# Riiid! Answer Correctness Prediction Kaggle Challenge: 4th Place Solution Summary

arXiv: https://arxiv.org/abs/2102.04250 · HTML: https://mlsanity.com/paper/2102.04250/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 9 references that are in the map.
- First posted on arXiv 3 Feb 2021.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [A Self-Attentive model for Knowledge Tracing](https://mlsanity.com/paper/1907.06837/) (2019)
- [EdNet: A Large-Scale Hierarchical Dataset in Education](https://mlsanity.com/paper/1912.03072/) (2019)
- [Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing](https://mlsanity.com/paper/2002.07033/) (2020)
- [SAINT+: Integrating Temporal Features for EdNet Correctness Prediction](https://mlsanity.com/paper/2010.12042/) (2020)

## Built on by (most cited first, 4 of 4)

- [Time Series Analysis for Education: Methods, Applications, and Future Directions](https://mlsanity.com/paper/2408.13960/) (2024)
- [Multi-granulariy Time-based Transformer for Knowledge Tracing](https://mlsanity.com/paper/2304.05257/) (2023)
- [Incremental Knowledge Tracing from Multiple Schools](https://mlsanity.com/paper/2201.06941/) (2022)
- [Improved Sales Forecasting using Trend and Seasonality Decomposition with LightGBM](https://mlsanity.com/paper/2305.17201/) (2023)

## Abstract (arXiv)

This paper presents my solution to the challenge "Riiid! Answer Correctness Prediction" on Kaggle hosted by Riiid Labs (2020), which scores 0.817 (AUC) and ranks 4th on the final private leaderboard. It is a single transformer-based model heavily inspired from previous works such as SAKT, SAINT and SAINT+. Novel ingredients that I believed to have made a difference are the time-aware attention mechanism, the concatenation of the embeddings of the input sequences and the embedding of continuous features.