Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analytics

• URL: http://arxiv.org/abs/2212.02985v2
• Date: Tue, 28 May 2024 11:10:16 GMT
• Title: Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analytics
• Authors: Yun-Wei Chu, Seyyedali Hosseinalipour, Elizabeth Tenorio, Laura Cruz, Kerrie Douglas, Andrew Lan, Christopher Brinton,
• Abstract summary: We propose a Multi-Layer Personalized Federated Learning (MLPFL) methodology to optimize inference accuracy over different layers of student grouping criteria. In our approach, personalized models for individual student subgroups are derived from a global model. Experiments on three real-world online course datasets show significant improvements achieved by our approach over existing student modeling benchmarks.
• Score: 8.642174401125263
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conventional methods for student modeling, which involve predicting grades based on measured activities, struggle to provide accurate results for minority/underrepresented student groups due to data availability biases. In this paper, we propose a Multi-Layer Personalized Federated Learning (MLPFL) methodology that optimizes inference accuracy over different layers of student grouping criteria, such as by course and by demographic subgroups within each course. In our approach, personalized models for individual student subgroups are derived from a global model, which is trained in a distributed fashion via meta-gradient updates that account for subgroup heterogeneity while preserving modeling commonalities that exist across the full dataset. The evaluation of the proposed methodology considers case studies of two popular downstream student modeling tasks, knowledge tracing and outcome prediction, which leverage multiple modalities of student behavior (e.g., visits to lecture videos and participation on forums) in model training. Experiments on three real-world online course datasets show significant improvements achieved by our approach over existing student modeling benchmarks, as evidenced by an increased average prediction quality and decreased variance across different student subgroups. Visual analysis of the resulting students' knowledge state embeddings confirm that our personalization methodology extracts activity patterns clustered into different student subgroups, consistent with the performance enhancements we obtain over the baselines.

Related papers

• Preview-based Category Contrastive Learning for Knowledge Distillation [53.551002781828146]
  We propose a novel preview-based category contrastive learning method for knowledge distillation (PCKD) It first distills the structural knowledge of both instance-level feature correspondence and the relation between instance features and category centers. It can explicitly optimize the category representation and explore the distinct correlation between representations of instances and categories.
  arXiv  Detail & Related papers  (2024-10-18T03:31:00Z)
• An Active Learning Framework for Inclusive Generation by Large Language Models [32.16984263644299]
  Large Language Models (LLMs) generate text representative of diverse sub-populations. We propose a novel clustering-based active learning framework, enhanced with knowledge distillation. We construct two new datasets in tandem with model training, showing a performance improvement of 2%-10% over baseline models.
  arXiv  Detail & Related papers  (2024-10-17T15:09:35Z)
• Dynamic Post-Hoc Neural Ensemblers [55.15643209328513]
  In this study, we explore employing neural networks as ensemble methods. Motivated by the risk of learning low-diversity ensembles, we propose regularizing the model by randomly dropping base model predictions. We demonstrate this approach lower bounds the diversity within the ensemble, reducing overfitting and improving generalization capabilities.
  arXiv  Detail & Related papers  (2024-10-06T15:25:39Z)
• Task Groupings Regularization: Data-Free Meta-Learning with Heterogeneous Pre-trained Models [83.02797560769285]
  Data-Free Meta-Learning (DFML) aims to derive knowledge from a collection of pre-trained models without accessing their original data. Current methods often overlook the heterogeneity among pre-trained models, which leads to performance degradation due to task conflicts. We propose Task Groupings Regularization, a novel approach that benefits from model heterogeneity by grouping and aligning conflicting tasks.
  arXiv  Detail & Related papers  (2024-05-26T13:11:55Z)
• Universal Semi-supervised Model Adaptation via Collaborative Consistency Training [92.52892510093037]
  We introduce a realistic and challenging domain adaptation problem called Universal Semi-supervised Model Adaptation (USMA) We propose a collaborative consistency training framework that regularizes the prediction consistency between two models. Experimental results demonstrate the effectiveness of our method on several benchmark datasets.
  arXiv  Detail & Related papers  (2023-07-07T08:19:40Z)
• An Information-Theoretic Approach for Estimating Scenario Generalization in Crowd Motion Prediction [27.10815774845461]
  We propose a novel scoring method, which characterizes generalization of models trained on source crowd scenarios and applied to target crowd scenarios. The Interaction component aims to characterize the difficulty of scenario domains, while the diversity of a scenario domain is captured in the Diversity score. Our experimental results validate the efficacy of the proposed method on several simulated and real-world (source,target) generalization tasks.
  arXiv  Detail & Related papers  (2022-11-02T01:39:30Z)
• Predicting student performance using sequence classification with time-based windows [1.5836913530330787]
  We show that accurate predictive models can be built based on sequential patterns derived from students' behavioral data. We present a methodology for capturing temporal aspects in behavioral data and analyze its influence on the predictive performance of the models. The results of our improved sequence classification technique are capable of predicting student performance with high levels of accuracy, reaching 90 percent for course-specific models.
  arXiv  Detail & Related papers  (2022-08-16T13:46:39Z)
• Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learning [7.040747348755578]
  Traditional learning-based approaches to student modeling generalize poorly to underrepresented student groups due to biases in data availability. We propose a methodology for predicting student performance from their online learning activities that optimize inference accuracy over different demographic groups such as race and gender.
  arXiv  Detail & Related papers  (2022-08-02T00:22:20Z)
• Zero-shot meta-learning for small-scale data from human subjects [10.320654885121346]
  We develop a framework to rapidly adapt to a new prediction task with limited training data for out-of-sample test data. Our model learns the latent treatment effects of each intervention and, by design, can naturally handle multi-task predictions. Our model has implications for improved generalization of small-size human studies to the wider population.
  arXiv  Detail & Related papers  (2022-03-29T17:42:04Z)
• Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data [85.43008636875345]
  We show that diverse representation in training data is key to increasing subgroup performances and achieving population level objectives. Our analysis and experiments describe how dataset compositions influence performance and provide constructive results for using trends in existing data, alongside domain knowledge, to help guide intentional, objective-aware dataset design.
  arXiv  Detail & Related papers  (2021-03-05T00:27:08Z)
• Adversarial Sample Enhanced Domain Adaptation: A Case Study on Predictive Modeling with Electronic Health Records [57.75125067744978]
  We propose a data augmentation method to facilitate domain adaptation. adversarially generated samples are used during domain adaptation. Results confirm the effectiveness of our method and the generality on different tasks.
  arXiv  Detail & Related papers  (2021-01-13T03:20:20Z)

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.