Skip to main content
SpringerLink
Log in

iMoodle: An Intelligent Gamified Moodle to Predict “at-risk” Students Using Learning Analytics Approaches

  • Chapter
  • First Online:
Data Analytics Approaches in Educational Games and Gamification Systems

Part of the book series: Smart Computing and Intelligence ((SMCOMINT))

Abstract

Online learning is gaining increasing attention by researchers and educators since it makes students learn without being limited in time or space like traditional classrooms. Particularly, several researchers have also focused on gamifying the provided online courses to motivate and engage students. However, this type of learning still faces several challenges, including the difficulties for teachers to control the learning process and keep track of their students’ learning progress. Therefore, this study presents an ongoing project which is a gamified intelligent Moodle (iMoodle) that uses learning analytics to provide dashboard for teachers to control the learning process. It also aims to increase the students’ success rate with an early warning system for predicting at-risk students, as well as providing real-time interventions of supportive learning content as notifications. The beta version of iMoodle was tested for technical reliability in a public Tunisian university for three months and few bugs were reported by the teacher and had been fixed. The post-fact technique was also used to evaluate the accuracy of predicting at-risk students. The obtained result highlighted that iMoodle has a high accuracy rate which is almost 90%.

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
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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

References

  1. Yassine, S., Kadry, S., & Sicilia, M. A. (2016). A framework for learning analytics in moodle for assessing course outcomes. In Global Engineering Education Conference (pp. 261–266).

    Google Scholar 

  2. Vozniuk, A., Govaerts, S., & Gillet, D. (2013). Towards portable learning analytics dashboards. In 13th International Conference on Advanced Learning Technologies (pp. 412–416).

    Google Scholar 

  3. Anohina, A. (2007). Advances in intelligent tutoring systems: Problem-solving modes and model of hints. Journal of Computers Communications & Control, 2(1), 48–55.

    Article  Google Scholar 

  4. Siemens, G. (2010). What are learning analytics?. Retrieved August 12, 2016, from http://www.elearnspace.org/blog/2010/08/25/what-are-learning-analytics/.

  5. Kim, S., Song, K., Lockee, B., & Burton, J. (2018). What is gamification in learning and education?. In Gamification in learning and education (pp. 25–38). Springer, Cham.

    Google Scholar 

  6. Gañán, D., Caballé, S., Clarisó, R., & Conesa, J. (2016). Analysis and design of an eLearning platform featuring learning analytics and gamification. In 2016 10th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS), IEEE (pp. 87–94).

    Google Scholar 

  7. Deterding, S., Sicart, M., Nacke, L., O’Hara, K., & Dixon, D. (2011). Gamification: Using game-design elements in non-gaming contexts. In Proceedings of the CHI 2011. Vancouver, BC, Canada.

    Google Scholar 

  8. Kapp, K. M. (2012). The gamification of learning and instruction: Game-based methods and strategies for training and education. Wiley.

    Google Scholar 

  9. Andrade, F. R. H., Mizoguchi, R., & Isotani, S. (2016). The Bright and Dark Sides of Gamification. In Proceedings of the International Conference on Intelligent Tutoring Systems, 9684, 1–11.

    Google Scholar 

  10. Villagrasa, S., Fonseca, D., Redondo, E., & Duran, J. (2018). Teaching case of gamification and visual technologies for education. Gamification in Education: Breakthroughs in Research and Practice: Breakthroughs in Research and Practice, p. 205.

    Google Scholar 

  11. Brewer, R., Anthony, L., Brown, Q., Irwin, G., Nias, J., & Tate, B. (2013). Using gamification to motivate children to complete empirical studies in lab environments. In 12th International Conference on Interaction Design and Children (pp. 388–391). New York.

    Google Scholar 

  12. Dichev, C., & Dicheva, D. (2017). Gamifying education: what is known, what is believed and what remains uncertain: A critical review. International Journal of Educational Technology in Higher Education, 14(1), 9.

    Article  Google Scholar 

  13. Garcia, J., Copiaco, J. R., Nufable, J. P., Amoranto, F., & Azcarraga, J. (2015). Code it! A gamified learning environment for iterative programming. In Doctoral Student Consortium (DSC)-Proceedings of the 23rd International Conference on Computers in Education (ICCE) (pp. 373– 378).

    Google Scholar 

  14. Hew, K. F., Huang, B., Chu, K. W. S., & Chiu, D. K. (2016). Engaging Asian students through game mechanics: Findings from two experiment studies. Computers & Education, 92, 221–236.

    Article  Google Scholar 

  15. Barata, G., Gama, S., Jorge, J., & Gonçalves, D. (2013). Engaging engineering students with gamification. In 5th international conference on Games and virtual worlds for serious applications (VSGAMES), IEEE (pp. 1–8).

    Google Scholar 

  16. Khalil, M., & Ebner, M. (2016). Learning analytics in MOOCs: Can data improve students retention and learning? In EdMedia + Innovate Learning. Association for the Advancement of Computing in Education (AACE) (pp. 581–588).

    Google Scholar 

  17. Van Barneveld, A., Arnold, K. E., & Campbell, J. P. (2012). Analytics in higher education: Establishing a common language. EDUCAUSE learning initiative, 1, 1–11.

    Google Scholar 

  18. Powell, S., & MacNeill, S. (2012). Institutional readiness for analytics. JISC CETIS Analytics Series, 1(8).

    Google Scholar 

  19. Dietz-Uhler, B., & Hurn, J. E. (2013). Using learning analytics to predict (and improve) student success: A faculty perspective. Journal of Interactive Online Learning, 12(1), 17–26.

    Google Scholar 

  20. Sampayo, C. F. (2013). Analytics and Recommendations. In Moodle Docs. Retrieved from https://moodle.org/plugins/view.php?plugin=block_analytics_recommendations.

