Abstract

In undergraduate theses, a good methodology section should describe the series of steps that were followed in performing the research. To assist students in this task, we develop machine-learning models and an app that uses them to provide feedback while students write. We construct an annotated corpus that identifies sentences representing methodological steps and labels when a methodology contains a logical sequence of such steps. We train machine-learning models based on language modeling and lexical features that can identify sentences representing methodological steps with 0.939 f-measure, and identify methodology sections containing a logical sequence of steps with an accuracy of 87%. We incorporate these models into a Microsoft Office Add-in, and show that students who improved their methodologies according to the model feedback received better grades on their methodologies.

Highlights

  • IntroductionIn the Mexican higher education system, most undergraduate students write a thesis (tesis de licenciatura) before graduation

  • In the Mexican higher education system, most undergraduate students write a thesis before graduation

  • We design a model to identify when a methodology has a logical sequence of steps, incorporating language model and content word features, achieving an accuracy of 87%

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Summary

Introduction

In the Mexican higher education system, most undergraduate students write a thesis (tesis de licenciatura) before graduation. Throughout the process, the advisor spends time reviewing the draft that the student is building and gradually offering suggestions. This process becomes a cycle until the document meets established standards and/or institutional guidelines. We focus on designing machine-learning models to detect and evaluate the quality of such steps in a Spanish-language student-written methodology section, and on incorporating such models into an interactive application that gives students feedback on their writing. We design a model to detect sentences that represent methodological steps, incorporating language model and verb taxonomy features, achieving 0.939 f-measure. We design a model to identify when a methodology has a logical sequence of steps, incorporating language model and content word features, achieving an accuracy of 87%. We incorporate the models into an Add-In for Microsoft Word, and measure how the application’s feedback improves student writing

Background
Guidelines
Annotation
Model: step identification
Language model features
Sentence location features
Verb taxonomy features
Sequencing element features
Model: logical sequence detection
Pilot test
User interface
Statistical analysis
Satisfaction survey
Findings
Discussion
Full Text
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