Skip to main content
SpringerLink
Log in

The Allocation of Submissions in Online Peer Assessment: What Can an Assessor Model Provide in This Context?

  • Conference paper
  • First Online:
Innovations in Smart Cities and Applications (SCAMS 2017)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 37))

Included in the following conference series:

Abstract

In online learning environments, the individual characteristics of learners have necessarily an influence on the reliability and the credibility of peer assessment. This paper focuses on the stage of allocating students’ submissions within online peer assessment process. Firstly, by providing an overview of the main applications of this process in online assessment tools and MOOCs. This overview considers mainly the methodologies of assigning submissions to learners for evaluation; and secondly, by proposing a model for the assessor based on the individual personal characteristics that shape her or his assessment profile. This profile plays a key role in the success of the peer assessment/feedback experience. We conclude this paper with a brief discussion of the potential that can provide the assessor model in the context of an approach that manages the allocation of submissions and considers the personal characteristics of the learners’ community.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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

References

  1. Reis, R., Escudeiro, P.: The role of virtual worlds for enhancing student-student interaction in MOOCs. In: User-Centered Design Strategies for Massive Open Online Courses (MOOCs), pp. 208–221. IGI Global (2016)

    Google Scholar 

  2. Daradoumis, T., et al.: A review on massive e-learning (MOOC) design, delivery and assessment. In: 2013 Eighth International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC). IEEE (2013)

    Google Scholar 

  3. Hmedna, B., et al.: Identifying and tracking learning styles in MOOCs: a neural networks approach. Int. J. Innov. Appl. Stud. 19(2), 267 (2017)

    Google Scholar 

  4. Yousef, A.M.F., et al.: The impact of rubric-based peer assessment on feedback quality in blended MOOCs. In: International Conference on Computer Supported Education. Springer, Cham (2015)

    Google Scholar 

  5. Zhao, C., et al.: Exploring the role of assessment in developing learners’ critical thinking in massive open online courses. In: European Conference on Massive Open Online Courses. Springer, Cham (2017)

    Google Scholar 

  6. Jiao, J., et al.: Improving learning in MOOCs through peer feedback: how is learning improved by providing and receiving feedback? In: Learning and Knowledge Analytics in Open Education, pp. 69–87. Springer, Cham (2017)

    Google Scholar 

  7. Khalil, H., Ebner, M.: MOOCs completion rates and possible methods to improve retention-a literature review. In: World Conference on Educational Multimedia, Hypermedia and Telecommunications (2014)

    Google Scholar 

  8. Ashenafi, M.M., Ronchetti, M., Riccardi, G.: Exploring the role of online peer-assessment as a tool of early intervention. In: SETE@ ICWL (2016)

    Google Scholar 

  9. Falchikov, N., Goldfinch, J.: Student peer assessment in higher education: a meta-analysis comparing peer and teacher marks. Rev. Educ. Res. 70(3), 287–322 (2000)

    Article  Google Scholar 

  10. Haber, J.: MOOCs, pp. 69–76. MIT Press, Cambridge (2014)

    Google Scholar 

  11. Shute, V.J., Rahimi, S.: Review of computer-based assessment for learning in elementary and secondary education. J. Comput. Assist. Learn. 33(1), 1–19 (2017)

    Article  Google Scholar 

  12. Balfour, S.P.: Assessing writing in MOOCs: automated essay scoring and calibrated peer review (TM). Res. Pract. Assess. 8, 40–48 (2013)

    Google Scholar 

  13. Zupanc, K., Bosnić, Z.: Automated essay evaluation with semantic analysis. Knowl. Based Syst. 120, 118–132 (2017)

    Article  Google Scholar 

  14. Falchikov, N.: Involving students in assessment. Psychol. Learn. Teach. 3(2), 102–108 (2004)

    Article  Google Scholar 

  15. Topping, K.: Peer assessment between students in colleges and universities. Rev. Educ. Res. 68(3), 249–276 (1998)

    Article  Google Scholar 

  16. Kollar, I., Fischer, F.: Peer assessment as collaborative learning: a cognitive perspective. Learn. Instr. 20(4), 344–348 (2010)

    Article  Google Scholar 

  17. Gielen, S., et al.: Goals of peer assessment and their associated quality concepts. Stud. High. Educ. 36(6), 719–735 (2011)

    Article  Google Scholar 

  18. Lu, J., Law, N.: Online peer assessment: effects of cognitive and affective feedback. Instr. Sci. 40(2), 257–275 (2012)

    Article  Google Scholar 

  19. Babik, D., et al.: Probing the landscape: toward a systematic taxonomy of online peer assessment systems in education. In: EDM (Workshops) (2016)

    Google Scholar 

  20. de Alfaro, L., Shavlovsky, M.: CrowdGrader: a tool for crowdsourcing the evaluation of homework assignments. In: Proceedings of the 45th ACM Technical Symposium on Computer Science Education. ACM (2014)

    Google Scholar 

  21. Cho, K., Schunn, C.D.: Scaffolded writing and rewriting in the discipline: a web-based reciprocal peer review system. Comput. Educ. 48(3), 409–426 (2007)

    Article  Google Scholar 

  22. Schunn, C., Godley, A., DeMartino, S.: The reliability and validity of peer review of writing in high school AP English classes. J. Adolesc. Adult Lit. 60(1), 13–23 (2016)

