Page 68 - Getting the Picture Modeling and Simulation in Secondary Computer Science Education

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Chapter 3
learning objectives of Computational Science — Magnusson’s component M1 (i.e. goals and objectives), and additionally to determine what data sources are suitable to monitor students’ learning outcomes when engaging in modeling activities, and finally to explore what specific challenges do the students experience when engaging in modeling activities.
We address the following research questions:
1. How can the intended learning outcomes of Computational Science
(modeling and simulation) be described in operational terms?
2. What data sources are suitable to monitor students’ learning
outcomes when engaging in modeling activities?
3. What specific challenges do the students experience when engaging
in modeling activities?
The first question addresses Magnusson’s component M1 — goals and
objectives. The second question contributes to Magnusson’s component M4 — methods of assessment — as we plan to use our findings as input for a later study into a CT assessment instrument (see chapter 5). The third question addresses Magnusson’s component M2 — students’ understanding as our findings will help to design teaching materials for modeling and simulation and thus indirectly contribute to Magnusson’s component M3 — instructional strategies.
3.1.2 Related work
Previous work on characterizing modeling is done mainly in the areas of mathematics and natural sciences; see the following section. Research on making students’ learning process and outcomes visible has focused mostly on CT aspects such as algorithmic thinking or programming. The employed assessment instruments range from tests with closed questions (Gouws et al., 2013b), tests with open questions (Meerbaum-Salant et al., 2013; Werner et al., 2012), surveys (Werner et al., 2012), recordings or observations of students at work (Meerbaum- Salant et al., 2013), examination of programming projects (Brennan & Resnick, 2012; Meerbaum-Salant et al., 2013; Werner et al., 2012) to interviews with students (Brennan & Resnick, 2012; Grover, 2011) and teachers (Meerbaum- Salant et al., 2013). In particular, Brennan and Resnick (2012) “are interested in the ways that design-based learning activities [...] support the development of computational thinking in young people” and they explore three approaches to assessment of the development of CT of the children engaged in such activities. They discuss strengths and limitations of each of these approaches extensively