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This study addresses gaps in the existing literature on students' understanding of Independent Learning (IL), whilst exploring the link between levels of IL, growth mindset, motivated strategies for learning and academic performance.

Three hundred and eighty-six university students recruited via opportunistic sampling completed an online survey to measure: understanding and level of IL, Motivated Strategies for Learning (MSL) (Duncan and McKeachie, 2005) and growth mindset (Dweck, 2000). Interaction with the university Virtual Learning Environment (VLE) and academic grades were also measured. A correlational design was implemented, and a Spearman Rho was calculated to explore the relationship between level of IL, MSL and growth mindset. A between-subjects design using independent measures t-test was employed to determine the significance of any difference in level of IL and VLE engagement according to academic grade.

Whilst most students: considered themselves an IL and understood what IL was, the majority erroneously believed it meant learning alone or without help. Level of IL, however, was positively associated with motivational beliefs (self-efficacy and mindset), cognitive strategies (rehearsal, elaboration, organisation and critical thinking), and metacognitive strategies (time management and self-regulation). Further, those with grades A-C scored significantly higher than those with grades D and below on cognitive strategies (elaboration and organisation). Those attaining higher grades also interacted with the VLE significantly more frequently and regularly than those attaining lower grades.

This study adds to the existing literature by highlighting the positive relationship between level of IL, MSL, mindset and academic achievement. It also addresses the under-explored potential for VLE engagement in predicting grades amongst on-campus courses. Given that cognitive strategies and VLE engagement differentiate the high and low achievers, interventions to develop such skills may enhance academic achievement.

Active and transformative learning (ATL) challenges the new era of teaching and learning in higher education. In this chapter a systematic approach to ATL theories and methodologies is provided. Authors provide a unique methodological framework for the integration of active learning practices in modern higher education curricula. This chapter also provides an integrated context for the overall discussion of the phenomenon in the volume. The recent arrivals of new disruptive artificial intelligence-enabled technologies such as OpenAI, ChatGPT, DeepAI and others are only few aspects of a new challenging era for the teaching, learning, innovation and sustainability in the higher education.

The objective of this study is to analyze the conceptual and domain overlap of organizational learning and organizational resilience; specifically, the adaptation or renewal domain in organizational resilience. From the findings, strategies to foster collective learning leading to organizational resilience are identified and outlined.

Recent organizational resilience conceptual models were analyzed to identify the conceptual overlap between the renewal and adaptation domain of organizational resilience and organizational learning. From the analysis of the models, implications were drawn based on the conceptual overlap found in organizational learning and the adaptable or renewal domain of organizational resilience.

To build the renewal or adaptation domain of organizational resilience, organizations must embody learning into a capability. Systems are then required for learning to remain continuous and foster knowledge acquisition, distribution, interpretation, and organizational memory that leads to dynamic capabilities for renewal and adaptation. The learning strategies must then focus renewing what is known in traditional approaches to organizational learning that supports experiential learning, developing systematic approaches to learning, and creating contexts to facilitate organizational learning. When this knowledge is aggregated to an organizational level, it contributes to resilience.

As organizational resilience grows in attention and importance; it is necessary to investigate similarities and conceptual domain overlap. This study contributes to this need and identifies what can be implemented in learning strategies for organizations’ resilience capacity.

Healthcare education is a huge industry with a significant social footprint and resilient impact on well-being and on the quality of life. It integrates diverse scientific domains and needs to continuously update its value proposition to reflect the need for preparing top-quality health professionals. It also has to support professional development and to manage effectively the accreditation of programs and the certification of skills and knowledge. In this chapter, the authors expand a theoretical framework about Active and Transformative Learning (ATL) that has introduced in the volume of ATL for STEAM disciplines and also discussed how artificial intelligent (AI) tools, such as OPENAI Chat GPT, can serve as transformers and value carriers for the implementation of ATL activities and use cases in healthcare education.

