平衡技术对学生成功预测模型的反事实解释的效果分析
An effect analysis of the balancing techniques on the counterfactual explanations of student success prediction models
AI总结：
本文通过开放大学学习分析数据集案例研究，分析了多种反事实生成方法在平衡处理后的有效性，为学生成功预测模型选择合适方法提供依据。
摘要：
在过去十年中，高等教育领域数字解决方案的使用出现了大规模增长。由于这一增长，大量数据使得先进的数据分析方法能够支持学习者并审视学习过程。学习分析中的一个主要研究方向是使用各种机器学习方法对学习者的成功进行预测建模。为了建立学习者和教师对此类方法和系统的信任，有必要探索能够使相关利益相关者深入理解底层机器学习模型的方法和方法论。在这一背景下，来自可解释机器学习工具的反事实解释具有很大前景。多种反事实生成方法颇具潜力，但特征必须具有可操作性和因果性才能有效。因此，确定哪种反事实生成方法在期望特性、稳定性和鲁棒性方面更适合学生成功预测模型至关重要。尽管近年来已发表了一些关于反事实解释在教育科学中应用的研究，但它们尚未讨论哪种反事实生成方法更适合这一问题。本文分析了常用的反事实生成方法在平衡处理后的有效性，例如 WhatIf 反事实解释、多目标反事实解释和最近实例反事实解释。本贡献展示了一个使用开放大学学习分析数据集的案例研究，以证明反事实解释的实际用途。结果说明了该方法的有效性，并描述了可以采取的具体步骤来改变模型的预测。
英文摘要：
In the past decade, we have experienced a massive boom in the usage of digital solutions in higher education. Due to this boom, large amounts of data have enabled advanced data analysis methods to support learners and examine learning processes. One of the dominant research directions in learning analytics is predictive modeling of learners' success using various machine learning methods. To build learners' and teachers' trust in such methods and systems, exploring the methods and methodologies that enable relevant stakeholders to deeply understand the underlying machine-learning models is necessary. In this context, counterfactual explanations from explainable machine learning tools are promising. Several counterfactual generation methods hold much promise, but the features must be actionable and causal to be effective. Thus, obtaining which counterfactual generation method suits the student success prediction models in terms of desiderata, stability, and robustness is essential. Although a few studies have been published in recent years on the use of counterfactual explanations in educational sciences, they have yet to discuss which counterfactual generation method is more suitable for this problem. This paper analyzed the effectiveness of commonly used counterfactual generation methods, such as WhatIf Counterfactual Explanations, Multi-Objective Counterfactual Explanations, and Nearest Instance Counterfactual Explanations after balancing. This contribution presents a case study using the Open University Learning Analytics dataset to demonstrate the practical usefulness of counterfactual explanations. The results illustrate the method's effectiveness and describe concrete steps that could be taken to alter the model's prediction.
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