TITLE: 
Pick the Moment: Identifying Critical Pedagogical Decisions Using Long-Short Term Rewards

ABSTRACT:
Identifying critical decisions is one of the most challenging decision-making problems in real-world applications. In this work, we propose a novel Reinforcement Learning (RL) based Long-Short Term Rewards (LSTR) framework for critical decisions identification. RL is a machine learning area concerning with inducing effective decision-making policies, following which result in the maximum cumulative reward. Many RL algorithms find the optimal policy via estimating the optimal Q-values, which specify the maximum cumulative reward the agent can receive. In our LSTR framework, the long term rewards are defined as Q-values and the short term rewards are determined by the reward function. Experiments on a synthetic GridWorld game and real-world Intelligent Tutoring System datasets show that the proposed LSTR framework indeed identifies the critical decisions in the sequences. Furthermore, our results show that carrying out the critical decisions alone is as effective as a fully-executed policy.

AUTHORS:
Song Ju, Guojing Zhou, Tiffany Barnes, Min Chi

NOTE:
Presented in the workshop as part of the ENCORE track. This paper is from EDM 2020 conference. The paper can be accessed at the following link: https://educationaldatamining.org/files/conferences/EDM2020/papers/paper_167.pdf