    Article  Google Scholar 

  23. Rice, W.: Moodle E-Learning Course Development. Packt Publishing Ltd., Birmingham (2015)

    Google Scholar 

  24. Using Workshop – MoodleDocs. https://docs.moodle.org/29/en/Using_Workshop#Grade_for_assessment. Accessed 2017

  25. Russell, J., et al.: Variability in students’ evaluating processes in peer assessment with calibrated peer review. J. Comput. Assist. Learn. 33(2), 178–190 (2017)

    Article  Google Scholar 

  26. Edx: Open Response Assessments (2017). https://edx.readthedocs.io/projects/open-edx-building-and-running-a-course/en/named-release-birch/exercises_tools/open_response_assessments/index.html

  27. Staubitz, T., et al.: Improving the peer assessment experience on MOOC platforms. In: Proceedings of the Third (2016) ACM Conference on Learning@ Scale. ACM (2016)

    Google Scholar 

  28. Piech, C., et al.: Tuned models of peer assessment in MOOCs. arXiv preprint arXiv:1307.2579 (2013)

  29. Goldin, I.M., Ashley, K.D.: Peering inside peer review with Bayesian models. In: Artificial Intelligence in Education. Springer, Heidelberg (2011)

    Google Scholar 

  30. Van den Berg, I., Admiraal, W., Pilot, A.: Design principles and outcomes of peer assessment in higher education. Stud. Higher Educ. 31(03), 341–356 (2006)

    Article  Google Scholar 

  31. IEEE P1484.2/D7, 2000-11-28: Draft Standard for Learning Technology. Public and Private Information (PAPI) for Learners (PAPI Learner) (2002). http://ltsc.ieee.org/wg2/. Accessed 25 Oct 2002

  32. Oubahssi, L., Grandbastien, M.: From learner information packages to student models: which continuum? In: International Conference on Intelligent Tutoring Systems. Springer, Heidelberg (2006)

    Google Scholar 

  33. Battou, A.: Approche granulaire des objets pédagogiques en vue de l’adaptabilité dans le cadre des Environnements Informatiques pour l’Apprentissage Humain (2012)

    Google Scholar 

  34. Qazdar, A., et al.: AeLF: mixing adaptive learning system with learning management system. Int. J. Comput. Appl. 119(15), 1–8 (2015)

    Google Scholar 

  35. Fini, A.: The technological dimension of a massive open online course: the case of the CCK08 course tools. Int. Rev. Res. Open Distrib. Learn. 10(5) (2009)

    Google Scholar 

  36. Brown, G.A., Bull, J., Pendlebury, M.: Assessing Student Learning in Higher Education. Routledge, New York (2013)

    Google Scholar 

  37. Jeffery, D., et al.: How to achieve accurate peer assessment for high value written assignments in a senior undergraduate course. Assess. Eval. Higher Educ. 41(1), 127–140 (2016)

    Article  MathSciNet  Google Scholar 

  38. Xiong, Y., et al.: A proposed credibility index (CI) in peer assessment. In: Poster Presented at the Annual Meeting of the National Council on Measurement in Education, Philadelphia, PA (2014)

    Google Scholar 

  39. Engelhard, G.: Examining rater errors in the assessment of written composition with a Many-Faceted Rasch model. J. Educ. Meas. 31(2), 93–112 (1994)

    Article  Google Scholar 

  40. Felder, R.M., Silverman, L.K.: Learning and teaching styles in engineering education. Eng. Educ. 78(7), 674–681 (1988)

    Google Scholar 

  41. Lan, C.H., Graf, S., Lai, K.R.: Enrichment of peer assessment with agent negotiation. IEEE Trans. Learn. Technol. 4(1), 35–46 (2011)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. IRF-SIC Laboratory, FSA, Ibn Zohr University, Agadir, Morocco

    Mohamed-Amine Abrache & Abdelkrim Bendou

  2. GMES Laboratory, ENSA, Ibn Zohr University, Agadir, Morocco

    Aimad Qazdar

  3. IRF-SIC Laboratory, ENCG, Ibn Zohr University, Agadir, Morocco

    Chihab Cherkaoui

Authors
  1. Mohamed-Amine Abrache
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aimad Qazdar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abdelkrim Bendou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chihab Cherkaoui
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohamed-Amine Abrache .

Editor information

Editors and Affiliations

  1. Computer Sciences Department, Faculty of Sciences and Techniques, Abdelmalek Essaadi University, Tangier, Morocco

    Mohamed Ben Ahmed

  2. Computer Sciences Department, Faculty of Sciences and Techniques, Abdelmalek Essaadi University, Tangier, Morocco

    Anouar Abdelhakim Boudhir

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Abrache, MA., Qazdar, A., Bendou, A., Cherkaoui, C. (2018). The Allocation of Submissions in Online Peer Assessment: What Can an Assessor Model Provide in This Context?. In: Ben Ahmed, M., Boudhir, A. (eds) Innovations in Smart Cities and Applications. SCAMS 2017. Lecture Notes in Networks and Systems, vol 37. Springer, Cham. https://doi.org/10.1007/978-3-319-74500-8_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-74500-8_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-74499-5

  • Online ISBN: 978-3-319-74500-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation