Successfully carrying out complex learning-tasks through guiding teams’ qualitative and quantitative reasoning

Abstract

This study investigated whether and how scripting learners' use of representational tools in a computer supported collaborative learning (CSCL)-environment fostered their collaborative performance on a complex business-economics task. Scripting the problem-solving process sequenced and made its phase-related part-task demands explicit, namely defining the problem and proposing multiple solutions, followed by determining suitability of the solutions and coming to a definitive problem solution. Two tools facilitated construction of causal or mathematical domain representations. Each was suited for carrying out the part-task demands of one specific problem-solving phase; the causal was matched to problem-solution phase and the mathematical (in the form of a simulation) to the solution-evaluation phase. Teams of learners (N = 34, Mean age = 15.7) in four experimental conditions carried out the part-tasks in a predefined order, but differed in the representational tool/tools they received during the collaborative problem-solving process. The tools were matched, partly matched or mismatched to the part-task demands. Teams in the causal-only (n = 9) and simulation-only (n = 9) conditions received either a causal or a simulation tool and were, thus, supported in only one of the two part-tasks. Teams in the simulation-causal condition (n = 9) received both tools, but in an order that was mismatched to the part-task demands. Teams in the causal-simulation condition (n = 7) received both tools in an order that matched the part-task demands of the problem phases. Results revealed that teams receiving part-task congruent tools constructed more task-appropriate representations and had more elaborated discussions about the domain. As a consequence, those teams performed better on the complex learning-task.

Using a three‐dimensional thinking graph to support inquiry learning

“…Causal mapping has been used to represent relations of cause and effect. Slof et al () examined its effects by asking students to construct a causal map from a given set of predefined concepts or solutions needed to solve a complex task. They found that students using causal maps justified their solutions better (i.e., reasoning) and demonstrated more cognitive and meta‐cognitive activities reflected in online discussions than those who did not create causal maps.…”

“…However, concept mapping alone has been found to be inadequate in supporting complex problem‐solving tasks, and particularly in eliciting and representing the process of applying knowledge to practice (Wang, Cheng, Chen, Mercer, & Kirschner, ). Causal map is a kind of concept map, which can be used to represent causal relationship, a specific type of relationship between concepts (Slof et al, ). Evidence map goes beyond conceptual knowledge by representing the reasoning process and building the relations between evidence and hypothesis (Suthers et al, ).…”

“…For example, Shute and Glaser () offered learners optional variables and linking words from which learners can pick to generate hypotheses. External representations have been employed to scaffold students' scientific reasoning, such as concept maps (Gijlers & de Jong, ), causal maps (Slof, Erkens, Kirschner, Janssen, & Jaspers, ), and evidence maps (Suthers, Vatrapu, Medina, Joseph, & Dwyer, ). These representational supports have been proved to be effective in facilitating hypothesis formulation or reasoning.…”

Using a visualisation-based and progressive learning environment as a cognitive tool for learning computer programming

“…Such visualisation-based cognitive tools (Jonassen, Carr, & Yueh, 1998) can help represent complex, abstract issues and processes that are difficult to convey in traditional formats. Research has shown the promising effects of such tools in improving students' knowledge and task performance in various contexts (Gijlers & de Jong, 2013;Slof, Erkens, Kirschner, Janssen, & Jaspers, 2012;Suthers, Vatrapu, Medina, Joseph, & Dwyer, 2008;Wang, Cheng, Chen, Mercer, & Kirschner, 2017;Wang, Wu, Kirschner, & Spector, 2018).…”

The effects of a visible-annotation tool for sequential knowledge construction on discourse patterns and collaborative outcomes

“…However, several studies have pointed out that representation tools for CSCL lack consideration for the specific characteristics and complexity of learning tasks based on a collaborative knowledge construction process (Barron, 2003;Slof, Erkens, Kirschner, Janssen, & Jaspers, 2012). For example, some studies have suggested that concept maps and threaded discussions face limitations in reducing collaboration load or supporting complex learning, which requires a variety of sub-activities (Eryilmaz, Alrushiedat, Kasemvilas, Mary, & Pol, 2009;Suthers & Hundhausen, 2003).…”