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Insight into cognitive structure

There is immense interest on the part of researchers and educators to diagnose a novice’s cognitive structure and compare it with an expert’s in order to identify the most appropriate ways to bridge the gap (Ifenthaler, Masduki, & Seel, 2009; Ifenthaler & Seel, 2005). Accordingly, by diagnosing these structures precisely, even partially, the educator comes closer to influencing it through instructional settings and materials. However, it is not possible to measure these internal representations of knowledge directly.

We argue that it is inevitable to identify economic, fast, reliable, and valid techniques to elicit and analyze cognitive structures (Ifenthaler, 2008). Being able to identify such techniques, one must be aware of the complex processes and interrelationships between internal and external representations of knowledge.

Seel (1991, p. 17) describes the function of internal representation of knowledge by distinguishing three zones – the object zone W as part of the world, the knowledge zone K, and the zone of internal knowledge representation R. As shown in Figure 1, there exist two classes of functions: (1) fin as the function for the internal representation of the objects of the world (internalization), and (2) fout as the function for the external re-representation back to the world (externalization). Both classes of functions are not directly observable. Hence, a measurement of cognitive structures is always biased as we are not able to more precisely define the above described functions of internalization and externalization (Ifenthaler, 2008). Additionally, the possibilities of externalization are limited to a few sets of sign and symbol systems (Seel, 1999) – characterized as graphical- and language-based approaches.

Lee and Nelson (2004) report various graphical forms of external representations for instructional uses and provide a conceptual framework for external representations of knowledge. Graphical forms of externalization include (1) knowledge maps, (2) diagrams, (3) pictures, (4) graphs, (5) charts, (6) matrices, (7) flowcharts, (8) organizers, and (9) trees. However, not all of these forms of externalization have been utilized for instruction and educational diagnosis (Ifenthaler, 2008a; Scaife & Rogers, 1996; Seel, 1999a). Other forms of graphical approaches are the structure formation technique (Scheele & Groeben, 1984), pathfinder networks (Schvaneveldt, 1990), mind tools (Jonassen, 2009; Jonassen & Cho, 2008), or causal diagrams (Al-Diban & Ifenthaler, in press).Language-based approaches include thinking-aloud protocols (Ericsson & Simon, 1993), teach-back procedures (Mandl, Gruber, & Renkl, 1995), cognitive task analysis (Kirwan & Ainsworth, 1992), and computer linguistic techniques (Pirnay-Dummer, Ifenthaler, & Spector, 2009; Seel, Ifenthaler, & Pirnay-Dummer, 2009).

As discussed above, there exist numerous approaches for the elicitation of knowledge for various diagnostic purposes. However, most approaches have not been tested for reliability and validity (Ifenthaler, 2008a; Seel, 1999a). Additionally, they are almost only applicable for single or small sets of data (Al-Diban & Ifenthaler, in press; Ifenthaler, 2008). Hence, new approaches are required which have not only been tested for reliability and validity but also provide a fast and economic way of analyzing larger sets of data. Additionally, approaches for educational diagnostics also need to move beyond the perspective of correct and incorrect solutions. As we moved into the 21st century, we argue that the application of alternative assessment and analysis strategies is inevitable for current educational diagnostics. The project will highlight recent empirical studies which report newly developed methodologies for educational diagnostics in two parts: Assessment and instructional implications.

Selected Publications:

Assessment part:

Ifenthaler, D., & Seel, N. M. (2011). A longitudinal perspective on inductive reasoning tasks. Illuminating the probability of change. Learning and Instruction, 21(4), 538-549. doi: 10.1016/j.learninstruc.2010.08.004

Ifenthaler, D. (2011). Identifying cross-domain distinguishing features of cognitive structure. Educational Technology Research & Development, 59(6), 817-840. doi: 10.1007/s11423-011-9207-4

Ifenthaler, D., Masduki, I., & Seel, N. M. (2011). The mystery of cognitive structure and how we can detect it. Tracking the development of cognitive structures over time.Instructional Science, 39(1), 41-61. doi: 10.1007/s11251-009-9097-6

Ifenthaler, D. (2010). Relational, structural, and semantic analysis of graphical representations and concept maps. Educational Technology Research and Development, 58(1),81-97. doi: 10.1007/s11423-008-9087-4

Pirnay-Dummer, P., Ifenthaler, D., & Spector, J. M. (2010). Highly integrated model assessment technology and tools. Educational Technology Research and Development, 58(1),3-18. doi: 10.1007/s11423-009-9119-8

Instructional implications part:

Pirnay-Dummer, P., & Ifenthaler, D. (2011). Reading guided by automated graphical representations: How model-based text visualizations facilitate learning in reading comprehension tasks. Instructional Science, 39(6), 901-919. doi: 10.1007/s11251-010-9153-2

Ifenthaler, D. (2009). Model-based feedback for improving expertise and expert performance. Technology, Instruction, Cognition and Learning, 7(2), 83-101.

Ifenthaler, D. (2010). Bridging the gap between expert-novice differences: The model-based feedback approach. Journal of Research on Technology in Education, 43(2), 103-117.

Ifenthaler, D. (2012). Determining the effectiveness of prompts for self-regulated learning in problem-solving scenarios. Journal of Educational Technology & Society, 15(1), 38-52.