The role of metacognitive skills in learning to solve problems

Introduction 3
the problem in its natural context and the possibility of manipulating
aspects of that problem. Learners should be able to change parameters
of the problem and subsequently interpret the effects of the changes.
Also the learner can set his or her own learning goals.
Cognitive tools can support the learning and problem solving process.
Cognitive tools are computer tools that are intended to engage
and facilitate cognitive processing (Kommers, Jonassen & Mayes, 1992).
Jonassen (1999) also calls them mind tools. The general idea behind
cognitive tools is that instead of designing a learning environment readymade,
learners can use cognitive tools in order to represent and construct
knowledge themselves. Such tools are, for example, visualization tools
that support representation of information in different formats. In order
to support the collaborative aspect in learning in the constructivist
paradigm, cognitive tools can be used, such as e-mail, chat, video conferencing
or shared document work spaces that enable the collaborative
writing of documents.
Games and simulations fit especially well in the constructivist paradigm.
Ideally, in games and simulations learners are able to explore and experience
the effects of their activities based on realistic case material.
Despite positive expectations about the use of games and simulations
in a constructivist learning context, research points out that these expectations
can not always be confirmed with empirical results. Several
studies indicate that learning results are often suboptimal. De Jong
and Van Joolingen (1998) reviewed several studies concerning the use
of simulations in scientific discovery learning. Their conclusion is that
students have problems with discovery learning. Moreover, they state
that using simulations without additional guidance and support will not
lead to optimal learning results. Characteristic problems that students
encounter fall into the classes hypothesis generation, designing experiments,
interpreting data and regulating learning.
Recent work on inductive discovery learning in simulations by Hulshof
(2001), Prins (2002) and van Rijn (2003) shows that learning results in
a simulation for the domain of Optics are similarly weak. Van Rijn however,
argues that there is a gap between the results found on the knowledge
tests used and the knowledge actually acquired by the students.
Basically, teachers expect learner behaviour (and focus their tests on
this expected behaviour) that is different from actual learner behaviour.
The author identifies three general principles that influence learner behaviour
in inductive learning tasks. First, learners prefer to formulate
hypotheses that are as simple as possible and also explanations that are
as simple as possible for the observed phenomena. Learners appear to