Advertisement

Higher Education Challenge Characterization to Implement Automated Essay Scoring Model for Universities with a Current Traditional Learning Evaluation System

  • José Carlos MachicaoEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 918)

Abstract

Higher education is currently challenged to respond to a massive interest in learning with a current model that shows increasing evidence of too much cost, effort and decreasing efficacy of the operational learning process. Artificial intelligence has gained presence as a solution, but the integration process is already reporting problems and will not be implemented easily, in particular for universities with low degree of automation integrated to their systems. Universities need to quickly adapt and develop organizational and individual competencies, and clarity about the elements for new learning evaluation systems. This work contributes to propose a model to help universities to define these new systems making the most of artificial intelligence tools for academic essays scoring.

Keywords

Higher education Automated essay scoring Artificial intelligence Knowledge management 

References

  1. 1.
    QS Quacquarelli Symonds: Understanding the Future of Higher Education. QS Website (2018). http://www.qs.com/understanding-future-higher-education/
  2. 2.
    Moran, H., Powell, J.: Running a tight ship: can universities plot a course through rough seas? Special Report for the Guardian, HSBC, UUK (2018). https://uploads.guim.co.uk/
  3. 3.
    Kerr, C.: The Uses of the University, 5th edn. Harvard University Press, Cambridge (2003)Google Scholar
  4. 4.
    Ferrer-Balas, D., et al.: Going beyond the rhetoric: system-wide changes in universities for sustainable societies. J. Cleaner Prod. 18(7), 607–610 (2010)CrossRefGoogle Scholar
  5. 5.
    INEI: Estadísticas de Educación Superior. Instituto Nacional de Estadísticas Website (2018). https://www.inei.gob.pe/estadisticas/indice-tematico/nivel-de-educacion-alcanzado-8034/
  6. 6.
    Vedder, R.: Seven Challenges Facing Higher Education. Forbes Magazine. The Center for College Affordability and Productivity (2017). https://www.forbes.com/sites/ccap/
  7. 7.
    Bok, D.: Universities in the Marketplace. The Commercialization of Higher Education. Princeton University Press, Princeton (2003)Google Scholar
  8. 8.
    McDonald, M.: Systematic Assessment of Learning Outcomes: Developing Multiple-Choice Exams. Jones & Barlett Learning, Boston (2002)Google Scholar
  9. 9.
    Warwick, K.: Artificial Intelligence: The Basics. Routledge, London (2013)Google Scholar
  10. 10.
    Connelly, J., Forsyth, P.: Essay Writing Skills: Essential Techniques to Gain Top Marks. Kogan Page Publishers, London (2012)Google Scholar
  11. 11.
    Page, E.B.: The imminence of grading essays by computer. Phi Delta Kappa 47, 238–243 (1966)Google Scholar
  12. 12.
    Shermis, M.D., Burstein, J.C.: Automated Essay Scoring: A Cross-Disciplinary Perspective. Routledge, New York (2003)CrossRefGoogle Scholar
  13. 13.
    Ramalingam, V., Pandian, A., Chetry, P., Nigam, H.: Automated essay grading using machine learning. In: National Conference on Mathematical Techniques and its Applications (NCMTA 2018) (2018).  https://doi.org/10.1088/1742-6596/1000/1/012030. http://iopscience.iop.org/articleGoogle Scholar
  14. 14.
    Shermis, M.D.: The challenges of emulating human behavior in writing assessment. Assessing Writ. J. 22, 91–99 (2014)CrossRefGoogle Scholar
  15. 15.
    Harvard University: Essay Structure. Harvard College Writing Centre (2017). https://writingcenter.fas.harvard.edu
  16. 16.
    Hoppe, U., Verdejo, M.F.: Artificial Intelligence in Education: Shaping the Future of Learning Through Intelligent Technologies. IOS Press, Amsterdam (2003)Google Scholar
  17. 17.
    Archibald, R., Feldman, D.: Explaining increases in higher education costs. J. High. Educ. 79(3), 268–295 (2008)CrossRefGoogle Scholar
  18. 18.
    Muller, V., Bostrom, N.: Future progress in artificial intelligence: a survey of expert opinion. In: Fundamental Issues of Artificial Intelligence. Springer (2016)Google Scholar
  19. 19.
    Dale, R., Moisl, H., Somers, H.: Handbook of Natural Language Processing. Business and Economics. CRC Press, Boca Raton (2000)CrossRefGoogle Scholar
  20. 20.
    Wissner-Gross, A.D., Freer, C.E.: Causal entropic forces. Phys. Rev. Lett. 110, 168702 (2013)CrossRefGoogle Scholar
  21. 21.
    Glaser, R.: Knowing, Learning, and Instruction. Lawrence Erlbaum Publishers, London (1989)Google Scholar
  22. 22.
    Bird, S., et al.: Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O’Reilly Media Inc., Sebastopol (2009)zbMATHGoogle Scholar
  23. 23.
    Le, Q., Mikolov, T.: Distributed Representations of Sentences and Documents. Google Inc. (2014)Google Scholar
  24. 24.
    Scikit Learn: Scikit-Learn Website (2018). http://scikit-learn.org
  25. 25.
    Norton, L.S.: Essay-writing: what really counts? High. Educ. J. 20(4), 411–442 (1990)CrossRefGoogle Scholar
  26. 26.
    Mumford, M.D., et al.: Leadership skills for a changing world: Solving complex social problems. Leadersh. Q. J. 11(1), 11–35 (2000)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.GestioDinámicaLimaPeru
  2. 2.Universidad ContinentalLimaPeru

Personalised recommendations