This study is predicated on the belief that there does not now exist, nor will there ever exist, any single theory of learning that is broad enough to account for all types of learning yet specific enough to be maximally useful in practical application. Perhaps this dichotomy is the reason for the apparent gap between existing theories of learning and the practice of instructional design. As an alternative to any supposed grand theory of learning—and following the lead of prominent thinkers in the fields of clinical psychology and language teaching—this study proposes a shift toward principles. It presents a principle-based conceptual framework of learning, and recommends use of the framework as a guide for creating domain-specific theories of learning. The purpose of this study was to review theories of learning in the behavioral, cognitive, constructive, human, and social traditions to identify principles of learning local to those theories that might represent specific instances of more universal principles, fundamentally requisite to the facilitation of learning in general. Many of the ideas reviewed have resulted from, or been supported by, direct empirical evidence. Others have been suggested based on observational or practical experience of the theorist. The ideas come from different points in time, are described from a variety of perspectives, and emphasize different aspects and types of learning; yet there are a number of common themes shared among them regarding the means by which learning occurs. It is hypothesized that such themes represent universal and fundamental principles of learning. These principles were the objective of the present study. They have been sought through careful review and analysis of both theoretical and empirical literature by methods of textual research (Clingan, 2008) and constant comparative analysis (Glaser & Strauss, 1967). By way of textual research a methodological lens was defined to identify general themes, and by way of constant comparative analysis these themes were developed further through the analysis and classification of specific instances of those themes in the texts reviewed. Ten such principles were identified: repetition, time, step size, sequence, contrast, significance, feedback, context, engagement, and agency. These ten facilitative principles were then organized in the context of a comprehensive principles-of-learning framework, which includes the four additional principles of potential, target, change, and practice.

The aim of the current research was to create and validate the concept of adapting the Internet of Everything to develop a technical and vocational education and training framework. This research was conducted using sequential exploratory mixed method design and classification type methodology in two qualitative and quantitative phases. The qualitative phase was conducted using the integrated review method and its validation by 11 experts. The results obtained from the qualitative phase in 9 factors are: 1. Fundamental assumptions (such as technological assumptions, human assumptions, environmental assumptions, and educational assumptions), 2. internal effective factors (such as vision, mission, and strategic goals of the technical and professional organization, Availability of resources and knowledge in the technical and professional organization), 3. External factors (such as challenges and level of technological development in society), 4. Key stakeholders (such as content developers, teaching-learning centers, learning providers, cloud space providers, and software providers), 5. Resources (such as providing technological infrastructure and human competence), 6. Actions (such as recognizing the characteristics and interests of the learner and providing feedback), 7. Output (such as improving attention, motivation, learner participation, improving the interaction between the provider and the learner, and creating creative thinking), 8. Outcomes (such as developing smart services in technical and professional education, developing smart space in technical and professional education, and developing technical and professional skills based on digital technology) and 9. Effects (such as sustainable development, energy efficiency, flexibility of social welfare). Then, based on this framework, in the second (quantitative) phase, it was validated using a descriptive survey method through a questionnaire tool. 51 specialists in the Internet of Things, technical and vocational training, and E-learning responded to this questionnaire. Data were analyzed using PLS3 software. The results confirmed the reliability of 9 factors, and by conducting various tests based on convergent and divergent indices, 13 items did not have sufficient validity and were removed from the final framework. This framework will help the stakeholders, especially the policymakers, to better understand the concept of the Internet of Everything in a glance and a comprehensive process, and by using the guide provided at the end of this research, it is possible to integrate and continuously improve the movement in the implementation of this technology in the technical and vocational training.:Chapter One: Introduction 14 