The effectiveness of lightweight automated support for learning about dynamic systems with qualitative representations

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


We developed an application which allows learners to construct qualitative representations of dynamic systems to aid them in learning subject content knowledge and system thinking skills simultaneously. Within this application, we implemented a lightweight support function which automatically generates help from a norm-representation to aid learners as they construct these qualitative representations. This support can be expected to improve learning. Using this function it is not necessary to define in advance possible errors that learners may make and the subsequent feedback. Also, no data from (previous) learners is required. Such a lightweight support function is ideal for situations where lessons are designed for a wide variety of topics for small groups of learners. Here, we report on the use and impact of this support function in two lessons: Star Formation and Neolithic Age. A total of 63 ninth-grade learners from secondary school participated. The study used a pretest/intervention/post-test design with two conditions (no support vs. support) for both lessons. Learners with access to the support create better representations, learn more subject content knowledge, and improve their system thinking skills. Learners use the support throughout the lessons, more often than they would use support from the teacher. We also found no evidence for misuse, i.e., 'gaming the system', of the support function.
Original languageEnglish
Title of host publicationSAC '24: Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing
Number of pages10
Publication statusPublished - Apr 2024


Dive into the research topics of 'The effectiveness of lightweight automated support for learning about dynamic systems with qualitative representations'. Together they form a unique fingerprint.

Cite this