基于GRM的在线校准研究

计算机化自适应测验的题库面临建设成本高且更新、扩充技术较复杂等问题.在线校准技术,可以将新题和旧题置于同一参数量尺上,降低了题库扩充成本.已有若干关于两级评分下新题的在线校准研究,但多级记分项目的在线校准却鲜见报道.该文先由拓展的夹逼平均法求取难度初值,并用多序列相关系数法求取区分度初值,再采用多步EM算法估计项目参数.Monte Carlo模拟结果表明:新题项目参数的估计值返真性较好,且参数的估计精度随着作答次数的小幅度增加而保持着逐渐提高的趋势.

The Online Calibration Based on Graded Response Model

Item bank of computerized adaptive test is faced with high construction cost and updated,expanded technology more complex.Not only can the application of the online calibration technology reduce cost,but also put the calibrated parameter values of the new items and the old in the same scale.Online calibration of new items under dichotomously scored models has achieved good results,but under polytomously scored items is reported rarely.To explore the performance of online calibration for polytomously scored items,a method to calculate the initial values of the multiple EM cycle method(MEM)is proposed based on graded response model(GRM),which is focus on the extended squeezing average method and the multiple-sequence correlation coefficient method to calculate as the initial parameters of the new item,then use the multiple EM cycle method to estimate parameters.Results of Monte Carlo simulation show that the parameter estimation of new items can get acceptable estimation accuracy and the parameters of new items are more accurate with a small increase of the sample size for calibration.