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Fair Knowledge Tracing in Second Language Acquisition: Analysis of Algorithmic Bias

Analysis of fairness in predictive models for second-language learning, evaluating bias across platforms and countries using Duolingo data.
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Home » Documentation » Fair Knowledge Tracing in Second Language Acquisition: Analysis of Algorithmic Bias

1. Introduction

Predictive modeling in education, particularly Knowledge Tracing (KT), aims to model student knowledge states to personalize learning. Traditional methods relied on human judgment, prone to biases from memory limits, fatigue, and positivity bias. Computational KT, introduced by Corbett and Anderson (1994), uses student interaction data (grades, feedback, participation) to predict future performance and adapt instruction.

While accuracy has been the primary focus, this research highlights a critical gap: algorithmic fairness. The study investigates whether predictive models in second-language acquisition (using Duolingo data) exhibit unintended biases against specific groups based on platform (iOS, Android, Web) or country development status (developed vs. developing).

2. Methodology & Experimental Setup

The study employs a comparative analysis framework to evaluate fairness alongside accuracy.

2.1 Datasets & Tracks

Three learning tracks from the Duolingo 2018 shared task dataset were used:

  • en_es: English speakers learning Spanish.
  • es_en: Spanish speakers learning English.
  • fr_en: French speakers learning English.

Data includes student exercise sequences, correctness, and metadata (client platform, country). Countries were classified as "Developed" or "Developing" based on standard economic indices (e.g., IMF classification).

2.2 Predictive Models

Two categories of models were evaluated:

  • Machine Learning (ML): Traditional models like Logistic Regression, Random Forests.
  • Deep Learning (DL): Neural network-based models, likely including variants of Deep Knowledge Tracing (DKT) or Transformer-based architectures.

The primary task was binary prediction: will the student answer the next exercise correctly?

2.3 Fairness Metrics

Fairness was assessed using group fairness metrics, comparing model performance across protected groups:

  • Platform Fairness: Compare accuracy, F1-score, or AUC between users on iOS, Android, and Web clients.
  • Geographic Fairness: Compare performance metrics between users from developed and developing countries.

Disparities in these metrics indicate algorithmic bias. A perfectly fair model would have equal performance across all groups.

3. Results & Findings

The study yielded four key findings, revealing significant trade-offs and biases.

3.1 Accuracy vs. Fairness Trade-off

Deep Learning (DL) models generally outperformed Machine Learning (ML) models in both accuracy and fairness. DL's ability to capture complex, non-linear patterns in sequential learning data leads to more robust predictions that are less reliant on spurious correlations linked to sensitive attributes.

3.2 Platform Bias (iOS/Android/Web)

Both ML and DL algorithms exhibited a noticeable bias favoring mobile users (iOS/Android) over non-mobile (Web) users. This could stem from data quality differences (e.g., interaction patterns, session length), interface design, or the demographic profiles typically associated with each platform. This bias risks disadvantaging learners who primarily access educational tools via desktop computers.

3.3 Geographic Bias (Developed vs. Developing)

ML algorithms showed a more pronounced bias against users from developing countries compared to DL algorithms. This is a critical finding, as ML models may learn and amplify historical inequities present in the training data (e.g., differences in prior educational access, internet reliability). DL models, while not immune, demonstrated greater resilience to this geographic bias.

Optimal Model Selection: The study suggests a nuanced approach:

  • Use Deep Learning for the en_es and es_en tracks for the best balance of fairness and accuracy.
  • Consider Machine Learning for the fr_en track, where its fairness-accuracy profile was deemed more suitable for that specific context.

4. Technical Analysis & Framework

4.1 Knowledge Tracing Formulation

At its core, Knowledge Tracing models the latent knowledge state of a student. Given a sequence of interactions $X_t = \{(q_1, a_1), (q_2, a_2), ..., (q_t, a_t)\}$, where $q_i$ is an exercise/question and $a_i \in \{0,1\}$ is the correctness, the goal is to predict the probability of correctness on the next exercise: $P(a_{t+1}=1 | X_t)$.

Deep Knowledge Tracing (Piech et al., 2015) uses a Recurrent Neural Network (RNN) to model this:

$h_t = \text{RNN}(h_{t-1}, x_t)$

$P(a_{t+1}=1) = \sigma(W \cdot h_t + b)$

where $h_t$ is the hidden state representing the knowledge state at time $t$, $x_t$ is the input embedding of $(q_t, a_t)$, and $\sigma$ is the sigmoid function.

4.2 Fairness Evaluation Framework

The study implicitly employs a group fairness paradigm. For a binary predictor $\hat{Y}$ and a sensitive attribute $A$ (e.g., platform or country group), common metrics include:

  • Statistical Parity Difference: $|P(\hat{Y}=1|A=0) - P(\hat{Y}=1|A=1)|$
  • Equal Opportunity Difference: $|P(\hat{Y}=1|A=0, Y=1) - P(\hat{Y}=1|A=1, Y=1)|$ (Used when true labels Y are known).
  • Performance Metric Disparity: Difference in accuracy, AUC, or F1-score between groups.

A smaller disparity indicates greater fairness. The paper's findings suggest DL models minimize these disparities more effectively than ML models across the defined groups.

5. Case Study: Framework Application

Scenario: An EdTech company uses a KT model to recommend review exercises in its language learning app. The model is trained on global user data.

