# Lost in Translation: Reimagining the Machine Learning Life Cycle in Education

arXiv: https://arxiv.org/abs/2209.03929 · HTML: https://mlsanity.com/paper/2209.03929/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 99 references that are in the map.
- First posted on arXiv 8 Sep 2022.

## Builds on (most cited first, 23 of 23)

- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [Measurement and Fairness](https://mlsanity.com/paper/1912.05511/) (2019)
- [A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle](https://mlsanity.com/paper/1901.10002/) (2019)
- [The Values Encoded in Machine Learning Research](https://mlsanity.com/paper/2106.15590/) (2021)
- [Problem Formulation and Fairness](https://mlsanity.com/paper/1901.02547/) (2019)
- [Roles for Computing in Social Change](https://mlsanity.com/paper/1912.04883/) (2019)
- [The Flaws of Policies Requiring Human Oversight of Government Algorithms](https://mlsanity.com/paper/2109.05067/) (2021)
- [Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing](https://mlsanity.com/paper/1811.03388/) (2018)
- [What's Sex Got To Do With Fair Machine Learning?](https://mlsanity.com/paper/2006.01770/) (2020)
- [AI Lifecycle Models Need To Be Revised. An Exploratory Study in Fintech](https://mlsanity.com/paper/2010.02716/) (2020)
- [Artificial Intelligence for Social Good: A Survey](https://mlsanity.com/paper/2001.01818/) (2020)
- [A Human-Centered Review of the Algorithms used within the U.S. Child Welfare System](https://mlsanity.com/paper/2003.03541/) (2020)
- [The four-fifths rule is not disparate impact: a woeful tale of epistemic trespassing in algorithmic fairness](https://mlsanity.com/paper/2202.09519/) (2022)
- [AI in Education needs interpretable machine learning: Lessons from Open Learner Modelling](https://mlsanity.com/paper/1807.00154/) (2018)
- [QuesNet: A Unified Representation for Heterogeneous Test Questions](https://mlsanity.com/paper/1905.10949/) (2019)
- [Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference](https://mlsanity.com/paper/1809.01357/) (2018)
- [Beyond "Fairness:" Structural (In)justice Lenses on AI for Education](https://mlsanity.com/paper/2105.08847/) (2021)
- [Equity and Artificial Intelligence in Education: Will "AIEd" Amplify or Alleviate Inequities in Education?](https://mlsanity.com/paper/2104.12920/) (2021)
- [Inferring Concept Prerequisite Relations from Online Educational Resources](https://mlsanity.com/paper/1811.12640/) (2018)
- [Time-varying Learning and Content Analytics via Sparse Factor Analysis](https://mlsanity.com/paper/1312.5734/) (2013)
- [Predicting University Students' Academic Success and Major using Random Forests](https://mlsanity.com/paper/1802.03418/) (2018)
- [Axes for Sociotechnical Inquiry in AI Research](https://mlsanity.com/paper/2105.06551/) (2021)

## Built on by (most cited first, 1 of 1)

- [Identifying Implicit Social Biases in Vision-Language Models](https://mlsanity.com/paper/2411.00997/) (2024)

## Abstract (arXiv)

Machine learning (ML) techniques are increasingly prevalent in education, from their use in predicting student dropout, to assisting in university admissions, and facilitating the rise of MOOCs. Given the rapid growth of these novel uses, there is a pressing need to investigate how ML techniques support long-standing education principles and goals. In this work, we shed light on this complex landscape drawing on qualitative insights from interviews with education experts. These interviews comprise in-depth evaluations of ML for education (ML4Ed) papers published in preeminent applied ML conferences over the past decade. Our central research goal is to critically examine how the stated or implied education and societal objectives of these papers are aligned with the ML problems they tackle. That is, to what extent does the technical problem formulation, objectives, approach, and interpretation of results align with the education problem at hand. We find that a cross-disciplinary gap exists and is particularly salient in two parts of the ML life cycle: the formulation of an ML problem from education goals and the translation of predictions to interventions. We use these insights to propose an extended ML life cycle, which may also apply to the use of ML in other domains. Our work joins a growing number of meta-analytical studies across education and ML research, as well as critical analyses of the societal impact of ML. Specifically, it fills a gap between the prevailing technical understanding of machine learning and the perspective of education researchers working with students and in policy.