The purpose of this study was to build a Random Forest supervised machine learning model in order to predict musical rater‐type classifications based upon a Rasch analysis of raters’ differential severity/leniency related to item use. Raw scores (N = 1,704) from 142 raters across nine high school solo and ensemble festivals (grades 9–12) were collected using a 29‐item Likert‐type rating scale embedded within five domains (tone/intonation, n = 6; balance, n = 5; interpretation, n = 6; rhythm, n = 6; and technical accuracy, n = 6). Data were analyzed using a Many Facets Rasch Partial Credit Model. An a priori k‐means cluster analysis of 29 differential rater functioning indices produced a discrete feature vector that classified raters into one of three distinct rater‐types: (a) syntactical rater‐type, (b) expressive rater‐type, or (c) mental representation rater‐type. Results of the initial Random Forest model resulted in an out‐of‐bag error rate of 5.05%, indicating that approximately 95% of the raters were correctly classified. After tuning a set of three hyperparameters (n_tree, m_try, and node size), the optimized model demonstrated an improved out‐of‐bag error rate of 2.02%. Implications for improvements in assessment, research, and rater training in the field of music education are discussed.

中文翻译：

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使用Rasch测度理论和随机森林预测操作评分者类型的分类：音乐演奏评估的观点

这项研究的目的是建立一个随机森林监督的机器学习模型，以便基于对评分者与项目使用相关的差异严重程度/宽容度的Rasch分析来预测音乐评估者类型的分类。使用嵌入在五个域（音调/语调，n = 6；平衡）中的29种Likert型评分量表，收集了9个高中独奏和合奏节（9-12年级）中142位评估者的原始得分（N = 1,704）。 ，n = 5；诠释，n = 6；节奏，n = 6；技术准确性，n = 6）。使用多方面Rasch部分信用模型分析数据。对29个差额估价人功能指数进行先验k均值聚类分析，得出了一个离散的特征向量，将估价人分为三种不同的估价人类型之一：（a）句法估价人类型，（b）表达估价人类型，或（c）心理表征评定者类型。初始随机森林模型的结果得出袋外错误率为5.05％，这表明大约95％的评估者已正确分类。调整一组三个超参数（n_树，_mtry和节点大小），优化的模型显示出2.02％的改进的袋外错误率。讨论了对音乐教育领域评估，研究和评估培训的改进的意义。