Knowledge State Networks for Effective Skill Assessment in Atomic Learning

TL;DR

This paper introduces Knowledge State Networks, a neural network-based framework that accelerates skill assessment in personalized online learning by predicting complete knowledge states from partial data, enabling faster and more efficient evaluations.

Contribution

The paper presents a novel neural network framework for rapid knowledge state assessment, improving speed and granularity in personalized skill-based online learning.

Findings

Significant reduction in the number of questions needed for assessment

Effective prediction of full knowledge states from partial information

Enables fine-grained skill assessments without lengthy testing

Abstract

The goal of this paper is to introduce a new framework for fast and effective knowledge state assessments in the context of personalized, skill-based online learning. We use knowledge state networks - specific neural networks trained on assessment data of previous learners - to predict the full knowledge state of other learners from only partial information about their skills. In combination with a matching assessment strategy for asking discriminative questions we demonstrate that our approach leads to a significant speed-up of the assessment process - in terms of the necessary number of assessment questions - in comparison to standard assessment designs. In practice, the presented methods enable personalized, skill-based online learning also for skill ontologies of very fine granularity without deteriorating the associated learning experience by a lengthy assessment process.