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There is an increase globally of students using technology to support their learning. The purpose of this paper is to outline the technical aspects of adaptive learning and contribute to the development of pedagogy that incorporates this method in teaching and learning.

This is a technical review article that summarises key guidance on the application of adaptive learning and then reflects on its application in a UK and Vietnamese context.

Initial analysis demonstrates that learning can occur asynchronously because of students engaging with adaptive learning. Issues and recommendations were derived from the reflections and practice of both UK and Vietnamese practitioners. Recommendations focussed on the more practical elements of constructing and maintaining adaptive learning. Questions were then constructed to make the decision of whether to implement adaptive learning into teaching and learning practices.

This academic commentary reflects on the implementation of asynchronous learning adaptive technologies in both the UK and Vietnam, specifically exploring the use of a “mastery path” and “computerised adaptive testing” to enhance student understanding.

This study aims to examine the relationship between the influence of technological disruption and the transformation of business models mediated by adaptive organization and organization learning.

In total, 116 top management teams from the member of the Indonesian Advertising Association (P3I) were recruited for this study. The data was obtained through an online survey and analyzed using the PLS-structural equation modeling (SEM) technique.

This study revealed the importance of organizational learning and adaptive organization in minimizing technology disruption and enabler of the business model transformation. In an always-changing environment, the adaptive organization is the core element and catalyst of firm transformation. The acceleration of business model transformation is empowered through establishing an organization's learning system by exploiting existing knowledge, exploring new knowledge and cultivating a learning culture.

In today’s fast-paced digital world and a constant state of flux, advertising agencies need to build a sustainable business model and structure that allows them to be flexible, adaptive to changes and efficient.

To the best of the authors’ knowledge, this study was the first to develop a model to mitigate technology disruption and enable necessary elements to create a transformation business model.

This paper aims to present a comprehensive overview of the patterns and trends of publications on artificial intelligence (AI) in personalised learning. It addresses the need to investigate the intellectual structure and development of this area in view of the growing amount of related research and practices.

A bibliometric analysis was conducted to cover publications on AI in personalised learning published from 2000 to 2022, including a total of 1,005 publications collected from the Web of Science and Scopus. The patterns and trends in terms of sources of publications, intellectual structure and major topics were analysed.

Research on AI in personalised learning has been widely published in various sources. The intellectual bases of related work were mostly on studies on the application of AI technologies in education and personalised learning. The relevant research covered mainly AI technologies and techniques, as well as the design and development of AI systems to support personalised learning. The emerging topics have addressed areas such as big data, learning analytics and deep learning.

This study depicted the research hotspots of personalisation in learning with the support of AI and illustrated the evolution and emerging trends in the field. The results highlight its latest developments and the need for future work on diverse means to support personalised learning with AI, the pedagogical issues, as well as teachers’ roles and teaching strategies.

This paper aims to examine employee-driven innovation (EDI) intertwined with learning, creating a new description combining these two concepts: employee-driven learning and innovation (EDLI). This paper provides insights into the nature of EDLI based on the existing theories and perspectives. It seeks to elaborate EDLI as an ongoing process in and through work.

The paper draws on Jaakkola’s (2020) guidance for structuring a conceptual article. The authors first describe the theoretical starting points related to EDI and then elaborate its relationship with learning at work, with the aim of structuring the key elements involved, drawing on and interpreting existing theory and knowledge.

In summary, advanced here are five premises for describing EDLI at work: (1) EDI and workplace learning are strongly intertwined phenomena, (2) learning in the EDI process occurs simultaneously at the intra-personal and inter-personal levels as a reciprocal process of adaptive and innovative learning, (3) innovations are only manifested in and are relevant to the specific cultural-historical and social context of particular enterprises, (4) the continuity of innovations and learning processes is enabled by participation and (5) triggers from outside the workplace, behind the innovation and the specific consequences (that transcend workplace boundaries) of the innovation anchor aspects of the process outside the workplace or work practice.

The paper advances a description and justification of EDLI. As such, it extends, connects and updates previously established theoretical models and explanations of this about EDIs. Based on the premises advanced here, the theoretical and practical contributions are discussed and the research gaps and needs for further research identified.

This study conducts a comparative study on the performance of reliability assessment methods based on adaptive surrogate models to accurately assess the reliability of automobile components, which is critical to the safe operation of vehicles.

In this study, different adaptive learning strategies and surrogate models are combined to study their performance in reliability assessment of automobile components.

By comparing the reliability evaluation problems of four automobile components, the Kriging model and Polynomial Chaos-Kriging (PCK) have better robustness. Considering the trade-off between accuracy and efficiency, PCK is optimal. The Constrained Min-Max (CMM) learning function only depends on sample information, so it is suitable for most surrogate models. In the four calculation examples, the performance of the combination of CMM and PCK is relatively good. Thus, it is recommended for reliability evaluation problems of automobile components.

