## 6 Discussion and Conclusions  
This paper presents a comparative analysis of various maze generation and solving algorithms implemented in an Android mobile application designed to help children aged five and above learn basic programming concepts and develop computational thinking skills. The application engages young learners by guiding a dog character through a maze using simple commands, providing an interactive and educational experience. By evaluating and comparing various maze generation algorithms within the context of a game-based mobile learning application, the research provides valuable insights into how different algorithms can be used to design and develop educational tools to foster computational thinking and programming skills in young learners. Compared to the existing research on maze generation algorithms, this paper contributes by evaluating existing and new algorithms in a different context, i.e., educational game design, and providing insights into which algorithms are best suited for different stages of learning basic programming concepts. The detailed comparison of various maze generation algorithms has highlighted their strengths and limitations in terms of time complexity, space complexity, and suitability for different educational contexts. Implementing these algorithms in a game-based mobile application has demonstrated their practical applicability. [PERSON]'s and Aldous-Broder algorithms were identified as the most time inefficient. Their time complexity increases exponentially with maze size due to the necessity of visiting every cell. On the other hand, algorithms like Sidewinder and Binary Tree demonstrated the fastest generation times for smaller mazes due to their simpler, more direct traversal methods. Moreover, the longest path ratio decreases as the maze size increases across all  
Figure 24: Maze solving comparison of Hunt and kill mazes (**a**) and Recursive backtracker mazes (**b**). algorithms. However, the Hunt and Kill and Recursive backtracker algorithms maintain a higher ratio of longest paths, making them suitable for generating progressively complex mazes crucial for advanced game levels. In addition, the study underscores the importance of selecting the appropriate maze-solving algorithm based on the specific characteristics of the maze generation method used. The Recursive call algorithm is highly efficient for simpler, more predictable mazes, while the Dijkstra algorithm offers reliable performance across varied maze structures. Although less efficient in complex mazes, the dead-end filling algorithm can be beneficial in specific scenarios where dead-end paths are fewer. Future research should further explore these dynamics, particularly focusing on optimizing solving algorithms for more complex and unpredictable maze structures. One of the primary implications is the importance of selecting the right maze generation algorithm based on the target audience's skill level and the desired learning outcomes. Simpler algorithms like Binary Tree and Sidewinder are ideal for beginners due to their straightforward nature, offering mazes with less complexity and fewer dead-ends. These characteristics are conducive to helping young learners grasp basic programming concepts without becoming overwhelmed. On the other hand, more complex algorithms like Hunt and Kill and Recursive backtracker are better suited for advanced learners, as they generate mazes with greater complexity and longer paths, which can better challenge and develop problem-solving skills. Additionally, the study underscores the potential for these algorithms to be used in adaptive learning environments, where the difficulty of the mazes can be dynamically adjusted based on the learner's progress. This adaptability can help maintain engagement and motivation, as the challenge level can be aligned with the learner's growing abilities.