A Course Teacher Recommendation Method Based on an Improved Weighted Bipartite Graph and Slope One

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.