An official website of the United States government
Abstract Automated, data‐driven decision making is increasingly common in a variety of application domains. In educational software, for example, machine learning has been applied to tasks like selecting the next exercise for students to complete. Machine learning methods, however, are not always equally effective for all groups of students. Current approaches to designing fair algorithms tend to focus on statistical measures concerning a small subset of legally protected categories like race or gender. Focusing solely on legally protected categories, however, can limit our understanding of bias and unfairness by ignoring the complexities of identity. We propose an alternative approach to categorization, grounded in sociological techniques of measuring identity. By soliciting survey data and interviews from the population being studied, we can build context‐specific categories from the bottom up. The emergent categories can then be combined with extant algorithmic fairness strategies to discover which identity groups are not well‐served, and thus where algorithms should be improved or avoided altogether. We focus on educational applications but present arguments that this approach should be adopted more broadly for issues of algorithmic fairness across a variety of applications.
more »
« less
Identifying and Mitigating Algorithmic Bias in Student Emotional Analysis
Algorithmic bias in educational environments has garnered increasing scrutiny, with numerous studies highlighting its significant impacts. This research contributes to the field by investigating algorithmic biases, i.e., selection, label, and data biases in the assessment of students’ affective states through video analysis in two educational settings: (1) an open-ended science learning environment and (2) an embodied learning context, involving 41 and 12 students, respectively. Utilizing the advanced High-speed emotion recognition library (HSEmotion) and Multi-task Cascaded Convolutional Networks (MTCNN), and contrasting these with the commercially available iMotions platform, our study delves into biases in these systems. We incorporate real student data to better represent classroom demographics. Our findings not only corroborate the existence of algorithmic bias in detecting student emotions but also highlight successful bias mitigation strategies. The research advances the development of equitable educational technologies and supports the emotional well-being of students by demonstrating that targeted interventions can effectively diminish biases.
more »
« less
- PAR ID:
- 10579828
- Publisher / Repository:
- Springer Nature Switzerland
- Date Published:
- Page Range / eLocation ID:
- 89 to 103
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Algorithmic fairness research has mainly focused on adapting learning models to mitigate discrimination based on protected attributes, yet understanding inherent biases in training data remains largely unexplored. Quantifying these biases is crucial for informed data engineering, as data mining and model development often occur separately. We address this by developing an information-theoretic framework to quantify the marginal impacts of dataset features on the discrimination bias of downstream predictors. We postulate a set of desired properties for candidate discrimination measures and derive measures that (partially) satisfy them. Distinct sets of these properties align with distinct fairness criteria like demographic parity or equalized odds, which we show can be in disagreement and not simultaneously satisfied by a single measure. We use the Shapley value to determine individual features’ contributions to overall discrimination, and prove its effectiveness in eliminating redundancy. We validate our measures through a comprehensive empirical study on numerous real-world and synthetic datasets. For synthetic data, we use a parametric linear structural causal model to generate diverse data correlation structures. Our analysis provides empirically validated guidelines for selecting discrimination measures based on data conditions and fairness criteria, establishing a robust framework for quantifying inherent discrimination bias in datamore » « less
-
null (Ed.)To help facilitate play and learning, game-based educational activities often feature a computational agent as a co-player. Personalizing this agent's behavior to the student player is an active area of research, and prior work has demonstrated the benefits of personalized educational interaction across a variety of domains. A critical research challenge for personalized educational agents is real-time student modeling. Most student models are designed for and trained on only a single task, which limits the variety, flexibility, and efficiency of student player model learning. In this paper we present a research project applying transfer learning methods to student player models over different educational tasks, studying the effects of an algorithmic "multi-task personalization" approach on the accuracy and data efficiency of student model learning. We describe a unified robotic game system for studying multi-task personalization over two different educational games, each emphasizing early language and literacy skills such as rhyming and spelling. We present a flexible Gaussian Process-based approach for rapidly learning student models from interactive play in each game, and a method for transferring each game's learned student model to the other via a novel instance-weighting protocol based on task similarity. We present results from a simulation-based investigation of the impact of multi-task personalization, establishing the core viability and benefits of transferrable student models and outlining new questions for future in-person research.more » « less
-
To help facilitate play and learning, game-based educational activities often feature a computational agent as a co-player. Personalizing this agent's behavior to the student player is an active area of research, and prior work has demonstrated the benefits of personalized educational interaction across a variety of domains. A critical research challenge for personalized educational agents is real-time student modeling. Most student models are designed for and trained on only a single task, which limits the variety, flexibility, and efficiency of student player model learning. In this paper we present a research project applying transfer learning methods to student player models over different educational tasks, studying the effects of an algorithmic "multi-task personalization" approach on the accuracy and data efficiency of student model learning. We describe a unified robotic game system for studying multi-task personalization over two different educational games, each emphasizing early language and literacy skills such as rhyming and spelling. We present a flexible Gaussian Process-based approach for rapidly learning student models from interactive play in each game, and a method for transferring each game's learned student model to the other via a novel instance-weighting protocol based on task similarity. We present results from a simulation-based investigation of the impact of multi-task personalization, establishing the core viability and benefits of transferrable student models and outlining new questions for future in-person research.more » « less
-
It is particularly important to identify and address issues of fairness and equity in educational contexts as academic performance can have large impacts on the types of opportunities that are made available to students. While it is always the hope that educators approach student assessment with these issues in mind, there are a number of factors that likely impact how a teacher approaches the scoring of student work. Particularly in cases where the assessment of student work requires subjective judgment, as in the case of open-ended answers and essays, contextual information such as how the student has performed in the past, general perceptions of the student, and even other external factors such as fatigue may all influence how a teacher approaches assessment. While such factors exist, however, it is not always clear how these may introduce bias, nor is it clear whether such bias poses measurable risks to fairness and equity. In this paper, we examine these factors in the context of the assessment of student answers to open response questions from middle school mathematics learners. We observe how several factors such as context and fatigue correlate with teacher-assigned grades and discuss how learning systems may support fair assessment. Keywords: halo effect, grading biases, fairness, subjective assessmentmore » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Book Chapter:
- https://doi.org/10.1007/978-3-031-64299-9_7
