The shift to BYOD (bring your own device) policies at schools requires browser-based programming tools that balance accessibility and functionality. We introduce WebTigerPython, a Python IDE combining novice-friendly features (Turtle graphics, robotics, error messages) with advanced capabilities (NumPy, Matplotlib). Its client-side execution and web worker architecture ensure non-blocking interactivity. Python code is run in WebAssembly, performing only about three times slower than native CPython but significantly faster than other web-based IDEs to which we compared it. Deployed in classrooms with 800+ daily users, WebTigerPython supports offline use, URL-based sharing of code, and aligns with existing curricula—demonstrating how web tools can rival local IDEs without compromising power or accessibility.

Clear, well-chosen names for variables and functions significantly enhance code readability and maintainability. In computer science education, teaching students to select appropriate identifiers is a critical task, especially in CS1. This study explores how large language models (LLMs) could assist in teaching this skill. While prior research has explored the use of LLMs in programming education, their precision and consistency in teaching code quality, particularly identifier selection, remains largely unexplored. For this purpose, this study investigated how well different LLMs can detect and report misleading identifiers. In a dataset of 33 code samples, we manually labeled misleading identifiers. On this dataset, we then tested five different LLMs on their ability to detect these misleading identifiers, measuring the overall accuracy, precision, recall, and f-score. Results revealed that the most successful model, GPT-4o, was able to correctly detect most of the manually flagged misleading variable names. However, it also tended to flag issues with variable identifiers in cases where the human evaluators would not, and refined prompting was not able to discourage this behavior.

With the growing demand for computational skills in the job market, it's imperative that lower secondary school students grasp basic programming concepts such as repetition, modularity, conditionals, and variables. Yet, many students perceive computer science as daunting and irrelevant. Physical computing offers a promising solution to this motivational gap. It enhances intrinsic motivation, self-efficacy, and positive attitudes towards technology. Moreover, they engage students who may not identify as typical programmers and foster essential computational thinking skills.

The use of microcontroller boards such as the Calliope Mini and BBC micro:bit is becoming increasingly popular in schools due to their versatility and affordability. This doctoral research aims to investigate the effectiveness and motivational potential of using microcontroller boards to introduce basic programming concepts to upper primary and lower secondary school students using Python. The primary focus is on the multi-context nature of microcontroller boards, exploring whether teaching programming concepts in different contexts, such as music, video games, and autonomous driving, can motivate a broader population of students compared to a single-context curriculum, such as Turtle Graphics or autonomous mobile robots. The research employs an educational design-based research approach. In the first cycle, a curriculum consisting of six lessons was developed and piloted in the context of gifted pull-out activities. The preliminary exploratory pilot study provides qualitative findings on students' responses to the curriculum, and algorithmic thinking gains were measured using a pre- and post-test. The results suggest that the curriculum has the potential to be an effective and engaging way to introduce basic programming concepts and that further research is needed to confirm these findings for larger populations. In the next educational design-based research cycle we plan to refine our measurement instruments and study design.