these skills in higher education. The students and instructors in this research study identify P2PL strategies to address these challenges for in-person and virtual classroom settings.