The Evaluation Of The Scoring Policies In National College Entrance Examination And Admission Using Business Intelligence

National Colleges Entrance Examination and Admission (NCEEA) is key part of the education system, plays a fundamental and oriented role in the personnel cultivation of University and basic education, and also has important influence in the society. NCEE fairness and impartiality ensure the authority of the NCEE system. Any change of the Scoring Policies in NCEEA attracts the attention of thousands of examinees as well as the whole society.The on-line admission works had been taken in 2000, so large amounts of the Entrance Examination historical data were accumulated. The historical data contains lots of data about the Scoring Policies in NCEEA. Regrettably, the valuable information, which contains a wealth of knowledge, was stored in the'black-box'for years. So the information can not been used and developed effectively. The potential of knowledge hidden in the historical data has not yet been effectively demonstrated and embodied. The Provinces and Cities Admission Offices (PCAO) is eager to extract valuable knowledge from the historical data by new technologies and methods. So the valuable knowledge can provide decision support for PCAO when amending or reforming of the Scoring Policies in NCEE.By research of the historical data, data warehouse, OLAP and data mining technologies were used in NCEEA data, some interesting and referential results are mined. And then an elevation method of the Scoring Policies in NCEEA was introduced.The contributions of this paper are in the following four aspects:1. Cleaning the historical data and establishing data warehouse.By analyzing the recent nine years historical enrolling data and requirements of the target system, data warehouse model was established. Then find the some problems in data quality and integration in the historical data. Finally, historical data have been extracted, transformed cleaned and then loaded to data warehouse.2. Data analysis on Scoring Policies in NCEEAThe data about Scoring Policies in NCEEA were extracted, and then the Multi-dimensional dataset has been modeled according to the analysis requirement. By employing the OLAP technology, some interesting and referential results are mined based on the analysis of the Scoring Policies examinees and matriculate examinees using Scoring Policies.3. Data mining on Scoring Policies in NCEEABy analyzing the Scoring Policies examinees, decision tree model and naive Bayes model were employed with the Scoring Policies data. Based on the two models training experiment and evaluation results, the implement effectiveness of decision tree model and naive Bayes model on Scoring Policies data was demonstrated. It also shows that some interesting and referential results are mined.4. Evaluation method on Scoring Policies in NCEEATo the end, some interesting results were found and presented by OLAP and data mining technologies in the paper. Based on these findings, the evaluation scheme for Scoring Policies is then given. This scheme can elevate the efficiency of the scoring policies used, also provide accurate and scientific decision making support when introducing new scoring policies, amending and reforming of the Scoring Policies in NCEEA.