Pith. sign in

REVIEW 10 cited by

Instructions and Guide for Diagnostic Questions: The NeurIPS 2020 Education Challenge

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2007.12061 v3 pith:U4PE3K4X submitted 2020-07-23 cs.CY cs.HCcs.LG

Instructions and Guide for Diagnostic Questions: The NeurIPS 2020 Education Challenge

classification cs.CY cs.HCcs.LG
keywords studentsquestionsanswersdiagnosticeducationaccuratelyeducationalpersonalized
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
Share X Bluesky LinkedIn Reddit HN
read the original abstract

Digital technologies are becoming increasingly prevalent in education, enabling personalized, high quality education resources to be accessible by students across the world. Importantly, among these resources are diagnostic questions: the answers that the students give to these questions reveal key information about the specific nature of misconceptions that the students may hold. Analyzing the massive quantities of data stemming from students' interactions with these diagnostic questions can help us more accurately understand the students' learning status and thus allow us to automate learning curriculum recommendations. In this competition, participants will focus on the students' answer records to these multiple-choice diagnostic questions, with the aim of 1) accurately predicting which answers the students provide; 2) accurately predicting which questions have high quality; and 3) determining a personalized sequence of questions for each student that best predicts the student's answers. These tasks closely mimic the goals of a real-world educational platform and are highly representative of the educational challenges faced today. We provide over 20 million examples of students' answers to mathematics questions from Eedi, a leading educational platform which thousands of students interact with daily around the globe. Participants to this competition have a chance to make a lasting, real-world impact on the quality of personalized education for millions of students across the world.

discussion (0)

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

Forward citations

Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. KG-SoftMAP: Soft Knowledge-Graph Priors for Bayesian Network Structure Learning from Sparse Discrete Data

    cs.LG 2026-06 unverdicted novelty 7.0

    KG-SoftMAP incorporates soft, confidence-weighted priors from a knowledge graph into MAP estimation for Bayesian network structure learning, recovering substantial directed structure from sparse discrete data where da...

  2. From Text to Parameters: Predicting Item Parameters from Embedding Regularization with Reliability and Design Ceilings

    cs.CL 2026-07 accept novelty 6.0

    Text embeddings recover 57-63% of the reliable variance in exam-item difficulty, and apparent differences in predictability across IRT parameters are mostly artifacts of calibration noise rather than text signal.

  3. Skill Neologisms: Towards Skill-based Continual Learning

    cs.LG 2026-05 unverdicted novelty 6.0

    Skill neologisms are optimized soft tokens that improve LLM performance on targeted skills without weight updates and allow zero-shot composition for continual learning.

  4. Skill Neologisms: Towards Skill-based Continual Learning

    cs.LG 2026-05 unverdicted novelty 6.0

    Skill neologisms are optimized soft tokens that enhance specific LLM skills and support zero-shot composition on synthetic and Skill-Mix tasks.

  5. Embedding Enhancement via Fine-Tuned Language Models for Learner-Item Cognitive Modeling

    cs.CL 2026-04 unverdicted novelty 6.0

    EduEmbed fine-tunes language models in two stages to add semantic information to learner-item embeddings and improve performance on cognitive diagnosis and adaptive testing tasks.

  6. Graph-Based Alternatives to LLMs for Human Simulation

    cs.CL 2025-11 conditional novelty 6.0

    GEMS formulates close-ended human-behavior simulation as link prediction on a heterogeneous graph and matches or exceeds LLM performance with three orders of magnitude fewer parameters across three datasets and three ...

  7. KG-SoftMAP: Soft Knowledge-Graph Priors for Bayesian Network Structure Learning from Sparse Discrete Data

    cs.LG 2026-06 conditional novelty 5.5

    A finite-strength logit prior from a weighted knowledge graph lets Bayesian network structure learning recover directed edges under extreme sparsity where data-only methods fail.

  8. Archetypes or ability? Clustering for modelling student mathematical competence

    cs.CY 2026-06 conditional novelty 5.0

    Clustering 119,034 UK maths mock-exam results shows student performance is dominated by overall ability, not discrete archetypes, with explainable models reaching ~78% accuracy.

  9. MCQ Difficulty Prediction via Modeling Learner Heterogeneity Using Data-Driven Cognitive Profiling

    cs.CY 2026-04 unverdicted novelty 5.0

    A framework using latent class analysis on student data to define personas, LLM simulations of their responses, and ridge regression improves IRT difficulty prediction for MCQs over baselines.

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

    cs.HC 2026-06 unverdicted novelty 4.0

    Replication of cold-start analysis in KT models on FoundationalASSIST shows performance varies by practice opportunities and problem types, highlighting reproduction challenges and SafeInsights utility.