Constructing nomological nets on the basis of process analyses to strengthen CSCL research

Due to the nature of collaborative learning, realising perfectly controlled experiments often requires an unreasonable amount of resources and sometimes it is not possible at all. Against this background, I propose to augment as good as feasible experimental design with a nomological net of relations between instructional support (intervention), learning processes and learning outcomes. Nomological networks are known from construct validity. In construct validity, the relations between variables (e.g. group differences, correlation matrices) are used to provide evidence for the validity of a measure. By adding multiple process and outcome variables together with the corresponding relations between intervention, process and outcome, the validity of causal relations found can be strengthened. I suggest adopting quality criteria from good research designs to evaluate the nomological nets. The resulting net needs to be (1) theory grounded, (2) situational, (3) feasible, (4) redundant, and (5) efficient. By making these nomological nets explicit and by designing them according to the presented criteria, CSCL research becomes more potent: the risk of inconclusive results is reduced while results that form a consistent nomological net can be interpreted with a stronger confidence, even if the experimental design has some flaws. If this becomes standard in CSCL research, it can be expected to contribute significantly better to knowledge accumulation in this area of research.

Abstract licence: CC BY-NC-ND

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