Bayesian-based traceability analysis of mixed teaching effect:Take meteorology and climatology
In the post-epidemic era,Mixed teaching has become a hot spot in the reform of teaching mode in colleges and universities.Aiming at the uncertainty in the evaluation of mixed teaching effect,The initial Bayesian network structure is constructed by using maximum spanning tree,According to the mutual information between the factors that affect the teaching effect,Breadth-first search is used to determine node order.The node order is input K2 algorithm to optimize the Bayesian network structure.Genie software is used to build an optimized Bayesian network model.A priori reasoning and a posteriori simulation show that the model fits the measured data well under the prior condition and good teaching results can be obtained by improving the rate of excellence in MOOC and the excellent rate of course report score under the posteriori condition.The complex internal relations between teaching methods and teaching effects is shown clearly by the model.The changing law is reflected directly and quantitatively.The guidance is provided to improve teaching.
mixed teachingtraceability analysis of teaching effectBayesian network structure
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