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110 10 PROMOTING ALGORITHMIC/COMPUTATIONAL THINKING... On the other hand, there was a strong correlation (r — 0.94 5 0.75) between groups’ responses, and the differences diminished towards the end of the learning session. Figure 10.8 shows the scores reported by the two groups on the subject of students’ forecasted involvement in following the learning unit (q2/11, q4/9, q5/13, q6/13; “I appreciate that the software makes possible to do (reconstruct/ orchestrate) ...”). The differences are significant (MANOVA, p = 0.0008). After the “seeing phases”, both groups were more motivated to be involved in the learning process than at the beginning of the session (group-Sa reported maximal score at this point). Considering the whole learning process, H-students’ scores indicate a continuous increase. 10.6.1 Limitations A first limitation of these studies is that the majority of H-students were females and most S-students were males (as mentioned above, this distribution is characteristic of these educational programmes). This fact could affect our results. For example, the significant differences we detected between groups regarding the anxiety factor could partly be caused by gender differences. This would be in line with several studies that investigated the effect of gender on computer anxiety. For example, Mcllroy et al. (2001) report persisting gender differences in self-reported computing anxiety. In a similar study (performed in the same country where our investigation was implemented), the authors also revealed a significant gender effect with respect to computer anxiety (Durndell & Haag, 2002). The only component where H-students’ scores were higher than those of their S-colleagues is participants’ appreciation regarding their forecasted involvement in following the learning unit (see Figure 10.8). But this result could also be perturbed by gender differences. For example, Khan, Ahmad, and Malik (2017) report that in the game-based learning context they have analysed girls outperformed boys in terms of engagement. Another limitation of our approach is that the learning session we designed included only one algorithm, a specific sorting algorithm. In addition, definitions of AT/CT emphasize that promoting this skill involves more than supporting students in assimilating basic computer algorithms (Shute, Sun, & Asbell-Clarke, 2017). 10.7 Conclusions One of the main conclusions of this research is that there are no unbridgeable differences between the ways H- and S-students relate to AT/CT promoter e-learning tools. We found strong correlations between both the performance
