## 6 Conclusions and Future Remarks
RQ3: Can MCMC help us validate this training in creative learning design in a TPD, giving the expected accuracy to our proposed results? The MCMC helped us achievegreat accuracy in our results. This would not be possible if we did not use this technique. MCMC is a very well-known mathematical model that has applications in almost all fields and educational settings. It is a great modeling tool in quantitative research. We suggest that it can be of great value even in unstructured data, like creativity and technology-enhanced learning design. Common WOA critique [9] is about finding the balance between exploration and exploitation. With MCMC, we have reliability in the decision-making procedure. Furthermore, in the conceptualization of the initial parameters, we do not rely on MCMC, which is not ideal as it distributes randomly, but we rely on WOA and its target solutions that have already been initiated. To sum up, we found that stochastic models can be of great value in social sciences and educational research, especially when involving multifaceted concepts like creativity, LD, and technology. In our training, we used a narrative literature review to design and evaluate. WOA helped us in finding the right exploration and exploitation areas and conceptualizing the design process. MCMC helped us in the validation of the outcome/training, making a more accurate analysis. For future work, case studies or other methods can be employed for the evaluation of this approach, providing evidence-based research for its validity. Additionally, this hybrid methodology could be validated by benchmark tests to further explore it from an algorithmic perspective. In addition, we should mention that mathematical models offer insights into model consequences and are simpler to falsify. They could also produce extensive data, which improves analysis. Because of these, we propose that it could be of great value during the design process, before reaching the application stage. In educational research literature reviews, we do not find many cases involving design processes through algorithms like WOA or MCMC. This was a limitation when we were searching for similar scientific cases. Conceptualization, K.R. and D.K. ; methodology, K.R. and D.K. ; validation, K.R., D.K. and Y.P. ; formal analysis, K.R. and D.K. ; investigation, K.R. and D.K. ; resources, D.K. ; data curation, D.K. and Y.P. ; writing--original draft preparation, K.R. and D.K. ; writing--review and editing, D.K. and Y.P. ; visualization, K.R. ; supervision, Y.P. ; project administration, D.K. ; funding acquisition, D.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. In this article published in an MDPI journal, we declare that all data supporting the reported results, including links to publicly archived datasets analyzed or generated during the study, are not subject to confidentiality or ethical restrictions. There are no other data for this specific study, other than those that support the conclusions of this article. All data in the article are made available by the authors for future research purposes. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} LD & Learning Design \\ TPD & Teacher's Professional Development \\ TPACK & Technological Pedagogical Content Knowledge \\ SAMR & Substitution, Augmentation, Modification, Redefinition \\ TPS & Think, Pair, Share \\ TBL & Team-Based Learning \\ \end{tabular}