Tapping into the Natural Language System with Artificial Languages when Learning Programming
<|reference_start|>Tapping into the Natural Language System with Artificial Languages when Learning Programming: Background: In times when the ability to program is becoming increasingly important, it is still difficult to teach students to become successful programmers. One remarkable aspect are recent findings from neuro-imaging studies, which suggest a consistent role of language competency of novice programmers when they learn programming. Thus, for effectively teaching programming, it might be beneficial to draw from linguistic research, especially from foreign language acquisition.   Objective: The goal of this study is to investigate the feasibility of this idea, such that we can enhance learning programming by activating language learning mechanisms.   Method: To this end, we conducted an empirical study, in which we taught one group of students an artificial language, while another group received an introduction into Git as control condition, before we taught both groups basic programming knowledge in a programming course.   Result: We observed that the training of the artificial language can be easily integrated into our curriculum. Furthermore, we observed that language learning strategies were activated and that participants perceived similarities between learning the artificial language and the programming language. However, within the context of our study, we did not find a significant benefit for programming competency when students learned an artificial language first.   Conclusion: Our study lays the methodological foundation to explore the use of natural language acquisition research and expand this field step by step. We report our experience here to guide research and to open up the possibilities from the field of linguistic research to improve programming acquisition.<|reference_end|>
@article{hartmann2024tapping,
    title={Tapping into the Natural Language System with Artificial Languages when
  Learning Programming},
    author={Elisa Madeleine Hartmann, Annabelle Bergum, Dominik Gorgosch, Norman
  Peitek, Sven Apel, Janet Siegmund},
    journal={arXiv preprint arXiv:2402.01657},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.01657},
    primaryClass={cs.CY cs.HC}
}