Although a lot of research has been conducted on adaptive surrogate-model-based reliability evaluation method, there are still relatively few studies on the comprehensive application of this method to the reliability evaluation of automobile component. In this study, different adaptive learning strategies and surrogate models are combined to study their performance in reliability assessment of automobile components. Specially, a superior surrogate-model-based reliability evaluation method combination is illustrated in this study, which is instructive for adaptive surrogate-model-based reliability analysis in the reliability evaluation problem of automobile components.

The purpose of this paper is to provide adaptive access to learning resources in the digital library.

A novel method using ontology-based multi-attribute collaborative filtering is proposed. Digital libraries are those which are fully automated and all resources are in digital form and access to the information available is provided to a remote user as well as a conventional user electronically. To satisfy users' information needs, a humongous amount of newly created information is published electronically in digital libraries. While search applications are improving, it is still difficult for the majority of users to find relevant information. For better service, the framework should also be able to adapt queries to search domains and target learners.

This paper improves the accuracy and efficiency of predicting and recommending personalized learning resources in digital libraries. To facilitate a personalized digital learning environment, the authors propose a novel method using ontology-supported collaborative filtering (CF) recommendation system. The objective is to provide adaptive access to learning resources in the digital library. The proposed model is based on user-based CF which suggests learning resources for students based on their course registration, preferences for topics and digital libraries. Using ontological framework knowledge for semantic similarity and considering multiple attributes apart from learners' preferences for the learning resources improve the accuracy of the proposed model.

The results of this work majorly rely on the developed ontology. More experiments are to be conducted with other domain ontologies.

The proposed approach is integrated into Nucleus, a Learning Management System (https://nucleus.amcspsgtech.in). The results are of interest to learners, academicians, researchers and developers of digital libraries. This work also provides insights into the ontology for e-learning to improve personalized learning environments.

This paper computes learner similarity and learning resources similarity based on ontological knowledge, feedback and ratings on the learning resources. The predictions for the target learner are calculated and top N learning resources are generated by the recommendation engine using CF.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

This study aims to develop theory on the process toward digital service innovation (DSI) and to generate insights into how companies deal with the rising complexity associated with DSI, both inside and outside of the organization, through organizational learning and alignment.

After purposeful sampling, in-depth, longitudinal case studies of three manufacturers are presented as illustration. Per case, multiple semi-structured interviews are conducted, and insights are validated through rich additional data gathering. Each company's DSI pathway is reconstructed with critical incident technique. Next, using systematic combining, a middle-range theory is developed by proposing a theoretical frame concerning the relations between DSI maturity, learning and alignment.

The authors posit that, as companies gradually develop and progress toward DSI maturity, they deal with a rising degree of complexity, fueling their learning needs. Companies that are apt to learn, pass through multiple cycles of learning and alignment to overcome specific complexities associated with different DSI stages, with each cycle unlocking new DSI opportunities and challenges.

The study applies a stage-based view on DSI combined with complexity management and organizational learning and alignment theory. It offers a theoretical frame and propositions to be used by researchers for future DSI studies and by managers to evaluate alternative DSI strategies and implementation steps.

This study was done to compare the modes of teaching mathematics in higher education in the United Arab Emirates (UAE). The three teaching methods were used as follow: before, during and after the COVID-19 pandemic. The three teaching methods are: (1). Normal on-campus face-to-face teaching and learning activity before the COVID-19 pandemic. (2). Full online teaching and learning activity during the COVID-19 pandemic. (3). Blended teaching and learning activity after the COVID-19 pandemic.

Over the last few years, there has been a considerable amount of literature investigating the efficacy of the various delivery modes: on-campus delivery (face-to-face), online delivery and blended learning (hybrid), in helping college students improve their mathematical skills. However, the extent to which one learner learns best has been hotly debated among the researchers. Therefore, this study aims to compare the efficacy of implementing three teaching and learning delivery modes before, while, and after the COVID-19 pandemic: on-campus delivery (face-to-face), online delivery and blended learning (hybrid) on academic achievement in mathematics at a higher education institution in the UAE. The main research question explores whether there is a statistically significant difference (p = 0.05) in students’ academic based on the delivery methods: on-campus face-to-face, online and blended learning. The participants in the study were students from one of the largest higher education institutions in the UAE, and all of them studied the same mathematics course before, during and after the COVID-19 pandemic. Student scores in the three academic semesters were thoroughly compared and analyzed using the ANOVA test to check if there is a significant difference between the three groups followed by a Tukey test to identify the significant difference in favor of which group. The results showed that there were significant differences in the mean scores in the students’ achievement in the mathematics courses favoring the blended learning delivery mode. The findings also show that the students’ achievement in mathematics using the on-campus face-to-face teaching and learning was better than the students’ achievement in mathematics using online teaching and learning delivery modes.

