pith. sign in

arxiv: 2606.11004 · v1 · pith:EE6KM6PLnew · submitted 2026-06-09 · 💻 cs.HC

A Case Study Reexamining the Cold-Start Problem in Knowledge Tracing Models and Implications for SafeInsights, an Education Research Infrastructure

Jiayi Zhang , Ryan S. Baker , Debshila Basu Mallick , Cristina Heffernan , Neil Heffernan This is my paper

Pith reviewed 2026-06-27 11:49 UTC · model grok-4.3

classification 💻 cs.HC
keywords knowledge tracingcold-start problemreplicationeducational data miningproblem types
0
0 comments X

The pith

Knowledge tracing model performance varies across student practice opportunities and problem types in a replication on newer data.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper replicates an earlier analysis of the cold-start problem in knowledge tracing models using a more recent dataset. It finds that model performance differs depending on how many times a student has practiced a skill and on the type of problem being solved. A sympathetic reader would care because previous claims about which models handle new students best may not hold uniformly across contexts. The study also points out difficulties in exactly reproducing educational data mining experiments. It suggests that certain privacy-preserving setups can make such replications easier while protecting privacy.

Core claim

The replication demonstrates that KT model performance varies across both student practice trajectories and problem types.

What carries the argument

The breakdown of model performance by number of practice opportunities and by problem type categories.

If this is right

  • KT model choice may depend on the problem formats in use.
  • Early practice predictions interact with problem type.
  • Reproducing these studies requires matching data distributions and implementations closely.
  • Privacy-preserving research setups can support replication work.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The variation might be tested by training models on data that mixes all problem types.
  • If the pattern holds, it suggests KT models could include problem-type aware components.
  • The replication challenges point to a need for standardized evaluation protocols in the field.

Load-bearing premise

The newer dataset, selected models, and problem type groupings permit a direct and fair comparison to the earlier analysis without unstated differences in data distribution or model implementation.

What would settle it

A re-run of the models on the dataset that shows no difference in performance across practice opportunities or problem types would disprove the variation result.

Figures

Figures reproduced from arXiv: 2606.11004 by Cristina Heffernan, Debshila Basu Mallick, Jiayi Zhang, Neil Heffernan, Ryan S. Baker.

Figure 2
Figure 2. Figure 2: AUC by Skills, Models, and Problem Types [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
read the original abstract

Knowledge tracing (KT) models are widely used to predict students' evolving knowledge states from their learning history. However, many KT models are evaluated using specific datasets, platforms, and learning contexts, raising questions about whether reported model performance replicates and generalizes across newer datasets that vary in context. This paper replicates and extends Zhang et al. (2021), which examined the cold-start problem in KT models and found that deep-learning-based KT models performed better, partly because of stronger predictions when students began practicing a skill. Using a more recent ASSISTments dataset, FoundationalASSIST, we replicate the previous analysis by evaluating model performance across opportunities to practice and extend the analysis by examining performance across problem types, including fill-in-the-blank, multiple-choice select-one, multiple-choice select-all, and order/sort problems. Results show that KT model performance varies across both student practice trajectories and problem types. Beyond the empirical replication, this study identifies practical challenges in reproducing educational data mining studies and serves as a proof of concept, showing how privacy-preserving research infrastructures such as SafeInsights can be leveraged to facilitate educational research and support replication analyses.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper replicates Zhang et al. (2021) on the cold-start problem in knowledge tracing (KT) models and extends the analysis to performance across problem types using the FoundationalASSIST dataset. It reports that KT model performance varies across student practice trajectories and problem types (fill-in-the-blank, multiple-choice select-one, select-all, order/sort), identifies reproduction challenges in EDM studies, and presents the work as a proof-of-concept for the SafeInsights privacy-preserving infrastructure.

