Skip to main content
SpringerLink
Log in

Competency analytics tool: Analyzing curriculum using course competencies

  • Published:
Education and Information Technologies Aims and scope Submit manuscript

Abstract

The applications of learning outcomes and competency frameworks have brought better clarity to engineering programs in many universities. Several frameworks have been proposed to integrate outcomes and competencies into course design, delivery and assessment. However, in many cases, competencies are course-specific and their overall impact on the curriculum design is unknown. Such impact analysis is important for analysing, discovering gaps and improving the curriculum design. Unfortunately, manual analysis is a painstaking process due to large amounts of competencies across the curriculum. In this paper, we propose an automated method to analyse the competencies and discover their impact on the overall curriculum design. We provide a principled methodology for discovering the impact of courses’ competencies using Bloom’s Taxonomy, Dreyfus’ model and the learning outcomes framework. We developed the Curriculum Analytics Tool (CAT) which generates the competency scores for the entire curriculum across two dimensions; Cognitive levels and Progression levels. We use the CAT to analyse the competencies of an undergraduate Information Systems Management core curriculum program. Using 14 courses and the corresponding 578 competencies, this paper shows how our method enables us to perform in-depth analysis on the curriculum by discovering the cognition and progression statistics. We further apply the tool for recommending competencies when launching new courses.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  • Baker, R. S. J.d., & Yacef, K. (2009). The state of educational data mining in 2009: A Review and Future Visions. Journal of Educational Data Mining, 1(1), 3–17.

  • Baumgartner, I., & Shankararaman, V. (2013). Actively linking learning outcomes and competencies to course design and delivery: experiences from an undergraduate information systems program in Singapore. In IEEE global engineering education conference (EDUCON 2013). Germany: Berlin.

    Google Scholar 

  • Bloom, B. S., Engelhart, M. D., Furst, E. J., Hilland, W. H., & Krathwohl, D. R. (1956). Taxonomy of educational objectives: handbook I: cognitive domain. New York: David McKay, 19, 56.

    Google Scholar 

  • Brabrand, C., & Dahl, B. (2009). Analyzing CS competencies using the SOLO taxonomy. New York: In the Proceedings of the 14th annual ACM SIGCSE conference on Innovation and technology in computer science education.

    Book  Google Scholar 

  • Burr, L., & Spennemann, D. H. (2004). Pattern of user behavior in university online forums. In International Journal of Instructional Technology and Distance Learning, 1(10), 11–28.

    Google Scholar 

  • Cook, D., & Swayne, D. F. (2007). Interactive and dynamic graphics for data analysis: with R and GGobi. Springer.

  • Dreyfus, H. L., & Dreyfus, S. E. (1986). Mind over machine: the power of human intuition and experience in the era of the computer. Oxford: Basil Blackwell.

    Google Scholar 

  • Ducrot, J., Miller, S., & Goodman, P. S. (2008). Learning outcomes for a business information systems undergraduate program. Communications of the Association for Information Systems, 23, 6.

    Google Scholar 

  • EU. (2014). The European qualifications framework. European Union Education and Culture DG.

  • Feiman-Nemser, S. (1990). Teacher preparation: structural and conceptual alternatives. In W. R. Houston (Ed.), Handbook of Research on Teacher Education (pp. 212–229). New York: McMillan.

    Google Scholar 

  • Gnana Singh, A. A., & Leavline, E. J. (2013). Competency-based calisthenics of learning outcomes for engineering education. International Journal of Education and Learning, 2(1), 25–34.

    Google Scholar 

  • Gottipati, S., & Shankararaman, V. (2014a). Learning analytics applied to curriculum analysis. AIS SIGED 2014 Proceedings. 2. http://aisel.aisnet.org/siged2014/2

  • Gottipati, S., & Shankararaman, V. (2014b). Analyzing course competencies: What can competencies reveal about the curriculum?. Proceedings of the 22nd International conference on computers in education ICCE 2014: November 30–December 4, 2014, Nara, Japan. 319–324. Research collection school of information systems.

  • Hartel, R. W., & Foegeding, E. A. (2004). Learning: objectives, competencies, or outcomes. Journal of Food Science Education, 3, 69–70.

    Article  Google Scholar 

  • Jin, H., Wu, T., Liu, Z., & Yan, J. (2009). Application of visual data Mining in Higher-Education Evaluation System (pp. 101–104). Washington, DC: In International Workshop on Education Technology and Computer Science.

    Google Scholar 

  • Judith, G. C., Ramiah, K., Weist, E. M. G., & Shortell, S. M. (2008). Development of a Core competency model for the master of public health degree. American Journal of Public Health, 98(9), 1598–1607.

    Article  Google Scholar 

  • Kennedy, D. (2007). Writing and using learning outcomes: a practical guide. Cork: Quality Promotion Unit, University College Cork. Internet address http://www.nairtl.ie/.

