Change Support to Maintain Quality in Learning Technology Systems

  • Claus PahlEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10108)


The quality of learning technology systems can degrade over time due to changes in learners, content and the software environment. This might occur as long-term evolution of the system to adjust the system to environmental changes or as part of short-term adaptation in order to adapt to the learner behaviour and knowledge. We present a quality management framework for changing environments that integrates evolution and adaptation. The framework sees evolution and adaption as two incarnation of change. A key concept is the notion of feedback that causes quality goals such as learning achievement or learner experience to be changed or the need to adjust the system to maintain existing goals. The aim is to support instructors, course designer and platform support in understanding the interaction of evolution and adaptation and to help them in designing, modelling, running and maintaining learning technology systems in order to achieve and maintain the expected quality.


Learning technology system Quality management Change 


  1. 1.
    Gagne, R.M.: Instructional Technology: Foundations. Routledge, New York (2013)Google Scholar
  2. 2.
    Dawson, M., Al Saeed, I., Wright, J., Omar, M.: Technology enhanced learning with open source software for scientists and engineers. In: INTED2013 Proceedings, pp. 5583–5589 (2013)Google Scholar
  3. 3.
    Kenny, C., Pahl, C.: Intelligent and adaptive tutoring for active learning and training environments. Interact. Learn. Environ. J. 17(2), 181–195 (2009)CrossRefGoogle Scholar
  4. 4.
    Tseng, J.C., Chu, H.C., Hwang, G.J., Tsai, C.C.: Development of an adaptive learning system with two sources of personalization information. Comput. Educ. 51(2), 776–786 (2008)CrossRefGoogle Scholar
  5. 5.
    Brusilovsky, P., Karagiannidis, C., Sampson, D.: Layered evaluation of adaptive learning systems. Int. J. Continuing Eng. Educ. Life Long Learn. 14(4–5), 402–421 (2004)CrossRefGoogle Scholar
  6. 6.
    Javed, M., Abgaz, Y.M., Pahl, C.: A pattern-based framework of change operators for ontology evolution. In: Meersman, R., Herrero, P., Dillon, T. (eds.) OTM 2009. LNCS, vol. 5872, pp. 544–553. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-05290-3_68 CrossRefGoogle Scholar
  7. 7.
    Zablith, F., Antoniou, G., d’Aquin, M., Flouris, G., Kondylakis, H., Motta, E., Sabou, M.: Ontology evolution: a process-centric survey. Knowl. Eng. Rev. 30(01), 45–75 (2015)CrossRefGoogle Scholar
  8. 8.
    Pahl, C.: Architecture Solutions for e-Learning Systems. IGI Global, Hershey (2007)Google Scholar
  9. 9.
    Melia, M., Pahl, C.: Constraint-based validation of adaptive e-learning courseware. IEEE Trans. Learn. Technol. 2(1), 37–49 (2009)CrossRefGoogle Scholar
  10. 10.
    Murray, S., Ryan, J., Pahl, C.: A tool-mediated cognitive apprenticeship approach for a computer engineering course. In: 3rd IEEE International Conference on Advanced Learning Technologies (ICALT 2003), pp. 2–6 (2003)Google Scholar
  11. 11.
    Holohan, E., Melia, M., McMullen, D., Pahl, C.: The generation of e-learning exercise problems from subject ontologies. In: 6th International Conference on Advanced Learning Technologies (ICALT 2006), pp. 967–969 (2006)Google Scholar
  12. 12.
    Wang, M.X., Bandara, K.Y., Pahl, C.: Integrated constraint violation handling for dynamic service composition. In: IEEE International Conference on Services Computing (SCC 2009) (2009)Google Scholar
  13. 13.
    Lei, X., Pahl, C., Donnellan, D.: An evaluation technique for content interaction in web-based teaching and learning environments. In: 3rd IEEE International Conference on Advanced Learning Technologies, pp. 294–295 (2003)Google Scholar
  14. 14.
    Shi, L., Al Qudah, D., Qaffas, A., Cristea, Alexandra, I.: Topolor: a social personalized adaptive e-learning system. In: Carberry, S., Weibelzahl, S., Micarelli, A., Semeraro, G. (eds.) UMAP 2013. LNCS, vol. 7899, pp. 338–340. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-38844-6_32 CrossRefGoogle Scholar
  15. 15.
    Jamshidi, P., Ghafari, M., Aakash, A., Pahl, C.: A framework for classifying and comparing architecture-centric software evolution research. In: 17th European Conference on Software Maintenance and Reengineering (2013)Google Scholar
  16. 16.
    Ahmad, A., Jamshidi, P., Pahl, C.: Classification and comparison of architecture evolution reuse knowledge – a systematic review. J. Softw. Evol. Process 26(7), 654–691 (2014)CrossRefGoogle Scholar
  17. 17.
    Pahl, C.: The life and times of a learning technology system: the impact of change and evolution. Int. J. Web Based Learn. Teach. Technol. 8(3), 24–41 (2013)CrossRefGoogle Scholar
  18. 18.
    Weyns, D., Caporuscio, M., Vogel, B., Kurti, A.: Design for sustainability = runtime adaptation U evolution. In: Proceedings of the 1st International Workshop on Sustainable Architecture: Global Collaboration, Requirements, Analysis (SAGRA 2015), pp. 62:1–62:7 (2015)Google Scholar
  19. 19.
    Yang, T.C., Hwang, G.J., Yang, S.J.H.: Development of an adaptive learning system with multiple perspectives based on students? Learning styles and cognitive styles. Educ. Technol. Soc. 16(4), 185–200 (2013)Google Scholar
  20. 20.
    Javed, M., Abgaz, Y.M., Pahl, C.: Ontology change management and identification of change patterns. J. Data Semant. 2(2–3), 119–143 (2013)CrossRefGoogle Scholar
  21. 21.
    Roca, J.C., Chiu, C.M., Martinez, F.J.: Understanding e-learning continuance intention: an extension of the technology acceptance model. Int. J. Hum. Comput. Stud. 64(8), 683–696 (2006)CrossRefGoogle Scholar
  22. 22.
    Raible, J., Bennett, L., Jowallah, R.: Factors influencing the selection of an adaptive learning technology within university and K-12. FDLA J. 1(1), 1 (2014)Google Scholar
  23. 23.
    Cody-Allen, E., Kishore, R.: An extension of the UTAUT model with e-quality, trust, and satisfaction constructs. In: Proceedings of the 2006 ACM SIGMIS CPR Conference on Computer Personnel Research, pp. 82–89. ACM (2006)Google Scholar
  24. 24.
    Pahl, C., Xiong, H.: Migration to PaaS clouds – migration process and architectural concerns. In: 7th International Symposium on the Maintenance and Evolution of Service-Oriented and Cloud-Based Systems (MESOCA 2013). IEEE (2013)Google Scholar
  25. 25.
    Pahl, C., Lee, B.: Containers and clusters for edge cloud architectures – a technology review. In: 3rd International Conference on Future Internet of Things and Cloud (FiCloud 2015) (2015)Google Scholar
  26. 26.
    Pahl, C.: Containerization and the PaaS cloud. IEEE Cloud Comput. 2(3), 24–31 (2015)CrossRefGoogle Scholar
  27. 27.
    Tseng, S.S., Su, J.M., Hwang, G.J., Hwang, G.H., Tsai, C.C., Tsai, C.J.: An object-oriented course framework for developing adaptive learning systems. Educ. Technol. Soc. 11(2), 171–191 (2008)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Free University of Bozen-BolzanoBolzanoItaly

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