Supporting learning of variable control in a computer-based biology environment: Effects of prompting college students to reflect on their own thinking

Abstract

While instruction on control of variables has been shown to be effective, especially when it encourages students to focus explicitly on rules or procedures, little evidence of application to novel problems has been obtained. We hypothesized that prompting students to understand their own learning processes while doing experiments involving control of variables would allow them to activate their repertoire of knowledge and strategies and learn in a way that would enhance transfer of learning. Students were assigned to one of four versions of a computer-based biology simulation learning environment, each employing a different type of prompt: reason justification, rule based, emotion focused, or none (control). Learning in this computer environment, college biology students designed and conducted experiments involving control of variables. Students' ability to solve both contextually similar (near transfer) and contextually dissimilar (far transfer) problems was assessed. The treatment groups performed equally well on contextually similar problems. However, on a contextually dissimilar problem, the reason justification group had significantly higher scores than the other groups. Qualitative data showed that the reason justification prompts directed students' attention to understanding when, why, and how to employ experiment design principles and strategies, and this in turn helped students to transfer their understanding to a novel problem.

How effective is instructional support for learning with computer simulations?

“…The first hypothesis argued that learners' self-generated explanations of the simulation outcome would be more effective than explanatory feedback of the computer program, and this in turn would be more effective than no feedback at all (Chi et al 1989(Chi et al , 1994Lin and Lehmann 1999;Moreno 2004).…”

“…To enhance the effectiveness of data interpretation, it seems helpful to offer learners feedback about implemented actions (Lin and Lehmann 1999;Moreno 2004). Differentiated feedback from the computer program about the simulation outcome can support learners' knowledge acquisition.…”

“…Other studies have substantiated the assumption that learners' descriptions and interpretations of the simulation outcome lead to more effective knowledge acquisition. Lin and Lehmann (1999) conducted a study in which learners were requested to justify the simulation outcome after they made use of computer-simulated experiments. This kind of instructional support led to a significant improvement of knowledge application on a transfer task.…”

“…As an instructional intervention to support data interpretation, learners either (a) were requested to describe and scientifically interpret the simulation outcome (Lin and Lehmann 1999) or (b) received a description and biological interpretation of the simulation outcome from the computer program (Moreno 2004). In the solution condition in which participants were asked to generate a solution, learners were requested to describe and to interpret their own simulation outcome in written form on their assignment (see ''Appendix'').…”

Metacognitive Scaffolding Agent Based on BDI Model for Interactive Learning Environments

“…The most influential theories with respect to agent technology, it"s the BDI paradigm. The agent use this model to allow metacognitive scaffolding and support mentioned in the previous section, the treatment of internal metacognitive reasoning of the learners during a cognitive activity, to promote their abilities to think and promote autonomy [7].…”