Programming education consistently faces challenges in bridging theory with practice and fostering students’ cognitive competencies. This 12-year longitudinal study (2011–2023) investigates an innovative competency-based teaching model in university C programming education that integrates six educational theories into a coherent framework with three dimensions (theoretical, practical, innovative), four integration mechanisms, and five combinatorial strategies. Using a mixed-methods approach with a quasi-experimental design, we studied 4,051 undergraduate students from a Chinese university. Results revealed significant enhancement in students’ cognitive abilities, as measured by Raven’s Standard Progressive Matrices (t (350) = 8.76, p < 0.001, d = 0.68), which strongly correlated with improved academic performance (r = 0.62), computational thinking (r = 0.71), and problem-solving skills (r = 0.67). The model creates multiple pathways for cognitive development through synergistic interactions between components, promoting collaboration and self-directed learning with effects extending beyond graduation. Multiple regression analysis identified three key predictors of cognitive enhancement: classroom engagement (β = 0.35), project completion (β = 0.28), and participation in innovation activities (β = 0.22). This study provides robust empirical evidence for the long-term efficacy of a competency-based model in programming education, presenting a transformative approach to STEM education reform particularly relevant in rapidly evolving technological landscapes.

Although previous research has explored the correlation between teacher characteristics and teaching quality, effective methods for identifying key factors that influence teaching quality are still lacking. This study aims to address this issue by developing an identification methodology based on a computational pedagogy research paradigm to identify the key characteristics of teachers and courses that influence their teaching quality. We developed quantitative models to quantify the characteristics of teaching quality, based on those identified in previous studies. Correlation and multiple correlation analyses were conducted to identify the key influencing characteristics, and grey correlation analysis was used to calculate the degree of correlation between these key characteristics and teaching quality. Our methodology was applied to 27 computer science discipline teachers and 82 courses, and validated with teaching data from eight additional teachers. Our findings demonstrate the effectiveness of our method in identifying the key influence characteristics of teachers and courses on teaching quality and confirm significant correlations between these key influential characteristics and teaching quality. This innovative approach provides new insights and tools for predicting and improving the teaching quality across disciplinary majors. Our research has significant implications for future education studies, particularly for the development of effective methods for identifying key factors that influence teaching quality. By providing a more comprehensive understanding of the key factors that influence teaching quality, our study can inform the development of evidence-based strategies to improve the teaching effectiveness for different disciplinary majors.

To scientifically and accurately recommend suitable teachers for university courses and improve teaching quality, designing an effective recommendation algorithm is necessary. Therefore, we construct quantitative models of teacher characteristics, course characteristics, and teaching evaluations under the theories and methods of education and build a sparse experimental data matrix based on the quantified data. On this basis, we propose a teacher recommendation algorithm (PRLFM) based on the improved latent factor model (LFM) and the improved PersonalRank algorithm. Firstly, the improved LFM is used to predict the evaluation scores of those courses that teachers have not taught. The scores which are higher than the specified threshold are used to fill the corresponding missing items in the sparse matrix to reduce the matrix's sparsity. Then, the bipartite graph model based on the teacher set and course set is constructed according to the filled experimental data matrix. The weight of edges in the bipartite graph is replaced by the teacher and course's evaluation score multiplied by the course difficulty, which reflects the correlation between course and evaluation score. Next, an improved probability transition matrix based on the bipartite graph is constructed. The access probability in the matrix is replaced by the node's out degree's reciprocal multiplied by the edge's weight. The correlation degree between the course and all teachers is quickly calculated using the matrix algorithm of PersonalRank. Finally, a teacher recommendation model is constructed to realize teachers' top-N recommendation by combining the correlation degree with teachers' characteristics. Experiments show that the PRLFM algorithm can effectively improve the accuracy of prediction and top-N recommendation. It solves the problem of lack of scientific basis in recommending suitable teachers for university courses and improving the teaching quality.

Colleges and universities attach great importance to the quality of undergraduate teaching. To virtually guarantee the course’s teaching quality, the key lies in recommending suitable teachers for the course scientifically. It is a seemingly simple but very complicated problem. Moreover, with the development of colleges and universities, new courses are continually set up, and new teachers are introduced, which further complicates the problem. The problem has not been solved well for many years. Therefore, we propose a course teacher recommendation model (FCTR-LFM) based on fuzzy clustering and the latent factor model (LFM) to solve this problem. Firstly, under the guidance of pedagogy theories and methods, we conduct quantitative modeling for teachers and courses’ relevant characteristics and combine the quantitative results with historical teaching scores to establish a large-scale sparse course teaching evaluation matrix as the recommendation dataset. Next, we adopt the improved fuzzy clustering model to realize teachers’ automatic clustering according to their characteristics and use the teacher cluster to reconstruct the teaching evaluation matrix, significantly reducing the dataset’s size and reducing the sparsity. Then, we used the improved LFM to predict the score items in the evaluation matrix, including the missing score items. Finally, the prediction evaluation scores are sorted according to the course, and the TOP-N recommendation of the course teachers is realized. The experimental results show that FCTR-LFM can realize the prediction and recommendation well using the optimized parameters. It effectively solves the problem that there is no scientific basis for recommending suitable teachers for the course for a long time.

In the past, the teaching of C programming courses was teacher-centered, and students’ practical ability, innovation ability, independent learning ability, and moral character were not effectively improved. In order to meet the requirements of teaching informatization, OBE philosophy, “Golden Course” construction, and ideological politics in the curriculum for course teaching, we have been reforming how C programming courses are taught since 2013 from the perspective of sustainable development in order to realize the synergistic promotion of knowledge imparting, ability training, and moral character shaping. First, we systematically reformed the teaching support system in eight dimensions: changing the teaching philosophy, enriching teaching resources, reconstructing the teaching environment, reshaping the course content, transforming the teaching process, innovating teaching methods, reforming course evaluations, and building ideological politics surrounding the ecology of the curriculum. On this basis, we divided the teaching classroom into three classrooms: theory, practical training, and innovative practice. We ensured that teaching resources, information technology, diversified evaluation, and moral character shaping were always integrated into the classroom. Then, we used a combination of “online and offline, in-class knowledge learning and extra-curricular autonomous practice, teachers’ careful lectures and seniors’ guidance, ability training and moral character shaping, and impart knowledge and innovative practice” to build a student-centered teaching model of “three classrooms–four integrations–five combinations”. Since the application of this model in course teaching, students have not only enhanced their sense of access to learning and improved their course performance, independent learning ability, and practical ability, but have also improved their innovation ability, with students achieving excellent results in thesis publication, patent applications, software copyright applications, discipline competitions, and innovation project applications. Students have cultivated a strong sense of social responsibility and high moral character, and employers are highly satisfied. This teaching model has been adopted and reused in 12 engineering courses and has achieved good application results. The teaching model can provide a reference for college engineering courses to build a student-centered information-based education ecology, create high-quality classrooms, and collaboratively improve students’ abilities and moral character.

The quality of course teaching is directly related to education quality. Many scholars have attempted to identify the associations between course-teaching quality and teachers’ characteristics, such as educational background, degree, professional title, age, teaching age, job burnout, and academic research. However, because these characteristics are mostly evolvable, research findings are inconsistent. Therefore, we attempted to identify the association between teaching styles that reflect teachers’ stable psychological quality, Technological Pedagogical Content Knowledge (TPACK), and teaching quality. To this end, we first collected data from three different disciplines at a university using the constructed teaching quality, TPACK, and course difficulty questionnaires, together with the TSTI scale proposed by Grigorenko and Sternberg. We constructed three matrices with different sparsities as experimental datasets using teachers with the teaching style and PTACK attributes, courses with the course difficulty attribute, and teaching quality. We then constructed a weighted bipartite graph with the teachers and courses in the matrix as nodes and the teaching quality divided by course difficulty as the weights of the edges. We proposed an improved Slope One algorithm based on a weighted bipartite graph to scientifically predict teachers’ teaching quality in untaught courses. Finally, we constructed a TOP-N recommendation model for course teachers that combined teaching style and TPACK features to achieve accurate recommendations for course teachers. The experiments show that our proposed solution is feasible and that the algorithmic model is effective. Therefore, we developed a scientific method to improve the quality of university course teaching.