Learner Level and Preference Prediction of E-learners for E-learning Recommender Systems

  • D. DeenadayalanEmail author
  • A. Kangaiammal
  • B. K. Poornima
Part of the Studies in Computational Intelligence book series (SCI, volume 771)


An effective e-learning system must identify learning content appropriate for the needs of the specific learner from among the many sources of learning content available. The recommendation system discussed here is a tool to address such competence. Identifying learner levels, and thereby identifying the appropriate learning content, is possible only if the learning content is prepared using a proven instructional strategy which covers various learner levels. Therefore, in the proposed method, the learning content is prepared using David Merrill’s First Principles of Instruction, a problem-based approach that has four phases of instruction: activation, demonstration, application and integration. These four phases are used to predict learner levels for three different types of media content, namely text, video and audio, and to determine a rating. The naïve Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. Learner ratings are used to predict learner preferences as to the type of content. To identify the learner’s level, the learner rating and the instructional phases which they prefer most are used. The same classifier is used to identify the level and preferences of the learner. To estimate predictive accuracy, a k-fold cross-validation technique is used. The experimental results show that the proposed classifier yields maximum accuracy of 0.9794 and a maximum kappa statistic of 0.9687 for learning level and preference, respectively.


E-learning Learning content Naive bayes David Merrill Learner preference Learner level k-Fold cross-validation 



This research work is supported by the University Grants Commission (UGC), New Delhi, India under Minor Research Projects Grant No. F MRP-6990/16 (SERO/UGC) Link No. 6990.


  1. 1.
    Salehi, Mojtaba, Mohammad Pourzaferani, and Seyed Amir Razavi. 2013. Hybrid attribute-based recommender system for learning material using genetic algorithm and a multidimensional information model. Egyptian Informatics Journal 14: 67–78.CrossRefGoogle Scholar
  2. 2.
    Poornima, B.K., D. Deenadayalan, and A. Kangaiammal. 2017. Text preprocessing on extracted text from audio/video using R. International Journal of Computational Intelligence and Informatics 6 (4): 267–278.Google Scholar
  3. 3.
    Sikka, Reema, Amita Dhankhar, and Chaavi Rana. 2012. A Survey Paper on E-Learning Recommender System. International Journal of Computer Applications 47 (9): 0975–888.CrossRefGoogle Scholar
  4. 4.
    Crespo, R.G., et al. 2011. Recommendation System based on user interaction data applied to intelligent electronic books. Elsevier, Human Behavior 27: 1445–1449.CrossRefGoogle Scholar
  5. 5.
    Parlakkilic, and Karslioglu. 2013. Rapid multimedia content development in medical education. Journal of Contemporary, Medical Education 1 (4): 245–251.CrossRefGoogle Scholar
  6. 6.
    Liu, Ying, and Jiajun Yanga. 2012. Improving ranking-based recommendation by social information and negative similarity, information technology and quantitative management (ITQM 2015). Procedia Computer Science 55: 732–740.CrossRefGoogle Scholar
  7. 7.
    Poornima, B.K., D. Deenadayalan, and A. Kangaiammal. 2015. Efficient content retrieval in E-learning system using semantic web ontology. Advances in Computer Science and Information Technology (ACSIT) 2 (6): 503–507. ISSN: 2393-9915.Google Scholar
  8. 8.
    Poornima, B.K., D. Deenadayalan, and A. Kangaiammal. 2015. Personalization of learning objects in e-learning system using David Merrill’s approach with web 3.0. International Journal of Applied Engineering Research (IJAER) 10 (85): 318–324.Google Scholar
  9. 9.
    Merrill, M.D., 2007. First principles of instruction: a synthesis. In Trends and issues in instructional design and technology, 2nd ed., 62–71.Google Scholar
  10. 10.
    Nirmala K., and M. Pushpa. 2012. Feature based text classification using application term set. International Journal of Computer Applications 52 (10): 0975–8887.CrossRefGoogle Scholar
  11. 11.
    Gupta, G.K. 2006. Introduction to data mining with case studies. Limited: Prentice-Hall Of India Pvt.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • D. Deenadayalan
    • 1
    Email author
  • A. Kangaiammal
    • 1
  • B. K. Poornima
    • 1
  1. 1.Department of Computer ApplicationsGovernment Arts CollegeSalemIndia

Personalised recommendations