  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.
