Advertisement

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

  • Mohamed-Amine Abrache
  • Aimad Qazdar
  • Abdelkrim Bendou
  • Chihab Cherkaoui
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 37)

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.

Keywords

Peer assessment Peer review Learner profile Assessor model MOOC Online assessment tools Allocation of submissions 

References

  1. 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. 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. 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. 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. 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. 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. 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. 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. 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)CrossRefGoogle Scholar
  10. 10.
    Haber, J.: MOOCs, pp. 69–76. MIT Press, Cambridge (2014)Google Scholar
  11. 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)CrossRefGoogle Scholar
  12. 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. 13.
    Zupanc, K., Bosnić, Z.: Automated essay evaluation with semantic analysis. Knowl. Based Syst. 120, 118–132 (2017)CrossRefGoogle Scholar
  14. 14.
    Falchikov, N.: Involving students in assessment. Psychol. Learn. Teach. 3(2), 102–108 (2004)CrossRefGoogle Scholar
  15. 15.
    Topping, K.: Peer assessment between students in colleges and universities. Rev. Educ. Res. 68(3), 249–276 (1998)CrossRefGoogle Scholar
  16. 16.
    Kollar, I., Fischer, F.: Peer assessment as collaborative learning: a cognitive perspective. Learn. Instr. 20(4), 344–348 (2010)CrossRefGoogle Scholar
  17. 17.
    Gielen, S., et al.: Goals of peer assessment and their associated quality concepts. Stud. High. Educ. 36(6), 719–735 (2011)CrossRefGoogle Scholar
  18. 18.
    Lu, J., Law, N.: Online peer assessment: effects of cognitive and affective feedback. Instr. Sci. 40(2), 257–275 (2012)CrossRefGoogle Scholar
  19. 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. 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. 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)CrossRefGoogle Scholar
  22. 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)CrossRefGoogle Scholar
  23. 23.
    Rice, W.: Moodle E-Learning Course Development. Packt Publishing Ltd., Birmingham (2015)Google Scholar
  24. 24.
    Using Workshop – MoodleDocs. https://docs.moodle.org/29/en/Using_Workshop#Grade_for_assessment. Accessed 2017
  25. 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)CrossRefGoogle Scholar
  26. 26.
  27. 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. 28.
    Piech, C., et al.: Tuned models of peer assessment in MOOCs. arXiv preprint arXiv:1307.2579 (2013)
  29. 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. 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)CrossRefGoogle Scholar
  31. 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. 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. 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. 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. 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. 36.
    Brown, G.A., Bull, J., Pendlebury, M.: Assessing Student Learning in Higher Education. Routledge, New York (2013)Google Scholar
  37. 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)MathSciNetCrossRefGoogle Scholar
  38. 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. 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)CrossRefGoogle Scholar
  40. 40.
    Felder, R.M., Silverman, L.K.: Learning and teaching styles in engineering education. Eng. Educ. 78(7), 674–681 (1988)Google Scholar
  41. 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)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.IRF-SIC Laboratory, FSAIbn Zohr UniversityAgadirMorocco
  2. 2.GMES Laboratory, ENSAIbn Zohr UniversityAgadirMorocco
  3. 3.IRF-SIC Laboratory, ENCGIbn Zohr UniversityAgadirMorocco

Personalised recommendations