The main study question was: is there a statistical significant difference at the significance level (a = 0.05) in students’ achievements in mathematics courses at higher education in the UAE, which can be attributed to the method of teaching? The descriptive statistics reveal that the average student’s score in the final exam after the COVID-19 pandemic is 65.7 with a standard deviation of 16.65, which are higher than the average student’s score in the final exam before the COVID-19 pandemic of 58.7 with a standard deviation 20.53, and both are higher than the average students’ score in the final exam during the COVID-19 pandemic 51.8 with standard deviation 21.48. Then, the ANOVA test reveals that there is a statistically significant difference between the three groups in the final exam marks. The researchers used the multiple comparison tests (Tukey test) to determine the difference. The Tukey test reveals that there is a statically significant difference between the average students’ score in the final exam after the COVID-19 pandemic and the average students’ score in the final exam during the COVID-19 pandemic, where p = 0.015 < 0.05 as well as there is a statically significant difference between the average students’ score in the final exam after the COVID-19 pandemic and the average students’ score in the final exam before the COVID-19 pandemic, where p = 0.000 < 0.05 in favor of the average students’ score in the final exam after the COVID-19 pandemic. On the other hand, there is a statically significant difference between the average students’ score in the final exam before the COVID-19 pandemic and the average students’ score in the final exam during the COVID-19 pandemic, where p = 0.016 < 0.05 in favor of the average students’ score in the final exam before the COVID-19 pandemic.

There are several limitations that may reduce the possibility of generalizing the expected results of the current study to students outside the study population: (1) The study is limited to students of a federally funded postsecondary education institution in the UAE, in which most students are studying in their non-native language. (2) The study is limited to the mathematics courses. (3) The achievement test used in the study is a standardized test developed by the college as a cross-campus summative assessment.

The hybrid education model, also known as blended learning, combines traditional face-to-face instruction with online learning components. When applied to teaching mathematics in higher education, this approach can have several implications and benefits. Here are some key points supported by references: (1) Enhanced Accessibility and Flexibility: hybrid models offer flexibility in learning, allowing students to access course materials, lectures and resources online. This flexibility can accommodate diverse learning styles and preferences. A study by Means et al. (2013) in “Evaluation of Evidence-Based Practices in Online Learning” highlights how blended learning can improve accessibility and engagement for students in higher education. (2) Personalized Learning Experience: by incorporating online resources, instructors can create a more personalized learning experience. Adaptive learning platforms and online quizzes can provide tailored feedback and adaptive content based on individual student needs (Freeman et al., 2017). This individualization can improve student performance and understanding of mathematical concepts. (3) Increased Student Engagement: the integration of online components, such as interactive simulations, videos and discussion forums, can enhance student engagement and participation (Bonk and Graham, 2012). Engaged students tend to have better learning outcomes in mathematics. (4) Improved Assessment and Feedback Mechanisms: hybrid models allow for the implementation of various assessment tools, including online quizzes, instant feedback mechanisms and data analytics, which can aid instructors in monitoring students’ progress more effectively (Means et al., 2013). This timely feedback loop can help students identify areas needing improvement and reinforce their understanding of mathematical concepts. (5) Cost-Effectiveness and Resource Optimization: integrating online materials can potentially reduce overall instructional costs by optimizing resources and enabling efficient use of classroom time (Graham, 2013). (6) Challenges and Considerations: despite the benefits, challenges such as technological barriers, designing effective online materials and ensuring equitable access for all students need to be addressed (Garrison and Vaughan, 2014). It requires thoughtful course design and continuous support for both students and instructors. When implementing a hybrid education model in teaching mathematics, instructors should consider pedagogical strategies, technological infrastructure and ongoing support mechanisms for students and faculty.

The research is the first research in the UAE to discuss the difference in teaching mathematics in higher education before, during and after the COVID-19 pandemic.

The purpose of this article is to expand knowledge on Swedish principals' professional development (PD) from the perspectives of superintendents. In particular, the article analyzes how superintendents understand and organize PD for principals.

Empirical data are derived from a strategic sample of ten (n = 10) superintendents. Transcribed interviews were analyzed in two steps. The first step was carried out inductively to identify prominent aspects of PD for principals. In the second step, the detected themes and categories were analyzed more deductively through the theoretical lens of learning in organizations.

The analysis revealed that the purpose of PD for principals and the principal leadership that must be nurtured from the perspective of superintendents spans a scale, from knowing what is already required to critically examining and exploring the unknown. In addition, the understanding of learning stretches from an individual enterprise to a collective activity. However, noteworthy differences between the superintendents were detected and organized into three ideal types.

Despite a profound research design and a careful selection of superintendents, the sample sets some limits because of the plurality within the decentralized Swedish school system.

The results can support strategies from superintendents, principals and educational authorities to build infrastructures that foster PD at different levels of school systems.

This article offers a novel perspective by analyzing principals' PD from the perspectives of superintendents.