This study is intended to provide an example of computational modeling (CM) experiment using machine learning algorithms. Specific outcomes modeled in this study are the predicted influences associated with the Science Writing Heuristic (SWH) and associated with the completion of question items for the Cornell Critical Thinking Test. The Student Task and Cognition Model in this study uses cognitive data from a large-scale randomized control study. Results of the computational model experiment provide for the possibility to increase student success via targeted cognitive retraining of specific cognitive attributes via the SWH. This study also illustrates that computational modeling using machine learning algorithms (MLA) is a significant resource for testing educational interventions, informs specific hypotheses, and assists in the design and development of future research designs in science education research.

这项研究旨在提供使用机器学习算法的计算建模（CM）实验的示例。在这项研究中建模的特定结果是与科学写作启发法（SWH）相关的预测影响，以及与康奈尔批判性思维测验的问题完成有关的预测影响。本研究中的学生任务和认知模型使用来自大规模随机对照研究的认知数据。计算模型实验的结果提供了通过SWH通过特定认知属性的定向认知再训练来提高学生成功率的可能性。这项研究还表明，使用机器学习算法（MLA）进行的计算建模是测试教育干预措施的重要资源，可以为特定的假设提供依据，