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January 2012 Volume 15 Number 1 - Educational Technology ...

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deductive reasoning inventory (33 minutes), and the experience with concept mapping test (five minutes). Next, the<br />

participants were given an introduction to concept maps and were shown how to use the HIMATT environment (ten<br />

minutes). After a short relaxation phase (five minutes), they answered the 13 multiple choice questions of the<br />

domain-specific knowledge test on the immune system and the consequences of virus infections (pretest; eight<br />

minutes). Then they received the article on the immune system and the consequences of virus infections and were<br />

introduced into the problem scenario. In total, all participants spent 25 minutes on the problem scenario.<br />

Additionally, participants in the experimental condition GP and DP received their reflective thinking prompt after <strong>15</strong><br />

minutes working on the problem scenario. The CG did not receive a reflective thinking prompt. They were allowed<br />

to take notes with paper and pencil. After another short relaxation phase (five minutes), the participants logged into<br />

the HIMATT environment and constructed a concept map on their understanding of the problem scenario (ten<br />

minutes). Finally, the participants answered the 13 multiple choice questions of the posttest on declarative<br />

knowledge (eight minutes).<br />

Data analysis<br />

In order to analyze the participants’ understanding of the problem scenario, we used the seven measures<br />

implemented in HIMATT (see Ifenthaler, 2010b; Pirnay-Dummer, et al., 2010). Accordingly, each of the<br />

participants’ concept maps was compared automatically against the reference map (expert solution based on the<br />

article). Table 1 describes the seven measures of HIMATT, which include four structural measures and three<br />

semantic measures (Ifenthaler, 2010a, 2010b; Pirnay-Dummer & Ifenthaler, 2010; Pirnay-Dummer, et al., 2010).<br />

HIMATT uses specific automated comparison algorithms to calculate similarities between a given pair of<br />

frequencies f1 (e.g. expert solution) and f2 (e.g. participant solution). The similarity s is generally derived by<br />

f1<br />

f 2<br />

s 1<br />

<br />

maxf1,<br />

f 2 <br />

which results in a measure of 0 ≤ s ≤ 1, where s = 0 is complete exclusion and s = 1 is identity. The other measures<br />

collect sets of properties. In this case, the Tversky similarity (Tversky, 1977) applies for the given sets A (e.g. expert<br />

solution) and B (e.g. participant solution):<br />

f ( A B)<br />

s <br />

f ( A B)<br />

f ( A B)<br />

f ( B A)<br />

and are weights for the difference quantities which separate A and B. They are usually equal ( = = 0.5)<br />

when the sources of data are equal. However, they can be used to balance different sources systematically, e.g.<br />

comparing a learner’s concept map which was constructed within five minutes to an expert’s concept map, which<br />

may be an illustration of the result of a conference or of a whole book (see Pirnay-Dummer & Ifenthaler, 2010). The<br />

Tversky similarity also results in a measure of 0 ≤ s ≤ 1, where s = 0 is complete exclusion and s = 1 is identity.<br />

Reliability scores exist for the single measures integrated into HIMATT. They range from r = .79 to r = .94 and are<br />

tested for the semantic and structural measures separately and across different knowledge domains (Pirnay-Dummer,<br />

et al., 2010). Validity scores are also reported separately for the structural and semantic measures. Convergent<br />

validity lies between r = .71 and r = .91 for semantic comparison measures and between r = .48 and r = .79 for<br />

structural comparison measures (Pirnay-Dummer, et al., 2010).<br />

Measure [abbreviation]<br />

and type<br />

Surface matching [SFM]<br />

Structural indicator<br />

Graphical matching [GRM]<br />

Structural indicator<br />

Table 1. Description of the seven HIMATT measures<br />

Short description<br />

The surface matching (Ifenthaler, 2010a) compares the number of vertices within two<br />

graphs. It is a simple and easy way to calculate values for surface complexity.<br />

The graphical matching (Ifenthaler, 2010a) compares the diameters of the spanning<br />

trees of the graphs, which is an indicator for the range of conceptual knowledge. It<br />

corresponds to structural matching as it is also a measure for structural complexity<br />

only.<br />

44

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