Adaptive Task Assignment in Online Learning Environments

TL;DR

This paper presents a novel adaptive algorithm called SBTS for personalized task assignment in online learning, which estimates student skills and dynamically suggests appropriate tasks to enhance learning outcomes.

Contribution

The paper introduces SBTS, a skill-based task selector inspired by multi-armed bandit algorithms, capable of adapting to student performance in real-time.

Findings

SBTS accurately estimates student skill levels.

The algorithm adapts quickly to diverse student models.

Effective in a Java programming course scenario.

Abstract

With the increasing popularity of online learning, intelligent tutoring systems are regaining increased attention. In this paper, we introduce adaptive algorithms for personalized assignment of learning tasks to student so that to improve his performance in online learning environments. As main contribution of this paper, we propose a a novel Skill-Based Task Selector (SBTS) algorithm which is able to approximate a student's skill level based on his performance and consequently suggest adequate assignments. The SBTS is inspired by the class of multi-armed bandit algorithms. However, in contrast to standard multi-armed bandit approaches, the SBTS aims at acquiring two criteria related to student learning, namely: which topics should the student work on, and what level of difficulty should the task be. The SBTS centers on innovative reward and punishment schemes in a task and skill matrix based on the student behaviour. To verify the algorithm, the complex student behaviour is modelled using a neighbour node selection approach based on empirical estimations of a students learning curve. The algorithm is evaluated with a practical scenario from a basic java programming course. The SBTS is able to quickly and accurately adapt to the composite student competency --- even with a multitude of student models.