Problem: Post-deployment analytics show that users in Country X (a developing nation) have a 15% higher rate of being incorrectly recommended exercises that are too difficult, leading to frustration and drop-off, compared to users in Country Y (a developed nation).

Analysis using this paper's framework:

  1. Identify Sensitive Group: Users from developing vs. developed countries.
  2. Audit Model: Calculate performance metrics (Accuracy, AUC) separately for each group. The observed 15% disparity in "appropriate difficulty recommendation rate" is a fairness violation.
  3. Diagnose: Is the model ML or DL? Per this study, an ML model is more likely to exhibit this geographic bias. Investigate feature distributions—perhaps the model over-relies on features correlated with country development (e.g., average connection speed, device type).
  4. Remediate: Consider switching to a DL-based KT architecture, which the study found to be more robust to this bias. Alternatively, apply fairness-aware training techniques (e.g., adversarial debiasing, re-weighting) to the existing model.
  5. Monitor: Continuously track the fairness metric post-intervention to ensure the bias is mitigated.

6. Future Applications & Directions

The implications of this research extend beyond second-language learning:

  • Personalized Learning at Scale: Fair KT models can enable truly equitable adaptive learning systems in MOOCs (like Coursera, edX) and intelligent tutoring systems, ensuring recommendations are effective for all demographics.
  • Bias Auditing for EdTech: This framework provides a blueprint for auditing commercial educational software for algorithmic bias, a growing concern for regulators and educators.
  • Cross-Domain Fairness: Future work should investigate fairness across other sensitive attributes: gender, age, socioeconomic status inferred from data, and learning disabilities.
  • Causal Fairness Analysis: Moving beyond correlation to understand the causes of bias—is it the data, the model architecture, or the learning context? Techniques from causal inference could be integrated.
  • Federated & Privacy-Preserving Fair Learning: Training fair models on decentralized user data without compromising privacy, a key direction for ethical AI in education.

7. References

  1. Baker, R.S., Inventado, P.S. (2014). Educational Data Mining and Learning Analytics. In: Larusson, J., White, B. (eds) Learning Analytics. Springer, New York, NY.
  2. Corbett, A. T., & Anderson, J. R. (1994). Knowledge tracing: Modeling the acquisition of procedural knowledge. User modeling and user-adapted interaction, 4(4), 253-278.
  3. Piech, C., Bassen, J., Huang, J., Ganguli, S., Sahami, M., Guibas, L. J., & Sohl-Dickstein, J. (2015). Deep knowledge tracing. Advances in neural information processing systems, 28.
  4. Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and Machine Learning: Limitations and Opportunities. fairmlbook.org.
  5. Duolingo. (2018). Second Language Acquisition Modeling (SLAM) Workshop Dataset. Retrieved from https://sharedtask.duolingo.com/
  6. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys (CSUR), 54(6), 1-35.

8. Expert Analysis & Commentary

Core Insight: This paper delivers a crucial, often-ignored truth in EdTech: high accuracy does not equate to equitable education. The authors convincingly demonstrate that standard Knowledge Tracing models, when deployed naively, systematically disadvantage entire cohorts of learners—specifically, those using web platforms and those in developing nations. The most striking finding is that simpler Machine Learning models aren't just less accurate; they are significantly less fair, acting as amplifiers of existing societal and digital divides. This positions algorithmic fairness not as a niche ethical concern, but as a core component of model performance and pedagogical efficacy.

Logical Flow: The argument is methodical. It starts by establishing the high stakes (personalized education) and the historical blind spot (fairness). It then sets up a clean, binary comparative experiment (ML vs. DL) across three distinct language learning contexts. The choice of fairness axes—platform and geography—is astute, reflecting real-world deployment variables that directly impact user experience. The results flow logically: DL's superior representational capacity yields not just better predictions, but fairer ones. The nuanced recommendation (DL for en_es/es_en, ML for fr_en) is refreshing, avoiding a one-size-fits-all dogma and acknowledging context-dependency, a hallmark of rigorous analysis.

Strengths & Flaws: The primary strength is its actionable, empirical focus. It moves beyond theoretical fairness discussions to provide measurable evidence of bias in a widely-used dataset (Duolingo). This is a powerful template for internal model auditing. However, the analysis has limitations. It treats "developed" and "developing" as monolithic blocks, glossing over immense heterogeneity within these categories (e.g., urban vs. rural users). The study also doesn't delve into why the biases exist. Is it feature representation, data volume per group, or cultural differences in learning patterns? As noted in the comprehensive survey by Mehrabi et al. (2021), diagnosing the root cause of bias is essential for developing effective mitigations. Furthermore, while DL appears fairer here, its "black box" nature could mask more subtle, harder-to-detect biases, a challenge highlighted in fairness literature.

Actionable Insights: For EdTech leaders and product managers, this research is a mandate for change. First, fairness metrics must be integrated into the standard model evaluation dashboard, alongside accuracy and AUC. Before deploying any adaptive learning feature, conduct an audit similar to this study. Second, prioritize Deep Learning architectures for core student modeling tasks, as they offer a better inherent guard against bias, corroborating trends seen in other domains where deep networks learn more robust features. Third, disaggregate your data. Don't just look at "global" performance. Slice metrics by platform, region, and other relevant demographics as a routine practice. Finally, invest in causal analysis to move from observing bias to understanding and engineering it out. The future of equitable EdTech depends on treating fairness with the same rigor as prediction accuracy.