Student assessment is one of the most fundamental tasks in the field of AI
Education (AIEd). One of the most common approach to student assessment is
Knowledge Tracing (KT), which evaluates a student's knowledge state by
predicting whether the student will answer a given question correctly or not.
However, in the context of multiple choice (polytomous) questions, conventional
KT approaches are limited in that they only consider the binary (dichotomous)
correctness label (i.e., correct or incorrect), and disregard the specific
option chosen by the student. Meanwhile, Option Tracing (OT) attempts to model
a student by predicting which option they will choose for a given question, but
overlooks the correctness information. In this paper, we propose
Dichotomous-Polytomous Multi-Task Learning (DP-MTL), a multi-task learning
framework that combines KT and OT for more precise student assessment. In
particular, we show that the KT objective acts as a regularization term for OT
in the DP-MTL framework, and propose an appropriate architecture for applying
our method on top of existing deep learning-based KT models. We experimentally
confirm that DP-MTL significantly improves both KT and OT performances, and
also benefits downstream tasks such as Score Prediction (SP).

本文提出了一种多任务学习框架，称为 Dichotomous-Polytomous Multi-Task Learning（DP-MTL），该框架综合了知识追踪和选项追踪，以更精确地评估学生的知识状态，并证实该框架可以明显提高知识追踪和选项追踪的性能，并 profit downstream tasks such as Score Prediction（SP）。

不留任何任务：多任务学习知识跟踪和选项跟踪，以提高学生评估能力

No Task Left Behind: Multi-Task Learning of Knowledge Tracing and Option Tracing for Better Student Assessment

Purpose: Manual feedback in basic RMIS training can consume a significant
amount of time from expert surgeons' schedule and is prone to subjectivity.
While VR-based training tasks can generate automated score reports, there is no
mechanism of generating automated feedback for surgeons performing basic
surgical tasks in RMIS training. In this paper, we explore the usage of
different holistic features for automated skill assessment using only robot
kinematic data and propose a weighted feature fusion technique for improving
score prediction performance.
Methods: We perform our experiments on the publicly available JIGSAWS dataset
and evaluate four different types of holistic features from robot kinematic
data - Sequential Motion Texture (SMT), Discrete Fourier Transform (DFT),
Discrete Cosine Transform (DCT) and Approximate Entropy (ApEn). The features
are then used for skill classification and exact skill score prediction. Along
with using these features individually, we also evaluate the performance using
our proposed weighted combination technique.
Results: Our results demonstrate that these holistic features outperform all
previous HMM based state-of-the-art methods for skill classification on the
JIGSAWS dataset. Also, our proposed feature fusion strategy significantly
improves performance for skill score predictions achieving up to 0.61 average
spearman correlation coefficient.
Conclusions: Holistic features capturing global information from robot
kinematic data can successfully be used for evaluating surgeon skill in basic
surgical tasks on the da Vinci robot. Using the framework presented can
potentially allow for real time score feedback in RMIS training.

本文研究了使用机器人运动数据中的整体特征进行自动化技能评估的方法，提出了一种加权特征融合技术来提高分数预测表现，并探讨了其在外科医生基础手术任务中的实际应用价值。实验结果表明，这些整体特征优于以前的基于 HMM 的状态 - of-the-art 方法，对 RMIS 培训中的外科医生技能评估具有潜在的实时评分反馈作用。