{'fraction_non_alphanumeric': 0.024650780608052588, 'fraction_numerical': 0.009860312243221035, 'mean_word_length': 5.310880829015544, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
The Five Million Piece Puzzle: Finding Answers in 500,000 Snap!-Projects

When learners use programming environments and languages or interact with them to construct their own knowledge, they create artifacts. These artifacts are a great source of insights into how learners use programming environments. Better knowledge of how learners use programming environments help us develop the theory of teaching programming languages. To this end, we analyzed a sample of 550,000 Snap!-projects, containing almost five million scripts, making this the first automated analysis of Snap!-projects of this magnitude. The majority of our initial results are in line with analyses of Scratch-projects of comparable scale: code smells such as standard names in sprites are found both in the Scratch-community and in these projects, and the complexity of projects in both languages is comparable. We also evaluated projects with regards to other factors, such as sprite naming, and the usage of blocks across the different categories. From our results, we can gain further insights into how learners use block-based programming environments, which ultimately assists us in developing teaching strategies for block-based languages.