This paper aims to extend the previous contributions about data-driven control in aircraft control system from academy and practice, respectively, combining iteration and learning strategy. More specifically, after returning output signal to input part, and getting one error signal, three kinds of data are measured to design the unknown controller without any information about the unknown plant. Using the main essence of data-driven control, iterative learning idea is introduced together to yield iterative learning data-driven control strategy. To get the optimal data-driven controller, other factors are considered, for example, adaptation, optimization and learning. After reviewing the aircraft control system in detail, the numerical simulation results have demonstrated the efficiency of the proposed iterative learning data-driven control strategy.

First, considering one closed loop system corresponding to the aircraft control system, data-driven control strategy is used to design the unknown controller without any message about the unknown plant. Second, iterative learning idea is combined with data-driven control to yield iterative learning data-driven control strategy. The optimal data-driven controller is designed by virtue of power spectrum and mathematical optimization. Furthermore, adaptation is tried to combine them together. Third, to achieve the combination with theory and practice, our proposed iterative learning data-driven control is applied into aircraft control system, so that the considered aircraft can fly more promptly.

A novel iterative learning data-driven strategy is proposed to efficiently achieve the combination with theory and practice. First, iterative learning and data-driven control are combined with each other, being dependent of adaptation and optimization. Second, iterative learning data-driven control is proposed to design the flight controller for the aircraft system. Generally, data-driven control is more wide in our living life, so it is important to introduce other fields to improve the performance of data-driven control.

To the best of the authors’ knowledge, this new paper extends the previous contributions about data-driven control by virtue of iterative learning strategy. Specifically, iteration means that the optimal data-driven controller is solved as one recursive form, being related with one gradient descent direction. This novel iterative learning data-driven control has more advanced properties, coming from data driven and adaptive iteration. Furthermore, it is a new subject on applying data-driven control into the aircraft control system.

This study proposes a collaborative federated learning (CFL) framework to address personal data transmission and retention issues for artificial intelligence (AI)-enabled video surveillance in public spaces.

This study examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Based on the requirements, this study proposes a CFL framework to gradually adapt AI models’ knowledge while reducing personal data transmission and retention. The framework uses three different federated learning strategies to rapidly learn from different new data sources while minimizing personal data transmission and retention to a central machine.

The findings confirm that the proposed CFL framework can help minimize the use of personal data without compromising the AI model's performance. The gradual learning strategies help develop AI-enabled video surveillance that continuously adapts for long-term deployment in public spaces.

This study makes two specific contributions to advance the development of AI-enabled video surveillance in public spaces. First, it examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Second, it proposes a CFL framework to minimize data transmission and retention for AI-enabled video surveillance. The study provides comprehensive experimental results to evaluate the effectiveness of the proposed framework in the context of facial expression recognition (FER) which involves large-scale datasets.

This paper aim to solve the problem of low assembly success rate for 3c assembly lines designed based on classical control algorithms due to inevitable random disturbances and other factors,by incorporating intelligent algorithms into the assembly line, the assembly process can be extended to uncertain assembly scenarios.

This work proposes a reinforcement learning framework based on digital twins. First, the authors used Unity3D to build a simulation environment that matches the real scene and achieved data synchronization between the real environment and the simulation environment through the robot operating system. Then, the authors trained the reinforcement learning model in the simulation environment. Finally, by creating a digital twin environment, the authors transferred the skill learned from the simulation to the real environment and achieved stable algorithm deployment in real-world scenarios.

In this work, the authors have completed the transfer of skill-learning algorithms from virtual to real environments by establishing a digital twin environment. On the one hand, the experiment proves the progressiveness of the algorithm and the feasibility of the application of digital twins in reinforcement learning transfer. On the other hand, the experimental results also provide reference for the application of digital twins in 3C assembly scenarios.

In this work, the authors designed a new encoder structure in the simulation environment to encode image information, which improved the model’s perception of the environment. At the same time, the authors used the fixed strategy combined with the reinforcement learning strategy to learn skills, which improved the rate of convergence and stability of skills learning. Finally, the authors transferred the learned skills to the physical platform through digital twin technology and realized the safe operation of the flexible printed circuit assembly task.

