Exploring STEM+Maker Education in Vocational Schools

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New generation information technologies such as artificial intelligence, big data, cloud computing, mobile internet, and the Internet of Things are deeply integrated with education. How should vocational education, closely linked to the market and employment, respond to the new situation? How should vocational schools innovate their educational paradigms?

In my opinion, vocational schools should not only focus on operational skills training but also guide students to transition from being users of artificial intelligence to participants and creators. How to cultivate innovative talents for future society, explore what to teach, what to learn, how to teach, and how to learn is an important research topic of “Artificial Intelligence + Education”.

Exploring STEM+Maker Education in Vocational Schools

—Taking the “Smart Restaurant Settlement System” as an Example

By | Liu Hailong, Chen Dongdong

1. “STEM+Maker” Supports Innovative Talent Cultivation

STEM+Maker

The “New Generation Artificial Intelligence Development Plan” issued by the State Council points out the direction for the development of artificial intelligence education at the vocational education stage in our country. Artificial intelligence is gradually becoming one of the important teaching contents in vocational education. Vocational teachers should focus on cultivating students’ logical thinking ability, problem-solving ability, hands-on ability, and innovative consciousness, keeping pace with the times and enhancing students’ potential to adapt to future competition.

STEM (Science, Technology, Engineering, and Mathematics) education and maker education are seen as key forces driving educational reform and are one of the best paths to cultivate students’ innovative and practical abilities. The integration of artificial intelligence enriches the connotation of STEM education and maker education, while also injecting fresh blood into the innovative development of STEM education and maker education, providing strong support for cultivating innovative talents with comprehensive innovation capabilities and qualities, which aligns with the training goals of vocational education students in the era of artificial intelligence. I will take the “Smart Restaurant Settlement System” as an example to explore the teaching methods and paths of artificial intelligence teaching in vocational schools under the “STEM+Maker” teaching model.

2. Analysis of the “Smart Restaurant Settlement System” Class Case

STEM+Maker

The “Smart Restaurant Settlement System” is a lesson in the artificial intelligence curriculum of Dongguan University of Technology. Teachers explain relevant knowledge and principles of artificial intelligence, guide students to use artificial intelligence technology to solve problems in life, cultivate students’ habits of active participation, exploration, innovation, and hands-on practice, improve students’ creative thinking ability and problem-solving ability, and develop students’ scientific literacy. I adopt the “STEM+Maker” model to carry out curriculum teaching in a project-based form, guiding students to use typical technologies of artificial intelligence (computer vision recognition, Python programming language) to create a smart settlement system that can quickly identify the prices of different types of dishes and automatically calculate the total price, solving the problem of frequent errors in manual payment in some restaurants.

(1) Teaching Objectives

1. Knowledge and Skills: ① Understand the basic classifications and common application scenarios of artificial intelligence; ② Master the design methods of intelligent hardware systems based on Arduino, and be familiar with the application methods of typical technologies of artificial intelligence—computer vision recognition and speech synthesis technology; ③ Master Python programming methods, and realize the drive of intelligent hardware such as main controllers and artificial intelligence modules through programming; ④ Master digital design and digital manufacturing methods, learn 3D design and 3D printing, plane design, and laser cutting, design and create functionally diverse structural models; ⑤ Understand the principle of threshold segmentation in image processing.

2. Process and Method: ① Introduce problems through life situations, clarify tasks; ② Teachers guide students to analyze problems and explain knowledge difficulties; ③ Students participate in project learning activities, cooperate in groups for creativity, design, structural construction, electronic control, and Python programming, completing production tasks.

3. Emotional Attitude and Values: ① Let students learn artificial intelligence and Python programming knowledge, deepen their understanding of artificial intelligence, increase their interest in designing artificial intelligence works, and stimulate students’ desire to learn artificial intelligence technology; ② Cultivate students’ ability to discover, analyze, and solve problems, exercise students’ collaborative abilities, and improve students’ scientific literacy.

4. Creativity: ① Use digital tools to express creativity, carry out design activities in structure, control, and programming, and cultivate students’ design thinking ability; ② Students participate in structural design and construction activities, design and control electronic control systems, and develop engineering thinking abilities; ③ Participate in programming activities (Python programming) to improve computational thinking abilities; ④ Design and create prototypes of the smart restaurant settlement system project.

(2) Teaching Design Based on the “STEM+Maker” Model

Based on constructivism and innovation theory, I adhere to the concept of “learning by doing,” organizing students to participate in project-based learning and experiential learning activities, fully leveraging students’ initiative and agency in learning, guiding students to explore the application of artificial intelligence technology around real-life situations (problems), collaborate in groups, and design and create works, exploring in practice with new perspectives and ways of thinking, enhancing interdisciplinary innovative capabilities and problem-solving abilities. The teaching design model based on the “STEM+Maker” model is shown in Figure 1.

Figure 1 “STEM+ Maker” Teaching Design Model

The “Smart Restaurant Settlement System” is a project that integrates emerging information technologies and multidisciplinary knowledge, and is a typical case of artificial intelligence course teaching. The development of this system involves artificial intelligence technologies (such as visual recognition, speech synthesis), digital design and manufacturing, electronic system construction, Python programming, and other technologies, as well as interdisciplinary knowledge. Before conducting teaching activities, teachers can design and organize teaching activities according to the teaching model.

1. Discover Problems Based on Real Life

Technological development profoundly affects people’s lives. When shopping, most people use WeChat or Alipay for payment, which is very convenient. Currently, some restaurants still rely on waiters to manually calculate prices and collect payments, which can sometimes lead to errors. There is a settlement system on the market that supports intelligent settlement but has its shortcomings. It quickly identifies and calculates prices by placing sensing chips in the dishes, but the chips can easily fail due to high-temperature disinfection of the dishes, and the cost is relatively high.

Teachers raise questions based on social realities, connecting students’ learned knowledge with real life, guiding students to determine the problems to be solved and the functions to be achieved by the project works based on the problems and needs. This aligns with the maker education that emphasizes “learning by doing,” both are oriented towards solving real problems, allowing students to discover real problems, seek creative solutions, and realize them through efforts, developing students’ innovative thinking and problem-solving abilities.

2. Analyze Problems and Explore Solutions

The realization of project works requires multidisciplinary knowledge. In this phase, teachers organize students to explore in cooperative groups, considering what functions the smart restaurant settlement system needs to achieve, analyzing what technologies and hardware and software devices are needed to achieve these functions, and how to verify the functions of the prototype, guiding students to explore a series of questions using multidisciplinary knowledge, enhancing their ability to analyze and solve problems. The teaching activity arrangement is as follows.

Question 1: What functions does the smart restaurant settlement system need to achieve? (Science)

Activity: Students discuss, summarize, and conclude.

Conclusion: Smartly recognize different colors of dishes; announce prices; simulate scanning for payment; automatically calculate total price.

Question 2: What technical means and methods are needed to realize the above functions? (Technology, Engineering, Mathematics)

Activity: Discuss the physical presentation of the work, choose materials; determine the hardware devices and programming methods needed for each function.

Conclusion: Use plane design and laser cutting to present the main structure of the work, choosing natural-textured, 2 mm thick plywood and 2 mm thick transparent acrylic for production. When designing the drawing, the installation positions of the smart hardware need to be considered. Dishes can be produced using 3D design and 3D printing methods. Different colors of PLA printing materials can be selected. Design the electronic system using an Arduino main controller, artificial intelligence module (computer vision module), and other auxiliary electronic modules. Use Python programming language for coding.

Smart restaurant settlement system hardware and software materials: Arduino main control board for processing and running programs, computer vision module for recognizing dish colors and calculating prices, LED light rings, buttons, potentiometers, MP3 playback modules, XH2.54 4pin electronic connection wires, lithium battery packs, 3D printed dishes, plywood/acrylic, Python programming software.

Extended exploration: Apply threshold segmentation technology.

Technical principle: Use the threshold segmentation method in image processing to process images, dividing pixels of different gray levels into different regions, thus achieving object recognition.

Question 3: How to verify the settlement effect? (Mathematics)

Activity: After students upload the program to the Arduino main controller based on the constructed restaurant model, place 1 dish, randomly 2 dishes, and 3 dishes on the pricing platform, then use a mobile phone to simulate payment, recording the data of each settlement and analyzing the effectiveness of the settlement results.

Question 4: What were the gains?

Activity: Share insights and experiences in design and production of the work and experimental results.

3. Project Prototype Design and Production

In the design and production phase of the project work, the teacher’s main role is to guide students to use cutting-edge technology and emerging technological means to solve complex problems encountered in the creative process, emphasizing the production of real works (i.e., physical results), and emphasizing “learning outcomes as works,” allowing students to experience the sense of achievement brought by physical and visual results, and naturally acquire knowledge and skills in this complete production process. The design of project works includes different types of challenging problems or tasks. Based on the characteristics of the project works and the functions to be achieved, I will arrange the specific teaching tasks as follows.

Figure 2 Program Flow

Task 1: Plane design and laser cutting of the restaurant structure. When students design the overall structure of the restaurant, they use plane design software AutoCAD to draw, and then operate the laser cutter to cut plywood and acrylic under the teacher’s guidance, finally assembling them.

Task 2: 3D design and 3D printing of the dish model. Use 123D Design 3D modeling software to design the shape of the dish, use Cura software to slice the model, and finally use a 3D printer to print. The color of the dish can be determined by the selected PLA printing material, with yellow, red, and blue materials available.

Task 3: Build the electronic control system and complete programming. Install the Arduino main control board, computer vision module, and LED light ring into the main structure of the restaurant, using electronic connection wires to connect the Arduino main control with other electronic modules, and test the power.

Use Python language for program design. Python is the programming language closest to artificial intelligence. Programming in Python helps students better understand the implementation process of artificial intelligence algorithms. The program’s functionality can be divided into two parts: one is to program the computer vision module to recognize different colors; the other is to control the system to realize voice announcements of prices, sensing dishes, simulating scanning for payment, and other functions. The program flow is shown in Figure 2.

Task 4: Test the functions of the work. Turn on the power, start the system, and place the dish on the designated platform. When the computer vision module detects the dish, it identifies the corresponding color based on preset values and calculates the price accordingly, simulating the entire payment and settlement process, testing the functionality and settlement effect of the work (as shown in Figure 3).

Figure 3 Function Testing of the Work

After completing the production of the project work, teachers organize students to verify and showcase the prototype, guiding them to re-examine the work, facilitating knowledge transfer and expansion. Of course, evaluation in the teaching process is also indispensable.

4. Work Verification and Share Display

After completing the previous phase, teachers organize students to verify the project works, judging whether the expected functions have been achieved: whether it can intelligently recognize dishes of different colors, whether it supports voice announcements of prices, whether it can simulate scanning for payment, and whether it can automatically calculate the total price. At the same time, students are organized to showcase their works on-site, explaining the design and production ideas, sharing insights, problems encountered, and solutions, and any new ideas or thoughts.

5. Evaluation and Reflection

① Iterative optimization of the work. Teachers lead students to re-examine the work, discussing how to upgrade and optimize the work. For example, how to design and produce a commercially usable smart restaurant settlement system under the premise of unlimited materials, equipment, and time.

② Expansion and application transfer. Teachers raise several questions to guide students to think about whether the methods, techniques, and means used in the design and production of project works can solve other problems in life, promoting deep thinking and enhancing students’ understanding of artificial intelligence knowledge and skills, and facilitating knowledge transfer. In this process, teachers repeatedly reinforce students’ innovative thinking and capabilities, improving their innovation abilities.

③ Evaluation of teaching effectiveness. To understand students’ learning situations, I designed a teaching activity evaluation form for a comprehensive evaluation of teaching effectiveness, which is beneficial for subsequent adjustments and improvements in teaching strategies to achieve better teaching outcomes. The evaluation of teaching activities includes self-evaluation, peer evaluation, and teacher evaluation. The evaluation criteria for teaching effectiveness are as follows.

Knowledge and Skills: Familiar with the core technologies and common application scenarios of artificial intelligence. Students should be able to understand the working principles of computer vision recognition modules and speech synthesis technologies; master the use of Python programming tools and basic programming methods in Python; proficiently master the use of plane and 3D drawing tools, drawing 2D and 3D graphics, and operating laser cutters and 3D printers to design and produce the main structure of the restaurant and dishes.

Creativity Performance: Able to build an intelligent restaurant electronic system based on Arduino smart hardware. Students should be able to drive the computer vision module through Python programming, writing programs for price announcements, dish sensing, simulating scanning for payment, and other functions; create prototypes of smart restaurant works, achieving intelligent recognition, price announcements, simulating scanning for payment, and automatic total price calculations, with appropriate expansions.

Collaboration Ability: In designing and producing smart restaurant works, group members cooperate with each other, thinking and exploring together. In the prototype production phase of the smart restaurant work, group members can clearly divide tasks based on the activity tasks. During the realization of the work, when encountering difficulties, group members help each other.

Problem-Solving Ability: Complete tasks within the specified time, realizing the production of the smart restaurant project work and expected functions. When encountering difficulties, actively communicate with peers or teachers, seeking solutions.

3. Summary and Reflection

STEM+Maker

Through presenting practical problems, I introduce the widely applicable artificial intelligence technology into the classroom, allowing students to experience and feel the impact of the new generation of information technology on people’s lives and work, understand and master the application methods of artificial intelligence technology in different fields, and stimulate their desire to learn about artificial intelligence.

Teachers adhere to the teaching concept of “student-centered, teacher-led” to scientifically organize teaching activities: on the one hand, providing students with ample time and opportunities, guiding them to analyze and break down problems, using their learned knowledge to seek solutions, especially in the design and production phase of the works, integrating knowledge from science, technology, engineering, mathematics, and art, promoting deep learning, and enhancing students’ ability to apply knowledge and solve problems; on the other hand, during the design and production of works, teachers timely encourage students’ unique ideas and designs, and provide guidance when students encounter problems, ensuring the smooth conduct of teaching activities. In the results sharing and reflection phase, teachers conduct comprehensive evaluations of students’ works, encouraging students to iterate, upgrade, and innovate their works, achieving the transformation from laboratory works to commercial products, improving students’ higher-order learning abilities and higher-order thinking abilities. In the evaluation of teaching effectiveness phase, teachers employ various methods to evaluate, grasping students’ learning situations regarding knowledge content, assessing their abilities in inquiry, communication, collaboration, and creative problem-solving, while discovering new issues and needs of students, providing a basis for adjusting teaching strategies, and laying the groundwork for achieving better teaching outcomes.

For vocational schools and vocational students, artificial intelligence is a relatively new field. Many schools have not previously offered relevant courses, and students have little or no knowledge of related topics, making teaching inevitably insufficient: first, students do not understand the relevant principles, application scenarios, and development directions of artificial intelligence, lacking accumulation of basic knowledge and understanding; second, learning artificial intelligence has a high technical threshold, and the application of software and hardware technologies and algorithm programming may exceed the cognitive range of vocational students, making them feel challenged; third, students’ collaborative abilities and problem analysis and solving abilities displayed in project activities are also somewhat lacking. I believe that when offering artificial intelligence courses in vocational schools, the following points should be noted: in arranging course cases, it should start from real-life problems, based on students’ existing knowledge and skills, to stimulate learning interest and cultivate students’ interdisciplinary problem-solving abilities; in setting course objectives, attention should be paid to students’ learning needs and interests, targeting educational goals, and strengthening the introduction and explanation of basic knowledge, relevant principles, and technical application methods of artificial intelligence; in course implementation, adhere to the principle of combining teacher-led and student-centered approaches, with teachers intervening appropriately, fully tapping students’ potential; in course resource construction, accumulate relevant resources on artificial intelligence in daily teaching, guiding students to obtain knowledge related to artificial intelligence through the internet.

Note: This article is a research achievement of the second batch of STEM education special research by the Guangdong Institute of Education in 2020 (Project No.: GDJY-2020-S-b064).

(Authors Liu Hailong is a lecturer at Xinyang University in Henan; Chen Dongdong is a teacher at Dongguan University of Technology in Guangdong)

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Source | “Digital Teaching in Primary and Secondary Schools” 2021 Issue 11

Editor | Zhu Yuanzhi

WeChat Editor | Li Zhonghua, Huo Congying (Intern)

WeChat Supervisor | Zhao Manshu

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