Study on design and practice of PBL teaching model based on STEM education concept

Table of Contents

Course implementation process

The AI Path Planning System is based on the structured reorganization of interdisciplinary knowledge of science, technology, engineering and mathematics required to guide students in completing an AI innovation practice project. The teaching content covers unified knowledge modules such as artificial intelligence, system prototype design, algorithm design (such as Dijkstra’s algorithm), innovation and entrepreneurship, as well as interdisciplinary knowledge fields involved in different projects. The teaching program includes four steps, namely course preparation, scientific exploration, engineering practice and communication reinforcement. The implementation of the course usually lasts until the third semester, ensuring that students have sufficient time to participate in the entire process of course project design. In the course preparation phase, 2 class hours of online teaching will be conducted to explain the talent cultivation objectives, teaching method requirements, and related teaching resources. And offline time was used to complete team building and quality development, totaling 4 class hours. Personality testing, product manager self recommendation, and other methods are adopted to quickly form teams, expand outdoor skills to enhance team cohesion, and encourage students to actively adapt and cooperate with teaching model reforms.

In the scientific exploration phase, there are 3 class hours of online learning and 5 class hours of offline research practice. After class practice includes students conducting industry visits, product research, group discussions, and completing assignments, totaling approximately 20 class hours. This section is divided into three parts: the firs is identifying problems. Teachers use case normals to teach a brief history of the application and development of world AI technology in map navigation, as well as industry research and learning. Offline, they organize students to experience AI products and ask questions to stimulate students’ thinking; The second is collaborative exploration. Teachers organize students to visit AI map companies offline to understand AI projects and engineering research and development processes, allowing students to explore future solutions for combining their own disciplines with AI technology in real scenarios, write an AI product experience report, and determine topics of interest based on the project pool resources provided by industry association enterprises; The third is the formulation of the plan, where the teacher explains the development process of the engineering project, sets up an evaluation form to guide the team to complete the conceptual research and development design of the product based on collaborative exploration, and conducts team research and sharing reports. The teacher should ensure that each team receives at least 2–4 communication and coaching sessions during this stage.

In the engineering practice phase, there are 4 h of online learning, 8 h of offline design practice classes, 2 h of post class product video production guidance, and 8 h of teacher project guidance. Students need to invest more than 30 h in pre – and post class learning. This step includes: (1) Design and construction. Firstly, the teacher responsible for software development explains the AI algorithm, guides the group to discuss development, reports the algorithm flowchart, and makes a technical roadmap report; Secondly, the teacher in charge of product prototype design will explain the embedded system design process, guide the team to integrate AI products, and prepare an evaluation form to systematically benchmark and evaluate the products, in order to improve the plan. This section includes the analysis and construction of mathematical models, as well as the engineering practice process of embedding and integrating AI products. (2) Testing and improvement, with the teacher responsible for system design teaching AI product functions and industrial design specifications, the teaching assistant team providing post class guidance on video production techniques for product demonstrations, the group conducting system design and testing optimization, and completing stage summaries through product prototype interaction design reports.

In the communication strengthening session, there are 2 class hours of online learning, 4 class hours of offline reporting, and approximately 15 class hours of post class AI product innovation and entrepreneurship incubation research and practice. In this stage, students will learn online about the project roadshow and product innovation and entrepreneurship incubation taught by teachers, understand the policies, funds, competitions and other resources for further sustainable development of this project, and carry out product innovation and entrepreneurship incubation practices. Finally, they will conduct a project roadshow and receive feedback and guidance from an expert review panel composed of AI industry associations, AI investment and financing experts or corporate executives, and teachers. Improve the project based on suggestions and share learning gains and reflections within the team. After the course is completed, excellent projects will be selected to continue cultivating and incubating in the “AI Student Innovation Studio”. Combined with school funding support, competition participation, science and technology innovation research guarantee, science and technology innovation honors and other incentive and guarantee mechanisms, it will promote the transformation and improvement of teaching achievements. The team reported and shared four times, including project topic selection report, product algorithm and technical roadmap report, product prototype and interaction design report. As a note, all three are process evaluations, accounting for 35%, daily attendance accounts for 15%, and AI product innovation incubation project roadshow (final evaluation accounts for 50%). The evaluation subjects include three categories: experts, peers, and individuals. After the course ended, the selected students participated in self-evaluation of course satisfaction and ability development, as well as in-depth interviews with learning summaries.

Teaching verification design

Research design

In order to better understand the PBLbSTEM, this work designed an application effect evaluation questionnaire. In addition to basic information, the questionnaire is divided into three parts: the first part is an assessment of learning engagement, including post class engagement time and deep learning; The second part is the assessment of ability development (namely the three literacy mentioned above); The third part is the evaluation of satisfaction, including learning experience and teaching implementation satisfaction. Except for the topic of “project participation” in the first part of the learning engagement assessment, other parts of the questionnaire use the Likert Psychological Response Scale and are scored on a scale of 5–7 based on the problem situation.

The initial draft questionnaire first randomly invited 100 selected course students for pre-questionnaire testing, and selected 25 students for testing interviews to revise and optimize the questionnaire’s wording. Next, the Alpha reliability coefficient method was used to test the reliability of the questionnaire. The Cronbach’s alpha coefficient for 60 items was 0.984, and the Alpha reliability coefficients for each sub dimension exceeded 0.942, indicating high reliability and internal consistency of the questionnaire. Meanwhile, factor analysis showed that the KMO values for each sub dimension were all greater than 0.7, and the Bartlett sphericity test was at the 0.01 level. The factor loadings of each dimension are all greater than 0.6, and the cumulative contribution rates of variance are all greater than 70%. The questionnaire has good validity and can reflect the overall effect of the course well.

Data collection and analysis

In November 2024, the teaching team conducted a questionnaire survey on 1000 students from our school who participated in the course selection in the past three rounds through Wechat Mini Program (namely Laibao). A total of 892 students from 5 engineering colleges and 7 engineering majors submitted questionnaires, accounting for 50.3% of the course selection population. Among them, there were 761 males (85.3%) and 84 females (14.7%). In addition to the survey questionnaire, the course also conducted course summaries and in-depth interviews with 45-planned/42-valid students to comprehensively understand the effectiveness of the course implementation. The comparative results show that the introduction of the new model can apparently stimulate students’ course engagement, effectively improve their three major competencies, and significantly improve course satisfaction. Where it needs to be declared that the participants provided their written informed consent to participate in this study. The research hypothesis is validated as follows:

The level of student engagement in learning has significantly increased

This study verifies the efficiency of student course participation from two aspects, namely time investment in after-school learning and deep learning. All students participated in group inquiry learning after class, with 71.9% of students participating in 4 ~ 6 times or more group discussions after class. Among them, 11% participated in up to 9 spontaneous group discussions after class, as shown in Fig. 3. The algorithm design stage and product molding design stage, which are the two stages of scientific exploration and engineering practice, are the most time-consuming stages for discussion, as shown in Fig. 4. 65.7% of students stated that they will continue to improve their incubation projects and participate in scientific and technological innovation competitions, research projects, or social practices after the course ends, as shown in Fig. 5.

Interdisciplinary and self-directed learning are the main features of the PBLbSTEM, which reflects the depth of students’ learning. The research results show that 91.2% of students believe that the frequency of “interdisciplinary learning combined AI with their own profession to solve problems” in the course is “slightly more”, “frequently” or even “very frequently”, while 89.1% of students believe that the frequency of “comparing, judging, analyzing and evaluating solutions to problems”, “not adhering to ready-made practices, independently thinking and innovating to solve problems”, and “reflecting and absorbing experience” in the course is “slightly more”, “frequently” or even “very frequently”; 91.2% of students believe that they tend to focus more on themselves in the course and often engage in self-directed exploration and learning with problem objectives; 91.4% of students often actively participate in group discussions, share learning skills, and actively seek advice from teachers. For example, ZMV (male) said that “this teaching method is great; my teacher provides us with a platform and direction, allowing us to explore everything on our own; without a doubt, this learning method is memorable and also promotes rapid growth”. It can be seen that the PBLbSTEM teaching model effectively increases students’ learning engagement, promotes deep learning and inquiry learning.

The students’ abilities have significantly improved. Table 1 reflects the evaluation status of students’ three major literacy indicators before and after the course. In terms of innovation literacy, students believe that the abilities that have made “good progress” and “great progress” include the following abilities. “Innovation consciousness, innovative thinking, and innovative practical ability”, “problem analysis and solving ability”, “interdisciplinary application ability” and “technology and tool use ability” and “product design and development capability” are above 87%, 87%, 83% and 80% respectively. In terms of engineering humanities literacy, students believe that the abilities that have made “good progress” and “great progress” include the following abilities. Both “sense of social responsibility” and “self reflection ability” are over 88%; And the “investigation and research ability”, “project management and communication cooperation ability” and “self-learning and lifelong learning ability”are over 83%. WKH, a student from the School of Electrical Engineering, described his progress in interdisciplinary skills and project communication management, as follows. “AI innovation project management is a field I have never been involved in before. I have found that the value of this course is higher than I imagined, and learning through practice is more efficient than simply studying from books. In terms of digital literacy, 92.4% of students believe that the course has “increased their understanding of their profession and the real-world application of Discrete Mathematics”, and 89.2% of students “increased AI projects interesting”. More than 92.8% of students believe that they have a better understanding of the practical significance of technological innovation, and over 93.4% of students believe that the curriculum will prevent them from easily giving up when facing academic difficulties in the future.

High student learning experience and teaching satisfaction. The students’ satisfaction with the implementation of PBLbSTEM teaching model, including teaching situation, teacher support, group cooperation, enterprise visits, team building expansion and learning environment, reached over 95%. MXX (male) said that “the course provides us with a rapid growth process. The most rare thing about learning AI projects and their development and production is that this course truly allows us to witness the growth of products in our hands in a very short period of time. Although it is not a very mature project, every step of its growth has been hand crafted and polished by our members. So, although it’s not an excellent work, it’s a perfect experience”. LLW (female) emphasized that “through this course, I have gained a group of like-minded friends. Walking on campus, we are all ordinary, but meeting each other makes us realize that we can also be innovative and creative. For the overall experience of the course, the memories are very pleasant, but the process is also very rushed, constantly in a cycle of discussion modification discussion”.

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