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Erschienen in:
The “Grey Area”: A Computational Approach to Model the Zone of Proximal Development Kapitel lesen Erstes Kapitel lesen

2017 | Supplement | Buchkapitel

Towards Automatic Assessment of Argumentation in Theses Justifications

verfasst von : Jesús Miguel García-Gorrostieta, Aurelio López-López, Samuel González-López

Erschienen in: Data Driven Approaches in Digital Education

Verlag: Springer International Publishing

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Abstract

Argumentation during the academic life is a critical skill when writing. This skill is needed to communicate clearly ideas and to convince the reader of the presented claims. However, not many students are good arguers and this is a skill difficult to master. This paper presents advances in the development of an argument assessment module. Such module supports students to identify argumentative paragraphs and determine the level of argumentation in the text. The task is achieved employing machine learning techniques with lexical features such as unigrams, bigrams, and argumentative markers categories. We based the module on an annotated collection of student writings, that serves for training. We performed an initial experiment to evaluate argumentative paragraph identification in the justification section of theses, reaching encouraging results, when compared against previously proposed approaches. The module is one component of a Thesis Writing Tutor, an Internet-based learning software for academic writing.

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Vorheriges Kapitel How Well Do Student Nurses Write Case Studies? A Cohesion-Centered Textual Complexity Analysis
Nächstes Kapitel Contextualizing the Co-creation of Artefacts Within the Nested Social Structure of a Collaborative MOOC
Fußnoten
1
In Spanish: TURET: Tutor para la Redacción de Tesis.
 
2
Spanish royal academy.
 
3
Advanced College-level Technician degree, study program offered in some countries.
 
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Metadaten
Titel
Towards Automatic Assessment of Argumentation in Theses Justifications
verfasst von
Jesús Miguel García-Gorrostieta
Aurelio López-López
Samuel González-López
Copyright-Jahr
2017
Buch
Data Driven Approaches in Digital Education

Print ISBN: 978-3-319-66609-9

Electronic ISBN: 978-3-319-66610-5

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-66610-5_5