The role of metacognitive skills in learning to solve problems

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havioural measures prove to be quite a labour-intensive way of collecting
data, this method remains to be preferred over self-report questionnaires
when a predictive quality for learning is deemed important.
The empirical studies of this research project indicate that the selfreport
measures do not correlate with the protocol measures of metacognition.
Three factors play a role in interpreting these results. First
one has to take the timing of the measurement into account (concurrent
versus non-concurrent). Second, the nature of the measurements differs
(indirect versus direct). And third, the concurrent measure is indeed an
individual measure but it is taken in a collaborative context. The lack of
concordance in this study is difficult to interpret. Future work concerning
metacognition could be related to effects of collaborative learning
versus individual learning.
Future studies could check the assumptions put forward in this thesis
concerning the processes measured by the MSLQ and protocol analysis.
It could also be investigated to what extent differences exist between
individual and socially-shared metacognition. Research in this area is
scarce (Iiskala, Vauras & Lehtinen, 2004). The effects of a collaborative
setting on metacognition might turn out to be in line with the ideas of
distributed cognition, especially in technology-rich environments (Hollan,
Hutchins & Kirsh, 2000). Researchers in this area argue that in
groups, cognitive processes may be distributed (Salomon, 1993). In that
sense, it is very likely that metacognitive processes are distributed across
team members and that one team member may take up the role of the
‘reflector’. Future research could also shed light on the perhaps related
‘free-rider’ phenomenon.
7.4 The value of a task model
Regarding the third and last research question, the added value of
the task model is investigated by contrasting a no-model with a model
condition in the third empirical study. The benefit of the task model
for knowledge acquisition is that students in the model condition reach
higher scores for KM model-specific knowledge than students in the nomodel
condition. Students in both conditions do not differ from each
other on the other types of knowledge nor on the transfer test. However,
students in the model condition take almost twice as long in the game
environment as students who do not have the model available. This
additional time is mostly spent in getting to know the model and in
learning to use it for solving problems. In conclusion, the task model
only has a limited influence on knowledge acquisition in KM Quest, and
it requires high investments of time and effort.