Abstract
The issue of educational evaluation has long been a research hotspot. Using big data analysis method to conduct educational evaluation can improve the pertinence and effectiveness of education. Conventional Apriori algorithm has certain limitations in the application of educational evaluation. This paper introduces an improved Apriori-Gen algorithm and describes its application in evaluation of actual effectiveness of ideological and political course of colleges and universities. Through conducting correlation analysis of network questionnaire data, the study requirements of college students can be acquired, so as to improve the teaching effectiveness of ideological and political course. Results show that it is effective to apply the proposed study method in educational evaluation.
Highlights
Implementation of reasonable educational evaluation is the premise for education decision making
This paper introduces an improved Apriori-Gen algorithm and describes its application in evaluation of actual effectiveness of ideological and political course of colleges and universities
Big data stresses on in-depth mining and analysis of multidimensional data so as to seek the implication relation and value behind data, which is beneficial for transforming educational evaluation from prediction based on small data to evidential decision based on comprehensive data
Summary
Implementation of reasonable educational evaluation is the premise for education decision making. An effective education evaluation relies on a comprehensive and solid evaluation basis. Big data stresses on in-depth mining and analysis of multidimensional data so as to seek the implication relation and value behind data, which is beneficial for transforming educational evaluation from prediction based on small data to evidential decision based on comprehensive data. With the aid of big data technology, educational evaluation is no longer made to support the decisional requirement of education management departments or education institutions only, but for all groups and individuals that are concerned about education or taking parts in education. Through analyzing students’ study requirements via big data, the pertinence and effectiveness of education can be improved. Data mining algorithms are many, including Apriori (Agrawal and Shafer, 1996; D’Angelo et al, 2016), K-means (Scitovski and Sabo, 2014), SVM (Support Virtual Machine) (Hu et al, 2015; Mu et al, 2017), EM (Expectation-Maximization) (Enders, 2003), Pagerank (Chen et al, 2007), Adaboost (Adaptive Boosting) (Hu, 2017a), KNN (K-Nearest Neighbor) (Hu, 2017b), Naive Bayes (Sitthi et al, 2016), etc
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