1-1. Background knowledge 14 1-2. Problem statement 16 1-3. Necessity and Importance 19 1-3-1. Theoretical Importance 20 1-3-2. Practical Importance 22 1-4. Research Objectives 23 1-5. Research Questions 23 1-6. Definition of terms 23 1-6-1. Internet of Things (IoT) 23 1-6-2. Internet of Everything (IoE) 23 1-6-3. Technical and Vocational Education and Training (TVET) 23 1-6-4. Vocational Education and Training (VET) 24 1-6-5. Vocational Training 24 1-6-6. Technical Education 24 1-6-7. Career and Technical Education (CTE) 24 1-7. Summary 24 Chapter Two: Theory 25 2-1. Introduction 25 2-2. Technical and Vocational Education and Training (TVET) 25 2-2-1. Theories related to this study 25 2-2-2. Innovative Approaches in Learning and Teaching 27 2-2-3. Technical and Vocational Education and Training (TVET) Worldwide 29 2-2-4. Technical and Vocational Education and Training in Iran 33 2-2-4-1. History 33 2-2-4-2. Main Mission 35 2-2-4-3. Target Groups 35 2-2-4-4. Center for Trainer Education and Technical-Vocational Research 35 2-2-4-5. Standard Development 36 2-2-4-6. Skill and Vocational Competence Assessment 37 2-2-4-7. International Presence 37 2-2-4-8. National Skill Dialogue 37 2-2-4-9. Current challenges of TVET in IRAN 37 2-2-4-10. TVET Typology In Iran 39 2-3. Formation of The Internet of Everything Concept 40 2-3-1. The Concept of the Internet 40 2-3-2. Concept of the Web 42 2-3-2-1. Advantages of using Web 2.0: 42 2-3-2-2. Disadvantages of using Web 2.0: 43 2-3-3. The Internet of Things (IoT) Concept 44 2-3-4. Internet of Everything (IoE) Concept 49 2-3-5. Elements of the Internet of Everything (IoE) 51 2-3-5-1. Things 51 2-3-5-2. People 51 2-3-5-3. Data 51 2-3-5-4. Process 51 2-3-6. Models of the Internet of Things (IoT) 51 2-3-7. Models of Internet of Things in Education 52 2-3-8. The Internet of Everything in Education 56 2-3-9. Internet of Everythings models in higher education 57 2-3-10. Information and Communication Technology (ICT) and Education 58 2-4. Research Background 61 2-5. Summary 65 Chapter Three: Method 66 3-1. Introduction 66 3-2. Research Design 66 3-3. Qualitative Phase 68 3-3-1. Research Method 68 3-3-2. Formation of the Objective and Research Question 68 3-3-3. Systematic Search and Background Selection 68 3-3-4. Qualitative Evaluation 70 3-3-5. Analysis and Integration 70 3-3-6. Dissemination of Findings 71 3-4. Quantitative Phase 71 3-4-1. Research Method 71 3-4-2. Population and Sampling Method 71 3-4-3. Research Instruments 72 3-4-4. Validity and Reliability of the Research Framework 73 3-4-5. Implementation Method 73 3-4-6. Data Analysis Methods 73 3-5. Summary 73 Chapter Four: Data 74 4-1. Introduction 74 4-2. Qualitative results 74 4-2-1. A. Inputs 77 4-2-2. 1) Infrastructures 77 4-2-2-1. 1-1) Political infrastructure 77 4-2-2-2. 1-2) Technological infrastructure 77 4-2-3. 2)Stakeholders 77 4-2-4. B. Activities 77 4-2-4-1. 1)Teaching-learning paradigm 77 4-2-4-2. 2)Instructional design process 78 4-2-5. C. Outputs 79 4-2-6. D. Outcomes and Impacts 80 4-2-7. E. Challenges 80 4-2-8. Qualitative Assessment 80 4-3. Analysis of Qualitative Assessment Results 81 4-3-1. Themes 81 4-3-2. Organizing Themes 85 4-3-3. Themes Network 85 4-4. Summary of Qualitative Phase 86 4-5. Quantitative results 86 4-5-1. Convergent Validity 88 4-5-1-1. Construct Validity 88 4-5-1-2. Average Variance Extracted 90 4-5-2. Discriminant Validity 91 4-5-2-1. Cross-Loading Criterion 91 4-5-2-2. Fornell-Larcker Criterion 92 4-5-2-3. Heterotrait-Monotrait (HTMT) Ratio Criterion 92 4-5-3. Reliability 93 4-6. Summary 94 Chapter Five: Discussion 95 5-1. Introduction 95 5-2. Theoretical Model of IoE based TVET Model 95 5-3. Conceptual Model of IoE based TVET Model 98 5-4. Practical Model of IoE based TVET Model 99 5-4-1. Awareness 100 5-4-2. Analyze and Define 101 5-4-3. Design and Development 101 5-4-4. Implement 102 5-4-5. Quality Assurance 104 5-5. Conclusion 105 5-5-1. Fundamental Assumptions 106 5-5-2. Internal Factors 107 5-5-3. External Factors 107 5-5-4. Stakeholders 108 5-5-5. Resources 109 5-5-6. Activities 109 5-5-7. Outputs 109 5-5-8. Outcomes 110 5-5-9. Impacts 110 5-6. Recommendations 110 5-6-1. Practical Recommendations 110 5-6-2. Research Recommendations 112 5-7. Limitations 112 5-7-1. Implementation Limitations 112 5-7-2. Research Limitations 112 5-7-3. Research Innovations 112 5-7-4. Contributions 113 5-8. Summary 113 References 117 Appendix 1 139 Appendix 2 150

The introduction of e-learning made way for advancements in learning and technology with individuals being exposed to electronic learning and teaching environments. At first, the introduction of e-learning into the educational sphere was intended to simply enhance traditional teaching and learning; however, technology then took the lead as a tool to materially enhance the concept of e-learning in education. Inevitably, technology’s impact on learning drove the delivery of electronic educational content but it also caused widespread debate about best practice in the design of e-learning systems. Since then, the phenomenal influx of technology enhancements that has been created has led most learners into a digital education era that cannot now function without it. At first, e-learning systems were forced to adapt to change as a result of e-learning trends and as a symbolic move from traditional learning to more innovative methods of learning and teaching. As such, e-learning remained affected by pedagogy, technology and curriculum changes outside of a structured, guided framework. Varying definitions exist as a result of the diverse understanding of the contributions and role of pedagogy and technology toward e-learning. There is a misconception and confusion of elearning attributed to the lack of a formally accepted definition which would identify with the need for pedagogy principles and guide researchers to apply models and frameworks to implement and improve the provision of e-learning systems. Although the effects of technology on learning are conclusive, the current dilemma is the lack of effective alignment of the pedagogy principles to suitable technology – an issue which has now become detrimental to learning. This study explores the various interpretations of e-learning definitions that allude to the incorporation of learning, technology and knowledge gained during e-learning interventions. However, as the research revealed a lack of any cohesive e-learning definition, this motivated the creation of a specific definition derived particularly for this study. In considering the role of technology in the e-learning environment, similar themes began to emerge that needed to be addressed holistically through e-learning. One of these themes was a need to focus on the formulation of a structured approach and pedagogical framework for the design and development of e-learning systems. The findings of the research identified e-learning frameworks and models that were in use. The outcome of an e-learning system framework drew on the research of extant models and frameworks and investigated the critical elements, particularly that of pedagogy in an e-learning environment. The proposed pedagogical framework for elearning was evaluated by means of a survey of organisations that produce e-learning systems. The findings of the survey were analysed to assess the alignment and relevance of the dimensions and elements in the framework to the design and development of e-learning systems. The proposed pedagogical e-learning framework is intended to add value to the design and development of e-learning systems with the core focus on pedagogy. In years to come, current and existing technologies and tools may become outdated, yet learning opportunities continue to evolve based on pedagogy, technology and curriculum requirements. By harmonising the synergy between pedagogy and technology, a pedagogically aligned e-learning framework can resolve the lack of pedagogy in elearning system design and development. / Information Science / M.Sc. (Information Systems)

The introduction of e-learning made way for advancements in learning and technology with individuals being exposed to electronic learning and teaching environments. At first, the introduction of e-learning into the educational sphere was intended to simply enhance traditional teaching and learning; however, technology then took the lead as a tool to materially enhance the concept of e-learning in education. Inevitably, technology’s impact on learning drove the delivery of electronic educational content but it also caused widespread debate about best practice in the design of e-learning systems. Since then, the phenomenal influx of technology enhancements that has been created has led most learners into a digital education era that cannot now function without it. At first, e-learning systems were forced to adapt to change as a result of e-learning trends and as a symbolic move from traditional learning to more innovative methods of learning and teaching. As such, e-learning remained affected by pedagogy, technology and curriculum changes outside of a structured, guided framework. Varying definitions exist as a result of the diverse understanding of the contributions and role of pedagogy and technology toward e-learning. There is a misconception and confusion of elearning attributed to the lack of a formally accepted definition which would identify with the need for pedagogy principles and guide researchers to apply models and frameworks to implement and improve the provision of e-learning systems. Although the effects of technology on learning are conclusive, the current dilemma is the lack of effective alignment of the pedagogy principles to suitable technology – an issue which has now become detrimental to learning. This study explores the various interpretations of e-learning definitions that allude to the incorporation of learning, technology and knowledge gained during e-learning interventions. However, as the research revealed a lack of any cohesive e-learning definition, this motivated the creation of a specific definition derived particularly for this study. In considering the role of technology in the e-learning environment, similar themes began to emerge that needed to be addressed holistically through e-learning. One of these themes was a need to focus on the formulation of a structured approach and pedagogical framework for the design and development of e-learning systems. The findings of the research identified e-learning frameworks and models that were in use. The outcome of an e-learning system framework drew on the research of extant models and frameworks and investigated the critical elements, particularly that of pedagogy in an e-learning environment. The proposed pedagogical framework for elearning was evaluated by means of a survey of organisations that produce e-learning systems. The findings of the survey were analysed to assess the alignment and relevance of the dimensions and elements in the framework to the design and development of e-learning systems. The proposed pedagogical e-learning framework is intended to add value to the design and development of e-learning systems with the core focus on pedagogy. In years to come, current and existing technologies and tools may become outdated, yet learning opportunities continue to evolve based on pedagogy, technology and curriculum requirements. By harmonising the synergy between pedagogy and technology, a pedagogically aligned e-learning framework can resolve the lack of pedagogy in elearning system design and development. / Information Science / M.Sc. (Information Systems)

Autism Spectrum Disorder (ASD) is part of the developmental disabilities. There are numerous symptoms for ASD patients, including lack of abilities in social interaction, communication obstacle and repeatable behaviors. Meanwhile, the rate of ASD prevalence has kept rising by the past 20 years from 1 out of 150 in 2000 to 1 out of 54 in 2016. In addition, the ASD population is quite large. Specifically, 3.5 million Americans live with ASD in the year of 2014, which will cost U.S. citizens $236-$262 billion dollars annually for autism services. So, it is critical to make an accurate diagnosis for preschool age children with ASD, in order to give them a better life. Instead of using traditional ASD behavioral tests, such as ADI-R, ADOS, and DSM-IV, we applied brain MRI images as input to make diagnosis. We revised 3D-ResNet structure to fit 110 preschool children's brain MRI data, along with Convolution 3D and VGG model. The prediction accuracy with raw data is 65.22%. The accuracy is significantly improved to 82.61% by removing the noise around the brain. We also showed the speed of ML prediction is 308 times faster than behavior tests.

The field of influencer marketing has experienced rapid growth in recent years. However, uncovering the true effectiveness of this marketing approach remains a significant challenge. This thesis addresses the challenge of predicting the effectiveness of influencer marketing campaigns by employing advanced machine learning techniques, specifically the Auto Machine Learning framework Autogluon. With the aim of democratizing machine learning and empowering businesses in the influencer marketing domain, this work leverages Autogluon to predict the interactions generated by influencers when posting affiliate links. By evaluating various settings of AutoGluon and assessing the performance using metrics such as R-squared score, we observed promising results with good predictive accuracy. The findings from our study contribute to critical discussions in the field. This research offers a streamlined and efficient approach to machine learning, reducing the need for extensive manual model tuning and enabling marketers to make informed decisions and optimize their campaign strategies. The outcomes of this study have practical implications for businesses, allowing them to effectively predict campaign interactions and maximize the impact of influencer marketing initiatives. By leveraging the power of automated machine learning, this thesis opens up new opportunities for businesses to harness the potential of influencer marketing in driving successful marketing campaigns. / Influencer marketing trenden har ökat markant de senaste aren men effektiviteten av denna marknadsföringsmetod är till stor del oviss. Denna avhandling utfårskar svårigheten med att förutse effekten av influencer marketing kampanjer med hjälp av avancerad maskininlärningsteknik, specifikt Auto Machine Learning-ramverket Autogluon. Med målet att demokratiserar och uppmuntra företag att använda maskininlärning, utforskar denna avhandling Autogluon för att förutse interageringar som genereras när influerare publicerar affiliate länkar. Genom att utvärdera olika inställningar av Autogluon och analysera olika data som till exempel R-kvadratvärde observerade vi lovande resultat med god förutsägbar precision. Resultaten från vår studie bidrar till kritiska diskussioner inom området. Denna forskning erbjuder en strömlinjeformad och effektiv metod för maskininlärning, vilket minskar behovet av omfattande manuellt modellarbete och möjliggör för marknadsförare att fatta informerade beslut och optimera sina kampanjstrategier. Resultaten av denna studie har praktiska implikationer för företag, vilket gör det möjligt för dem att effektivt förutse interaktioner i kampanjer och maximera effekten av influencer marketingvertyg. Genom att applicera automatiserad maskininlärning öppnar denna avhandling nya möjligheter för företag att dra nytta av potentialen hos influencer marketing för att driva framgångsrika marknadsföringskampanjer.

The purpose of the study has been to investigate and shed light on practical measures companies take to create a culture that promotes learning, as well as the role of technology. It is an important topic for organizations to face major challenges regarding reskilling and upskilling of employees, to ensure that the company has the right skills for the future. We have examined 11 companies where we took a closer look at their organizational structure, how they use leadership around learning, their vision, mission and strategy for learning, what technology is used for learning (and how and why exactly these technologies), how they create and buy digital content / courses, what can be measured and used as guidelines for driving learning and finally we have looked at the challenges production companies are especially faced with. Our analysis shows that there are some common denominators in which competencies and tasks must exist and take place internally to be able to facilitate work towards a learning culture, that learning is designed after having a high business relevance, that the top management's attitude to learning is essential, that learning technologies are used frequently, there is data on learning activities that can be used as guidelines and that production companies need to take special measures to be able to include their entire workforce in their learning initiatives. Our analysis also shows that the quality and usefulness of learning technologies has accelerated in recent years and has been given a leading role in organizations' investments to improve the learning culture. In the report, we propose that companies realize the power they have to influence how learning is done by setting up and working with the essential building blocks of the learning ecosystem we have identified. / Syftet med studien har varit att undersöka och belysa praktiska åtgärder företag tar sig an för att skapa en kultur som främjar lärande, samt vilken roll teknologi har. Det är ett viktigt ämne för att organisationer står inför stora utmaningar gällande upplärning och omskolning av anställda, för att säkerställa att företaget har rätt kompetenser. Vi har undersökt 11 företag där vi har tittat närmare på deras organisationsstruktur, hur de använder sig av ledarskap kring lärande, deras vision, mission och strategi för lärande, vilka teknologier som används för lärande (samt hur och varför just dessa teknologier), hur de skapar och köper in digitalt innehåll/kurser, vad som kan mätas och användas som riktlinjer för att driva lärande och till sist har vi tittat på utmaningar produktionsföretag speciellt ställs inför. Vår analys visar att det finns några gemensamma nämnare i vilka kompetenser och arbetsuppgifter som ska finnas internt för att lättare kunna arbeta mot en lärande kultur, att lärande designas efter att ha hög affärsrelevans, att högsta ledningens inställning till lärande är essentiell, att lärande teknologier används flitigt, det finns data kring lärande aktiviteter som kan användas som riktlinjer samt att produktionsbolag behöver vidta särskilda åtgärder för att kunna inkludera hela sin arbetsstyrka i sina lärande initiativ. Vår analys visar även att kvaliteten på och nyttan av lärande teknologier har accelererat de senaste åren och har fått en huvudroll i organisationers investeringar för att förbättra lärande kulturen. I rapporten föreslår vi att företag inser makten de har att påverka hur lärande går till genom att sätta upp och arbeta med de olika delarna vi belyser är essentiella i ett lärande ekosystem.

This thesis examines the possibilities of linking historiographical education and drama curriculum in primary school using action research. The theoretical part examines various educational and psychological bases that support the idea of linking drama and historiographical education and defines the possibilities of the involvement of drama as a teaching method in the primary school. The practical part contains specific proposal of two lessons with historiographical themes and also includes evaluation of their implementation in 4th and 5th class. Both lessons are designed and implemented in accordance with the teachings of Three-phase, respecting the psychological aspects of the target group. Lessons use mainly drama methods, marginally methods of curriculum Reading and writing to critical thinking, Experiential learning, Project teaching and Cooperative learning are used. Action research is evaluated through observation and detailed description of teacher reflection.

<p>Over the past decade, distributed representation learning has emerged as a popular alternative to conventional centralized machine learning training. The increasing interest in distributed representation learning, specifically federated learning, can be attributed to its fundamental property that promotes data privacy and communication savings. While conventional ML encourages aggregating data at a central location (e.g., data centers), distributed representation learning advocates keeping data at the source and instead transmitting model parameters across the network. However, since the advent of deep learning, model sizes have become increasingly large often comprising million-billions of parameters, which leads to the problem of communication latency in the learning process. In this thesis, we propose to tackle the problem of communication latency in two different ways: (i) learning private representation of data to enable its sharing, and (ii) reducing the communication latency by minimizing the corresponding long-range communication requirements.</p> <p><br></p> <p>To tackle the former goal, we first start by studying the problem of learning representations that are private yet informative, i.e., providing information about intended ''ally'' targets while hiding sensitive ''adversary'' attributes. We propose Exclusion-Inclusion Generative Adversarial Network (EIGAN), a generalized private representation learning (PRL) architecture that accounts for multiple ally and adversary attributes, unlike existing PRL solutions. We then address the practical constraints of the distributed datasets by developing Distributed EIGAN (D-EIGAN), the first distributed PRL method that learns a private representation at each node without transmitting the source data. We theoretically analyze the behavior of adversaries under the optimal EIGAN and D-EIGAN encoders and the impact of dependencies among ally and adversary tasks on the optimization objective. Our experiments on various datasets demonstrate the advantages of EIGAN in terms of performance, robustness, and scalability. In particular, EIGAN outperforms the previous state-of-the-art by a significant accuracy margin (47% improvement), and D-EIGAN's performance is consistently on par with EIGAN under different network settings.</p> <p><br></p> <p>We next tackle the latter objective - reducing the communication latency - and propose two timescale hybrid federated learning (TT-HF), a semi-decentralized learning architecture that combines the conventional device-to-server communication paradigm for federated learning with device-to-device (D2D) communications for model training. In TT-HF, during each global aggregation interval, devices (i) perform multiple stochastic gradient descent iterations on their individual datasets, and (ii) aperiodically engage in consensus procedure of their model parameters through cooperative, distributed D2D communications within local clusters. With a new general definition of gradient diversity, we formally study the convergence behavior of TT-HF, resulting in new convergence bounds for distributed ML. We leverage our convergence bounds to develop an adaptive control algorithm that tunes the step size, D2D communication rounds, and global aggregation period of TT-HF over time to target a sublinear convergence rate of O(1/t) while minimizing network resource utilization. Our subsequent experiments demonstrate that TT-HF significantly outperforms the current art in federated learning in terms of model accuracy and/or network energy consumption in different scenarios where local device datasets exhibit statistical heterogeneity. Finally, our numerical evaluations demonstrate robustness against outages caused by fading channels, as well favorable performance with non-convex loss functions.</p>

This master thesis presents a machine learning framework for real-time gesture and skeleton-based action recognition, integrated with the Unity game engine. The system aims to enhance human-computer interaction (HCI) in gaming and 3D related applications through natural movement recognition, by training a model on skeleton tracking data. The framework is trained to accurately categorize and identify gestures such as kicks and punches, enabling a more immersive gaming experience not existing in traditional controllers. After studying the evolution of HCI and how machine learning has transformed and reshaped the interaction paradigm, the prototype system is built through data collection, augmenting, and preprocessing, followed by training and evaluating a Long Short-Term Memory (LSTM) neural network model for gesture classification. The model is integrated into Unity via Unity Sentis using Open Neural Network Exchange (ONNX) format, enabling efficient real-time action recognition in 3D space. Each component of the pipeline is available and adaptable for future custom- ization and needs, skeleton tracking and Unity integration is built using the ZED 2i camera and ZED SDK. Experimental results demonstrate that the system presented can achieve over 90% accuracy in identifying predefined gestures. As a bridging solution tailored for Unity, this framework offers a practical solution to action recognition that could be found useful in future applications. This work contributes to advancing human-computer interaction and offers a foundation for further development in gesture-based Unity game design.