## 4 Discussion  
The key observations based on the results presented in the preceding section are as follows:  
1. The research combining the use of machine learning and gamification is a vivid and still growing field as indicated by the rising number of papers published each year since at least 2018. [RQ1]
2. This research is not limited to a handful of countries, and even though there are leaders, as for now, they have not achieved a substantial advantage against the rest of the countries in terms of the research quantity. [RQ2]
3. The numerical prevalence of conference publications over journal articles is an expected phenomenon in a quickly developing research field, as conference publications usually have a much shorter submit-to-publish time than most journals. [RQ3] \begin{table}
\begin{tabular}{c c c} \hline \hline
**Ref.** & **Title** & **Cited** \\ \hline
[17] & Knowledge discovery of game design features by mining user & 22 \\  & generated feedback & 22 \\
[36] & Impact of Gamification on Code review process—An Experimental Study & 19 \\
[37] & Gamification and Machine Learning Inspired Approach for Classroom & 12 \\
[38] & Passed Learning & 12 \\
[39] & & 12 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._4. There is not a single scientific journal that could be considered by authors writing on combining machine learning and gamification as their default venue of publication. This diversity in journal selection can only be partly explained by different application areas addressed by the analyzed papers. We hope this observation will motivate members of the gamification/machine learning research community to propose a new thematic section or Special Issue to attract future publications in this vein and thus create a hub for sustained scientific discussion. [RQ3]
5. There was a considerable research interest identified in five of the six considered themes (and few publications mixing more than one theme). Gamification supporting machine learning was the most frequently addressed theme for almost the entire period considered. The second in popularity was machine learning supporting gamification, which has gained a lot of interest in the most recent years. The themes of combining machine learning and gamification, using gamification in machine learning education, and using machine learning in gamification research (which took the subsequent spots in popularity ranking) were characterized by subsequent ups and downs with no clear trend emerging. [RQ4]
6. The research on the application of machine learning and gamification is not confined to a single area of activity: in fact, we were able to identify 15 distinct areas targeted by the described applications. While finding education to be the most popular of them was predictable, much less so were the areas that took the subsequent spots: research and development, and healthcare. Nonetheless, there are many areas that could benefit from applications combining machine learning and gamification, yet were omitted (e.g., software development) or under-represented (e.g., tourism), suggesting the directions of future research in this vein. [RQ5]
7. The papers reporting the application of supervised learning have a strong numerical advantage over those reporting the application of unsupervised learning, with an even lesser share indicating the use of reinforcement learning. Artificial neural networks of various kinds were most often indicated as the applied machine learning techniques, though they are far from dominating the scene (15% of analyzed papers), with a gamut of other techniques also in use. The most typical software environment for the application of machine learning and gamification was Python (indicated in 17 papers out of 29 that provided such data) with scikit-learn library (indicated in 10 papers). [RQ6]
8. The applications of machine learning and gamification relied slightly more often on web than mobile technology, and (with a somewhat less frequency) augmented reality than virtual reality.