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Table of contents

1. Student Bio

2. Learning Goals Statement

3. EDS431 Learning Experience

4. Core of the Assessment

  • Background and Justification
  • Methodology
  • Results
  • Conclusion

5. Independent Self-Development

6. Evidence

7. Wraparound reflective writing

8. References

1. Student bio

Name: Fangjing Chen

Undergraduate Major: Early Childhood Education, Zhejiang Normal University

Postgraduate Major: Digital Education, Xi’an Jiaotong-Liverpool University

Professional Experience:

  • 2020–Present: Educator at Zhejiang Normal University Kindergarten Education Group.
  • Responsible for front-line early childhood education and teaching tasks.

Honours and Awards:

  • Recognized as a Category E High-Level Talent in Zhejiang Province
  • First Prize in the 2023 Zhejiang Province Outstanding Educational Activity Competition
  • First Prize in the National Teaching Skills Competition
  • First Prize in the Yangtze River Delta Teaching Skills Competition

Research Experience:

  • Contributed as a team member to the National Basic Education Achievement Awards. The project, “Reconstructing Learning Scenarios: Innovative Practices in Holistic Education in Kindergarten”, was awarded the Second Prize in the 2022 National Basic Education Teaching Achievement Evaluation.
  • Participated in the selection of Zhejiang Provincial Model Courses as part of the team project “Song Culture Curriculum for Kindergartens”, which was awarded as a Zhejiang Provincial Model Kindergarten Course.

Academic Contributions:

  • Strategies for Promoting Sustainability in Project Activities Using OKR Scaffolding, a funded research project in Hangzhou, Zhejiang Province.
  • Co-authored “Practical Dilemmas and Countermeasures for Improving the Quality of Early Childhood Teacher Teams: An ERG Theory Perspective”, published in Early Childhood Education (Educational Science Edition), Issue 11, 2023.
  • Presented “Strategies for Promoting Preschool Children’s Inquiry Sustainability in STEM Activities through Computational Thinking” at the 18th Academic Annual Meeting of the Chinese Education Association for Information Technology.

Research Interests:

  • Interdisciplinary Approaches to STEAM Education
  • Integration of Computational Thinking into Early Childhood Curricula
  • Generative AI-Driven Innovations in Teaching and Learning

 

2. Learning goals statement

At the outset of the EDS431 module, I set a clear and ambitious academic goal for myself: to achieve a Distinction.

After reviewing the module’s learning objectives, I further refined my personal learning goals as follows:

  • To learn and apply effective methods for designing both online and offline courses, and critically evaluate these methods using philosophical and theoretical knowledge;
  • To master the selection, design, and sharing of media resources, learning activities, and assessment tasks that meet specific course needs in collaboration with team members;
  • To employ the principles and methodologies of Design-Based Research (DBR) to design and develop course modules based on real-world cases, ensuring they are adaptable to various institutional and educational contexts.

As the module progressed, my understanding of these goals deepened, and I adjusted them accordingly:

  • To acquire and apply the skills needed to prototype course designs using the Figma platform, integrate tools from the Moodle platform for online and offline course development, and test and optimize these designs in real-world contexts using DBR;
  • To critically evaluate and refine course design methods using frameworks such as Constructivist Theory, the Technology Acceptance Model (TAM), Game-Based Learning Theory, Collaborative Learning Theory, and Learning Style Theory;
  • To design, optimize, and evaluate the “Dental Discovery 101” course module, adapting it to practical teaching contexts while generating theoretical insights with broader applicability.

Throughout the learning process, I have consistently adhered to these goals and made steady progress by engaging with theoretical study, case analysis, and practical design tasks provided by the module, working toward their achievement.

3. EDS431 learning experience(Expand to See More)

During my study of the EDS431 module, I have developed a systematic understanding of digital education course design from both theoretical and practical dimensions. Significantly, the course content helped me move beyond my previously fragmented and experience-based perspective, gradually forming a clear trajectory that spans from theoretical learning to technological mastery and ultimately to practical application (see Figure 1). As a teacher dedicated to integrating artificial intelligence into kindergarten education, this course has provided essential support in leveraging AI to enhance instructional design and personalize student learning. Subsequently, I will critically reflect on my learning journey from four angles—theoretical learning, skill acquisition, module-based learning, and guest lectures—thus presenting the key elements that have left a profound impression on me.

截屏2024-12-15 13.14.40.png

Fig1 Course Learning Journey

I have organized these impactful learning elements in Figure 2.

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Fig 2 Impressive Learning Elements of the Learning Journey

Theoretical Learning:

Within the EDS431 module, I learned and applied the Design-Based Research (DBR) methodology. Critically engaging with the iterative cycle of digital course design has deepened my understanding of the reciprocal relationship between theory and practice. Moreover, by exploring Project-Based Learning (PBL), I became aware of how designing authentic contexts can foster critical thinking and collaboration skills. In addition, delving into game-based learning theories allowed me to distinguish among Gamification, Game-Based Learning (GBL), and Game-Based Pedagogy (GBP). These conceptual clarifications not only honed my theoretical acuity but also highlighted multiple vantage points for effectively integrating game elements into course design.

Skill Acquisition:

From a critical standpoint, being a non-computer major initially heightened my anxiety and stress when applying technologies. However, under Dr. Lina’s systematic and step-by-step guidance, I gradually mastered operations on the Model platform and H5P tools, thereby developing interactive and visually compelling digital course content. Furthermore, I explored HTML and the fishbone diagram technique, significantly improving my abilities in course structuring and problem analysis. This process revealed that practical design capabilities are indispensable for digital education course developers. Indeed, the EDS431 module enabled me to confront my weaknesses and, by extension, opened new pathways for more advanced learning.

Guest Lectures:

The guest lectures offered fresh perspectives that critically challenged and enriched my research direction. Our team is investigating how Generative AI can support kindergarten teachers in course design. In this vein, Yukyeong Song’s lecture illuminated the principles underlying the design of personalized learning pathways; Yexiang Wu’s introduction to platform-based AI tools broadened my technical understanding of enhancing learners’ experiences; and Weicui’s exploration of Prompt Engineering inspired new strategies for course feedback and optimization. By aligning closely with my research interests, these lectures not only anchored my theoretical understanding but also provided practical reference points for implementation.

Module-Based Learning:

Moreover, the module’s learner-centered approach compelled me to reconsider how course design must revolve around learners’ needs. Critically examining various modes of engagement, I built a systematic theoretical framework through lectures, engaged in face-to-face discussions during tutorials with Dr. Lina, and benefited from interactive group collaborations. Notably, our group encompassed diverse work statuses (part-time and full-time) and disciplinary backgrounds (computer science, fine arts, English, information management, and early childhood education). Although this diversity introduced certain challenges, it simultaneously created opportunities for richer dialogue and innovative thinking. As the group leader, I confronted the pressure of motivating members toward shared goals. Nevertheless, by sharing online resources, facilitating discussions, and orchestrating effective communication, I successfully navigated the complexities of collaboration and ultimately guided the team toward achieving our established objectives.

In conclusion, the EDS431 module, by integrating theoretical knowledge, technical skill development, expert insights, and learner-centered collaboration, has constructed a more coherent and multifaceted framework for my understanding of digital education course design. Critically reflecting on this journey, I have not only reinforced the synergy between theory and practice but also sharpened my technical competencies and honed my collaborative skills within a diverse team context. More importantly, this experience sets the stage for applying AI-driven innovations in early childhood education, thus offering a strategic direction for continued exploration in the ever-evolving landscape of digital pedagogy.

4. Core of the assessment

This section provides a comprehensive overview of the core components of the assessment in EDS431. It is organized into the following sub-sections: Background and Justification、Methodology、Results、Conclusion.

Through these sub-sections, the Core of the part aims to provide an in-depth understanding of how theoretical foundations and practical applications converge in the design, implementation, and evaluation of digital education courses.

A. Background and Justification

Background

“Dental Discovery 101” is an interactive learning game specifically designed for dental hygiene and assisting students. It aims to support students in mastering dental terminology, diagnostic skills, and practical application through virtual clinical scenarios. By incorporating a blended learning model that combines online and offline components, the course enables students to enhance their retention of terminology and improve practical skills while completing engaging tasks. The high demands for proficiency in terminology and practical skills in dental education, coupled with the challenges faced by students in balancing theoretical and practical learning, make this innovative course design particularly valuable.

Problem Statement

Although “Dental Discovery 101” has several strengths, it also exhibits the following limitations:

1. Lack of collaborative learning opportunities

The course primarily supports individual learning, while collaborative learning has been shown to significantly improve learning outcomes (Wang et al., 2009). Collaborative environments can greatly enhance student interaction and discussion (Hsiao et al., 2014) and have demonstrated utility and educational value on medical education platforms (Taveira-Gomes et al., 2014).

2. Lack of personalized learning paths

The current course does not allow students to select learning paths based on their knowledge levels, which limits their self-regulation and problem-solving abilities (Scheiter & Gerjets, 2007; Panadero, 2017).

3. Limited feedback mechanisms

The course primarily provides punitive feedback in the form of point deductions, lacking immediate and detailed positive reinforcement. Such a design can weaken student motivation and increase cognitive load (Hattie & Timperley, 2007; Deci & Ryan, 2000). Timely and detailed feedback is essential for deep knowledge acquisition and effective learning while also reducing anxiety (Tsai et al., 2015; Belschak & Den Hartog, 2009).

Goals

To address the limitations of the current “Dental Discovery 101” course and enhance learning outcomes, the optimized design aims to achieve the following goals:

1. Enhance collaborative learning

To address the lack of collaborative learning opportunities, this optimization introduces blended collaborative tasks combining online and offline activities. These tasks are designed to strengthen student interaction and teamwork. Studies have shown that blended learning environments effectively engage students and promote knowledge construction (Van et al., 2005; Garrison & Kanuka, 2004).

2. Develop personalized learning paths

To overcome the absence of personalized learning paths, the optimization incorporates flexible difficulty selection and adaptive learning technologies. Based on the SCDGBL framework, course content will dynamically adjust difficulty levels, allowing students to choose their learning paths according to their knowledge levels. Research indicates that adaptive learning paths significantly improve knowledge retention and reduce cognitive load (Sweller et al., 1998).

3. Optimize feedback mechanisms

To resolve the issue of limited feedback mechanisms, the optimization introduces diversified feedback forms, including Immediate Elaborative Feedback (IEF) and positive reinforcement strategies. For instance, when students provide incorrect answers, the system will offer detailed explanations through animations or visual aids to help them understand and correct their errors. When students provide correct answers, they will receive motivational rewards, such as digital badges or progression to advanced roles like “Dental Expert” (Alt, 2021). Timely and detailed feedback enhances the learning experience, deepens understanding of complex knowledge, and significantly reduces anxiety (Hattie & Timperley, 2007; Tsai et al., 2015).

Significance

This optimized design not only addresses the primary issues of traditional dental education but also aligns with trends in digital education and personalized learning, providing an innovative case study for the field of educational technology. By integrating collaborative learning, adaptive learning paths, and diversified feedback, “Dental Discovery 101” significantly enhances students’ learning experiences and knowledge application skills while offering valuable insights for game-based learning designs in other medical disciplines.

B. Methodology(Expand to See More)

This section outlines the methodological framework used to develop and optimize the Dental Discovery 101 course, which is based on a five-phase design thinking model: Empathy, Define, Ideate, Prototype and Test.

The main objective of this section is to critically analyze the actions taken in each stage, which I will discuss in four areas: actions, methods, challenges and solutions, and contributions.

Step 1. Empathy

During the “empathy” phase, I used simulated user interactions and visualization tools to understand user needs, behaviours, and pain points to support course design optimization. Key actions included

  • user interviews via the EDS431 AI TA
  • empathy mapping using Miro(Fig 3).

In the process, to address the challenge of superficial AI emotional feedback, I refined the interview prompts to gain more specific emotional insights. The findings revealed three key issues: static learning paths with limited personalization, insufficient feedback mechanisms, and a punitive scoring system that affects motivation. These three issues aligned with the pain points identified in Evaluation 1, validating my previous analysis and providing a solid foundation for the design phase to follow.

Fangjing Chen's User Journal Map.png

https://miro.com/app/board/uXjVLEJviM0=/?share_link_id=191533708807

Fig3 User Journey Map

Step2 Define

During the definition phase, I used the XMind tool to analyze data from the empathy phase to identify the core problem(Fig 4), root causes, and potential solutions. Key actions included:

  • Developing a problem statement: “Students face static learning pathways, inadequate feedback mechanisms, and a punitive grading system.”
  • Root cause analysis: forced progress, minimal feedback, and discouraging grading.
  • offer solutions: dynamic difficulty adjustment, personalized feedback, and progress-based rewards.

These theoretical underpinnings I have detailed theoretical support for in the Background section of Assessment1, so my thinking was very clear in completing the Xmind mapping process.

I also prioritized solutions based on feasibility, impact and alignment with user needs. However, because I lacked real-world product development experience, I could only prioritize these elements from an empirical perspective.

Overall, this phase was user-centered, and I clarified user needs and identified feasible solutions, which laid a solid foundation for the ideation phase.

Fangjing Chen's Xmind.png

https://xmind.ai/share/gq5spzwj?xid=ehrfKlEt

Fig4 Definition X-Mind

Step 3: Ideate

The ideation phase is critical to guiding subsequent prototyping and implementation, so I'll explain it in detail.

I used the SCAMPER approach (Substitute, Combine, Adapt, Modify, Repurpose, Eliminate, Reverse) to address the core issues of Dental Discovery 101: static learning paths, limited feedback mechanisms, and punitive scoring systems. Problem-oriented analysis (based on user feedback and empathy mapping) and theoretical integration (Constructivist Learning Theory, Multimedia Learning Theory, and Cognitive Load Theory, to name a few) informed the development of innovative solutions(Fig 5).

  • Substitute: Learning objectives were redefined to include critical thinking and clinical decision-making tasks, replacing the sole focus on terminology memorization. This approach leverages authentic contexts to enhance practical application skills.
  • Combine: Virtual reality (VR) and augmented reality (AR) technologies were integrated with collaborative platforms (e.g., Moodle discussion forums) to enhance immersive experiences and foster collaborative learning (Merchant et al., 2014; Radianti et al., 2020).
  • Adapt: Dynamic difficulty adjustments and skip-level features were designed to cater to diverse learner needs, thereby promoting self-regulation and deeper learning (Nadolny et al., 2020).
  • Modify: Content depth was expanded by incorporating complex case analyses and guided animations, reducing cognitive load and enhancing learning outcomes (Sweller, 1998).
  • Put to Other Uses: The course design was extended to applications in patient education, clinical skill training, and interdisciplinary learning, supporting knowledge transfer and cognitive expansion.
  • Eliminate: Redundant tasks and rules were removed to focus on key learning objectives, simplifying the user experience and minimizing cognitive load (Mayer, 2005).
  • Reverse: Task sequences were optimized, allowing learners to skip mastered content and dynamically review mistakes, thereby boosting self-efficacy and motivation (Scheiter & Gerjets, 2007; Panadero, 2017).

I also encountered some challenges during the process. First, to address the problem of too many solutions and unclear prioritization, I used feasibility, impact, and user consistency criteria to rank solutions. Second, to address technical complexity and resource constraints, I used a phased implementation approach, starting with core functionality and gradually adding advanced tools.

This phase resulted in practical, theoretically supported solutions with clear priorities that provided a solid foundation for prototyping.

Fangjing Chen's Ideate.png

https://miro.com/welcomeonboard/eTd1VGVOazFjR3IwQk1qTVVWWFdTYlZvOUczVzkySkhuM1hpUUp6TGxHMnYzSWgwNUZsNnVrV0VuZStnZkxpMnBjREdLMGlpUGRuc2RPcllpbitBN216S0hSdXl1VFMrcVA1enR4bEhZU21xcjY1UjEvVzJIeGlGc1ZTN2tnb3UhZQ==?share_link_id=333863318261

Fig 5 SCAMPER MAP

Step 4:Prototype

During the prototype phase, I utilized Figma to design and develop the core interface and functionality of Dental Discovery 101(The specific interface design will be presented in the 6 Results section), building on previous theoretical foundations. Figma was chosen for its robust user interface design, ease of use, and accessibility to community resources, all of which enriched the practicality and creativity of the design.

Key Actions:

  • Main interface and navigation: Design the game title, start options, and clear navigation menus for easy access.
  • Personalized Learning Path: Includes difficulty levels (Beginner, Intermediate, Advanced, Proficient) and dynamic review of incorrect answers for increased flexibility.
  • Collaboration Mode: Integrated solo and collaborative options for increased interactivity.
  • Feedback and Progress System: Visual charts and scoring mechanisms to provide performance insights and suggestions.
  • Task Dashboard: Added task descriptions, answer submissions, and hints for a better understanding of complex content.
My prototype of this design submitted through the Learning Mall platform received a peer-review score of 75.5 out of 100 (excellent), confirming its innovation and educational effectiveness. However, during the implementation process, I also identified limitations in the technical implementation of my prototype design that need to be improved, and this part of the optimization I will state in detail in the next step REVERSE.
Overall, the Figma-based prototype successfully provided personalized learning paths, detailed feedback, and immersive learning experiences in line with Cognitive Load Theory and Personalized Learning Principles, and was validated through peer feedback.

Step 5 Test

During the testing phase, our team collaborated to implement and test Dental Discovery 101 using the AI Tutor page on the Learning Mall platform, which was chosen for its ease of rollout to a broad student audience, ease of design with the plug-ins within it, and ability to address three core issues: lack of collaborative learning opportunities, limited personalized learning paths, and insufficient feedback mechanisms.

The testing process consisted of three key components:

  • welcome page testing: Evaluate user navigation to the core content (welcome page, challenge levels, and module drawer) and validate reward mechanisms such as a dental badge bar and leaderboards to increase engagement.
  • level-based challenge testing: assesses the interactivity and learning effectiveness of tasks, records completion times, and analyzes feedback on task difficulty to meet learner needs.
  • feedback mechanism testing: validate the real-time feedback features of AI Tutor and Peer Feedback, and analyze the role of the badge system in maintaining learner motivation.

My role in the team included designing and testing the welcome page, documenting course optimization, and writing the results section. Our final results will be presented in detail in C. Result. Of course, there are some limitations to our design, which I will detail in the D. Conclusion section.

B. Video of Empathy for the Methodology section

B. Video of Define for the Methodology section

B. Video of Empathy for the Methodology section

C. Results

Through the design and optimization process, I achieved the following three outcomes:

1. Optimized Course Plan

I revised the objectives of Dental Discovery 101 and refined the implementation process to enhance clarity and accessibility. The restructured course documentation is now more systematic and intuitive, making it easier for educators and learners to interpret and apply.

2. Prototype Design

I developed five key interfaces using the Figma platform. These included the course’s main navigation, personalized learning path selection, task panel, feedback and progress system, and collaboration mode switch. These designs emphasize personalization, interactivity, and innovation, providing a solid foundation for subsequent development and implementation.

3. Course Demonstration Video

A comprehensive video was created to showcase the overall structure of Dental Discovery 101, including interface design, task execution, and user interactions. The video further validated the coherence and feasibility of the optimized design.

The following is a transcript of the audio explanation in the video:

Welcome to Dental Discovery 101! Next, I will introduce the interface of this course game.Once you enter the learning interface, you’ll find three main sections:

  • Navigation Bar: Located on the left, this includes the welcome page and challenge pages for the four levels. You can easily select the content you want to explore and click to enter the relevant page.
  • Main Display Area: In the center, this area shows the core course content. You can scroll through the modules and interact with the material dynamically.
  • Module Drawer: On the right, you can contact the AI tutor via Dental AI anytime to ask questions. You can also see your online peers and get motivation through community learning. The Dental Badge Bar shows your experience points for completed tasks, encouraging you to continue completing challenges for rewards. The Leaderboard shows your peers’ progress, motivating you to improve. Once you complete all tasks, you’ll earn the “Dental Specialist” badge as the ultimate goal.

Now, let’s walk through the learning process and content students will experience in each module.

First is the Exploration Page. On this page, students will get an introduction to Dental Discovery 101 and access the latest course updates via the Announcement Forum. If they have questions, they can post them in the Question Forum, and the course instructor will reply within three business days. The BBB Button takes students to the live course page, available every Thursday at 6:00 PM. Students can join live sessions or review recorded content after the class. To accommodate different learning styles, we also provide a video introduction to quickly help students understand the course essentials.In the Course Design and Implementation section, we present the course background, objectives, evaluation standards, and learning theories, emphasizing a student-centred design. This allows students to understand the goals behind the course and engage actively, offering feedback and suggestions.

The second is Level-Based Challenges. Students can freely choose which of the four levels to challenge. Depending on their proficiency, they can repeat any level to reinforce their understanding. Each level has four steps, and after completing each, students can click Mark Down to track progress. They’ll earn star rewards for each completed challenge. Once all levels are completed, they’ll earn the “Dental Specialist” badge.

Let’s take Level 1 as an example to understand how the challenges work.

First, students will complete asynchronous learning to reinforce the knowledge required for this level. After learning, they’ll go through each challenge step-by-step, guided by highlighted steps from STEP 1 to STEP 4.

STEP 1: Name the Teeth  

Students drag and drop numbers to label the tooth quadrants. After confirming, they can check if the answers are correct. Incorrect answers won’t allow dragging, helping students check and reinforce their knowledge interactively.

STEP 2: Sound Spell Challenge

Students listen to audio content to identify the tooth quadrants. This step ensures they can correctly pronounce and understand the dental terminology. They’ll upload their pronunciation to the Pronunciation Test Forum and get peer feedback to improve their pronunciation.

STEP 3: Test Your Pronunciation

Students upload their audio and participate in peer evaluations. This helps them refine their pronunciation and deepen their understanding by listening to others.

STEP 4: What is the Conditions

Students view real-life case images and scenarios to identify dental conditions and choose the correct answers. This step enhances clinical thinking by connecting theory with practical application.

This concludes the demonstration of Dental Discovery 101. If you have any questions, feel free to contact us.

C. Optimized course plan

C. Prototype(Interface1-5)

C. Course Demonstration Video

D. Conclusion

EDS 431 learning, relies on the “Dental Discovery 101” project, encompassing the entire process from problem identification to design development and solution evaluation, providing invaluable hands-on experience in digital education course design. Through this journey, I gained deeper insights into creating interactive, learner-centered courses. Key takeaways included the importance of iterative optimization, integrating user feedback, and balancing theoretical frameworks with practical applications.

Limitations:

Despite achieving its initial objectives, the design has certain limitations. Firstly, the prototype relies on the Figma platform, which lacks the technical depth for full functional implementation. Secondly, testing was primarily conducted in simulated scenarios, with limited involvement from real learners, which impacts the accuracy and representativeness of user feedback.

Future Directions:

To address these limitations, future iterations could involve collaboration with software developers to create a fully functional platform. Expanding the testing scope to include diverse learner groups in authentic educational settings would enhance the reliability and generalizability of the results. Furthermore, integrating advanced features such as adaptive AI tutors and augmented reality could significantly enhance the interactivity and effectiveness of the course.

In conclusion, “Dental Discovery 101” demonstrates its potential as an innovative, learner-centered educational tool, laying a solid foundation for further exploration and application in dental education.

5. Independent self-development

Introduction

Independent self-development played a pivotal role in the design and development of Dental Discovery 101. It served as a valuable complement to classroom learning and aligned closely with the intrinsic requirements of the PBL (Problem-Based Learning) framework. In this course, Dr. Lina provided problem-solving pathways that broadened our perspectives and encouraged self-driven exploration. Through this process, I learned to leverage my capabilities and utilize diverse resources to identify appropriate solutions, thereby enhancing my instructional design skills.

Specific Practices in Independent Learning(Fig 6)

1. Mastering Figma Interface Design Skills

While designing the prototype for Dental Discovery 101, I learned Figma’s operation processes via Xiaohongshu, including designing and organizing interface components, optimizing navigation bars, and implementing dynamic interactive elements. These efforts helped me develop core UI design skills, providing technical support for subsequent prototype development.

2.Learning Prompt Engineering for AI

To enhance the feedback mechanism and user experience in the course, I studied prompt engineering through the Dedao learning app and Bilibili. This knowledge enabled me to refine AI interaction prompts, improving task clarity and operational efficiency for users.

3.Application of Reference Management Tools

During the literature review and theoretical framework development, I learned how to efficiently manage references using Zotero and EndNote via tutorials on Bilibili. These tools allowed me to quickly locate, organize, and cite resources, thereby improving the academic rigor and scientific validity of the course design.

Recommendations for Others

1. Engage in Frontline Educational Practices

Immersion in frontline education provides an invaluable opportunity to understand learners’ needs and gain hands-on teaching experience. Integrating theory with practice ensures instructional designs are grounded in real-world contexts.

2. Observe Exemplary Course Design Practices

Beyond acquiring knowledge in class, closely observe the methods employed by instructors, such as Dr. Lina’s approaches to course design, creating digital environments, and integrating technology. These strategies serve as exemplary references for digital course design.

Future Applications and Implications

The skills and knowledge gained through independent self-development have significantly advanced my abilities across various areas. As a kindergarten teacher, while early childhood education cannot yet achieve full digital integration like higher education, the principles I learned—such as the Design Thinking Model, PBL learning methods, and the SCAMPER approach—can be applied flexibly to project-based courses, fostering innovation in early childhood teaching. This learning capacity will further support my career development as I explore forward-thinking teaching strategies and inject fresh ideas into future instructional designs.

In conclusion, independent self-development has been an essential part of the course design process and remains a cornerstone of continuous professional growth.

截屏2024-12-15 20.46.41.png.1

Fig 6 Specific Practices in Independent Learning

6. Evidence

All the evidence from the EDS431 learning process has been presented in its entirety in each section.

7. Wraparound reflective writing

As the team leader, I initially believed my primary responsibility was to facilitate communication, particularly because Dr. Lina mentioned that the final deliverable for this course was an individual assignment. At that stage, team collaboration did not seem essential. However, as the project progressed, I realized that achieving better outcomes required leveraging the strength of the team, working together, and striving toward a shared goal

The biggest challenge for me in this collaboration was coordinating schedules and managing the division of labor, especially when integrating diverse backgrounds and expertise. To address these issues, we utilized online communication tools and held regular meetings to ensure clear task assignments and synchronized progress. However, I also recognized that my leadership in team communication could be further improved. Ultimately, only four team members actively participated in the Realize project, while two others declined due to scheduling conflicts, despite multiple invitations. This was a regrettable aspect of our collaboration.

Looking ahead, I hope that other groups aiming to solve problems through teamwork can initiate discussions, planning, division of labor, and implementation as early as possible. This would prevent the kind of last-minute rush we experienced near the deadline. Despite this, I feel proud of our team’s achievements. The EDS431 team collaboration enabled us to present the Realize phase at its best. I believe my team members and I gave our utmost effort and delivered an outstanding result.

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