Tapping into the Natural Language System with Artificial Languages when
Learning Programming
- URL: http://arxiv.org/abs/2402.01657v1
- Date: Fri, 12 Jan 2024 07:08:55 GMT
- Title: Tapping into the Natural Language System with Artificial Languages when
Learning Programming
- Authors: Elisa Madeleine Hartmann, Annabelle Bergum, Dominik Gorgosch, Norman
Peitek, Sven Apel, Janet Siegmund
- Abstract summary: 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.
We observed that the training of the artificial language can be easily integrated into our curriculum.
However, within the context of our study, we did not find a significant benefit for programming competency when students learned an artificial language first.
- Score: 7.5520627446611925
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- Language Evolution with Deep Learning [49.879239655532324]
Computational modeling plays an essential role in the study of language emergence.
It aims to simulate the conditions and learning processes that could trigger the emergence of a structured language.
This chapter explores another class of computational models that have recently revolutionized the field of machine learning: deep learning models.
arXiv Detail & Related papers (2024-03-18T16:52:54Z) - Learning a Hierarchical Planner from Humans in Multiple Generations [21.045112705349222]
We present natural programming, a library learning system that combines programmatic learning with a hierarchical planner.
A user teaches the system via curriculum building, by identifying a challenging yet not impossible goal.
The system solves for the goal via hierarchical planning, using the linguistic hints to guide its probability distribution.
arXiv Detail & Related papers (2023-10-17T22:28:13Z) - PwR: Exploring the Role of Representations in Conversational Programming [17.838776812138626]
We introduce Programming with Representations (PwR), an approach that uses representations to convey the system's understanding back to the user in natural language.
We find that representations significantly improve understandability, and instilled a sense of agency among our participants.
arXiv Detail & Related papers (2023-09-18T05:38:23Z) - Leveraging Large Language Model and Story-Based Gamification in
Intelligent Tutoring System to Scaffold Introductory Programming Courses: A
Design-Based Research Study [6.773393436953262]
This study explores how large language models and.
gamblers can scaffold coding learning and increase.
Chinese students sense of belonging in introductory programming courses.
arXiv Detail & Related papers (2023-02-25T04:07:03Z) - Language Cognition and Language Computation -- Human and Machine
Language Understanding [51.56546543716759]
Language understanding is a key scientific issue in the fields of cognitive and computer science.
Can a combination of the disciplines offer new insights for building intelligent language models?
arXiv Detail & Related papers (2023-01-12T02:37:00Z) - Leveraging Language to Learn Program Abstractions and Search Heuristics [66.28391181268645]
We introduce LAPS (Language for Abstraction and Program Search), a technique for using natural language annotations to guide joint learning of libraries and neurally-guided search models for synthesis.
When integrated into a state-of-the-art library learning system (DreamCoder), LAPS produces higher-quality libraries and improves search efficiency and generalization.
arXiv Detail & Related papers (2021-06-18T15:08:47Z) - Ten Quick Tips for Deep Learning in Biology [116.78436313026478]
Machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling.
Deep learning has become its own subfield of machine learning.
In the context of biological research, deep learning has been increasingly used to derive novel insights from high-dimensional biological data.
arXiv Detail & Related papers (2021-05-29T21:02:44Z) - Including Signed Languages in Natural Language Processing [48.62744923724317]
Signed languages are the primary means of communication for many deaf and hard of hearing individuals.
This position paper calls on the NLP community to include signed languages as a research area with high social and scientific impact.
arXiv Detail & Related papers (2021-05-11T17:37:55Z) - Fostering learners' self-regulation and collaboration skills and
strategies for mobile language learning beyond the classroom [0.0]
The chapter argues that support should focus on the development of two vital learning skills, namely being able to self-regulate and to collaborate effectively.
The ultimate aim is to enable the provision of individual adaptive learning paths to facilitate language learning beyond the classroom.
arXiv Detail & Related papers (2021-03-20T15:57:59Z) - Experience Grounds Language [185.73483760454454]
Language understanding research is held back by a failure to relate language to the physical world it describes and to the social interactions it facilitates.
Despite the incredible effectiveness of language processing models to tackle tasks after being trained on text alone, successful linguistic communication relies on a shared experience of the world.
arXiv Detail & Related papers (2020-04-21T16:56:27Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.