Supporting learners: Increasing complexity?

doi:10.1016/j.chb.2006.10.008

Computers in Human Behavior

Volume 23, Issue 3, May 2007, Pages 1162-1166

https://doi.org/10.1016/j.chb.2006.10.008 Get rights and content

Abstract

The different papers in this special issue all addressed learning with complex tasks. All of these papers reached only partially the expected results. This discussion on possible factors that may explain these unexpected results. A first issue that is questioned is the functionality of the tools in the studies. Secondly, the learners lack of compliance is addressed. It may have been that the learners did not take the opportunities offered to them. Third, the use of the support by the learners itself is questioned. Although some methodological issues can be raised, the different papers made a worthwhile attempt to grasp the complexity in a learning environment. Moreover, they highlight the importance of a consolidated framework to determine relevant factors that should be considered when dealing with complexity.

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Cited by (13)

The effects of inspecting and constructing part-task-specific visualizations on team and individual learning
2013, Computers and Education
Citation Excerpt :
One should take into account that using other classes of visualizations (e.g., tables, time charts, pictures) affect cognitive behavior differently (Suthers, 2001; Vekiri, 2002) and that obtained results cannot automatically be generalized to those classes. Second, whereas many domains (e.g., business-economics, meteorology, physics) require multiple problem representations, the effects of a particular design depend on the characteristics of the learning task and the involved knowledge domains (Elen & Clarebout, 2007). When designing tools and/or learning environments, one should take this into account.
This study examined whether inspecting and constructing different part-task-specific visualizations differentially affects learning. To this end, a complex business-economics problem was structured into three phase-related part-tasks: (1) determining core concepts, (2) proposing multiple solutions, and (3) coming to a single solution. Each phase was foreseen with a part-task-specific representational tool facilitating visualization of the domain-content (i.e., a conceptual, causal and simulation tool respectively for the subsequent phases). Whereas all teams of learners (N = 17) were scripted to carry out the part-tasks in the predefined order, teams were instructed to (1) inspect expert visualizations (n = 8) or (2) construct their own domain-specific visualizations (n = 9). Results indicate that constructing visualizations, in comparison to inspecting them, evokes more meaningful discussion of the domain-content beneficially affecting team complex learning-task performance and individual learning gains (i.e., higher post-test score).
The effects of constructing domain-specific representations on coordination processes and learning in a CSCL-environment
2012, Computers in Human Behavior
Citation Excerpt :
In their discussions, learners can easily refer to the representation enabling learners to create shared understanding of these different viewpoints and negotiate about them (Erkens & Janssen, 2008; Mercer, Littleton, & Wegerif, 2004; Mühlpfordt & Stahl, 2007). Although the educational benefits of representational tools are widely recognized, some studies report mixed or even negative findings and, thus, question how and why such tools might foster complex learning (Bera & Liu, 2006; Elen & Clarebout, 2007; Lee & Nelson, 2005; Leutner, Leopold, & Sumfleth, 2009). The research reported on here addresses this by first describing several pitfalls regarding the design of representational tools aimed at fostering complex learning.
This study examined the effects of scripting learners’ use of two types of representational tools (i.e., causal and simulation) on their online collaborative problem-solving. Scripting sequenced the phase-related part-task demands and made them explicit. This entailed (1) defining the problem and proposing multiple solutions (i.e., problem-solution) and (2) evaluating solutions and coming to a definitive solution (i.e., solution-evaluation). The causal tool was hypothesized to be best suited for problem solution and the simulation tool for solution evaluation. Teams of learners in four experimental conditions carried out the part-tasks in a predefined order, but differed in the tools they received. Teams in the causal-only and simulation-only conditions received either a causal or a simulation tool for both part-tasks. Teams in the causal-simulation and simulation-causal conditions received both tools in suited and unsuited order respectively. Results revealed that teams using the tool suited to each part-task constructed more task appropriate representations and were better able to share and negotiate knowledge. As a consequence, they performed better on the complex learning-task. Although all learners individually gained more domain knowledge, no differences were obtained between conditions.
Fostering complex learning-task performance through scripting student use of computer supported representational tools
2010, Computers and Education
Citation Excerpt :
