The Influence of Learner Characteristics on Conducting Scientific Inquiry Within Microworlds

The Influence of Learner Characteristics on Conducting Scientific Inquiry Within Microworlds Janice D. Gobert Juelaila J. Raziuddin Michael A. Sao Pedro Learning Sciences and Technologies Program, Worcester Polytechnic Institute, 100 Institute Road Worcester, MA 01609 USA Abstract In this study, we address whether learner characteristics can provide data to inform adaptive scaffolding of scientific inquiry skills in our learning environment, Science Assistments. We found that academic efficacy positively predicted students’ skills at generating hypotheses; another subscale, skeptical of school relevance, negatively predicted students’ skills at conducting controlled experiments, specifically controlling for variables strategies (CVS). methodological advantage over many in that we do not rely on self-report measures of cognitive processing as this presents barriers to face validity (Crippen et al., 2009). Rather, we use students’ log files as indices of students’ strategy use (Winne et al., 2000); log files have been shown to be valid measure of processing quality (Sins et al., 2007). We hypothesize that during science inquiry within an educational learning environment, students’ goal orientation and self-efficacy may influence the selection of strategies for inquiry. Some key findings in accordance with our hypothesis are as follows. Mastery learning goals (goal of deep learning) are positively correlated with deep processing (cf. Elliot et al., 1999; Sins et al., 2007) and are a good predictor of achievement (Wolters, 2004). Performance-approach goals (goal to demonstrate good performance) are associated with high grades and exam performance (Harackiewicz et al., 2000; Pintrich, 2000; Wolters, 2004). Performance-avoidance goals (goal to avoid poor performance) have been shown to be associated with poorer learning (Skaalvik, 1997) and self-handicapping behaviors (Elliot, et al., 1999; Urdan, 2004). High self- efficacy (belief that one can achieve the task at hand). is associated with deeper processing of material (Bandura, 1997; Pintrich, 1999), including in science microworlds (Sins et al., 2007) and has been found to positively predict student pre-post content learning and science inquiry skills (Nelson & Ketelhut, 2008). Keywords: science learning environment; log files; inquiry learning; learner characteristics Introduction Social cognitive theory has long recognized the inter- relationships between the learner, the learning environment, and the learner’s behaviors, and in recent years, understanding these relationships has become an important area of research (Crippen et al., 2009; Payne et al., 2007). With the proliferation of science learning environments, it is important to unpack the relationship between learner characteristics and learning processes in these environments. This is important because current conceptualizations of knowledge ontologies for science stress content, skills, and nature of science (Perkins, 1986), but these alone are not sufficient to account for the wide range of scores exhibited in achievement outcomes (Gobert & Baker, 2010). Additionally, developing empirical models of these relationships remains a challenge and thus, there is, to date, no integrated theoretical model. The results of this are twofold: 1) instructional designers are left with minimal data with which to address learner characteristics, and 2), providing adaptive support to students for conducting inquiry rich science learning environments on the basis of learner characteristics is nearly impossible. In this study, we address whether learner characteristics might provide data to inform scaffolding of scientific inquiry skills in our learning environment, Science Assistments (Gobert et al., 2007, 2009, 2010). In terms of inquiry skills, we measure hypothesizing, controlling for variables (CVS), interpreting data, and communicating findings. We use a variety of measures including log files, which capture student’s interactions within the learning environment, as well as open-format explanations. Our study provides a Methodology Participants Participants were 70 eighth grade students, ranging in age from 12-14 years, from a public middle school in Central Massachusetts. Students belonged to one of six class sections and had one of two science teachers. Materials Science Assistments Learning Environment (www.scienceassistments.org) is a learning environment for Physics, Life Science, and Earth Science that supports students to conduct scientific inquiry with microworlds. Density Microworlds. Our “Mass and Density” microworlds, focus on the Massachusetts curricular framework’s “properties of matter” learning strand for mass, volume, and density. We have two microworlds in which