Investigations of a complex, realistic task: Intentional, unsystematic, and exhaustive experimenters

Abstract

This study examines how students' experimentation with a virtual environment contributes to their understanding of a complex, realistic inquiry problem. We designed a week‐long, technology‐enhanced inquiry unit on car collisions. The unit uses new technologies to log students' experimentation choices. Physics students (n = 148) in six diverse high schools studied the unit and responded to pretests, posttests, and embedded assessments. We scored students' experimentation using four methods: total number of trials, variability of variable choices, propensity to vary one variable at a time, and coherence between investigation goals and experimentation methods. Students made moderate, significant overall pretest to posttest gains on physics understanding. Coherence was a strong predictor of learning, controlling for pretest scores and the other experimentation measures. We identify three categories of experimenters (intentional, unsystematic, and exhaustive) and illustrate these categories with examples. The findings suggest that students must combine disciplinary knowledge of the investigation with intentional investigation of the inquiry questions in order to understand the nature of the variables. Mechanically executing well‐established experimentation procedures (such as varying one variable at a time or comprehensively exploring the experimentation space) is less likely to lead students to valuable insights about complex tasks. Our proposed categories extend and refine previous efforts to categorize experimenters by linking scientific procedures with understanding of the science discipline. © 2011 Wiley Periodicals, Inc. J Res Sci Teach 48: 745–770, 2011

Distributed Revisiting: an Analytic for Retention of Coherent Science Learning

“…These afford students the opportunity to evaluate their understanding and make new links between ideas across activities. The significant advantage of revisiting the dynamic visualization is consistent with evidence for their value when they are implemented with guidance in online units (McElhaney & Linn, 2011;Ryoo & Linn, 2012;Svihla & Linn, 2012a), and also consistent with a differentiation between simple recalling and more effortful relearning (Rawson & Dunlosky, 2011).…”

Exploring effectiveness of simulation-based inquiry learning in science with integration of formative assessment

“…B Reconceptualization (Hsu 2008;Jaakkola and Nurmi 2008;Jaakkola et al 2011;Lazonder and Ehrenhard 2014;McElhaney and Linn 2011;Olympiou and Zacharia 2012;Olympiou et al 2013;Renken and Nunez 2013;Srisawasdi et al 2013;Srisawasdi and Kroothkeaw 2014;Srisawasdi and Sornkhatha 2014;Suits and Srisawasdi 2013;Trundle and Bell 2010;Zacharias et al 2008), the results from both of our two studies indicated that secondary school students had better conceptual achievement of science by interacting with inquiry-based learning of science by simulation. These findings indicate, and could be argued, that SimIn-FA in secondary education can be used to enhance students' conceptual development of science concepts and their understanding on buoyancy-driven concepts, in particular.…”

“…To address the learning problems in science outlined in the previous section, simulation-based inquiry (SimIn) has increasingly become a pedagogical approach for enhancing students' conceptual learning and development in school science (Srisawasdi and Kroothkeaw 2014;Srisawasdi and Sornkhatha 2014) where they can perform their own investigations, while at the same time develop knowledge of the concept modeled by the simulation Lazonder et al 2010;Suits and Srisawasdi 2013;Vreman-de Olde et al 2013). Previous studies have shown that the SimIn learning environment, as an active agent in the process of conceptual change, effectively promotes better conceptual understanding in science for students (Hsu 2008;Jaakkola and Nurmi 2008;Jaakkola et al 2011;Lazonder and Ehrenhard 2014;McElhaney and Linn 2011;Olympiou and Zacharia 2012;Olympiou et al 2013;Renken and Nunez 2013;Srisawasdi and Kroothkeaw 2014;Srisawasdi and Sornkhatha 2014;Suits and Srisawasdi 2013;Trundle and Bell 2010;Zacharias et al 2008). Further, in the interest of conceptual change research, it is a challenge to extend strategic ways to use SimIn for making conceptual change occur efficiently.…”

Supporting learners’ experiment design

“…Low prior knowledge learners lack internal information about concepts and meaningful relationships between concepts, and they show less sophisticated strategies than high prior knowledge learners (Alexander and Judy 1988;Schauble et al 1991). As a result, in inquiry learning low prior knowledge learners often conduct unsystematic experiments in which learners fail to vary the appropriate variable and in which there is a mismatch between the research question and the conducted experimental trials, whereas high prior knowledge learners show goaloriented inquiry behavior and conduct well-structured experiments (Hmelo et al 2000;McElhaney and Linn 2011). In other words, high prior knowledge learners are better equipped to structure a task themselves than low prior knowledge learners, which could explain why we found significantly higher learning gains in favour of the EDT for low prior knowledge learners only.…”