Skip to main content
SpringerLink
Log in

Using Process Visualization and Early Warning Based on Learning Analytics to Enhance Teaching and Learning

  • Conference paper
  • First Online:
Artificial Intelligence and Security (ICAIS 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1252))

Included in the following conference series:

  • 1645 Accesses

Abstract

With the rapid development of educational big data, learning analytics (LA) has been put forward. Without a commercial system which need numerous resources to construct, our study makes full use of the data from existing learning management system, exercises & examination system, and then makes analysis and prediction, finally optimizes the teaching and learning. Specifically, the visualized data can be presented timely to students and teachers, which can enable them to rethink and solve the problem. Logical regression, naive bayes classifier (NBC), support vector machine algorithm (SVM), K-means and artificial neural network (ANN) are used to predict the students’ risk of failure, and NBC has the best prediction effect on our dataset. The early warning is issued, so that the personalized preventive measures can be taken. According to the results of the comparative experiment, the scores of learning analytics group are more than 8% higher than those of the traditional group. Overall, it is easy to implement, and has high cost performance and certain reference value.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

Similar content being viewed by others

References

  1. Bienkowski, M., Feng, M., Means, B.: Enhancing teaching and learning through educational data mining and learning analytics: an issue brief. Department of Education, Office of Educational Technology. Technical report, Washington, D.C., US (2012)

    Google Scholar 

  2. Educational data mining, July 2011. http://www.educationaldatamining.org/

  3. Papamitsiou, Z., Economides, A.A.: Learning analytics and educational data mining in practice: a systematic literature review of empirical evidence. J. Educ. Technol. Soc. 17(4), 49–64 (2014)

    Google Scholar 

  4. Aldowah, H., Al-Samarraie, H., Fauzy, W.M.: Educational data mining and learning analytics for 21st century higher education: a review and synthesis. Telematics Inform. 37, 13–49 (2019)

    Article  Google Scholar 

  5. Ray, S., Saeed, M.: Applications of educational data mining and learning analytics tools in handling big data in higher education. In: Alani, M.M., Tawfik, H., Saeed, M., Anya, O. (eds.) Applications of Big Data Analytics, pp. 135–160. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-76472-6_7

    Chapter  Google Scholar 

  6. Yu, L., Yang, Q.Y., Nan, Z.: Model driven educational big data mining for enhancing teaching and learning: an interview with Dr. Mimi Recker from Utah state university. Open Educ. Res. 1, 4–9 (2018)

    Google Scholar 

  7. Xiao, W., Ni, C.B., Li, R.: Research on learning early warning based on data mining abroad: review and prospect. Distance Educ. China 2, 70–78 (2018)

    Google Scholar 

  8. Jiang, Q., Zhao, W., Li, Y.F., et al.: Research on learning analytics dashboard based on big data. China Educ. Technol. 1, 112–120 (2017)

    Google Scholar 

  9. Arnold, K.E., Pistilli, M.D.: Course signals at Purdue. In: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pp. 267–270 (2012)

    Google Scholar 

  10. Hu, Y.L., Gu, X.Q., Luo, J.T.: An analysis of education-decision support oriented by educational effectiveness: from the perspective of learning analytics. Mod. Distance Educ. Res. 6, 41–47 (2014)

    Google Scholar 

  11. Xiao, W., Ni, C.B., Li, R.: Research on learning early warning based on data mining in foreign countries: review and prospect. Distance Educ. China 2, 70–78 (2018)

    Google Scholar 

  12. Romero-Zaldivar, V.-A., Pardo, A., Burgos, D., Kloos, C.D.: Monitoring student progress using virtual appliances: a case study. Comput. amp Educ. 58(4), 1058–1067 (2012)

    Article  Google Scholar 

  13. Kavitha, M.G., Raj, L.: Educational data mining and learning analytics - educational assistance for teaching and learning. Int. J. Comput. Organ. Trends (IJCOT) 41(1) (2017)

    Google Scholar 

  14. Macfadyen, L.P., Dawson, S.: Mining LMS data to develop an early warning system for educators: a proof of concept. Comput. Educ. 54(2), 588–599 (2010)

    Article  Google Scholar 

  15. Zhao, H.Q., Jiang, Q., Zhao, W., et al.: Empirical research of predictive factors and intervention countermeasures of online learning performance on big data-based learning analytics. E-educ. Res. 1, 62–69 (2017)

    Google Scholar 

  16. Popoola, S.I., Atayero, A.A., Badejo, J.A., John, T.M., Odukoya, J.A., Omole, D.O.: Learning analytics for smart campus: data on academic performances of engineering undergraduates in Nigerian private university. Data Brief 17, 76–94 (2018)

    Article  Google Scholar 

  17. Mishra, A., Bansal, R., Singh, S.N.: Educational data mining and learning analysis. In: International Conference on Cloud Computing, Honolulu, HI, US (2017)

    Google Scholar 

  18. Kop, R.: The design and development of a personal learning environment: researching the learning experience. Media Inspirations for Learning: What Makes the Impact? Technical report, Canada (2010)

    Google Scholar 

  19. Marbouti, F., Diefes-Dux, H.A., Madhavan, K.: Models for early prediction of at-risk students in a course using standards-based grading. Comput. Educ. 103, 1–15 (2016)

    Article  Google Scholar 

  20. He, W., Yen, C.J.: Using data mining for predicting relationships between online question theme and final grade. J. Educ. Technol. Soc. 15(3), 77–88 (2012)

    Google Scholar 

  21. Pardo, A., Han, F., Ellis, R.A.: Combining university student self-regulated learning indicators and engagement with online learning events to predict academic performance. IEEE Trans. Learn. Technol. 10(1), 82–92 (2017)

    Article  Google Scholar 

  22. Yang, Y.Q., Zhou, D.Q., Yang, X.J.: A multi-feature weighting based k-means algorithm for MOOC learner classification. Comput. Mater. Continua 59(2), 625–633 (2019)

    Article  Google Scholar 

  23. Xi, X.F., Sheng, V.S., Sun, B.Q.: An empirical comparison on multi-target regression learning. Comput. Mater. Continua 56(2), 185–198 (2018)

    Google Scholar 

  24. Wang, T.J., Wu, T., Ashrafzadeh, A.H.: Crowdsourcing-based framework for teaching quality evaluation and feedback using linguistic 2-tuple. Comput. Mater. Continua 57(1), 81–96 (2018)

    Article  Google Scholar 

  25. Hu, Y.H., Lo, C.L., Shih, S.P.: Developing early warning systems to predict students online learning performance. Comput. Hum. Behav. 36, 469–478 (2014)

    Article  Google Scholar 

  26. Zhang, Z.H., Liu, W., Zhi, H.: Learning dashboard: a novel learning support tool in the big data era. Mod. Distance Educ. Res. 3, 100–107 (2014)

    Google Scholar 

  27. Lee, U.J., Sbeglia, G.C., Ha, M., Finch, S.J., Nehm, R.H.: Clicker score trajectories and concept inventory scores as predictors for early warning systems for large stem classes. J. Sci. Educ. Technol. 24(6), 848–860 (2015)

    Article  Google Scholar 

  28. Chen, Z.J., Zhu, X.L.: Research on prediction model of online learners academic achievement based on educational data mining. China Educ. Technol. 371(12), 75–81 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Chengdu University of Information Technology, No. 24 Block 1, Xuefu Road, Chengdu, 610225, China

    MaoYang Zou, Ting Wang, Hong Xu, XiaoJie Li & Xi Wu

  2. Chengdu Institute of Computer Application, University of Chinese Academy of Sciences, No. 9 the 4th Section of South Renmin Road, Chengdu, 610041, China

    MaoYang Zou

Authors
  1. MaoYang Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ting Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hong Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. XiaoJie Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xi Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to MaoYang Zou .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Jinwei Wang

  3. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zou, M., Wang, T., Xu, H., Li, X., Wu, X. (2020). Using Process Visualization and Early Warning Based on Learning Analytics to Enhance Teaching and Learning. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Communications in Computer and Information Science, vol 1252. Springer, Singapore. https://doi.org/10.1007/978-981-15-8083-3_16

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-8083-3_16

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-8082-6

  • Online ISBN: 978-981-15-8083-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation