Course implementation course of
The AI Path Planning System is based on the structured reorganization of interdisciplinary information of science, expertise, engineering and arithmetic required to information college students in finishing an AI innovation practice mission. The teaching content material covers unified information modules corresponding to synthetic intelligence, system prototype design, algorithm design (corresponding to Dijkstra’s algorithm), innovation and entrepreneurship, in addition to interdisciplinary information fields concerned in numerous initiatives. The teaching program contains 4 steps, particularly course preparation, scientific exploration, engineering practice and communication reinforcement. The implementation of the course often lasts till the third semester, guaranteeing that college students have adequate time to take part in all the course of of course mission design. In the course preparation part, 2 class hours of on-line teaching will probably be performed to elucidate the expertise cultivation aims, teaching technique necessities, and associated teaching sources. And offline time was used to finish workforce constructing and high quality growth, totaling 4 class hours. Personality testing, product supervisor self suggestion, and different strategies are adopted to shortly type groups, develop out of doors expertise to boost workforce cohesion, and encourage college students to actively adapt and cooperate with teaching model reforms.
In the scientific exploration part, there are 3 class hours of on-line studying and 5 class hours of offline analysis practice. After class practice contains college students conducting business visits, product analysis, group discussions, and finishing assignments, totaling roughly 20 class hours. This part is split into three elements: the firs is figuring out issues. Teachers use case normals to show a quick historical past of the applying and growth of world AI expertise in map navigation, in addition to business analysis and studying. Offline, they arrange college students to expertise AI merchandise and ask inquiries to stimulate college students’ considering; The second is collaborative exploration. Teachers arrange college students to go to AI map corporations offline to grasp AI initiatives and engineering analysis and growth processes, permitting college students to discover future options for combining their very own disciplines with AI expertise in actual situations, write an AI product expertise report, and decide matters of curiosity based on the mission pool sources supplied by business affiliation enterprises; The third is the formulation of the plan, the place the trainer explains the event course of of the engineering mission, units up an analysis type to information the workforce to finish the conceptual analysis and growth design of the product based on collaborative exploration, and conducts workforce analysis and sharing experiences. The trainer ought to be sure that every workforce receives at the least 2–4 communication and teaching classes throughout this stage.
In the engineering practice part, there are 4 h of on-line studying, 8 h of offline design practice courses, 2 h of publish class product video manufacturing steerage, and 8 h of trainer mission steerage. Students want to speculate greater than 30 h in pre – and publish class studying. This step contains: (1) Design and building. Firstly, the trainer accountable for software program growth explains the AI algorithm, guides the group to debate growth, experiences the algorithm flowchart, and makes a technical roadmap report; Secondly, the trainer in cost of product prototype design will clarify the embedded system design course of, information the workforce to combine AI merchandise, and put together an analysis type to systematically benchmark and consider the merchandise, as a way to enhance the plan. This part contains the evaluation and building of mathematical fashions, in addition to the engineering practice course of of embedding and integrating AI merchandise. (2) Testing and enchancment, with the trainer accountable for system design teaching AI product capabilities and industrial design specs, the teaching assistant workforce offering publish class steerage on video manufacturing methods for product demonstrations, the group conducting system design and testing optimization, and finishing stage summaries by way of product prototype interplay design experiences.
In the communication strengthening session, there are 2 class hours of on-line studying, 4 class hours of offline reporting, and roughly 15 class hours of publish class AI product innovation and entrepreneurship incubation analysis and practice. In this stage, college students will be taught on-line in regards to the mission roadshow and product innovation and entrepreneurship incubation taught by academics, perceive the insurance policies, funds, competitions and different sources for additional sustainable growth of this mission, and perform product innovation and entrepreneurship incubation practices. Finally, they’ll conduct a mission roadshow and obtain suggestions and steerage from an skilled assessment panel composed of AI business associations, AI funding and financing consultants or company executives, and academics. Improve the mission based on recommendations and share studying features and reflections inside the workforce. After the course is accomplished, glorious initiatives will probably be chosen to proceed cultivating and incubating within the “AI Student Innovation Studio”. Combined with college funding help, competitors participation, science and expertise innovation analysis assure, science and expertise innovation honors and different incentive and assure mechanisms, it’s going to promote the transformation and enchancment of teaching achievements. The workforce reported and shared 4 instances, together with mission matter choice report, product algorithm and technical roadmap report, product prototype and interplay design report. As a notice, all three are course of evaluations, accounting for 35%, every day attendance accounts for 15%, and AI product innovation incubation mission roadshow (ultimate analysis accounts for 50%). The analysis topics embrace three classes: consultants, friends, and people. After the course ended, the chosen college students participated in self-evaluation of course satisfaction and skill growth, in addition to in-depth interviews with studying summaries.
Teaching verification design
Research design
In order to higher perceive the PBLbSTEM, this work designed an utility impact analysis questionnaire. In addition to fundamental data, the questionnaire is split into three elements: the primary half is an evaluation of studying engagement, together with publish class engagement time and deep studying; The second half is the evaluation of skill growth (particularly the three literacy talked about above); The third half is the analysis of satisfaction, together with studying expertise and teaching implementation satisfaction. Except for the subject of “project participation” within the first half of the educational engagement evaluation, different elements of the questionnaire use the Likert Psychological Response Scale and are scored on a scale of 5–7 based on the issue scenario.
The preliminary draft questionnaire first randomly invited 100 chosen course college students for pre-questionnaire testing, and chosen 25 college students for testing interviews to revise and optimize the questionnaire’s wording. Next, the Alpha reliability coefficient technique was used to check the reliability of the questionnaire. The Cronbach’s alpha coefficient for 60 objects was 0.984, and the Alpha reliability coefficients for every sub dimension exceeded 0.942, indicating excessive reliability and inside consistency of the questionnaire. Meanwhile, issue evaluation confirmed that the KMO values for every sub dimension have been all better than 0.7, and the Bartlett sphericity take a look at was on the 0.01 stage. The issue loadings of every dimension are all better than 0.6, and the cumulative contribution charges of variance are all better than 70%. The questionnaire has good validity and can replicate the general impact of the course properly.
Data assortment and evaluation
In November 2024, the teaching workforce performed a questionnaire survey on 1000 college students from our college who participated within the course choice previously three rounds by way of Wechat Mini Program (particularly Laibao). A complete of 892 college students from 5 engineering schools and 7 engineering majors submitted questionnaires, accounting for 50.3% of the course choice inhabitants. Among them, there have been 761 males (85.3%) and 84 females (14.7%). In addition to the survey questionnaire, the course additionally performed course summaries and in-depth interviews with 45-planned/42-valid college students to comprehensively perceive the effectiveness of the course implementation. The comparative outcomes present that the introduction of the brand new model can apparently stimulate college students’ course engagement, successfully enhance their three main competencies, and considerably enhance course satisfaction. Where it must be declared that the individuals supplied their written knowledgeable consent to take part on this examine. The analysis speculation is validated as follows:
The stage of scholar engagement in studying has considerably elevated
This examine verifies the effectivity of scholar course participation from two facets, particularly time funding in after-school studying and deep studying. All college students participated in group inquiry studying after class, with 71.9% of college students taking part in 4 ~ 6 instances or extra group discussions after class. Among them, 11% participated in as much as 9 spontaneous group discussions after class, as proven in Fig. 3. The algorithm design stage and product molding design stage, that are the 2 levels of scientific exploration and engineering practice, are essentially the most time-consuming levels for dialogue, as proven in Fig. 4. 65.7% of college students acknowledged that they’ll proceed to enhance their incubation initiatives and take part in scientific and technological innovation competitions, analysis initiatives, or social practices after the course ends, as proven in Fig. 5.
The quantity of group discussions after class.
Distribution of publish class group dialogue time levels.
Follow up concepts for the mission after the course ends (a number of selection).
Interdisciplinary and self-directed studying are the principle options of the PBLbSTEM, which displays the depth of college students’ studying. The analysis outcomes present that 91.2% of college students imagine that the frequency of “interdisciplinary learning combined AI with their own profession to solve problems” within the course is “slightly more”, “frequently” and even “very frequently”, whereas 89.1% of college students imagine 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” within the course is “slightly more”, “frequently” and even “very frequently”; 91.2% of college students imagine that they have an inclination to focus extra on themselves within the course and usually interact in self-directed exploration and studying with downside aims; 91.4% of college students usually actively take part in group discussions, share studying expertise, and actively search recommendation from academics. For instance, ZMV (male) stated 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 may be seen that the PBLbSTEM teaching model successfully will increase college students’ studying engagement, promotes deep studying and inquiry studying.
The college students’ skills have considerably improved. Table 1 displays the analysis standing of college students’ three main literacy indicators earlier than and after the course. In phrases of innovation literacy, college students imagine that the talents which have made “good progress” and “great progress” embrace the next skills. “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 phrases of engineering humanities literacy, college students imagine that the talents which have made “good progress” and “great progress” embrace the next skills. 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 college students “increased AI projects interesting”. More than 92.8% of college students imagine that they’ve a greater understanding of the sensible significance of technological innovation, and over 93.4% of college students imagine that the curriculum will stop them from simply giving up when dealing with educational difficulties sooner or later.
High scholar studying expertise and teaching satisfaction. The college students’ satisfaction with the implementation of PBLbSTEM teaching model, together with teaching scenario, trainer help, group cooperation, enterprise visits, workforce constructing growth and studying setting, reached over 95%. MXX (male) stated 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 (feminine) emphasised 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”.