Towards an Empirical Perspective on Multi-Level Modeling and a Comparison with Conventional Meta Modeling
Sybren de Kinderen, Monika Kaczmarek, Kristina Rosenthal · 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C) · 2021
Although prior work exist focusing on how, when, and why multi-level modeling (MLM) could be applied, presently, surprisingly little is known about how learning processes of MLM proceed, what modeling difficulties learners experience, or what the comprehensibility of multi-level models, compared to their conventional counterparts, is. As a response, we take an empirical perspective on MLM. We are planning for a study collecting data on subjects’ modeling processes in order to (1) identify, by using the concept of cognitive breakdowns, modeling difficulties these subjects face while using MLM, and (2) by combining various theories from cognitive linguistics, e.g., to analyze how participants decide upon selected abstractions. In this position paper, we present the design of the planned study as well as theoretical lenses we will follow. We also share first experiences from a conducted pilot study, in terms of its focus and set-up.