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OCR
10.5 RESULTS REGARDING STUDENTS’ PERFORMANCE 103 array (being informed about the results of the comparison operations) (Figure 7.7.e); — (Q7) to answer 13 questions regarding the “black-box stage” that had just finished and the “parallel simulation stage” that was about to start; — s6: (arousing aesthetic emotions) to watch a nice parallel simulation of the six sorting algorithms (different sorting algorithms are visualized as they are working side by side on different colour scale bars) (Figure 7.7.f); — (Q8) to answer 4 questions regarding the “parallel simulation stage” that had just finished; — (09) to answer 9 questions regarding their global impressions on the e-learning session they had just finished. 10.5 Results regarding students’ performance As already mentioned, the software registered all the errors students had made (including the type of errors: wrong parameters or wrong action) and the number of requests for help. (Students were made aware of this beforehand.) Our first analysis (Study 6) focused on the following research questions: — What kind of differences can be observed between the performance results of sciences- and humanities-oriented students in an AT/CT promoter learning environment? —Can differences be bridged by properly calibrated (possibly adaptive) learning strategies? The dependent variable was the level the students attained in the studied algorithm. We selected as testing points: phase 3 (reconstruction task), phase 4 (white-box task), and phase 5 (black-box task). Students’ orientation (S-students (science); H-students (Humanities)) was the independent variable, and the type of learning experience was a controlled variable. We expected that: — S-students would assimilate the algorithm faster than their H-colleagues. — The majority of H-students would also understand the algorithm at an acceptable level. — Carefully designed and properly calibrated learning strategies could fit both groups, moving them closer to each other. The evaluation process was based on the number of errors made by the students as well as on the number of help requests they made. Analysing the online reports, we concluded that 95.65% of the S-students (group-Sa) assimilated the studied algorithm. In the case of H-students, this percentage was 73.91% (group-Ha). These conclusions reflect the students’ performance during their last task (orchestrating the algorithm on a random sequence stored in a black-box array). Focusing on those students who assimilated the algorithm, the average of the errors they made decreased as follows:
