Does teaching students how to explicitly model the causal structure of systems improve their understanding of these systems?

Abstract

Does teaching students how to explicitly model the causal structure of systems improve their understanding of these systems?. European Journal of Engineering Education, 39(4): 391-411Access to the published version may require subscription. N.B. When citing this work, cite the original published paper. Permanent link to this version:http://urn.kb.se/resolve?urn=urn:nbn:se:fhs:diva-5242 1 Does teaching students how to explicitly model the causal structure of systems improve their understanding of these systems?If students really understand the systems they study, they would be able to tell how changes in the system would affect a result. This demands that the students understand the mechanisms that drive its behaviour. The study investigates potential merits of learning how to explicitly model the causal structure of systems. The approach and performance of 15 system dynamics students, who are taught to explicitly model the causal structure of the systems they study, was compared with the approach and performance of 22 engineering students, who do generally not receive such training. The task was to bring a computer simulated predator-and-prey ecology to equilibrium. The system dynamics students were significantly more likely than the engineering students to correctly frame the problem. They were not much better at solving the task, however. It seemed that they had only learnt how to make models and not how to use them for reasoning.Keywords: engineering education research; modelling; external representations, dynamic systems, qualitative reasoning IntroductionThe literature on modelling tends to focus on the quantitative model that results when the laws of physics have been applied and relevant measurements made. The goal of the modelling process is a model that lends itself to mathematical treatment (e.g., Lesh & Doerr, 2003;Redish & Smith, 2008). The underlying qualitative model appears to be seen as self-evident and/or implicit in the quantitative model. True as this may be,we cannot be certain that it is evident to the students. When the mathematical model has been manipulated and transformed, and a result calculated, the result has to be given meaning (Bissel & Dillon, 2000; Kehler & Lester, 2003;Redish & Smith, 2008). The students need to understand why a result is as it is. In other words, they have to be able to interpret their results. Furthermore, if they 2 really understand the system under study they would be able to tell changes in the system would affect a result. This demands that the students understand the qualitative structure of the system, i.e., the mechanisms that drive its behaviour.The aim of this study is to investigate the potential merits of training students in how to explicitly model the qualitative, or causal, structure of the systems they study.Do students who receive such training understand the systems they study better than students who do not? It might be the case that students spontaneously model the causal structure when the need arises, or that it is sufficient that the cau...

Teaching the dynamics of the growth of a business venture through transparent simulations

“…Nonetheless, transparent simulations involve additional information in the form of diagrams, which benefit only those learners who can correctly understand and interpret those diagrams (Landriscina, 2013). For example, students who are not conversant with systems dynamics have difficulty recognizing and comprehending dynamic structures as they are not able to properly read and interpret causal-loop-diagrams and stock and flow diagrams (Alessi, 2000;Davidsen & Spector, 2015;Jensen, 2014).…”

Effectiveness of blended physics laboratory experimentation on pre-service physics teachers’ understanding of the nature of science

“…These group of authors argued that physicality is important in which manipulation of concrete materials & apparatus (Chan & Black, 2006) and better understanding of multidimensional object/system (Jones et al, 2003) is possible. Similarly, real experiment is better because virtual simulations tend to create virtual worlds (Jensen, 2014;Lindwall & Ivarsson, 2010). Others still claim that not only virtual experimentation, but real experimentation challenges also learning the physical world.…”

“…This group of academics prefers virtual experiments to real/physical ones. However, virtual experimentation is criticized since it deprives of students hands-on manipulation (Hofstein & Lunetta, 2004), oversimplification (Bernhard, 2018), create virtual worlds (Jensen, 2014;Lindwall & Ivarsson, 2010), and mainly unrelated with student learning. Still others acknowledge the affordances of each modes of experimentation (Gire et al, 2010;Marshall & Young, 2006).…”

“…Recently, authors have argued that blended experimentation is the best way to take advantage of real and virtual experimentation using affordances from the two. For example, many studies reported that blending real with virtual physics experimentation benefited students than real or virtual experimentations alone (de Jong et al, 2013;Olympiou & Zacharia, 2012, 2014. However, this group of scholars are criticized for taking middle line without strong arguments.…”