Teaching the Art of Computer Programming at a Distance by Generating Dialogues using Deep Neural Networks

Yu, Yijun; Wang, Xiaozhu; Dil, Anton and Rauf, Irum (2019). Teaching the Art of Computer Programming at a Distance by Generating Dialogues using Deep Neural Networks. In: 28th ICDE World Conference on Online Learning, 3-7 Nov 2019, Dublin, Ireland, (In Press).

URL: https://wcol2019.ie/

Abstract

While teaching the art of Computer Programming, students with visual impairments (VI) are disadvantaged, because speech is their preferred modality. Existing accessibility assistants can only read out predefined texts sequentially, word-for-word, sentence-for-sentence, whilst the presentations of programming concepts could be conveyed in a more structured way. Earlier we have shown that deep neural networks such as Tree-Based Convolutional Neural Networks (TBCNN) and Gated Graph Neural Networks (GGNN) can be used to classify algorithms across different programming languages with over 90% accuracy. Furthermore, TBCNN or GGNN have been shown useful for generating natural and conversational dialogues from natural language texts. In this paper, we propose a novel pedagogy called “Programming Assistant”, by creating a personal tutor that can respond to voice commands, which trigger an explanation of programming concepts, hands-free. We generate dialogues using DNNs, which substitute code with the names of algorithms characterising the programs, and we read aloud descriptions of the code. Furthermore, the application of the dialogue generation can be embodied into an Alexa Skill, which turns them into fully natural voices, forming the basis of a smart assistant to handle a large number of formative questions in teaching the Art of Computer Programming at a distance.

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