Publication
A Computerized Adaptive Competency-based Placement Test to Determine the Optimal Entry Point in Online Courses
Kim Alexa Schwarz; Leo Sylvio Rüdian; Christian Kellermann
In: Communications in Computer and Information Science. European Conference on Artificial Intelligence (ECAI-2024), International Workshop on AI in Education and Educational Research (AIEER), located at European Conference on Artificial Intelligence, October 19-20, Santiago de Compostela, Spain, Pages 161-174, CCIS, Vol. 2519, ISBN 978-3-031-93408-7, Springer, 2025.
Abstract
Personalization in online learning can be found in several do-
mains, such as language learning, chemistry, or math education. Going
beyond these domains is rarely seen. Due to fixed learning progressions,
learners enrolled must complete the entire course to succeed. Learners’
prior knowledge is often not considered. This can lead to disengagement
as learners are not challenged appropriately if task difficulty levels do
not align with their skills. To allow a flexible course entry, we introduce
a placement test to estimate learners’ competency levels employing the
item response theory and implement a computerized competency-based
adaptive placement test in real-world online courses of the KI-Campus.
We illustrate the challenges of applying item response theory with mul-
tiple competencies in a real-world setting and demonstrate how to iden-
tify a domain-independent, optimal course entry point tailored to the
learner. This application promises to optimize the learning time con-
sidering learners’ prior knowledge, which in turn, can reduce drop-out
rates.