Amirhajlou Leila, Sohrabi Zohre, Alebouyeh Mahmoud Reza, Tavakoli Nader, Haghighi Roghye Zare, Hashemi Akram, Asoodeh Amir
Department of Medical Education, Iran University of Medical Sciences, Tehran, Iran.
Department of Anesthesiology and Pain Medicine, Iran University of Medical Sciences, Tehran, Iran.
J Educ Health Promot. 2019 Jun 27;8:108. doi: 10.4103/jehp.jehp_394_18. eCollection 2019.
Predicting residents' academic performance is critical for medical educational institutions to plan strategies for improving their achievement.
This study aimed to predict the performance of residents on preboard examinations based on the results of in-training examinations (ITE) using various educational data mining (DM) techniques.
This research was a descriptive cross-sectional pilot study conducted at Iran University of Medical Sciences, Iran.
A sample of 841 residents in six specialties participating in the ITEs between 2004 and 2014 was selected through convenience sampling. Data were collected from the residency training database using a researcher-made checklist.
The analysis of variance was performed to compare mean scores between specialties, and multiple-regression was conducted to examine the relationship between the independent variables (ITEs scores in postgraduate 1 year [PGY1] to PG 3 year [PGY3], sex, and type of specialty training) and the dependent variable (scores of postgraduate 4 year called preboard). Next, three DM algorithms, including multi-layer perceptron artificial neural network (MLP-ANN), support vector machine, and linear regression were utilized to build the prediction models of preboard examination scores. The performance of models was analyzed based on the root mean square error (RMSE) and mean absolute error (MAE). In the final step, the MLP-ANN was employed to find the association rules. Data analysis was performed in SPSS 22 and RapidMiner 7.1.001.
The ITE scores on the PGY-2 and PGY-3 and the type of specialty training were the predictors of scores on the preboard examination ( = 0.129, < 0.01). The algorithm with the overall best results in terms of measuring error values was MLP-ANN with the condition of ten-fold cross-validation (RMSE = 0.325, MAE = 0.212). Finally, MLP-ANN was utilized to find the efficient rules.
According to the results of the study, MLP-ANN was recognized to be useful in the evaluation of student performance on the ITEs. It is suggested that medical, educational databases be enhanced to benefit from the potential of DM approach in the identification of residents at risk, allowing instructors to offer constructive advice in a timely manner.
预测住院医师的学业成绩对于医学教育机构制定提高其成绩的策略至关重要。
本研究旨在使用各种教育数据挖掘（DM）技术，根据培训期间考试（ITE）的结果预测住院医师预考的成绩。
本研究是在伊朗医科大学进行的一项描述性横断面试点研究。
通过便利抽样选取了2004年至2014年间参加ITE的六个专业的841名住院医师作为样本。使用研究者制作的清单从住院医师培训数据库中收集数据。
进行方差分析以比较各专业之间的平均分数，并进行多元回归以检验自变量（研究生1年级[PGY1]至研究生3年级[PGY3]的ITE分数、性别和专业培训类型）与因变量（研究生4年级的预考分数）之间的关系。接下来，使用三种DM算法，包括多层感知器人工神经网络（MLP-ANN）、支持向量机和线性回归，构建预考分数的预测模型。基于均方根误差（RMSE）和平均绝对误差（MAE）分析模型的性能。在最后一步，使用MLP-ANN来寻找关联规则。数据分析在SPSS 22和RapidMiner 7.1.001中进行。
PGY-2和PGY-3的ITE分数以及专业培训类型是预考分数的预测因素（ = 0.129，<0.01）。在测量误差值方面总体结果最佳的算法是具有十折交叉验证条件的MLP-ANN（RMSE = 0.325，MAE = 0.212）。最后，使用MLP-ANN来寻找有效规则。
根据研究结果，MLP-ANN被认为在评估学生ITE成绩方面是有用的。建议加强医学教育数据库，以利用DM方法在识别有风险的住院医师方面的潜力，使教师能够及时提供建设性的建议。
