Advertisement

Measurement of Quality of a Course

Analysis to Analytics
  • Jérémie Seanosky
  • David Boulanger
  • Colin Pinnell
  • Jason Bell
  • Lino Forner
  • Michael Baddeley
  • Kinshuk
  • Vivekanandan Suresh Kumar
Chapter
Part of the Lecture Notes in Educational Technology book series (LNET)

Abstract

Traditionally, the quality of a course offering is measured based on learner feedback at the end of the offering. This chapter offers a method to measure the quality of a course offering—continually, formatively, and summatively—using factors such as the quality of resources used, learner motivation, learner capacity, learner competency growth, and instructor competence. These factors are represented in a Bayesian belief network (BBN) in a system called MI-IDEM. MI-IDEM receives streams of data corresponding to these factors as and when they become available, which leads to estimates of quality of the course offering based on individual factors as well as an overall quality of the offering. Continuous, formative, and summative course quality measurements are imperative to identify weaknesses in the learning process of students and to assist them when they need help. This chapter professes the need for a comprehensive measurement of course quality and ensuing initiatives to personalize and adapt course offerings. It presents two case studies of this novel approach: first, measurement of the quality of a course offering in a blended online learning environment and second, measurement of the quality of training course offering in an industry environment.

Keywords

Course quality assessment Analysis versus analytics Mixed-initiative instructional design evaluation model Learning analytics Blended online instruction Continuous assessment 

Notes

Acknowledgements

We would like to acknowledge support from NSERC/iCORE/Xerox/Markin Research Chair, NSERC Discovery Grants, and Athabasca University, Canada.

References

  1. Boulanger, D., Seanosky, J., Baddeley, M., Kumar, V., & Kinshuk. (2014). Learning analytics in the energy industry: Measuring competences in emergency procedures. In Proceedings of the 2014 IEEE Sixth International Conference on Technology for Education (T4E) (pp. 148–155).Google Scholar
  2. Boulanger, D., Seanosky, J., Kumar, V., Panneerselvam, K., & Somasundaram, T. S. (2015). Smart learning analytics. In G. Chen, V. Kumar, Kinshuk, R. Huang & S. C. Kong (Eds.), Emerging issues in smart learning (pp. 289–296). Berlin: Springer.Google Scholar
  3. Bloom, B. S., Hastings, J. T., & Madaus, G. F. (1971). Handbook on formative and summative assessment of student learning. New York: McGraw Hill.Google Scholar
  4. El Kadi, M., & Kumar, V. (2010). MI-IDEM: A model to evaluate instructional design using Bayesian belief networks. In Proceedings of the 2010 9th International Conference on Information Technology Based Higher Education and Training (ITHET) (pp. 1–8).Google Scholar
  5. Forner, L., Kumar, V., & Kinshuk. (2013). Assessing design of online courses using bayesian belief networks. In Proceedings of the 2013 IEEE Fifth International Conference on Technology for Education (T4E) (pp. 36–42).Google Scholar
  6. Hrmo, R., Kristofiakova, L., & Kučerka, D. (2012). Developing the information competencies via e-learning and assessing the qualities of e-learning text. In Proceedings of the 2012 15th International Conference on Interactive Collaborative Learning (ICL) (pp. 1–4).Google Scholar
  7. Kumar, V., Boulanger, D., Seanosky, J., Panneerselvam Kinshuk, K., & Somasundaram, T. S. (2014a). Competence analytics. Journal of Computers in Education, 1(4), 251–270.CrossRefGoogle Scholar
  8. Kumar, V., Kinshuk, Clemens C., Harris, S. (2014b). Causal models and big data learning analytics. In Kinshuk & R. Huang (Eds.) Ubiquitous learning environments and technologies. Springer’s lecture notes in educational technology series (pp. 31–44).Google Scholar
  9. Reumann, M., Mohr, M., Diez, A., & Dossel, O. (2008). Assessing learning progress and quality of teaching in large groups of students. In Proceedings of the 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS) (pp. 2877–2880).Google Scholar
  10. Seanosky, J., Boulanger, D., Kumar, V., & Kinshuk. (2015). Unfolding learning analytics for big data. In G. Chen, V. Kumar, Kinshuk, R. Huang & S. C. Kong (Eds.), Emerging issues in smart learning (pp. 377–384). Berlin: Springer.Google Scholar
  11. Smolin, D., & Butakov, S. (2012). Applying artificial intelligence to the educational data: An example of syllabus quality analysis. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 164–169).Google Scholar
  12. Tervakari, A., Silius, K., & Kailanto, M. (2013). Students’ participation in a social media enhanced learning environment. In Proceedings of the 2013 IEEE Global Engineering Education Conference (EDUCON) (pp. 871–879).Google Scholar
  13. Zheng, L., El-Bishouty, M., Pinnell, C., Bell, J., Kumar, V., & Kinshuk. (2015). A framework to automatically analyze regulation. In G. Chen, V. Kumar, Kinshuk, R. Huang & S. C. Kong (Eds.), Emerging issues in smart learning (pp. 23–30). Berlin: Springer.Google Scholar
  14. Zheng, L., Kumar, V., & Kinshuk. (2014). The role of co-regulation in synchronous online learning environment. In Proceedings of the 2014 IEEE Sixth International Conference on Technology for Education (T4E) (pp. 241–244).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Jérémie Seanosky
    • 1
  • David Boulanger
    • 1
  • Colin Pinnell
    • 1
  • Jason Bell
    • 1
  • Lino Forner
    • 2
  • Michael Baddeley
    • 3
  • Kinshuk
    • 1
  • Vivekanandan Suresh Kumar
    • 1
  1. 1.Athabasca UniversityEdmonton ABCanada
  2. 2.Holland CollegeCharlottetownCanada
  3. 3.CNRL HorizonCalgary ABCanada

Personalised recommendations