  21. Petropoulou, O., Kasimatis, K., Dimopoulos, I., & Retalis, S. (2014). LAe-R: A new learning analytics tool in Moodle for assessing students’ performance. Bulletin of the IEEE Technical Committee on Learning Technology, 16(1), 1–13.

    Google Scholar 

  22. Da Silva, J. M. C., Hobbs, D., & Graf, S. (2014). Integrating an at-risk student model into learning management systems. In Nuevas Ideas en Informática Educativa TISE (pp. 120–124).

    Google Scholar 

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

    Article  Google Scholar 

  24. Tlili, A., Essalmi, F., & Jemni, M., Chang, M., & Kinshuk. (2018). iMoodle: An Intelligent Moodle Based on Learning Analytics. In Intelligent Tutoring System (pp. 476–479).

    Google Scholar 

  25. Tlili, A., Essalmi, F., Jemni, M., Kinshuk, & Chen, N. S. (2016). Role of personality in computer based learning. Computers in Human Behavior, 64, 805–813.

    Article  Google Scholar 

  26. Santos, O. C. (2016). Emotions and personality in adaptive e-learning systems: an affective computing perspective. In Emotions and personality in personalized services (pp. 263–285). Springer, Cham.

    Google Scholar 

  27. Lombriser, P., Dalpiaz, F., Lucassen, G., & Brinkkemper, S. (2016). Gamified requirements engineering: Model and experimentation. Springer.

    Google Scholar 

  28. Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York: Springer.

    Book  Google Scholar 

  29. Tlili, A., Essalmi, F., Jemni, M., Kinshuk., & Chen, N. S. (2018). A complete validated learning analytics framework: Designing issues from data preparation perspective. International Journal of Information and Communication Technology Education (IJICTE), 14(2), 1–16.

    Google Scholar 

  30. Shankar. S. & Purosothmana, T. (2009). Utility sentient frequent itemset mining and association rule mining. A literature survey and comparative study. International Journal of Soft Computing Applications, 4, 81–95.

    Google Scholar 

  31. Chan, C. C. H., Ming-Hsiu, L., & Yun-chiang, K. (2007). Association rules mining for knowledge management: A case study of library services. In Proceedings of the 9th WSEAS international conference on Mathematical methods and computational techniques in electrical engineering (pp. 1–6).

    Google Scholar 

  32. Wolff, A., Zdrahal, Z., Nikolov, A., & Pantucek, M. (2013). Improving retention: Predicting at-risk students by analysing clicking behaviour in a virtual learning environment. In Proceedings of the third international conference on learning analytics and knowledge (pp. 145–149).

    Google Scholar 

  33. Levy, Y. (2007). Comparing dropouts and persistence in e-Learning courses. Computers & Education, 48(2), 185–204.

    Article  Google Scholar 

  34. Billings, D. M. (1987). Factors related to progress towards completion of correspondence courses in a baccalaureate nursing programme. Journal of Advanced Nursing, 12(6), 743–750.

    Article  Google Scholar 

  35. Xenos, M., Pierrakeas, C., & Pintelas, P. (2002). A survey on student dropout rates and dropout causes concerning the students in the course of informatics of the Hellenic Open University. Computers & Education, 39(4), 361–377.

    Article  Google Scholar 

  36. Macfadyen, L. P., & Dawson, S. (2010). Mining LMS data to develop an “early warning system” for educators: A proof of concept. Computers & Education, 54(2), 588–599.

    Article  Google Scholar 

  37. Arnold, K. E., & Pistilli, M. D. (2012). Course signals at Purdue: Using learning analytics to increase student success. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 267–270).

    Google Scholar 

  38. Liu, D. Y. T., Froissard, J. C., Richards, D., & Atif, A. (2015). An enhanced learning analytics plugin for Moodle: Student engagement and personalised intervention.

    Google Scholar 

  39. Kotsiantis, S. B., Pierrakeas, C. J., & Pintelas, P. E. (2003). Preventing student dropout in distance learning using machine learning techniques. In International Conference on Knowledge-Based and Intelligent Information and Engineering Systems pp. 267–274.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Research Laboratory of Technologies of Information and Communication & Electrical Engineering (LaTICE), Tunis Higher School of Engineering (ENSIT), University of TUNIS, Tunis, Tunisia

    Mouna Denden, Fathi Essalmi & Mohamed Jemni

  2. Smart Learning Institute of Beijing Normal University, Beijing, China

    Ahmed Tlili & Ronghuai Huang

  3. School of Computing and Information Systems, Athabasca University, Athabasca, Canada

    Maiga Chang

  4. University of North Texas, 3940 N. Elm Street, G 150, Denton, TX, 76207, USA

    Kinshuk

Authors
  1. Mouna Denden
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ahmed Tlili
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fathi Essalmi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mohamed Jemni
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Maiga Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kinshuk
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ronghuai Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mouna Denden .

Editor information

Editors and Affiliations

  1. Smart Learning Institute, Beijing Normal University, Beijing, China

    Ahmed Tlili

  2. School of Computing and Information Systems, Athabasca University, Edmonton, AB, Canada

    Maiga Chang

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Denden, M. et al. (2019). iMoodle: An Intelligent Gamified Moodle to Predict “at-risk” Students Using Learning Analytics Approaches. In: Tlili, A., Chang, M. (eds) Data Analytics Approaches in Educational Games and Gamification Systems. Smart Computing and Intelligence. Springer, Singapore. https://doi.org/10.1007/978-981-32-9335-9_6

Download citation

  • DOI: https://doi.org/10.1007/978-981-32-9335-9_6

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-32-9334-2

  • Online ISBN: 978-981-32-9335-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation