Document Type thesis Author Name Bachmann, Matthew Knapp URN etd-043012-114659 Title Biology Microworld to Assess Students’ Content Knowledge and Inquiry Skills and Leveraging Student Modeling to Prescribe Design Features for Scaffolding Learning Degree MS Department Computer Science Advisors Janice Gobert, Advisor Joe Beck, Advisor Keywords User Modeling Assistments Science Assistments Computer Science Biology Date of Presentation/Defense 2011-05-05 Availability unrestricted
It is the underlying presupposition of the Science Assistments research (http://www.scienceassistments.org) that students need to leave school with a basic understanding of science and grounding in inquiry skills (NSES, 1996; NRC, 2011). We also believe that the current standard for assessing these skills, the Massachusetts Comprehensive Assessment System, is inadequate in terms of the rote- oriented multiple-choice tests.
This thesis describes the creation of a simulation, or microworld, of an animal cell. This content is aligned with the Massachusetts science frameworks for middle school Life Science (Massachusetts Department of Education, 2006). Our microworld, Simcell, gives students an opportunity to form hypotheses, design experiments to test these hypotheses, and analyze their data collected during the experiment. The microworlds track students' actions in log files that can be analyzed by the system to provide fine tuned assessments of students, and based on these assessments, in the future, we will provide dynamic help though scaffolds to students who are struggling with inquiry (Gobert et al, 2007; 2009; Gobert et al, in press).
Over the course of two studies, this biology microworld was designed, developed, and fined tuned through the use of domain experts and student pilot data. We also analyzed the student logs in order to try to model students' learning so we can predict useful times for the system to come in and help. In study one we identify a potential point to remediate struggling students. In study two we conducted a series of logistic and linear regressions to predict student knowledge. However, due to the large number of different variables and the relatively small size of the dataset, we could not be confident in the results that were obtained. Many attempts to reduce the number of variables used in the model were tried, but these methods did not yield more promise than the original set.
Finally, we finish this report with a new path for researchers to consider, namely, looking at the data in different ways in order to find a way of viewing the data that would allow for known successful student modeling techniques such as Bayesian Knowledge Tracing.
Files MB_Final_Thesis_2.pdf simcellONETests.pdf simcellTWOTests.pdf
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