Due to a change in higher education and adult education ideas and practices globally that have become more learner-centered, higher education is undergoing a transformation at a rate never before seen. Education has also evolved into a lifetime endeavor as the importance of higher education and adult learning has grown. In light of the fact that it offers guidance on how people can find purpose in their lives, transformative learning theory has a prominent position in higher education and adult education. By critically examining their presumptions and expectations and updating them to support higher education students' successful learning, educators can transform their theory and practice of instruction through active and transformative learning. Adapting to the changing capacities brought on by digitization, technological advancements, growing technological connectivity, global market expansion, mobility and migration, and workplace diversity is becoming more and more difficult for higher education institutions. The idea of active and transformative learning and transformative learning strategies are discussed in detail in this chapter to help readers understand their importance and function in effective teaching and learning in the transforming world of higher education. This chapter's major contribution to Active and Transformative Learning: Digital Transformation in Education is the provision of a comprehensive guide and strategy on how to successfully incorporate digital technologies into the teaching and learning process in order to improve student engagement, knowledge acquisition, and the growth of critical thinking skills.

Creating suitable learning conditions in university classrooms continues to be a topical issue in higher education. The above means that a continued search for innovative teaching and learning strategies in universities remains a priority issue. The study therefore investigated conditions that facilitate the effective implementation of cooperative learning (CL) as well as how CL influences the academic performance of students in universities in Zimbabwe.

The research adopted a quantitative research approach located in the descriptive research design. A sample of 341 undergraduate students selected using a simple random sampling strategy from a sample frame of 701 undergraduate students participated in the study. The study first used a pre-test and later, a post-test to determine the initial and end of semester (EOS) academic performance levels of students, respectively, on a Calculus 1 course. A self-constructed structured questionnaire was also used for data collection on the views of students towards the use of CL with particular reference to the 5 dimensions of positive interdependence (PDI), promotive interaction (PI), individual and group accountability (IGA), social and interpersonal skills (SS) and group processing (GP). The questionnaire was validated using confirmatory factor analysis. Structural equation modelling was used to test proposed relationships. A total of 341 questionnaires were administered through an email survey. Of the total, 149 completed questionnaires were received from the students, giving a return rate of 43.7%.

The results showed that positive interdependence, promotive interaction, social and interpersonal skills, individual and group accountability as well as group processing were important antecedents to the effective implementation of CL in universities. The results further showed that CL plays a significant role in improving the academic performance of university students; hence, academic performance was viewed as a significant consequence of CL.

This is the first study on the application of CL to enhance the academic performance of university students in the context of Zimbabwe. As a result, caution should be exercised when generalising the results as more studies to either confirm or disconfirm these results in the context of Zimbabwe may still be required.

These results have implications on policy and practice with regards to active teaching strategies in universities. With regards to practice, the results demonstrated that the use of group activities that allow students to share knowledge and support each other while the teacher plays a facilitating role is important for enhancing students' academic performance. With regards to policy, the results showed that universities can enhance the academic performance of students if policies that promote student-centered teaching and learning approaches to ensure that teaching in universities is more student-led than lecturer-led are developed.

While many studies have been conducted in other contexts, to establish the influence of CL on the academic performance of university students, there is no known study on the influence of CL that has been conducted in the context of Zimbabwean universities. The results of this study therefore are an eye opener on the role of CL in enhancing the academic performance of students in Zimbabwean universities.

(Digital) servitization, referring to service-driven strategies and their increasing implementation in manufacturing, is one of the most rapidly growing areas in industrial service research. However, the cultural change involved in successful servitization is a phenomenon that is widely observed but poorly understood. This research aims to clarify the processes of social construction as manufacturers change their organizational culture to transform into industrial service providers.

This research takes a systematic approach to integrate disparate literature on servitization into a cohesive framework for cultural change, which is purposefully augmented by rationale culled from organizational learning and sensemaking literature.

The organizational learning framework for cultural change in servitization introduces a dynamic perspective on servitizing organizations by explaining social processes between organizational and member-level cultural properties. It identifies three major cultural orientations toward service, digital and learning that govern successful servitization.

This research contributes to the servitization literature by presenting a new approach to reframe and explore cultural change processes across multiple levels, thus providing a concrete starting point for further research in this area.