Paz, L., Dörr, B., Altmeyer, K., Peters, N., & Werth, D.(2023)
In: EDULEARN23 Proceedings, DOI:

Link zur Publikation:


Almost all companies expect their employees to undergo continuous training. Moreover, continuing education is important for personal development and professional change.

Even though high budgets are spent on further education, their long-lasting benefit is often not confirmed. Research shows that it is crucial to match learning offers with individual needs, preferences and characteristics.

In the field of education, it is common to assign people to specific learning types or styles (e.g., visual or verbal). However, prior research questions the existence of distinct and persistent types of learners but rather refers to certain learning approaches a person can take. Consequently, to provide personalized recommendations for continuing education courses that align with individual learning approaches, a tool that assesses a learner’s current learning behavior, preferences, and prerequisites is required.

The present paper describes the conceptual development and technical implementation of a subjective rating-based instrument assessing learning approaches and compiling them to learning profiles. On this basis, the newly-developed tool provides learners with advice on individually matching learning formats and strategies to improve their learning habits. The tool is tailored for employees interested in continuing education.

The presented learning profiles matching tool is divided into four parts. The first part explores the learner’s preferred characteristics of learning formats. These characteristics were deduced by analyzing existing digital learning formats and categorizing them into four dimensions: synchronicity, interaction with the teacher/instructor, collaboration with other learners, and digitality. The learner’s preference for each characteristic is assessed with three items, and based on their responses, participants receive recommendations for suitable learning formats.
The second part evaluates the learner’s use of learning strategies using scales adapted from prior literature. This section includes cognitive learning strategies, metacognitive learning strategies, and internal resource time management. Based on their results, learners receive advice on how to improve their learning strategies.

The third part of the questionnaire investigates the frequency and quality of the learner’s learning breaks. Based on their responses, they receive advice on how to optimize their breaks to make subsequent learning phases more efficient.

The final part of the questionnaire examines a learner’s circadian rhythm to determine their chronotype. Based on their responses, learners receive suggestions on the best times of day to complete courses or learning tasks that require high levels of concentration.
The presented learning profile matching tool provides learners with valuable insights into their learning preferences and strategies, which can be used to increase their learning success. Regarding practical implementations, recommendation systems can use this information to make more suitable learning recommendations. Moreover, companies can save money by suggesting more suitable trainings to their employees that lead to more sustainable learning benefits.