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LitCrit: exploring intentions as a basis for automated feedback on Related Work.
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Abstract:
Learning the skill of academic writing is critical for post-graduate (PG) students to be successful, yet many struggle to master the required standard. Feedback can play a formative role in developing these skills, but many students do not find sufficiently helpful the kinds of feedback available to them. As the Related Work section is known to be particularly difficult for PG students to master that is the focus of this thesis. To date, models of academic writing have been built on observational studies of academic articles. In contrast, we carry out a user study to explore what content experts look for in Related Work and how this differs from PG students. We claim that by understanding what experts look for in Related Work and what aspects PG students struggle with, a useful author intention model can be developed to support writing feedback for Related Work sections. Our work demonstrates reliable annotation of the model intentions. Developing on existing algorithms, designed to identify rhetorical intentions in academic writing, we build a supervised machine learning classifier, showing how features focused on Related Work sections improve recognition of content aspects. Carrying out a study to rate the quality of Related Work, we demonstrate that the model is a good proxy for predicting quality, validating the choice of intentions in our model. In addition to recognising author intentions, we automate the generation of feedback based on observations of intentions that are present and missing, taking into account areas that PG students struggle to recognise. The thesis also contributes a new prototype writing analytic tool, called LitCrit, that supports visualising the intention narrative of Related Work and presents feedback. We claim this visualisation approach changes the PG student’s perception of Related Work, and demonstrate through a user study that it does draw attention to aspects previously missed bringing PG student responses in line with experts. Finally, we explore the performance of our classifier, originally set within the Computational Linguistics discipline, to that of Computer Graphics. This shows us that while performance may be lower when care is taken to understand those features which are discipline dependent, there is scope for improvement. Also, while a discipline may have the same intentions present in a section, their structural presentation may differ impacting feature choice.
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Keyword:
academic writing skills; author intentions; automated feedback; computational linguistics; LitCrit; model; Related Work; writing analytic tool
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URL: https://doi.org/10.7488/era/440
https://hdl.handle.net/1842/37139
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| 102 |
Towards Programming in Natural Language: Learning New Functions from Spoken Utterances
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In: International journal of semantic computing, 14 (2), 249–272 ; ISSN: 1793-351X, 1793-7108 (2020)
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| 103 |
Discovering and analysing lexical variation in social media text
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| 104 |
Modelling speaker adaptation in second language learner dialogue
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| 106 |
Personal-ITY: A novel youtube-based corpus for personality prediction in Italian
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| 107 |
Stop the Morphological Cycle, I Want to Get Off: Modeling the Development of Fusion
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In: Proceedings of the Society for Computation in Linguistics (2020)
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| 108 |
Defining distinctiveness: A computational and experimental analysis
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| 109 |
Are They There Yet? Revisiting the Accuracy Level of Computer-Generated Translation
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In: Journal Polingua: Scientific Journal of Linguistics, Literature and Education, Vol 9, Iss 1, Pp 1-4 (2020) (2020)
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| 110 |
Variables Must be Limited to a Single Feature
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In: Proceedings of the Annual Meetings on Phonology; Proceedings of the 2019 Annual Meeting on Phonology ; 2377-3324 (2020)
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| 111 |
Learning to Parse Grounded Language using Reservoir Computing
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In: ICDL-Epirob 2019 - Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics ; https://hal.inria.fr/hal-02422157 ; ICDL-Epirob 2019 - Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics, Aug 2019, Olso, Norway. ⟨10.1109/devlrn.2019.8850718⟩ ; https://ieeexplore.ieee.org/abstract/document/8850718 (2019)
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| 112 |
Statistische Modelle für die hierarchische phrasenbasierte maschinelle Übersetzung ... : Statistical models for hierarchical phrase-based machine translation ...
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| 114 |
Methods for taking semantic graphs apart and putting them back together again ...
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| 115 |
Proposition-based summarization with a coherence-driven incremental model ...
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Fang, Yimai. - : Apollo - University of Cambridge Repository, 2019
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| 116 |
Dated Phylogenies Shed Light on the ancestry of Sino-Tibetan languages ...
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| 117 |
A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics ...
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| 118 |
Dated Phylogenies Shed Light on the ancestry of Sino-Tibetan languages ...
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| 119 |
Ground truth for Neue Zürcher Zeitung black letter period ...
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| 120 |
Source code and data accompanying the paper «An automated framework for fast cognate detection and Bayesian phylogenetic inference in computational historical linguistics» ...
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