UPDATED: 1 FEB, 2022

SAINT+: Integrating Temporal Features for EdNet Correctness Prediction

by Dongmin Shin, Yugeun Shim, Hangyeol Yu, Seewoo Lee, Byungsoo Kim, Youngduck Choi

We introduce an addition to SAINT in SAINT+, where temporal features are added to the list of inputs used to train our model.

Definition:

SAINT(Separated Self-AttentIve Neural Knowledge Tracing) is one of our knowledge tracing model that captures complex relations between educational exercises and student responses to assess a student’s knowledge state. Learn more about it here
Empirical evaluations show a 1.25% increase in AUC compared to SAINT, and a maximum 3.61% increase in AUC compared to other deep learning-based knowledge tracing models such as DKT, DKVMN, and SAKT.

/

Loading PDF…

Get your score in 40 min!

Just do 1/4 of a full test and get actionable insights.

R.test is an AI-powered diagnostic test platform that evaluates student’s test readiness. Our mission is to get rid of inefficiency and inequality from test prep industry by making assessments more adaptive, accessible, and reliable.

â“’ 2023 Riiid, Inc. All Rights Reserved

521, Teheran-ro, Gangnam-gu, Seoul, Korea

contactus@rtest.ai

College Board® is a trademark registered by the College Board, which is not affiliated with, and does not endorse, this website.

Neither Riiid, Inc. or R.test is affiliated with College Board® and do not have access to College Board's proprietary data.

ACT® is a registered trademark of ACT, inc. This website is not endorsed or approved by ACT, inc.

Neither Riiid, Inc. or R.test is affiliated with ACT, Inc. and do not have access to ACT’s proprietary data.