Skip to main content
SpringerLink
Log in

Towards Efficient Teacher Assisted Assignment Marking Using Ranking Metrics

  • Conference paper
  • First Online:
Computers Supported Education (CSEDU 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 739))

Included in the following conference series:

  • 797 Accesses

Abstract

This paper describes a tool with supporting methodology for efficient teacher assisted marking of open assignments based on student answer ranking metrics. It includes a methodology for how to design tasks for markability. This improves marking efficienty and reduces cognitive strain for the teacher during marking, and also allows for easily giving feedback to students on common pitfalls and misconceptions to improve both the learning outcome for the students as well as the teacher’s productivity by reducing the time needed for marking open assignments. An advantage with the method is that it is language agnostic as well as generally being agnostic to the discipline of the course being assessed. The ranking metrics also provide implicit plagiarism detection.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://www.researchgate.net/post/What_is_the_most_efficient_way_of_designing_and_marking_student_assignments.

  2. 2.

    http://news.bbc.co.uk/2/hi/uk_news/magazine/7531132.stm.

  3. 3.

    Stop-word lists: https://code.google.com/archive/p/stop-words/.

  4. 4.

    Selenium WebDriver: http://seleniumhq.org.

References

  1. Barstad, V., Goodwin, M., Gjøsæter, T.: Predicting source code quality with static analysis and machine learning. In: Proceedings of NIK 2014. Norsk Informatikkonferanse (2014)

    Google Scholar 

  2. Bloom, B.S., Others, A.: Handbook on Formative and Summative Evaluation of Student Learning. McGraw-Hill, New York (1971)

    Google Scholar 

  3. Braham, R.: A knowledge metric with applications to learning assessment. In: Proceedings of KEOD 2010, pp. 5–9. SciTePress - Science and Technology Publications (2010)

    Google Scholar 

  4. Buckley, E., Cowap, L.: An evaluation of the use of Turnitin for electronic submission and marking and as a formative feedback tool from an educator’s perspective. Br. J. Educ. Tech. 44(4), 562–570 (2013)

    Article  Google Scholar 

  5. Dretske, F.: Knowledge and the Flow of Information. MIT Press, Cambridge (1981)

    MATH  Google Scholar 

  6. Dretske, F.: Perception, Knowledge and Belief: Selected Essays. Cambridge University Press, Cambridge (2000)

    Book  Google Scholar 

  7. Dretske, F.I.: Naturalizing the Mind. MIT Press, Cambridge (1997)

    Google Scholar 

  8. Eurostat: Education and training in the EU - facts and figures (2013)

    Google Scholar 

  9. Foltz, P.W., Laham, D., Landauer, T.K., Foltz, P.W., Laham, D., Landauer, T.K.: Automated essay scoring: applications to educational technology. In: Proceedings of EdMedia 1999, vol. 1999, pp. 939–944 (1999)

    Google Scholar 

  10. Kakkonen, T., Myller, N., Timonen, J., Sutinen, E.: Automatic essay grading with probabilistic latent semantic analysis. In: Proceedings of the Second Workshop on Building Educational Applications Using NLP, EdAppsNLP 2005, pp. 29–36. Association for Computational Linguistics, Stroudsburg (2005)

    Google Scholar 

  11. Lin, D.: An information-theoretic definition of similarity. In: Proceedings of the 15th International Conference on Machine Learning, pp. 296–304. Morgan Kaufmann (1998)

    Google Scholar 

  12. Moen, R.D., Nolan, T.W., Provost, L.P.: Quality Improvement Through Planned Experimentation. McGraw-Hill, New York (1999)

    Google Scholar 

  13. Rehder, B., Schreiner, M.E., Wolfe, M.B.W., Laham, D., Landauer, T.K., Kintsch, W.: Using latent semantic analysis to assess knowledge: some technical considerations. Discourse Process. 25(2–3), 337–354 (1998)

    Article  Google Scholar 

  14. Ricketts, C., Brice, J., Coombes, L.: Are multiple choice tests fair to medical students with specific learning disabilities? Adv. Health Sci. Educ. 15(2), 265–275 (2009)

    Article  Google Scholar 

  15. Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27(379–423), 623–656 (1948)

    Article  MathSciNet  MATH  Google Scholar 

  16. Sikora, A.S.: Mathematical theory of student assessment through grading (2015)

    Google Scholar 

  17. Ulltveit-Moe, N., Assev, S., Gjøsæter, T., Øysæd, H.: Streamlining assessment using a knowledge metric. In: Proceedings of CSEDU 2016, pp. 190–197. SCITEPRESS - Science and Technology Publications (2016)

    Google Scholar 

  18. Valenti, S., Neri, F., Cucchiarelli, A., Valenti, S., Neri, F., Cucchiarelli, A.: An overview of current research on automated essay grading. J. Inf. Technol. Educ. Res. 2(1), 319–330 (2003)

    Google Scholar 

  19. Winship, C., Mare, R.D.: Regression models with ordinal variables. Am. Sociol. Rev. 49, 513–524 (1984)

    Article  Google Scholar 

  20. Zen, K., Iskandar, D., Linang, O.: Using Latent Semantic Analysis for automated grading programming assignments. In: 2011 International Conference on Semantic Technology and Information Retrieval (STAIR), pp. 82–88 (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information and Communication Technology, University of Agder, Jon Lilletuns vei 9, 4879, Grimstad, Norway

    Nils Ulltveit-Moe, Sigurd Assev & Halvard Øysæd

  2. NC-Spectrum, Kviteseidgata 18, 3850, Kviteseid, Norway

    Terje Gjøsæter

Authors
  1. Nils Ulltveit-Moe
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Terje Gjøsæter
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sigurd Assev
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Halvard Øysæd
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Nils Ulltveit-Moe , Terje Gjøsæter , Sigurd Assev or Halvard Øysæd .

Editor information

Editors and Affiliations

  1. University of Salerno, Fisciano, Italy

    Gennaro Costagliola

  2. Ulster University, Belfast, United Kingdom

    James Uhomoibhi

  3. University of Denver, Denver, Colorado, USA

    Susan Zvacek

  4. Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

    Bruce M. McLaren

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Ulltveit-Moe, N., Gjøsæter, T., Assev, S., Øysæd, H. (2017). Towards Efficient Teacher Assisted Assignment Marking Using Ranking Metrics. In: Costagliola, G., Uhomoibhi, J., Zvacek, S., McLaren, B. (eds) Computers Supported Education. CSEDU 2016. Communications in Computer and Information Science, vol 739. Springer, Cham. https://doi.org/10.1007/978-3-319-63184-4_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-63184-4_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63183-7

  • Online ISBN: 978-3-319-63184-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation