Skip to main content

Temporal Processes Associating with Procrastination Dynamics

  • Conference paper
  • First Online:
  • 3093 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12748))

Abstract

Procrastination, as an act of voluntarily delaying tasks, is particularly pronounced among students. Recent research has proposed several solutions to modeling student behaviors with the goal of procrastination modeling. Particularly, temporal and sequential models, such as Hawkes processes, have proven to be successful in capturing students’ behavioral dynamics as a representation of procrastination. However, these discovered dynamics are yet to be validated with psychological measures of procrastination through student self-reports and surveys. In this work, we fill this gap by discovering associations between temporal procrastination modeling in students with students’ chronic and academic procrastination levels and their goal achievement. Our analysis reveals meaningful relationships between the learning dynamics discovered by Hawkes processes with student procrastination and goal achievement based on student self-reported data. Most importantly, it shows that students who exhibit inconsistent and less regular learning activities, driven by the goal to outperform or perform not worse than other students, also reported a higher degree of procrastination.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   99.99
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

Learn about institutional subscriptions

Notes

  1. 1.

    The implementation can be found in https://github.com/persai-lab/AIED2021_Hawkes.

  2. 2.

    Please note that these are based on correlation, and are not causal effects.

References

  1. Agnihotri, L., Baker, R.S., Stalzer, S.: A procrastination index for online learning based on assignment start time. In: 13th International Conference on Educational Data Mining (2020)

    Google Scholar 

  2. Asarta, C.J., Schmidt, J.R.: Access patterns of online materials in a blended course. Decis. Sci. J. Innov. Educ. 11(1), 107–123 (2013)

    Article  Google Scholar 

  3. Azevedo, R., Feyzi-Behnagh, R.: Dysregulated learning with advanced learning technologies. In: AAAI Fall Symposium: Cognitive and Metacognitive Educational Systems (2010)

    Google Scholar 

  4. Cerezo, R., Esteban, M., Sánchez-Santillán, M., Núñez, J.C.: Procrastinating behavior in computer-based learning environments to predict performance: a case study in Moodle. Front. Psychol. 8, 1403 (2017)

    Article  Google Scholar 

  5. Du, N., Farajtabar, M., Ahmed, A., Smola, A.J., Song, L.: Dirichlet-Hawkes processes with applications to clustering continuous-time document streams. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 219–228 (2015)

    Google Scholar 

  6. Dvorak, T., Jia, M.: Do the timeliness, regularity, and intensity of online work habits predict academic performance? J. Learn. Anal. 3(3), 318–330 (2016)

    Article  Google Scholar 

  7. Elliot, A.J., McGregor, H.A.: A 2 \(\times \) 2 achievement goal framework. J. Pers. Soc. Psychol. 80(3), 501 (2001)

    Article  Google Scholar 

  8. Elliot, A.J., Murayama, K.: On the measurement of achievement goals: critique, illustration, and application. J. Educ. Psychol. 100(3), 613 (2008)

    Article  Google Scholar 

  9. Elvers, G.C., Polzella, D.J., Graetz, K.: Procrastination in online courses: performance and attitudinal differences. Teach. Psychol. 30(2), 159–162 (2003)

    Article  Google Scholar 

  10. Geigle, C., Zhai, C.: Modeling MOOC student behavior with two-layer hidden Markov models. In: Proceedings of the Fourth ACM Conference on Learning@ Scale, pp. 205–208 (2017)

    Google Scholar 

  11. Halpin, P.F., von Davier, A.A., Hao, J., Liu, L.: Measuring student engagement during collaboration. J. Educ. Meas. 54(1), 70–84 (2017)

    Article  Google Scholar 

  12. Hansen, C., Hansen, C., Hjuler, N., Alstrup, S., Lioma, C.: Sequence modelling for analysing student interaction with educational systems. arXiv preprint arXiv:1708.04164 (2017)

  13. Hawkes, A.G.: Spectra of some self-exciting and mutually exciting point processes. Biometrika 58(1), 83–90 (1971)

    Article  MathSciNet  Google Scholar 

  14. Kazerouni, A.M., Edwards, S.H., Hall, T.S., Shaffer, C.A.: DevEventTracker: tracking development events to assess incremental development and procrastination. In: Proceedings of the 2017 ACM Conference on Innovation and Technology in Computer Science Education - ITiCSE 2017, pp. 104–109. ACM Press, Bologna (2017)

    Google Scholar 

  15. Kim, K.R., Seo, E.H.: The relationship between procrastination and academic performance: a meta-analysis. Personality Individ. Diff. 82, 26–33 (2015)

    Article  Google Scholar 

  16. Lan, A.S., Spencer, J.C., Chen, Z., Brinton, C.G., Chiang, M.: Personalized thread recommendation for MOOC discussion forums. In: Berlingerio, M., Bonchi, F., Gärtner, T., Hurley, N., Ifrim, G. (eds.) ECML PKDD 2018. LNCS (LNAI), vol. 11052, pp. 725–740. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-10928-8_43

    Chapter  Google Scholar 

  17. Mirzaei, M., Sahebi, S., Brusilovsky, P.: SB-DNMF: a structure based discriminative non-negative matrix factorization model for detecting inefficient learning behaviors. In: 2020 IEEE/WIC/ACM International Joint Conference On Web Intelligence And Intelligent Agent Technology. WI-IAT (2020)

    Google Scholar 

  18. Ng, A.: Clustering with the k-means algorithm. Machine Learning (2012)

    Google Scholar 

  19. Ogata, Y.: On Lewis’ simulation method for point processes. IEEE Trans. Inf. Theory 27(1), 23–31 (1981)

    Article  Google Scholar 

  20. Ogata, Y.: Statistical models for earthquake occurrences and residual analysis for point processes. J. Am. Stat. Assoc. 83(401), 9–27 (1988)

    Article  Google Scholar 

  21. Park, J., Yu, R., Rodriguez, F., Baker, R., Smyth, P., Warschauer, M.: Understanding student procrastination via mixture models. International Educational Data Mining Society (2018)

    Google Scholar 

  22. Pychyl, T.A., Lee, J.M., Thibodeau, R., Blunt, A.: Five days of emotion: an experience sampling study of undergraduate student procrastination. J. Soc. Behav. Pers. 15(5), 239 (2000)

    Google Scholar 

  23. Schouwenburg, H.C.: Academic procrastination. In: Schouwenburg, H.C. (ed.) Procrastination and Task Avoidance. The Springer Series in Social Clinical Psychology, pp. 71–96. Springer, Heidelberg (1995). https://doi.org/10.1007/978-1-4899-0227-6_410.1007/978-1-4899-0227-6_4

    Chapter  Google Scholar 

  24. Sirois, F.M., Yang, S., van Eerde, W.: Development and validation of the General Procrastination Scale (GPS-9): a short and reliable measure of trait procrastination. Personality Individ. Diff. 146, 26–33 (2019)

    Article  Google Scholar 

  25. Steel, P.: The nature of procrastination: a meta-analytic and theoretical review of quintessential self-regulatory failure. Psychol. Bull. 133(1), 65 (2007)

    Article  MathSciNet  Google Scholar 

  26. Steel, P.: Arousal, avoidant and decisional procrastinators: do they exist? Personality Individ. Differ. 48(8), 926–934 (2010)

    Article  Google Scholar 

  27. Steel, P., König, C.J.: Integrating theories of motivation. Acad. Manag. Rev. 31(4), 889–913 (2006)

    Article  Google Scholar 

  28. Valera, I., Gomez-Rodriguez, M.: Modeling adoption and usage of competing products. In: 2015 IEEE International Conference on Data Mining, pp. 409–418. IEEE (2015)

    Google Scholar 

  29. Yao, M., Sahebi, S., Behnagh, R.F.: Analyzing student procrastination in MOOCs: a multivariate Hawkes approach. International Educational Data Mining Society (2020)

    Google Scholar 

  30. Yao, M., Zhao, S., Sahebi, S., Behnagh, R.F.: Relaxed clustered Hawkes process for procrastination modeling in MOOCs. arXiv preprint arXiv:2102.00093 (2020)

  31. Yao, M., Zhao, S., Sahebi, S., Behnagh, R.F.: Stimuli-sensitive Hawkes processes for personalized student procrastination modeling. arXiv preprint arXiv:1608.05745 (2020)

  32. Zhou, K., Zha, H., Song, L.: Learning triggering kernels for multi-dimensional Hawkes processes. In: International Conference on Machine Learning, pp. 1301–1309. PMLR (2013)

    Google Scholar 

  33. Zimmerman, B.J.: Investigating self-regulation and motivation: historical background, methodological developments, and future prospects. Am. Educ. Res. J. 45(1), 166–183 (2008)

    Article  Google Scholar 

Download references

Acknowledgement

This paper is based upon work supported by the National Science Foundation under Grant Number 1917949.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mengfan Yao .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yao, M., Sahebi, S., Behnagh, R.F., Bursali, S., Zhao, S. (2021). Temporal Processes Associating with Procrastination Dynamics. In: Roll, I., McNamara, D., Sosnovsky, S., Luckin, R., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science(), vol 12748. Springer, Cham. https://doi.org/10.1007/978-3-030-78292-4_37

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-78292-4_37

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78291-7

  • Online ISBN: 978-3-030-78292-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics