Triple scheme of learning support design for scientific discovery learning based on computer simulation: experimental research

Abstract

Learning support studies involving simulation-based scientific discovery learning have tended to adopt an ad hoc strategies-oriented approach in which the support strategies are typically pre-specified according to learners' difficulties in particular activities. This article proposes a more integrated approach, a triple scheme for learning support design on the basis of the systematic analysis of the internal conditions of scientific discovery learning. The triple learning support scheme involves: (a) interpretative support that helps learners with knowledge access and the generation of meaningful and integrative understandings; (b) experimental support that scaffolds learners in systematic and valid experimental activities; and (c) reflective support that increases learners' self-awareness of the discovery processes and prompts their reflective abstraction and integration. Two experiments were conducted with eighth graders (13-year-olds) to examine the effects of these learning supports embedded into a simulation program on floating and sinking. The overall results support the main hypotheses that learning supports in a simulation environment should be directed towards the three perspectives to invite meaningful, systematic, and reflective discovery learning.

How effective is instructional support for learning with computer simulations?

“…Zhang et al (2004) distinguished three spheres of learning support in their scheme of scientific discovery learning with computer simulations: (a) Interpretative support scaffolds learners to access and activate prior knowledge as well as to generate appropriate hypotheses; (b) Experimental support scaffolds learners in designing verifiable scientific experiments, predicting and observing simulation outcomes, and drawing appropriate conclusions; (c) Reflective support helps learners to increase their self-awareness of the learning process and supports the activation of abstract and reflective integration of their discoveries. The four categories of learning difficulties with computer simulations suggested by de Jong and van Joolingen (1998) can be integrated (cf.…”

“…Empirical studies have pointed out that students frequently encounter problems with processes of scientific discovery learning Kirschner et al 2006;Manlove et al 2006;Rey 2010;Zhang et al 2004). However, by providing learning environments for students by using computer simulations, specific instructional support can foster successful knowledge acquisition .…”

Teacher support in computer-supported lab work: bridging the gap between lab experiments and students’ conceptual understanding

“…Other studies have highlighted the positive effects of integrating reflection prompts with support tools providing interpretative or experimental support in their inquiry process (Reid et al 2003;Zhang et al 2004). The support tools in SCY-Lab were designed to support students' reflection in their process of analysing and reporting their results from the gel electrophoresis experiment.…”

“…Furthermore, especially Blow-achieving^students benefitted from this type of support. Other studies have highlighted the positive effects of integrating reflection prompts with prompts that give students interpretative or experimental support in their inquiry process (Reid et al 2003;Zhang et al 2004). In a non-experimental study, Sandoval and Reiser (2004) focused on the effect of prompts in relation to students' reflection in peer discussion.…”

Detangling the Interrelationships Between Self-Regulation and Ill-Structured Problem Solving in Problem-Based Learning

“…In historical inquiry, learners need to constantly monitor their state of understanding in light of new information to evaluate whether the new information helps answer the questions and whether the coherence between an event and causes has been achieved in the inquiry process. In science inquiry, the learner may continuously engage in a mindful coordination between hypotheses and evidence gathered from experiments and draw evidence-based conclusions (Klahr & Dunbar, 1998;Zhang, Chen, Sun, & Reid, 2004). At times, the process may lead back to the planning and execution of another experiment (Pedaste et al, 2015).…”