Fairer but Not Fair Enough On the Equitability of Knowledge Tracing
Fairer but Not Fair Enough On the Equitability of Knowledge Tracing
复制标题
关于知识追踪的公平性更公平但不够公平
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
E. Brunskill
中科院分区：
文献类型：
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作者：
Shayan Doroudi;E. Brunskill
自适应教育技术有能力满足理论上的个别学生的需求，但是在某些情况下，个性化程度可能远远超过所需的，这可能会导致学生的不平等结果。在本文中，我们使用仿真来证明，尽管知识追踪算法比给所有学生的实践数量要公平得多，但当这些算法依靠不准确的模型时，这种算法仍然是不公平的。这可能是由于两个因素而产生的：（1）使用适合汇总学生人群的学生模型，以及（2）使用对学生学习做出错误假设的学生模型。特别是，我们证明了贝叶斯知识追踪算法和n连续的正确响应启发式启发式易受关注的敏感，但是使用添加因子模型的知识追踪可能更公平。本文的更广泛的信息是，在设计学习分析算法时，我们需要明确考虑该算法是否相对于不同的学生人群进行了公平的作用，如果没有，我们如何使我们的算法更加公平。
Adaptive educational technologies have the capacity to meet the needs of individual students in theory, but in some cases, the degree of personalization might be less than desired, which could lead to inequitable outcomes for students. In this paper, we use simulations to demonstrate that while knowledge tracing algorithms are substantially more equitable than giving all students the same amount of practice, such algorithms can still be inequitable when they rely on inaccurate models. This can arise as a result of two factors: (1) using student models that are fit to aggregate populations of students, and (2) using student models that make incorrect assumptions about student learning. In particular, we demonstrate that both the Bayesian knowledge tracing algorithm and the N-Consecutive Correct Responses heuristic are susceptible to these concerns, but that knowledge tracing with the additive factor model may be more equitable. The broader message of this paper is that when designing learning analytics algorithms, we need to explicitly consider whether the algorithms act fairly with respect to different populations of students, and if not, how we can make our algorithms more equitable.