License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.SLATE.2017.11
URN: urn:nbn:de:0030-drops-79577
URL: https://drops.dagstuhl.de/opus/volltexte/2017/7957/
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Correia, Helder ; Leal, José Paulo ; Paiva, José Carlos

Enhancing Feedback to Students in Automated Diagram Assessment

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OASIcs-SLATE-2017-11.pdf (0.4 MB)


Abstract

Automated assessment is an essential part of eLearning. Although comparatively easy for multiple choice questions (MCQs), automated assessment is more challenging when exercises involve languages used in computer science. In this particular case, the assessment is more than just grading and must include feedback that leads to the improvement of the students' performance.

This paper presents ongoing work to develop Kora, an automated diagram assessment tool with enhanced feedback, targeted to the multiple diagrammatic languages used in computer science. Kora builds on the experience gained with previous research, namely: a diagram assessment tool to compute differences between graphs; an IDE inspired web learning environment for computer science languages; and an extensible web diagram editor.

Kora has several features to enhance feedback: it distinguishes syntactic and semantic errors, providing specialized feedback in each case; it provides progressive feedback disclosure, controlling the quality and quantity shown to each student after a submission; when possible, it integrates feedback within the diagram editor showing actual nodes and edges on the editor itself.

BibTeX - Entry

@InProceedings{correia_et_al:OASIcs:2017:7957,
  author =	{Helder Correia and Jos{\'e} Paulo Leal and Jos{\'e} Carlos Paiva},
  title =	{{Enhancing Feedback to Students in Automated Diagram Assessment}},
  booktitle =	{6th Symposium on Languages, Applications and Technologies (SLATE 2017)},
  pages =	{11:1--11:8},
  series =	{OpenAccess Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-056-9},
  ISSN =	{2190-6807},
  year =	{2017},
  volume =	{56},
  editor =	{Ricardo Queir{\'o}s and M{\'a}rio Pinto and Alberto Sim{\~o}es and Jos{\'e} Paulo Leal and Maria Jo{\~a}o Varanda},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2017/7957},
  URN =		{urn:nbn:de:0030-drops-79577},
  doi =		{10.4230/OASIcs.SLATE.2017.11},
  annote =	{Keywords: automated assessment, diagram assessment, feedback generation,language environments, e-learning}
}

Keywords: automated assessment, diagram assessment, feedback generation,language environments, e-learning
Collection: 6th Symposium on Languages, Applications and Technologies (SLATE 2017)
Issue Date: 2017
Date of publication: 04.10.2017


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