Skip to main content

An Assessment Framework for Online Active Learning Performance

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12830))

Abstract

Under the influence of COVID-19, online learning has become the primary way for students to continue their education. At all stages of online learning, active learning is a useful strategy promoting optimal understanding. However, there is a lack of relevant research on how to evaluate students’ active learning performance. This paper presents an online active learning assessment framework based on the learning pyramid and learning dimension theory. After the division of course modules according to the learning pyramid theory, the active learning assessment is performed from five dimensions: (1) positive attitudes and perceptions about learning; (2) acquiring and integrating knowledge; (3) extending and refining knowledge; (4) using knowledge meaningfully, and (5) productive habits of mind. By identifying patterns from each online course module’s weblog data, instructors can assess students’ active learning conveniently from the beginning to the end of the online course. This study helps instructors understand learners’ learning situations and adopt corresponding strategies to adjust teaching activities to ensure high-quality teaching activities. Simultaneously, learners can also actively change their learning status according to active learning assessment to improve the learning effect.

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

References

  1. Demuyakor, J.: Coronavirus (COVID-19) and online learning in higher institutions of education: a survey of the perceptions of Ghanaian international students in China. Online J. Commun. Media Technol. 10(3), 1–9 (2020)

    Article  Google Scholar 

  2. Li, Z.: With the suspension of classes, online education is facing new challenges. People’s Daily Overseas Edition. http://paper.people.com.cn/rmrbhwb/html/2020-02/18/content_1971524.htm. Accessed 1 Dec 2020

  3. Li, C., Lalani, F.: The COVID-19 pandemic has changed education forever. This is how. The World Economic forum COVID action platform. https://www.weforum.org/agenda/2020/04/coronavirus-education-global-covid19-online-digital-learning/. Accessed 1 Dec 2020

  4. Kiryacu, C.: Temeljna nastavna umijeća. https://www.azoo.hr/images/razno/TEMELJNA_NASTAVN.pdf. Accessed 1 Dec 2020

  5. Timmerman, B., Lingard, R.: Assessment of active learning with upper division computer science students. In: 33rd Annual Frontiers in Education, vol. 3, p. S1D-7 (2003)

    Google Scholar 

  6. Reeves, T.C.: Alternative assessment approaches for online learning environments in higher education. J. Educ. Comput. Res. 23(1), 101–111 (2000)

    Article  MathSciNet  Google Scholar 

  7. Graff, M.: Cognitive style and attitudes towards using online learning and assessment methods. Electron. J. e-Learn. 1(1), 21–28 (2003)

    Google Scholar 

  8. Mateo, J., Sangrà, A.: Designing online learning assessment through alternative approaches: facing the concerns. Eur. J. Open Distance E-Learn. 10(2), 1–7 (2007)

    Google Scholar 

  9. Gaytan, J., McEwen, B.C.: Effective online instructional and assessment strategies. Am. J. Distance Educ. 21(3), 117–132 (2007)

    Article  Google Scholar 

  10. Suen, H.K.: Peer assessment for massive open online courses (MOOCs). Int. Rev. Res. Open Distrib. Learn. 15(3), 312–327 (2014)

    Google Scholar 

  11. Guerrero-Roldán, A.E., Noguera, I.: A model for aligning assessment with competences and learning activities in online courses. Internet High. Educ. 38, 36–46 (2018)

    Article  Google Scholar 

  12. Swan, K., Shen, J., Hiltz, S.R.: Assessment and collaboration in online learning. J. Asynchronous Learn. Netw. 10(1), 45–62 (2006)

    Google Scholar 

  13. Vonderwell, S., Liang, X., Alderman, K.: Asynchronous discussions and assessment in online learning. J. Res. Technol. Educ. 39(3), 309–328 (2007)

    Article  Google Scholar 

  14. James, P.A., et al.: Performance assessment framework for measuring online student learning outcome. In: Proceedings of the 120th Annual Conference, Atlanta, Georgia (2013)

    Google Scholar 

  15. Formanek, M., Wenger, M.C., Buxner, S.R., Impey, C.D., Sonam, T.: Insights about large-scale online peer assessment from an analysis of an astronomy MOOC. Comput. Educ. 113, 243–262 (2017)

