Evaluation modeling in English teaching based on frequent itemset mining algorithm

As of the end of 2013, China's higher education enrollment was already higher than 30%, signaling a shift from elite education to mass education in terms of student enrollment. A competitive intelligence capabilities in English evaluation model (AKA) was established using frequent itemset mining algorithm with data collected on undergraduates' competitive intelligence capabilities in English. The model uses modified association rules mining algorithm of Boolean database (Ad-Apri) and succeeds in achieving higher efficiencies by trimming the database effectively and reducing pressures on both the processor and the memory by calculating Boolean vector during the computation, resulting in significant reduction of the time conventionally used for calculating high-order frequent itemsets. The performance of the algorithm was tested in the experiment with self-constructed data and the algorithm was applied to the English scores of undergraduates of a university during 2009-2012 in support of education and teaching processes.