Peer review is a popular feedback mechanism in higher education that actively engages students and provides researchers with a means to assess student engagement. However, there is little empirical support for the durability of peer review, particularly when using data predictive modeling to analyze student comments. This study uses Na\"ive Bayes modeling to analyze peer review data obtained from an undergraduate visual literacy course over five years. We expand on the research of Friedman and Rosen and Beasley et al. by focusing on the Na\"ive Bayes model of students' remarks. Our findings highlight the utility of Na\"ive Bayes modeling, particularly in the analysis of student comments based on parts of speech, where nouns emerged as the prominent category. Additionally, when examining students' comments using the visual peer review rubric, the lie factor emerged as the predominant factor. Comparing Na\"ive Bayes model to Beasley's approach, we found both help instructors map directions taken in the class, but the Na\"ive Bayes model provides a more specific outline for forecasting with a more detailed framework for identifying core topics within the course, enhancing the forecasting of educational directions. Through the application of the Holdout Method and $\mathrm{k}$-fold cross-validation with continuity correction, we have validated the model's predictive accuracy, underscoring its effectiveness in offering deep insights into peer review mechanisms. Our study findings suggest that using predictive modeling to assess student comments can provide a new way to better serve the students' classroom comments on their visual peer work. This can benefit courses by inspiring changes to course content, reinforcement of course content, modification of projects, or modifications to the rubric itself.

翻译：同行评议是高等教育中一种广受欢迎的反馈机制，它能够积极调动学生参与，并为研究者提供评估学生参与度的手段。然而，关于同行评议效果的持久性，尤其是在使用数据预测模型分析学生评语方面，目前缺乏实证支持。本研究采用朴素贝叶斯模型，对一门本科视觉素养课程五年间收集的同行评议数据进行分析。我们在Friedman与Rosen以及Beasley等人的研究基础上，着重关注学生评语的朴素贝叶斯建模。我们的研究结果凸显了朴素贝叶斯模型的实用性，特别是在基于词性分析学生评语时，名词成为最显著的类别。此外，当使用视觉同行评议量规检验学生评语时，谎言因子成为主导因素。将朴素贝叶斯模型与Beasley的方法进行比较，我们发现两者都有助于指导教师把握课程方向，但朴素贝叶斯模型通过更详细的框架识别课程核心主题，为预测提供了更具体的纲要，从而增强了教育方向的预测能力。通过应用保留法及经过连续性校正的$\mathrm{k}$折交叉验证，我们验证了模型的预测准确性，强调了其在深入理解同行评议机制方面的有效性。我们的研究结果表明，使用预测模型评估学生评语，可以为更好地服务于学生对其视觉同行作品的课堂评语提供新途径。这有助于课程改进，包括启发课程内容更新、强化课程内容、调整项目设计或修改量规本身。