    Article  Google Scholar 

  16. Tsai, F.H., Tsai, C.C., Lin, K.Y.: The evaluation of different gaming modes and feedback types on game-based formative assessment in an online learning environment. Comput. Educ. 81, 259–269 (2015)

    Article  Google Scholar 

  17. Ikejiri, R., Oura, H., Fushikida, W., Anzai, Y., Yamauchi, Y.: Evaluating learning in the MOOC about the study of history. Educ. Technol. Res. 41(1), 77–89 (2019)

    Google Scholar 

  18. Marzano, R.J., Pickering, D., McTighe, J.: Assessing student outcomes: performance assessment using the dimensions of learning model. Association for Supervision and Curriculum Developmen, Alexandria (1993)

    Google Scholar 

  19. Jaques, E.: The development of intellectual capability: a discussion of stratified systems theory. J. Appl. Behav. Sci. 22(4), 361–383 (1985)

    Article  Google Scholar 

  20. Letrud, K., Hernes, S.: The diffusion of the learning pyramid myths in academia: an exploratory study. J. Curric. Stud. 48(3), 291–302 (2016)

    Article  Google Scholar 

  21. Dale, E., Nyland, B.: Cone of learning. Educ. Med. (1960)

    Google Scholar 

  22. Sousa, D.A.: How the brain learns, 3rd edn. Corwin, Thousand Oaks (2006)

    Google Scholar 

  23. Baranowski, M.: Single session simulations: the effectiveness of short congressional simulations in introductory American government classes. J. Polit. Sci. Educ. 2(1), 33–49 (2006)

    Article  Google Scholar 

  24. Peng, H., Chen, Y., Hu, H.: Research and exploration of online to offline (O2O) blended teaching for talent cultivating. In: 2020 International Conference on Advanced Education, Management and Information Technology (AEMIT 2020), pp. 191–194. Atlantis Press, French (2020)

    Google Scholar 

  25. Pekrun, R., Goetz, T., Titz, W., Perry, R.P.: Academic emotions in students’ self-regulated learning and achievement: a program of qualitative and quantitative research. Educ. Psychol. 37(2), 91–105 (2002)

    Article  Google Scholar 

  26. Wang, L., Hu, G., Zhou, T.: Semantic analysis of learners’ emotional tendencies on online MOOC education. Sustainability 10(6), 1–19 (2018)

    Google Scholar 

  27. Medhat, W., Hassan, A., Korashy, H.: Sentiment analysis algorithms and applications: A survey. Ain Shams Eng. J. 5(4), 1093–1113 (2014)

    Article  Google Scholar 

  28. Sentiment Analysis. https://github.com/Edward1Chou/SentimentAnalysis. Accessed 1 Dec 2020

  29. Al Amrani, Y., Lazaar, M., El Kadiri, K.E.: Random forest and support vector machine based hybrid approach to sentiment analysis. Procedia Comput. Sci. 127, 511–520 (2018)

    Article  Google Scholar 

  30. Chan, A.Y.K., Chow, P.K., Cheung, K.S.: Student participation index: student assessment in online courses. In: Liu, W., Shi, Y., Li, Q. (eds.) ICWL 2004. LNCS, vol. 3143, pp. 449–456. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-27859-7_58

    Chapter  Google Scholar 

  31. Hwang, C.L., Yoon, K.: Multiple Attribute Decision Making: Methods and Applications. Springer, New York (1981). https://doi.org/10.1007/978-3-642-48318-9

    Book  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

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

Liu, C., Zou, D., Chan, W.H., Xie, H., Wang, F.L. (2021). An Assessment Framework for Online Active Learning Performance. In: Li, R., Cheung, S.K.S., Iwasaki, C., Kwok, LF., Kageto, M. (eds) Blended Learning: Re-thinking and Re-defining the Learning Process.. ICBL 2021. Lecture Notes in Computer Science(), vol 12830. Springer, Cham. https://doi.org/10.1007/978-3-030-80504-3_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-80504-3_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-80503-6

  • Online ISBN: 978-3-030-80504-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics