A-4-5Literacy-Oriented Teaching Community: Implementation of Literacy-Oriented Courses
Project Name A-4-5Literacy-Oriented Teaching Community: Implementation of Literacy-Oriented Courses
Course Number 1516
Course Name Total Quality Management (TQM)
Instructor Dept. of Industrial Engineering & Management / Assistant Prof. Joey Chung
Course Date 114th Academic Year, 1st Semester (Sep. 08, 2025 – Jan. 05, 2026)
Sessions 5 to 7, Total Weeks: 18
Participants ■Internal Teachers: 10 (person-times)
■Internal Students: 55 (person-times)
Execution Status 1. Student Learning Outcomes (Knowledge, Ability, and Attitude):
In this semester's "Total Quality Management" course, student learning outcomes demonstrated a three-level leap from "theoretical cognition" to "practical application," and finally to "sustainability literacy":
  • Knowledge: Students not only mastered core TQM theories (e.g., PDCA Cycle, Customer Satisfaction, Continuous Improvement) but also successfully integrated these traditional quality concepts with the UN Sustainable Development Goals (SDGs). Through classroom guidance and a special lecture by Professor Wu Jia-Huang, students understood that "Quality Management" is no longer just about pursuing product yield, but encompasses a macro vision of achieving SDG 12 (Responsible Consumption and Production) and SDG 9 (Industry, Innovation, and Infrastructure) through reducing variation and optimizing processes.
  • Ability: The most significant outcome of this course is that students acquired the practical ability to "solve real-world problems." Students moved beyond paper-based theories to apply cross-disciplinary technical tools for quality improvement. For example, students demonstrated the use of Information System Development (SQL/PHP) to resolve human errors in inventory management; the application of Automatic Control (PLC/Sensors) to achieve error-proofing (Poka-Yoke) mechanisms; and even the utilization of AI and Big Data Analysis to optimize educational resource allocation. This shows that students can perfectly integrate Industrial Engineering quality methods (e.g., Fishbone Diagrams, Check Sheets) with modern technological tools (IoT, RFID).
  • Attitude: Students transformed from passive receivers into "improvers of social problems." In their final projects, we observed students' concern for both "campus issues" (e.g., library queuing, bookstore purchasing) and "social issues" (e.g., remote education resources, energy saving in rentals). They proactively identified pain points and conducted improvements with the goals of "improving efficiency and reducing waste." This attitude of internalizing "quality awareness" into "care for people and the environment" is the most valuable output of literacy-oriented teaching.
2. Course Execution Description:
This course adopted a dual model of "Problem-Based Learning (PBL)" and "Situational Teaching," strongly supported by the co-prepared resources of the teacher community. The execution process was divided into three stages:
  • Stage 1: Concept Implantation & SDG Connection (Situational Guidance)
Utilizing the community's co-prepared materials, SDG issues were introduced synchronously while teaching TQM chapters. For instance, when teaching "Continuous Improvement," students were guided to think about how to eliminate the "Seven Wastes" in campus life. Through the sharing of industry status in Vietnam and Thailand by foreign teachers, students learned about the high standards for quality and sustainability in cross-national supply chains, establishing an international perspective on quality.
  • Stage 2: Expert Paradigm & Tool Deepening (Deep Learning)
Professor Wu Jia-Huang from National Yang Ming Chiao Tung University was invited to give a lecture on "Quality Anomaly Handling," shifting the teaching scene from the classroom to a high-tech manufacturing site. Students learned how the industry uses "Commonality Analysis" and "Big Data" to diagnose quality problems. This helped students understand that the statistical tools learned in class (such as $C_{pk}$) are key to saving millions in costs and energy in the real world.
  • Stage 3: Field Practice & System Development (Practical Output)
For the final project, students were required to form groups to find a real field (e.g., the campus bookstore, rental apartments, factory production lines) and conduct improvements using the PDCA process. The course encouraged students to "not just propose suggestions, but build prototypes." Under the instructor's guidance, students translated quality problems into system requirements and actually developed functioning software/hardware solutions, fully experiencing the complete quality improvement journey of "Problem Discovery → Root Cause Analysis → Countermeasure Implementation → Effect Verification."
 

 
  3. Student Implementation and Demonstration of Learning:
A total of 7 student groups completed final project implementations this semester. The results were fruitful and concretely responded to the integration of TQM and SDGs:
  • Process Automation & Error-Proofing (SDG 9 Industry, Innovation): Group 2 (Automated Parts Sorting System) and Group 7 (Automated Rental Management System) utilized PLC control and IoT sensor technologies respectively to replace traditional manual judgment and meter reading. This not only achieved "Fool-proofing" in quality management but also significantly improved operational precision and energy management efficiency.
  • Digital Transformation & Waste Elimination (SDG 12 Responsible Consumption): Group 1 (Retail Inventory Management) and Group 6 (Smart Bookstore System) developed SQL database systems to replace traditional paper-based operations. Group 6 specifically calculated that after the system goes online, it can save thousands of triplicate forms and hundreds of hours of queuing time per semester, digitizing the quality spirit of "eliminating waste."
  • Service Quality Enhancement & Social Care (SDG 4 Quality Education): Group 3 (Library RFID System) resolved the customer (reader) pain point of waiting, enhancing service satisfaction. Group 4 (Automated Learning System) used Fishbone diagrams to analyze the root causes of scarce educational resources in remote areas and proposed an AI note-generation solution, demonstrating the potential of quality technology in social welfare applications.
These reports fully prove that students have broken free from the traditional framework of "quality control equals inspection" and have shifted to using information technology and systems thinking to construct comprehensive quality solutions with a spirit of sustainability.

Execution Status
4. Off-site Teaching Situation:
  • Location: I203 Computer Lab
  • Description: To implement the core spirit of "Management by Fact" in Total Quality Management (TQM), this course arranged off-site teaching at the computer lab to guide students in operating the industry-standard statistical software, Minitab. The teaching focused on transforming the Statistical Process Control (SPC) theory learned in class into practical operational skills. Students used Minitab to generate X-bar R Control Charts, Pareto Charts, and Process Capability Analysis graphs. Through this practice, students deeply realized that TQM is not just a management philosophy but requires precise statistical tools to monitor process Variation. Students learned how to distinguish between "common causes" and "special causes" of variation from raw data, which is a critical ability for problem diagnosis and decision-making in quality improvement activities, effectively bridging theory and practice.
5. Multiple Assessment Results:
This course moved away from traditional assessment based solely on paper-and-pencil tests, adopting a "360° Comprehensive Assessment Framework" that combines teacher, peer, and self-assessment, and introducing a literacy-oriented Rubric. The specific execution methods were as follows:
  • Construction of Literacy-Oriented Rubric: To ensure objectivity and guidance in grading, four major assessment dimensions were established:
    • Application of Quality Tools (30%): Assessing whether students correctly selected and used TQM tools (e.g., Fishbone diagrams, Control charts) to analyze root causes.
    • SDG Impact Assessment (25%): Examining whether the project concretely quantified the contribution to the environment (e.g., waste reduction, energy saving) or society (e.g., educational equity) before and after improvement.
    • Data Analysis Ability (25%): Assessing the ability to use Minitab or Excel for data support, rather than proposing countermeasures based solely on intuition.
    • Innovation & System Implementation (20%): Encouraging students to showcase creativity by developing automated systems (e.g., SQL, PLC) to solve quality problems.
  • Implementation of 360° Assessment Mechanism:
    • Teacher Assessment: The instructor scored the logic and completeness of the final report based on the Rubric.
    • Peer Assessment: During the final presentation, student groups assessed each other. This was not only for grading but also a process of observational learning, allowing students to reflect on their own shortcomings by learning from the strengths of other groups (e.g., Group 2's automation, Group 4's AI application).
    • Self-Assessment: Each student was required to write a "Reflection and Self-Assessment" chapter in their final report, reviewing their contribution, difficulties encountered, and problem-solving journey during the project execution, thereby strengthening their metacognitive abilities.
 
智慧化顧客社群知識管理系統規劃與開發專業成長研究社群
社群名稱:智慧化顧客社群知識管理系統規劃與開發專業成長研究社群
社群成員:7人(召集人:蔡志明老師)
活動成果:
  • 召集人蔡志明老師已完成半年產業研習,強化理論與產業實務連結,並將研習所得經驗融入社群活動規劃中。The project leader, Chih-Ming Tsai, has completed a six-month industry internship, strengthening the connection between theory and industry practice. He has integrated the insights gained from this experience into the planning of community activities.
  • 後續可持續發展為「AI & MIG研究社群」,持續深化顧客知識管理與智慧科技應用研究。It can be developed into an “AI & MIG research community” to continue deepening research on customer knowledge management and smart technology applications.
  • 社群目前執行「AI結合顧客知識管理市場調查研究與系統開發」產學合作計畫(主持人:蔡志明老師),研究成果已同步應用於教學課程中,提升學生之實務研究與系統應用能力。The community is currently executing the industry-academia collaboration project “AI-Integrated Customer Knowledge Management Market Research and System Development” (project leader: Chih-Ming Tsai). Research outcomes have been synchronously applied to teaching curricula, enhancing students' practical research and system application capabilities.
  • 結合MIG Square之「網際網路與電子商務」課程,延伸研究主題「顧客知識管理之智慧化AI技術應用與架構設計」並落實於教學當中,探討AI技術於電商產業顧客知識管理系統中的應用與設計案例,促進教師與學生對技術整合的理解。Integrating with the “Internet and E-Commerce” course at MIG Square, extending research themes and organizing related activities “Smart AI Technology Applications and Architecture Design for Customer Knowledge Management”. Exploring AI technology applications and design cases within customer knowledge management systems in the e-commerce industry to enhance faculty and student understanding of technology integration.
  • 顧客社群參與機制與知識共創模式建立:透過焦點小組團體討論,建構顧客知識擷取流程與回饋機制,強化社群互動與創新思維。Establishing Customer Community Engagement Mechanisms and Knowledge Co-creation Models: Through focus groups and practical design, constructing customer knowledge extraction processes and feedback mechanisms to strengthen community interaction and innovative thinking.
  • AI技術於顧客資料分析與決策應用:未來可舉辦實作課程,導入實際數據進行AI分析與應用模型演練,提升數據處理與決策支援能力。AI Applications in Customer Data Analysis and Decision-Making: Conducting hands-on courses that incorporate real-world data for AI analysis and application model exercises, enhancing data processing and decision support capabilities.