History of Model-It™

Theoretical advancements

Implementations and Findings

Research Questions as stated in Jackson et al., 2000:

Quality of Building and Testing Models:
  • Could students build good quality models in a short period of time?
  • What strategies did students develop for testing their models?
  • Was there a relationship between the quality of their testing and the quality of the resultant model?
Conversations as Evidence for Understanding Modeling:
  • How did students think and talk about modeling while engaged in modeling activities?
  • How do student conversations reflect their understanding of the modeling process?
Also:
What is the impact of the above features on the behavior of the students?

 

 

Throughout the implementations, the researchers had recruited participants from a local high school in the context of the ScienceWare project a research project in collaboration with science teachers to develop, pilot and assess a three-year science curriculum with emphasis on modeling and project-based activities (Jackson at al., 2000).


Implementations of 1994:

There were 3 studies in 1994 (Figure 1), investigating the potentialities of the software in high school contexts. The researchers had piloted the first version of the software with 6 ninth grade students. They conducted one-hour sessions for one-week periods (a total of around 4 hours), using the software twice; once time as the final, open-ended project of the class and then with the same students later in the year. Students had brief manuals that guided them through the process of using the software and constructing their models. They were collaborating in pairs of 2 and were able to create their own designs ad represent the scientific phenomena observed based on their choice.

Later on, they used 22 ninth-grade participants that were working in pairs for 4 50-minute class periods. The researchers videotaped and audio recorded the students while using Model-It™ to create their scientific models. In the third study, the same students (tenth-graders at that point) worked in pairs for a one-week period to create their scientific representations in Model-It™. In this last study, students were, again videotaped and audio recorded, as well as interviewed by the researchers upon the end of the implementation.

Figure 1: Subjects and data from the 3 studies conducted in 1994


Implementations of 1997:

Pilot testing involved 22 ninth-grade students who had spent several months researching ecosystems of a stream close to their school. For that, they had gathered data and had identified the quality of the water. They used Model-It™ at the end of the school year to create models of the ecosystem they had investigated. In groups of two and in a week’s time, they learned how to use the software and designed simple models. Students made hypotheses and tested them in order to construct their initial models. Later in the week, their task was to create their own model to represent either biodiversity, cultural eutrophication, or land uses. The role of the teachers was to provide assistance whenever needed. Towards the end of the week the researcher led a class discussion related to the constructed models, providing opportunities for the students to describe their models. In that year, Stratford, Krajcik, and Soloway used 50 students in an ecology class to analyze the final models of the software. They videotaped several conversations and interviewed 16 of the students while working on their models on streams. The analysis of the data indicated tat 75% of the models were running well when tested, they were meaningful coherent, and accurate.



Figure 2: A concept map that students created, related to the stream ecosystem they investigated.

Overall, the findings indicate that students can easily create and run simple scientific models, even if they have no knowledge on the subject to-be-learned. Model-It™ can meet the diverse learner needs and facilitate several kinds of modeling experiences (Jackson, Stratford, Krajcik, & Soloway, 2000). Modeling is a significant skill that facilitates understanding of several topics of the curriculum, not only science and mathematics, but social sciences, history, etc. It’s importance lies in the fact that it enhances understanding of dynamic changes in systems. Qualitative modeling software, in particular promotes understanding of alternative ways of explaining complexity (Jackson, Stratford, Krajcik, & Soloway, 2000) and participate in real- life and virtual communities. Beyond that, creating concept maps to represent the complex relationships between the elements of a system is crucial for qualitative understanding and participation.
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