In this article, we empirically validate Tinto's Student Integration model, in particular, the predictions the model makes regarding both students’ academic outcomes and their dropout decisions. In doing so, we analyze three decades’ worth of student enrollments at an Australian university and present a novel methodological approach using graph embedding techniques to capture both structural and neighborhood-based features of the co-enrollment network. In keeping with Tinto's model, we find that not only do these embedded representations of students’ social network predict their final grade point average (GPA), but also are able to successfully classify students who dropout. Our results show that these embedded representations of a student's social network can achieve $F_1$ -scores of up to 0.79 when classifying dropout and explain up to 10% of the variance in student's final GPA. When controlling for a small set of covariates and variables common to the literature, this performance increases to 0.83 and 24%, respectively. Furthermore, the performance of these methods is robust to both changes in their parameterization and to corruption of the underlying social networks. Importantly, this implies that hyperparameters may be selected to reduce the computational demands of this method without loss of predictive power. The novelty of this method, and its ability to identify student dropout, merits further investigation to preemptively identify at-risk students.

中文翻译：

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共同注册网络嵌入中的持久性和性能：Tinto学生整合模型的经验验证

在本文中，我们通过经验验证了Tinto的学生整合模型，尤其是该模型对学生的学业成绩和辍学决策所做的预测。为此，我们分析了一家澳大利亚大学三十年来的入学人数，并提出了一种使用图嵌入技术来捕获共同入学网络的结构特征和基于邻域特征的新颖方法论方法。与Tinto的模型保持一致，我们发现，这些嵌入的学生社交网络表示不仅可以预测他们的最终平均绩点（GPA），而且还可以成功地对辍学的学生进行分类。我们的结果表明，学生社交网络的这些嵌入式表示可以实现$ F_1 $ -对辍学分类时得分最高为0.79，并解释了学生最终GPA差异的10％。当控制文献中常见的一小部分协变量和变量时，此性能分别提高到0.83和24％。此外，这些方法的性能对于其参数设置的更改以及底层社交网络的破坏都是强大的。重要的是，这意味着可以选择超参数以减少该方法的计算需求，而不会损失预测能力。这种方法的新颖性及其识别学生辍学的能力，值得进一步研究以优先识别有风险的学生。