Significance. If the replication is faithful, the findings would demonstrate context-sensitivity of KT models to trajectories and item formats, with practical implications for model deployment in education. The explicit discussion of reproduction barriers and the SafeInsights demonstration would strengthen the case for standardized, privacy-preserving replication infrastructures in the field.

major comments (2)
  1. [Methods] Methods section: the manuscript provides no explicit confirmation, comparison tables, or parameter lists showing that preprocessing steps, skill tagging, opportunity counting, train/test splits, and hyper-parameters on FoundationalASSIST match those used in Zhang et al. (2021). Without this, differences in reported performance cannot be confidently attributed to dataset or problem-type factors rather than implementation artifacts, undermining the replication component of the central claim.
  2. [Abstract] Abstract and Results: the headline claim that 'performance varies across both student practice trajectories and problem types' is stated without any quantitative metrics, error bars, statistical tests, or exclusion criteria in the provided text, making the empirical result unverifiable from the abstract and weakening the extension to problem types.
minor comments (1)
  1. [Methods] The paper should include a dedicated subsection or table explicitly listing all deviations (or lack thereof) from the 2021 pipeline to allow readers to assess replication fidelity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thoughtful review and constructive comments. We have carefully considered each point and provide our responses below, along with planned revisions to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Methods] Methods section: the manuscript provides no explicit confirmation, comparison tables, or parameter lists showing that preprocessing steps, skill tagging, opportunity counting, train/test splits, and hyper-parameters on FoundationalASSIST match those used in Zhang et al. (2021). Without this, differences in reported performance cannot be confidently attributed to dataset or problem-type factors rather than implementation artifacts, undermining the replication component of the central claim.

    Authors: We agree that explicit documentation of replication fidelity is essential. Although our implementation followed the procedures described in Zhang et al. (2021) as closely as possible given the differences in dataset structure, the current manuscript does not include a side-by-side comparison. In the revised version, we will add a dedicated subsection and table in the Methods section that lists the preprocessing steps, skill tagging approach, opportunity counting method, train/test split strategy, and hyper-parameter settings used in our study alongside those from the original paper. This will allow readers to evaluate the replication more rigorously. revision: yes

  2. Referee: [Abstract] Abstract and Results: the headline claim that 'performance varies across both student practice trajectories and problem types' is stated without any quantitative metrics, error bars, statistical tests, or exclusion criteria in the provided text, making the empirical result unverifiable from the abstract and weakening the extension to problem types.

    Authors: The abstract is intended as a concise overview, but we acknowledge that including some quantitative indicators would improve verifiability. In the revision, we will update the abstract to report key performance metrics (e.g., AUC differences across opportunity bins and problem types) and note that detailed statistical analyses, including error bars where appropriate, are presented in the results section. We will also clarify any exclusion criteria applied in the analysis. revision: yes

Circularity Check

0 steps flagged

No significant circularity: purely empirical replication with independent results on new data

full rationale

This is an empirical replication and extension study that evaluates standard KT models (DKT, DKVMN, etc.) on the FoundationalASSIST dataset, reporting performance metrics across practice opportunities and problem types. No mathematical derivations, equations, fitted parameters presented as predictions, or ansatzes appear in the analysis chain. The central claims rest on direct computation of model accuracies on the new dataset rather than reducing to self-citations, self-definitions, or renamings. The citation to Zhang et al. (2021) functions as the replication baseline (with shared authorship noted but not load-bearing for the new empirical observations), and the paper explicitly frames its contribution as identifying reproduction challenges and demonstrating SafeInsights infrastructure rather than deriving results from prior outputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Empirical replication study containing no mathematical derivations, fitted parameters, or postulated entities.

pith-pipeline@v0.9.1-grok · 5755 in / 1070 out tokens · 20706 ms · 2026-06-27T11:49:51.198698+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

35 extracted references · 3 canonical work pages

  1. [1]

    Ryan Baker and Stephen Hutt. 2025. Morf: a post-mortem. InProceedings of the 15th International Learning Analytics and Knowledge Conference, 797–802

    2025

  2. [2]

    Ryan S. Baker. 2019. Challenges for the future of educational data mining: the baker learning analytics prizes.Journal of Educational Data Mining, 11, 1, 1–17

    2019

  3. [3]

    Baker, and Vincent Aleven

    Conrad Borchers, Jiayi Zhang, Ryan S. Baker, and Vincent Aleven. 2024. Us- ing think-aloud data to understand relations between self-regulation cycle characteristics and student performance in intelligent tutoring systems. In Proceedings of the 14th Learning Analytics and Knowledge Conference, 529–539

    2024

  4. [4]

    Youngduck Choi et al. 2020. EdNet: a large-scale hierarchical dataset in educa- tion. InInternational Conference on Artificial Intelligence in Education. Springer International Publishing, Cham, 69–73

    2020

  5. [5]

    Corbett and John R

    Albert T. Corbett and John R. Anderson. 1994. Knowledge tracing: modeling the acquisition of procedural knowledge.User Modeling and User-Adapted Interaction, 4, 4, 253–278

    1994

  6. [6]

    Data to trust: co-designing privacy-preserving research infrastructure with members of the K–12 ecosystem

    2025. Data to trust: co-designing privacy-preserving research infrastructure with members of the K–12 ecosystem. In https://osf.io/6p3xy/

    2025

  7. [7]

    Fancsali

    Stephen E. Fancsali. 2014. Causal discovery with models: behavior, affect, and learning in cognitive tutor algebra. InProceedings of the 7th International Conference on Educational Data Mining

    2014

  8. [8]

    Mingyu Feng, Neil Heffernan, Kathleen Collins, Cristina Heffernan, and Robert F. Murphy. 2023. Implementing and evaluating ASSISTments online math homework support at large scale over two years: findings and lessons learned. InInternational Conference on Artificial Intelligence in Education. Springer Nature Switzerland, Cham, 28–40

    2023

  9. [9]

    Josh Gardner, Christopher Brooks, Juan Miguel Andres, and Ryan S. Baker

  10. [10]

    In2018 IEEE International Conference on Big Data

    MORF: a framework for predictive modeling and replication at scale with privacy-restricted MOOC data. In2018 IEEE International Conference on Big Data. IEEE, 3235–3244

  11. [11]

    Theophile Gervet, Kenneth Koedinger, Justin Schneider, and Tom Mitchell

  12. [12]

    When is deep learning the best approach to knowledge tracing?Journal of Educational Data Mining, 12, 3, 31–54

  13. [13]

    Heffernan and Cristina Lindquist Heffernan

    Neil T. Heffernan and Cristina Lindquist Heffernan. 2014. The ASSISTments ecosystem: building a platform that brings scientists and teachers together for minimally invasive research on human learning and teaching.International Journal of Artificial Intelligence in Education, 24, 4, 470–497

    2014

  14. [14]

    Emily Jensen, Stephen Hutt, and Sidney D’Mello. 2019. Generalizability of sensor-free affect detection models in a longitudinal dataset of tens of thousands of students. InProceedings of the 12th International Conference on Educational Data Mining

    2019

  15. [15]

    Koedinger, Ryan S

    Kenneth R. Koedinger, Ryan S. Baker, Kyle Cunningham, Alida Skogsholm, Brett Leber, and John Stamper. 2010. A data repository for the EDM community: the PSLC DataShop. InHandbook of Educational Data Mining, 43–56

    2010

  16. [16]

    Morgan Lee, Alexander Frenk, Eamon Worden, Kunal Gupta, Tien Pham, Emma Croteau, and Neil Heffernan. 2025. Investigating the robustness of knowledge tracing models in the presence of student concept drift. (2025). arXiv: 2511.007 04

    2025

  17. [17]

    Xingyu Li, Yizhou Fan, Tong Li, Mladen Raković, Sumer Singh, João van der Graaf, and Dragan Gašević. 2024. The FLoRA engine: using analytics to measure and facilitate learners’ own regulation activities. (2024). arXiv: 2412.09763

    work page arXiv 2024

  18. [18]

    Qinyi Liu, Lin Li, Valdemar Švábensk `y, Conrad Borchers, and Mohammad Khalil. 2026. Measuring the impact of student gaming behaviors on learner modeling. InProceedings of the LAK26: 16th International Learning Analytics and Knowledge Conference, 106–116

    2026

  19. [19]

    Marian Cristian Mihaescu and Paul Stefan Popescu. 2021. Review on publicly available datasets for educational data mining.Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 11, 3, e1403

    2021

  20. [20]

    Baker, Adriana de Carvalho, and Jaclyn Ocumpaugh

    Luc Paquette, Ryan S. Baker, Adriana de Carvalho, and Jaclyn Ocumpaugh

  21. [21]

    InInternational Conference on User Modeling, Adaptation, and Personalization

    Cross-system transfer of machine learned and knowledge engineered Cold-Start Problem in KT Models and Implications for SafeInsights PELE, June 27– July 03, 2026, Seoul, Republic of Korea models of gaming the system. InInternational Conference on User Modeling, Adaptation, and Personalization. Springer International Publishing, Cham, 183– 194

    2026

  22. [22]

    Philip I. Jr. Pavlik, Hao Cen, and Kenneth R. Koedinger. 2009. Performance factors analysis—a new alternative to knowledge tracing. Online submission. (2009)

    2009

  23. [23]

    Radek Pelánek. 2017. Bayesian knowledge tracing, logistic models, and beyond: an overview of learner modeling techniques.User Modeling and User-Adapted Interaction, 27, 3, 313–350

    2017

  24. [24]

    Guibas, and Jascha Sohl-Dickstein

    Chris Piech, Jonathan Bassen, Jonathan Huang, Surya Ganguli, Mehran Sahami, Leonidas J. Guibas, and Jascha Sohl-Dickstein. 2015. Deep knowledge tracing. InAdvances in Neural Information Processing Systems. Vol. 28

    2015

  25. [25]

    Maria Ofelia Z San Pedro, Ryan SJ d Baker, Sujith M Gowda, and Neil T Heffer- nan. 2013. Towards an understanding of affect and knowledge from student interaction with an intelligent tutoring system. InInternational conference on artificial intelligence in education. Springer, 41–50

    2013

  26. [26]

    Valdemar Švábenský, Brendan Flanagan, Eliana Daniela López Zapata, and Atsushi Shimada. 2026. Open datasets in learning analytics: trends, challenges, and best practice.ACM Transactions on Knowledge Discovery from Data, 20, 4, 1–56

    2026

  27. [27]

    Trust by design: applying the five safes to responsible K–12 data access

    2025. Trust by design: applying the five safes to responsible K–12 data access. In https://osf.io/6p3xy/files/5ar68

    2025

  28. [28]

    Walonoski and Neil T

    James A. Walonoski and Neil T. Heffernan. 2006. Detection and analysis of off- task gaming behavior in intelligent tutoring systems. InInternational Conference on Intelligent Tutoring Systems. Springer Berlin Heidelberg, Berlin, Heidelberg, 382–391

    2006

  29. [29]

    Zichao Wang et al. 2020. Instructions and guide for diagnostic questions: the NeurIPS 2020 education challenge. (2020). arXiv: 2007.12061

    work page arXiv 2020

  30. [30]

    Philip H Winne and Nancy E Perry. 2000. Measuring self-regulated learning. InHandbook of self-regulation. Elsevier, 531–566

    2000

  31. [31]

    Eamon Worden, Cristina Heffernan, Neil Heffernan, and Shashank Sonkar

  32. [32]

    FoundationalASSIST: an educational dataset for foundational knowledge tracing and pedagogical grounding of LLMs. (2026). arXiv: 2602.00070[cs.CY]

    work page arXiv 2026

  33. [33]

    Jiani Zhang, Xingjian Shi, Irwin King, and Dit-Yan Yeung. 2017. Dynamic key-value memory networks for knowledge tracing. InProceedings of the 26th International Conference on World Wide Web, 765–774

    2017

  34. [34]

    Jiayi Zhang, Conrad Borchers, Vincent Aleven, and Ryan S. Baker. 2024. Using large language models to detect self-regulated learning in think-aloud protocols. InProceedings of the International Conference on Educational Data Mining. International Educational Data Mining Society

    2024

  35. [35]

    Jiayi Zhang, Rohini Das, Ryan Baker, and Ryan Scruggs. 2021. Knowledge trac- ing models’ predictive performance when a student starts a skill. InProceedings of the 14th International Conference on Educational Data Mining. Paris, France, 625–629

    2021