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Supporting complex assessment processes

Published: 18 October 2017 Publication History

Abstract

Assessment is a key element in the teaching-learning process. Every formal education implies some kind of assessment, and the results of this assessment contribute to the quality improvement in that process. Currently, most assessment scenarios are very complex and require tools to support them. Establishing a good assessment approach requires continuous decision-making processes to guarantee fair assessments. In this paper the TraMeA assessment support system is presented centering on its characteristics that can help in those decision making processes. The proposal is exemplified with real examples derived from the evaluation of Final Year Projects.

References

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  • (2018)Improving essay peer grading accuracy in massive open online courses using personalized weights from student's engagement and performanceJournal of Computer Assisted Learning10.1111/jcal.1231635:1(110-120)Online publication date: 28-Sep-2018

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TEEM 2017: Proceedings of the 5th International Conference on Technological Ecosystems for Enhancing Multiculturality
October 2017
723 pages
ISBN:9781450353861
DOI:10.1145/3144826
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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  • University of Salamanca: University of Salamanca

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 October 2017

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Author Tags

  1. Complex assessment
  2. planning assessment
  3. tools for e-assessment

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  • Research-article
  • Research
  • Refereed limited

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TEEM 2017

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TEEM 2017 Paper Acceptance Rate 84 of 109 submissions, 77%;
Overall Acceptance Rate 496 of 705 submissions, 70%

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Cited By

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  • (2018)Improving essay peer grading accuracy in massive open online courses using personalized weights from student's engagement and performanceJournal of Computer Assisted Learning10.1111/jcal.1231635:1(110-120)Online publication date: 28-Sep-2018

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