An effect analysis of the balancing techniques on the counterfactual explanations of student success prediction models

• URL: http://arxiv.org/abs/2408.00676v1
• Date: Thu, 1 Aug 2024 16:19:08 GMT
• Title: An effect analysis of the balancing techniques on the counterfactual explanations of student success prediction models
• Authors: Mustafa Cavus, Jakub Kuzilek,
• Abstract summary: One of the dominant research directions in learning analytics is predictive modeling of learners' success using various machine learning methods. Several counterfactual generation methods hold much promise, but the features must be actionable and causal to be effective. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Robust Counterfactual Explanations under Model Multiplicity Using Multi-Objective Optimization [0.8702432681310401]
  Counterfactual explanation (CE) is not robust when there are multiple machine-learning models with similar accuracy. In this paper, we propose robust CEs that introduce a new viewpoint, and a method that uses multi-objective optimization to generate it. We believe that this research can serve as a valuable foundation for various fields, including explainability in machine learning, decision-making, and action planning based on machine learning.
  arXiv  Detail & Related papers  (2025-01-10T08:57:50Z)
• Causal Inference Tools for a Better Evaluation of Machine Learning [0.0]
  We introduce key statistical methods such as Ordinary Least Squares (OLS) regression, Analysis of Variance (ANOVA) and logistic regression. The document serves as a guide for researchers and practitioners, detailing how these techniques can provide deeper insights into model behavior, performance, and fairness.
  arXiv  Detail & Related papers  (2024-10-02T10:03:29Z)
• Unified Explanations in Machine Learning Models: A Perturbation Approach [0.0]
  Inconsistencies between XAI and modeling techniques can have the undesirable effect of casting doubt upon the efficacy of these explainability approaches. We propose a systematic, perturbation-based analysis against a popular, model-agnostic method in XAI, SHapley Additive exPlanations (Shap) We devise algorithms to generate relative feature importance in settings of dynamic inference amongst a suite of popular machine learning and deep learning methods, and metrics that allow us to quantify how well explanations generated under the static case hold.
  arXiv  Detail & Related papers  (2024-05-30T16:04:35Z)
• The Common Stability Mechanism behind most Self-Supervised Learning Approaches [64.40701218561921]
  We provide a framework to explain the stability mechanism of different self-supervised learning techniques. We discuss the working mechanism of contrastive techniques like SimCLR, non-contrastive techniques like BYOL, SWAV, SimSiam, Barlow Twins, and DINO. We formulate different hypotheses and test them using the Imagenet100 dataset.
  arXiv  Detail & Related papers  (2024-02-22T20:36:24Z)
• MACE: An Efficient Model-Agnostic Framework for Counterfactual Explanation [132.77005365032468]
  We propose a novel framework of Model-Agnostic Counterfactual Explanation (MACE) In our MACE approach, we propose a novel RL-based method for finding good counterfactual examples and a gradient-less descent method for improving proximity. Experiments on public datasets validate the effectiveness with better validity, sparsity and proximity.
  arXiv  Detail & Related papers  (2022-05-31T04:57:06Z)
• Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement [75.00655434905417]
  Explainable Artificial Intelligence (XAI) is an emerging research field bringing transparency to highly complex machine learning (ML) models. This paper offers a comprehensive overview over techniques that apply XAI practically for improving various properties of ML models. We show empirically through experiments on toy and realistic settings how explanations can help improve properties such as model generalization ability or reasoning.
  arXiv  Detail & Related papers  (2022-03-15T15:44:28Z)
• Graph-based Ensemble Machine Learning for Student Performance Prediction [0.7874708385247353]
  We propose a graph-based ensemble machine learning method to improve the stability of single machine learning methods. Our model outperforms the best traditional machine learning algorithms by up to 14.8% in prediction accuracy.
  arXiv  Detail & Related papers  (2021-12-15T05:19:46Z)
• Individual Explanations in Machine Learning Models: A Case Study on Poverty Estimation [63.18666008322476]
  Machine learning methods are being increasingly applied in sensitive societal contexts. The present case study has two main objectives. First, to expose these challenges and how they affect the use of relevant and novel explanations methods. And second, to present a set of strategies that mitigate such challenges, as faced when implementing explanation methods in a relevant application domain.
  arXiv  Detail & Related papers  (2021-04-09T01:54:58Z)
• Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations [64.85696493596821]
  In computer vision applications, generative counterfactual methods indicate how to perturb a model's input to change its prediction. We propose a counterfactual method that learns a perturbation in a disentangled latent space that is constrained using a diversity-enforcing loss. Our model improves the success rate of producing high-quality valuable explanations when compared to previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-03-18T12:57:34Z)
• A Diagnostic Study of Explainability Techniques for Text Classification [52.879658637466605]
  We develop a list of diagnostic properties for evaluating existing explainability techniques. We compare the saliency scores assigned by the explainability techniques with human annotations of salient input regions to find relations between a model's performance and the agreement of its rationales with human ones.
  arXiv  Detail & Related papers  (2020-09-25T12:01:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.