The demand for educational quality presupposes valid and reliable analytical instruments for educational research. However, many instruments are developed with little or no theoretical justification, which leads to doubtful findings and no contribution to the improvement of educational quality (cf. Novak, 1998). Accordingly, the development of new analytical instruments to capture key latent variables associated with human learning and cognition requires a solid theoretical foundation.

One central interest of psychological and educational research is internal cognitive processes and systems, which are described by theoretical constructs such as mental models and schemata (cf. Seel, 1991). However, mental models and schemata are theoretical constructs of science which are not observable. Accordingly, researchers can only learn about mental models or schemata if (1) individuals communicate their internal systems (Seel, 1991) and if (2) valid and reliable instruments are used to analyze them (Seel, 1999).

The demand for an appropriate evaluation tool for assessing internal cognitive systems led to the development of the SMD-Technology (Surface, Matching, Deep Structure). Based on the theory of mental models (cf. Johnson-Laird, 1983; Seel, 1991), the SMD- Technology uses graphical representations or concept maps to assess individual processes in persons solving complex problems at multiple intervals over time. The computer-based and automated SMD-Technology is composed of three levels - Surface, Matching, and Deep-Structure (cf. Ifenthaler, 2006).

The SMD-Technology was tested for objectivity, reliability, and validity in three experimental studies with 106 participants and different subject domains (cf. Ifenthaler, 2006). The objectivity of the SMD-Technology was guaranteed by the computer-based and automated realization of the instrument. The test-retest reliability was used within a control group in which no substantial change of the Surface, Matching, and Deep-Structure outcomes was measured. The construct validity of the SMD-Technology was tested on convergent and discriminant constructs (cf. Pirnay-Dummer, 2006).

Additionally, the SMD-Technology was part of a series of comparative studies of different quantitative and qualitative methodologies conducted in order to determine the methodologies strength, unique characteristics, and report collective validity (see Johnson et al., 2006).

Please refer to the following works when using SMD Technology:

  • Ifenthaler, D. (2008). Relational, structural, and semantic analysis of graphical representations and concept maps. Educational Technology Research and Development. doi: 10.1007/s11423-008-9087-4
  • Ifenthaler, D. (2006). Diagnose lernabhängiger Veränderung mentaler Modelle. Entwicklung der SMD-Technologie als methodologisches Verfahren zur relationalen, strukturellen und semantischen Analyse individueller Modellkonstruktionen. Freiburg: FreiDok.
  • Johnson, T. E., O’Connor, D. L., Spector, J. M., Ifenthaler, D., & Pirnay-Dummer, P. (2006). Comparative study of mental model research methods: Relationships among ACSMM, SMD, MITOCAR & DEEP methodologies. In A. J. Cañas & J. D. Novak (Eds.), Cncept maps: Thery, methodology, technology. Procedings of the Second International Conference on Concept Mapping, Voume 1 (pp. 87-94). San José: Universidad de Costa Rica.
  • Johnson, T. E., Ifenthaler, D., Pirnay-Dummer, P., & Spector, J. M. (2009). Using concept maps to assess individuals and team in collaborative learning environments. In P. L. Torres & R. C. V. Marriott (Eds.), Handbook of research on collaborative learning using concept mapping (pp. 358-381). Hershey,PA: Information Science Publishing.