    Google Scholar 

  • Kennedy, D., Hyland A., Ryan N. (2009). Learning outcomes and competences: introducing bologna objectives and tools, B 2.3–3, 1–18.

  • Khairuddin, N.N., Khairuddin, H. (2008). Application of Bloom's taxonomy in software engineering assessments. In Proceedings of the 8th conference on applied computer science (ACS'08), Subhas C. Misra, Roberto Revetria, Les M. Sztandera, Mihaiela Iliescu, Azami Zaharim, and Hamed Parsiani (Eds.). World scientific and engineering academy and society (WSEAS), Stevens point, Wisconsin, USA, pp 66–69.

  • Krathwohl, D. R. (2002). A revision of Bloom's Taxonomy: an overview. Benjamin S. Bloom, University of Chicago. Theory Into Practice, 42(4), 216.

    Google Scholar 

  • Lister, R., & Leaney, J. (2003). Introductory programming, criterion-referencing, and bloom. In Proceedings of the 34th SIGCSE technical symposium on computer science education (pp. 143–147). New York: ACM.

    Chapter  Google Scholar 

  • Mazza, R., & Milani, C. (2004). GISMO: a graphical interactive student monitoring tool for course management systems. In In international conference on technology enhanced learning, Milan (pp. 1–8).

    Google Scholar 

  • Mostow, J., Beck, J., Cen, H., Cuneo, A., Gouvea, E., Heiner, C. (2005). An educational data mining tool to browse tutor-student interactions: Time will tell! In Proceedings of the Workshop on Educational Data Mining. 15–22.

  • O’Neill, G., & Murphy, F. (2010). Guide to taxonomies of learning. In UCD teaching and learning/resources. Available http://www.ucd.ie/t4cms/ucdtla0034.pdf

    Google Scholar 

  • Passow, H. (2012). Which ABET competencies do engineering graduates find most important in their work? Journal of Engineering Education, 101(1), 95–118.

    Article  Google Scholar 

  • Proli, D., & Dondi, C. (2011). Analysis of learning outcomes approach implementation in European higher education. Project number: 167178-LLP-1-2009-ES-KA1-KA1EQF, European Union Education and Culture DG.

  • Raykova, M., Kostadinova, H., & Totkov, G. (2011). Adaptive test system based on revised Bloom's taxonomy. In Proceedings of the 12th International Conference on Computer Systems and Technologies. ACM, New York: pp. 504–509.

  • Rodolfa, E. R., Bent, R. J., Eisman, E., Nelson, P. D., Rehm, L., & Ritchie, P. (2005). A cube model for competency development: implications for psychology educators and regulators. Professional Psychology: Research and Practice, 36, 347–354.

    Article  Google Scholar 

  • Romero, C., Gutierrez, S., Freire, M., & Ventura, S. (2008). Mining and visualizing visited trails in web-based educational systems (pp. 182–185). Montreal, Canada: In international conference on educational data mining.

    Google Scholar 

  • Scott, T. (2003). Bloom's taxonomy applied to testing in computer science classes. Journal Computer Small Colloid, 19(1), 267–274.

    Google Scholar 

  • Shankararaman, V., & Ducrot, J. (2016). Leveraging competency framework to improve teaching and learning: a methodological approach. Journal of Education and Information Technologies, 21, 1299.

    Article  Google Scholar 

  • Shen, R., Yang, F., & Han, P. (2002). Data analysis center based on e-learning platform. In Workshop the internet challenge (pp. 19–28). Berlin, Germany: Technology and applications.

    Google Scholar 

  • Vignan, S., Senthilkumar, G., Marthi, N., & Goteti, P. (2011). Integrating learning outcomes and Bloom's taxonomy in web application development course: Experiences from corporate training. In the Proceedings of IEEE Students' Technology Symposium (TechSym), January, Kharagpur, India, pp 11–16.

  • Wheeler, D. (2007). Using a summative assessment alignment model and the revised Bloom's taxonomy to improve curriculum development, instruction and evaluation. Doctoral dissertation: Syracuse University.

    Google Scholar 

  • Whetten, D.A. (2007). Principles of effective course design: What I wish I had known about Learning-Centered Teaching 30 Years Ago. Journal of Management Education 31(3), 339–345, pp. 347–357.

  • Yoo, J., Yoo, S., Lance, C., Hankins, J. (2006). Student progress monitoring tool using treeview. In proceedings of the 37th Technical Symposium on Computer Science Education, SIGCSE’06. ACM Press. March 1–5, Houston: 373–377.

Download references

Author information

Authors and Affiliations

  1. School of Information Systems, Singapore Management University, Singapore, Singapore

    Swapna Gottipati & Venky Shankararaman

Authors
  1. Swapna Gottipati
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Venky Shankararaman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Swapna Gottipati.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gottipati, S., Shankararaman, V. Competency analytics tool: Analyzing curriculum using course competencies. Educ Inf Technol 23, 41–60 (2018). https://doi.org/10.1007/s10639-017-9584-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10639-017-9584-3

Keywords

Search

Navigation