Such tools can facilitate students’ construction and discussion of different representations of the domain and, thereby, guiding their learning process and foster their learning-task performance (Fischer et al., 2002; Wegerif et al., 2010). Although its importance is widely recognized, there are, however, also studies that report mixed or negative effects on learning (Elen & Clarebout, 2007; Van Drie et al., 2005). An important reason for these contrasting findings seems that the complexity of the learning-task is not properly taken into account when designing representational tools.
This study investigated whether scripting student use of computer supported representational tools fostered students’ collaborative performance of a complex business-economics problem. Scripting the problem-solving process sequenced and made its phase-related part-task demands explicit, namely (1) determining core concepts, (2) proposing multiple solutions, and (3) coming to a final solution. The representational tools facilitated students in constructing specific representations of the domain (i.e., conceptual, causal, or mathematical) and were each suited for carrying out the part-task demands of a specific phase. Student groups in four experimental conditions had to carry out all part-tasks in a predefined order, but differed in the representational tool(s) they received during their collaborative problem-solving process. In three mismatch conditions, student groups received either a conceptual, causal, or simulation representational tool which supported them in only carrying out one of the three part-tasks. In the match condition, student groups received the three representational tools in the specified order, each matching the part-task demands of a specific problem phase. The results revealed that student groups in the match condition constructed more task-appropriate representations and had more elaborated and meaningful discussions about the domain. As a consequence, those student groups performed better on the complex learning-task. However, similar results were obtained by student groups who only received a representational tool for constructing causal representations for all part-tasks.
Guiding students' online complex learning-task behavior through representational scripting
2010, Computers in Human Behavior
This study investigated the effects of representational scripting on students’ collaborative performance of a complex business-economics problem. The scripting structured the learning-task into three part-tasks, namely (1) determining core concepts and relating them to the problem, (2) proposing multiple solutions to the problem, and (3) coming to a final solution to the problem. Each provided representation (i.e., conceptual, causal, or simulation) was suited for carrying out a specific part-task. It was hypothesized that providing part-task congruent support would guide student interaction towards better learning-task performance. Groups in four experimental conditions had to carry out the part-tasks in a predefined order, but differed in the representation they received. In three mismatch conditions, groups only received one of the representations and were, thus, only supported in carrying out one of the part-tasks. In the match condition, groups received all three representations in the specified order (i.e., representational scripting). The results indicate that groups in the match condition had more elaborated discussions about the content of the knowledge domain (i.e., concepts, solutions and relations) and were better able to share and to negotiate about their knowledge. As a consequence, these groups performed better on the learning-task. However, these differences were not obtained for groups receiving only a causal representation of the domain.
The relationships between learner variables, tool-usage behaviour and performance
2009, Computers in Human Behavior
A variety of learner variables have been asserted to affect learners’ tool-usage behaviour. However, direct empirical evidence on which learner variables influence tool-usage behaviour and how is limited. In order to better understand the impact of learner variables on tool use, the current study investigates the relationships between learner variables, the quantitative aspects of tool-usage behaviour and its outcome (i.e., performance). More specifically, the focus is on how the variation in tool use is related to prior knowledge and goal orientation and how this variation affects performance. Tool-use data were extracted from log-file data collected in an open-ended learning environment [Clarebout, G. (2005). The enhancement of optimal tool use in open learning environments (Doctoral dissertation). Katholieke universiteit Leuven, Leuven.]. Results partly revealed the hypothesized relationships between learner variables, tool-usage behaviour (the proportion of time spent on tools) and performance. The results suggest that in order to have a more thorough understanding of the relationships between learner variables, tool-usage behaviour and learning outcomes, log-file data may need to be complemented with direct observations to acknowledge the qualitative aspects of the tool-usage behaviour (i.e., mixed method approach).
Computer-Supported Collaborative L2 Learning
2019, Chinese Language Learning Sciences

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Supporting learners: Increasing complexity?

Abstract

Motivational challenges experienced in highly complex learning environments

Coping with complex environments: The effects of navigation support and a transparant interface on learning iwth a computer simulation of decision support theory

International Journal of Machine Studies

The use of instructional interventions: Lean learning environments as a solution for a design problem