Adaptive Task Assignment in Online Learning Environments
Per-Arne Andersen, Christian Kr{\aa}kevik, Morten Goodwin, Anis Yazidi

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…
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Taxonomy
TopicsIntelligent Tutoring Systems and Adaptive Learning · Online Learning and Analytics · Advanced Bandit Algorithms Research
