Fuzzy decision analysis for the selection of mobile-based programming learning applications

Python programming has gained great popularity in recent years with increasing interest in artificial intelligence applications. With the spread of mobile devices, the number of applications for Python programming has also increased. In this study, a fuzzy decision model has been developed that can be used in the selection of programming teaching applications, especially Python. The decision tree of the model was created with the proposed problem-specific criteria. The decision mechanism is based on the Analytical Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Four different programming teaching applications were used in the model evaluation process. Criteria that may affect the selection of applications were determined through literature review and expert opinions. These criteria were then converted into comparison matrix-based surveys. Seven field experts participated in the study and the survey results were analyzed through semi-structured interviews. The results obtained with Extended Fuzzy AHP and TOPSIS are reported comparatively. Within the evaluations, Sololearn received the highest weight among the mobile applications in the research with an importance level of 0.379 (Ci = 0.585). Grading criteria according to weight values are "content" (0.438), "instruction" (0.338) and "application" (0.224) in the main categories, while in sub-categories "comprehensibility" (0.163), "accuracy" (0.162), "feedback" (0.131), "teaching strategy" (0.129), "currentness" (0.100), "ease of use" (0.081), "interaction tools" (0.075), "functionality" (0.053), "pricing" (0.036), "security" (0.036), "support and documentation" (0.019), "integrity" (0.013), "customizability" (0.002) and "platform flexibility" (0.000). Research findings are limited to the model developed within these criteria and the opinions of the participants. In future studies, the decision model can be applied to Python or other programming languages with a different or larger group of experts. It is thought that this study will contribute to the literature in the evaluation of mobile learning applications.