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The research paper examines the complex relationship between gamification, student engagement and academic performance in educational environments. The study employed a structural equation model that highlights important connections among key constructs within the educational setting.

This research aims to explore the connection between gamification, student engagement and academic performance in educational settings. The study employs various statistical techniques such as factor analysis, Kaiser–Meyer–Olkin (KMO), Bartlett’s test, component transformation matrix, correlation and regression analysis, descriptive statistics, ANOVA, coefficients and coefficient correlations, residual statistics and confirmatory factor analysis (CFA) to analyze the data.

It was found that active participation by the instructor and good time management skills have a positive impact on student engagement levels (β = 0.380, p < 0.001; β = 0.433 and p < 0.001). However, peer interaction does not significantly predict student engagement (β = −0.068 and p = 0.352). Additionally, there is a positive correlation between student engagement and performance (β = 0.280 and p < 0.001).

The study highlights the importance of innovative design to fully utilize gamification. Future research should consider design, user characteristics and educational context. The findings can guide informed decisions about gamification in education, fostering motivation and learning objectives.

The study presents a reliable tool for assessing student engagement and performance in educational settings, demonstrating high Cronbach’s alpha and robust reliability. It identifies student engagement and time management as significant predictors of Global Learning Outcome. The findings can inform decisions on implementing gamification in educational settings, promoting intrinsic motivation and aligning with learning objectives.

The research highlights the transformative impact of gamification on educational practices, highlighting its potential to enhance student experiences, motivate, promote diversity and improve long-term academic performance, highlighting the trend of integrating technology into education.

In today’s ever-changing education landscape, it is essential to incorporate innovative techniques to keep students engaged and enthusiastic about learning. Gamification is one such approach that has become increasingly popular. It is a concept that takes inspiration from the immersive world of games to enhance the overall learning experience.

This article aims to identify and describe the research outcomes of studies that have employed the theoretical framework of lesson study (LS) in initial science teacher preparation programs. The focus is on the impact of LS on preservice teachers’ (PST) pedagogical and content knowledge, beliefs, routines and norms for professional learning and instructional practices.

A systematic approach was employed to compile pertinent literature by initially searching scholarly databases using specific keywords and phrases related to prospective science teacher preparation. Seventeen studies, encompassing both qualitative research and mixed-methods research, met the inclusion criteria and significantly contributed to the study’s findings. The authors independently conducted a coding process, applying a predefined code scheme based on Lewis et al.'s (2019) theoretical framework. The outcomes of the coding process were compared, and reliability tests were conducted to ensure the consistency of the coding.

In preservice science teacher (PSST) education, LS proves transformative, enriching pedagogical and content knowledge, shaping beliefs, fostering collaboration and influencing instructional practices. Its collaborative, reflective and iterative nature significantly contributes to the professional growth of preservice science teachers, preparing them for effective, student-centered teaching practices. Further investigation is warranted in the realm of LS, particularly concerning preservice science teachers and their beliefs.

This literature review on science PSTs is one of the pioneering efforts to employ the professional development framework crafted by Lewis et al. (2019).

This study aims to investigate a mediation model in which generative learning positively affects marketing innovation and both organizational control and relationship openness mediate the relationship between learning intent and generative learning of international joint ventures (IJVs) in emerging markets. We also decipher the degree of necessity of these factors for generative learning and of generative learning for marketing innovation.

A sample of 181 marketing managers of IJVs in Vietnam, an emerging market, was surveyed to collect data. Partial least squares structural equation modeling (PLS-SEM) was employed to test the net effect, and necessary condition analysis (NCA) was used to decipher the degree of necessity.

The PLS-SEM results demonstrate that the effect of learning intent on generative learning is fully mediated by organizational control and relationship openness, which in turn leads to marketing innovation. The NCA findings reveal that all three factors, namely learning intent, organizational control and relationship openness, serve as necessary conditions for generative learning. However, generative learning does not play the role of a necessary condition for marketing innovation.

The study findings suggest that IJVs in emerging markets should pay attention not only to the net effects of those factors but also to their degrees of necessity for generative learning in order to achieve marketing innovation.

The study contributes to the literature by confirming the mediating roles of organizational control and relationship openness in the relationship between learning intent and generative learning. Furthermore, it is among the first to decipher the degrees of necessity of these factors for generative learning and of generative learning for the marketing innovation of IJVs in emerging markets.

Focussing on the perspective of business event leaders, this study aims to explore the future of transformative experience (TE) events, recognising a paradigm shift from organising conventional events to designing and guiding TEs in the meetings, incentives and conferences as exhibitions (MICE) context.

Using a qualitative interview-based design, insights from 20 international business events industry leaders were gathered and analysed by using thematic analysis through a multi-step process with MAXQDA.

The findings discuss the future of transformative events by identifying the paradigm shift towards TE in business events and outline key dimensions of the leader’s and team’s mindset and skills. Five design principles for TE events in the MICE sector are identified: design for change; emotionally experiential environments; personal engagement; responsibility; and transformative measurement.

The study offers a snapshot of how transformative events of the future could be designed and suggests a series of practical insights for MICE event leaders and organisers seeking to leverage events as a catalyst for intentional transformation, positive impact and long-lasting change.

The study adds to the emerging body of knowledge on TEs and contributes to an extended stakeholder perspective, namely, that of business event leaders and their teams who are instrumental in facilitating transformative events. An original framework for designing TE MICE events is offered as a theoretical contribution.

This scoping review article aimed to systematically search the literature on the use, purpose and barriers of information and communication technology (ICT) for culturally and linguistically diverse (CALD) older adults in the host country. The review also conducted a search for literature on programs that aim to develop digital literacy skills for CALD older adults.

A scoping review approach was utilized to identify and search the available literature, and to map the key concepts. A total of 23 relevant articles were included following a systematic search across seven databases.

The review suggests that CALD older adults use ICT for various purposes, including social interaction, health management, information gathering, immigration, everyday routines and leisure. The barriers faced by these older adults in using ICT encompass fear and limited knowledge, language issues, lack of interest, limited access to technology and health concerns. The existing training programs focus on enhancing self-efficacy and confidence and promoting attitudinal changes toward ICT.

Despite the presence of empirical studies, few scoping or systematic reviews have focused on CALD older adults and technology. Research developments related to the technology use of CALD older adults and associated training programs have been fragmented and sporadic, resulting in gaps in the evidence base. This review fills such a gap to better understand the research status connected to CALD older people’s technology adoption and use.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

The challenges facing 21st-century society are becoming increasingly complex, requiring the development of new citizen competencies. This study aims to validate an educational model focused on developing complex thinking in higher education students. Current educational models lack future-ready competencies, necessitating the emergence of new models to guide future generations toward the common good.

This was an adaptation of the causal-layered analysis (CLA) applied to 415 participants from higher education institutions in Mexico, Panama and Spain. Sessions were designed to present the proposed educational model and explore participants’ perceptions of its significance and contributions to future education.

Key findings include the following: participants perceived complexity as difficult and challenging; causes of problems were linked to outdated educational models requiring replacement by those that develop students’ competencies; participants envisioned changes that would develop individuals capable of understanding and transforming society; and participants recognized the model’s transformative potential, offering a novel proposal for 21st-century education.

This research sought to gather opinions from different stakeholders using the CLA methodology, providing a deep understanding of participants’ perspectives on the proposed solution.

The aim of this study was to measure the level of challenges faced by novice teachers in South Africa. Findings suggest a need for professional development courses to upskill teachers with effective pedagogies that can incorporate the social and emotional components into teaching and learning.

This study applied a descriptive research methodology by administering a questionnaire to 143 novice teachers. The data analysis technique was the Rasch model.

The findings indicated high item and person reliability, with a good item fit and polarity values that are compatible with the Rasch model. The three major challenges identified are uninvolved parents, discipline problems and a lack of guidance and counselling. These challenges can be related to social and emotional learning (SEL) components.

The study used quantitative methods and discovered the challenges that novice teachers face. If the research uses a combination of qualitative methods, it will be possible to better identify the specific causes of the above three challenges of novice teachers.

Due to the complex nature of South African society, many novice teachers are overwhelmed by the challenges they face when entering the profession. These challenges are often embedded in societal risk factors, which complicate the transition from student teacher to novice teacher. The major challenges identified in this study can be related to SEL components, as the challenges are closely linked to the psychological and social backgrounds of the students. Teachers in this study indicated that they found it difficult to deal with these challenges at the beginning of their careers.

By identifying the challenges facing new teachers in South Africa, they will be better prepared for their work in schools. Therefore, they will improve the above situation to continue developing professionally.

The findings indicated high item and person reliability, with a good item fit and polarity values that are compatible with the Rasch model. Teachers in this study indicated that they found it difficult to deal with these challenges in the beginning of their careers. Professional development courses are suggested to help teachers deal with issues such as discipline, uninvolved parents and guidance and counselling effectively. Higher education programmes should also include these topics in their curricula for student teachers. A greater emphasis on training teachers in their pastoral roles is suggested.

This study aims to increase the novelty of clothing design and fabric texture. The element library that can be used for design is systematically summarized. The element database can also be continuously filled according to the existing logic to realize the diversity of design. Improve the theory of fashion design, expand the designer's design ideas and improve design efficiency. Clear design steps and logic can help students and machines learn the design process and promote the development of intelligent design. And verify the feasibility of the simulation software to assist pleated clothing design.

Firstly, according to the logical framework of origami theory, different innovative designs and combined designs are made for the basic units of hyperbolic paraboloid, and the element library that can be used for design is systematically summarized. This database can also be continuously filled according to the existing logic to realize the diversity of design. Secondly, it summarizes three methods of pleated element filling clothing – uniform filling method, the irregular filling method and geometric addition method – that improve the theory of fashion design, expand the designer's design ideas and improve design efficiency. Clear design steps and logic can help students and machines learn the design process and promote the development of intelligent design. Finally, the virtual software is used to simulate the effect of pleated clothing, and the three-dimensional simulation software 3dclo is used to make an empirical study on the application of hyperbolic paraboloid origami in clothing pleated design to verify the feasibility of the simulation software to assist pleated clothing design.

The theoretical results of hyperbolic paraboloid origami are collected and arranged to establish the element library of hyperbolic paraboloid origami. The results expand the designer's design ideas and auxiliary design technology and improve the design efficiency using a sample of hyperbolic paraboloid fabric to verify its practicability and three-dimensional clothing simulation software for exploring the design. The design rules of hyperbolic paraboloid clothing and the realization method of fabric are summarized, including the expansion and combing of elements, the application of size and shape and the method of combination.

Owing to the hyperbolic paraboloid origami’s length shrinkage, the loose computation of clothing requires targeted computation. This paper solely applies a paper model for estimating the shrinkage, and then we tend to subsequently explore the way to precisely compute the porosity, to determine the existing differences in the two-dimensional shrinkage of hyperbolic paraboloid creases of varying materials and to know if the clothing after large-scale production is capable of reaching the anticipated value.

The exploration of this experiment brings a new 3D experiment process to the design process.

This experiment brings new possibilities for the development of virtual fitting and virtual display in the industry.

This study combines hyperbolic paraboloid origami and clothing and combs and expands the unit with logical thinking to expand the designer's design ideas.

The purpose of this paper is to fill a gap in the literature regarding the open innovation management approaches that small and medium-sized enterprises (SMEs) can use to access digital technologies and incorporate them into their organizational processes. The research question is: What organizational and process-level managerial actions do SMEs take to successfully access and implement digital technologies within their organizational processes?

Using Guertler et al.'s (2020) Action Innovation Management Research (AIM-R) framework, this study examines the digital transformation experiences of 10 European SMEs to gain insights into the managerial actions that foster successful digital transformation.

The findings of the paper reveal two major contributions. First, a digital transformation roadmap for SMEs is proposed, with a focus on accessing external resources and reconfiguring internal ones to ease their digital transformation journey. Second, three distinct paradoxes that influence the digital transformation process in SMEs are highlighted, providing useful insights into the challenges and tensions SMEs face during this journey.

This paper provides a unique perspective on the digital transformation of SMEs by examining the managerial actions required for successful technology adoption and revealing the paradoxes that may emerge during this transformative process. The findings form the basis for future research, deepening our understanding of digital transformation in SMEs and providing actionable advice to managers and practitioners navigating this journey.