Skip to main content
SpringerLink
Log in

Enhanced Features of SLOs: Focus on Specialization

  • Chapter
Smart Learning Objects for Smart Education in Computer Science

  • 953 Accesses

Abstract

Smart LOs being reusable items in terms of generative capabilities may also offer new opportunities to create individual and highly adaptable content for learning processes. As it was shown in the previous chapters, reusability is a central topic in LO research. However, reusability cannot be generally understood without the educational context. The main goal of reusability is to adapt the teaching content to the context of use in some learning processes. The adaptive aspects of reusability should be discussed from a wider perspective than it was done so far. We need to have a framework enabling to connect reuse issues with the educational context in order we could be able first to specialize and then having the specialized SLO to consider the adaptability problem in some well-defined manner. Therefore, the aim of this chapter is to introduce such a framework and discuss the SLO problem.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 54.99
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. Acher M, Collet P, Lahire P, France RB (2012) FAMILIAR: a domain-specific language for large scale management of feature models. Sci Comput Program 78(6):657–681

    Article  Google Scholar 

  2. PEPM 2013 – ACM SIGPLAN (2013) Workshop on partial evaluation and program manipulation (PEPM’13)

    Google Scholar 

  3. Anderson L, Krathwohl D (2001) Taxonomy for learning, teaching and assessing: a revision of Bloom’s taxonomy of educational objectives. Longman, New York

    Google Scholar 

  4. Bespalova K, Burbaite R, Štuikys (2013) MP-ReTool tools. http://proin.ktu.lt/metaprogram/MP-ReTool/

  5. Bagheri E, Noia TD, Ragone A, Dragan Gasevic D (2010) Configuring software product line feature models based on stakeholders’ soft and hard. In: 14th international software product line conference. Springer, Berlin

    Google Scholar 

  6. Bhattacharya T, Prasath R, Bhattacharya B (2013) Qualitative learning outcome through computer assisted instructions. In: Mining intelligence and knowledge exploration. Springer, Tamil Nadu, pp 567–578

    Google Scholar 

  7. Castledine A, Chalmers C (2011) LEGO robotics: an authentic problem-solving tool? Des Technol Educ Int J 16(3):19–27

    Google Scholar 

  8. Capuano N, Gaeta M, Salerno S, Mangione GR (2011) An ontology-based approach for context-aware e-learning. In: 2011 third international conference on intelligent networking and collaborative systems (INCoS). IEEE, Fukuoka, pp 789–794

    Google Scholar 

  9. Czarnecki K, Helsen S, Eisenecker U (2005) Staged configuration through specialization and multilevel configuration of feature models. Softw Process Improv Pract 10(2):143–169

    Article  Google Scholar 

  10. Das M, Bhaskar M, Chithralekha T, Sivasathya S (2010) Context aware E-learning system with dynamically composable learning objects. Int J Comput Sci Eng 2(4):1245–1253

    Google Scholar 

  11. Fowler M, Beck K, Brant J, Opdyke W, Roberts D (1999) Refactoring: improving the design of existing code. Addison Wesley, Westford, Massachusetts. http://www.refactoring.com/

  12. Futamura Y (2000) Partial evaluation of computation process – an approach to a compiler-compiler. Higher-Order Symb Comput 12(4):381–391

    Google Scholar 

  13. Giacobazzi R, Jones ND, Mastroeni I (2012) Obfuscation by partial evaluation of distorted interpreters. In: Proceedings of the ACM SIGPLAN 2012 workshop on partial evaluation and program manipulation, PEPM’12. ACM, New York, pp 63–72

    Google Scholar 

  14. Gheyi R, Massoni T, Borba P (2011) Automatically checking feature model refactorings. J Univ Comput Sci 17(5):684–711

    Google Scholar 

  15. Gray JA (2003) Toeing the line: experiments with line-following algorithms. Technical report. http://www.fll-freak.com/misc/01-jgray_report.pdf

  16. Hartmann H, Trew T (2008) Using feature diagrams with context variability to model multiple product lines for software supply chains. In: Software product line conference, 2008. SPLC’08. 12th international. IEEE, pp 12–21

    Google Scholar 

  17. Ilomäki L, Jaakkola T, Lakkala M, Nirhamo L, Nurmi S, Paavola S, Rahikainen M, Lehtinen, E (2003) Principles, models and examples for designing learning objects (LOs). Pedagogical guidelines in CELEBRATE. A working paper for the European Commission, CELEBRATE Project, IST-2001-35188, May

    Google Scholar 

  18. Jones ND, Gomard CK, Sestoft P (1993) Partial evaluation and automatic program generation. Peter Sestoft

    Google Scholar 

  19. Jones ND (1996) An introduction to partial evaluation. ACM Comput Surv 28(3):480–503, September

    Article  Google Scholar 

  20. Jones ND, Sestoft P, Søndergaard PH (1985) An experiment in partial evaluation: the generation of a compiler generator. In: Jouannaud JP (ed) Rewriting techniques and applications, Dijon. Lecture notes in computer science, vol 202 Springer, Berlin, pp 124–140

    Google Scholar 

  21. Le Meur AF, Lawall JL, Consel Ch (2002) Towards bridging the gap between programming languages and partial evaluation. PEPM’02, 14–15 Jan 2002

    Google Scholar 

  22. Mens T, Tourw T (2004) A survey of software refactoring. IEEE Trans Softw Eng 30(2):126–139

    Article  Google Scholar 

  23. Martin S, Vallance M, van Schaik P, Wiz C (2010) Learning spaces, tasks and metrics for effective communication in Second Life within the context of programming LEGO NXT Mindstorms™ robots: towards a framework for design and implementation. J Virtual Worlds Res 3(1)

    Google Scholar 

  24. Murakami M (2012) An application of partial evaluation of communicating processes to system security. Int J Found Comp Sci Technol (IJFCST) 2(4):15–27

    Google Scholar 

  25. RobotC – Improved movement (2007) Robotics Academy. http://www.doc.ic.ac.uk/~ajd/Robotics/RoboticsResources/ROBOTC%20%20Improved%20Movement.pdf, p 19

  26. Štuikys V, Bespalova K, Burbaitė R (2014) Generative learning object (GLO) specialization: Teacher’s and learner’s view. In: Proceedings 20th international conference, ICIST, Druskininkai, Lithuania, Springer, 9–10 Oct 2014

    Google Scholar 

  27. Štuikys V, Burbaitė R, Damaševičius R (2013) Teaching of computer science topics using meta-programming-based GLOs and LEGO robots. Inf Educ 12(1):125–142

    Google Scholar 

  28. Štuikys V, Damaševičius R (2013) Meta-programming and model-driven meta-program development: principles, processes and techniques. Springer

    Google Scholar 

  29. Shuhidan S, Hamilton M, D’Souza D (2009) A taxonomic study of novice programming summative assessment. In: Proceedings of 11th Australasian computing education conference (ACE 2009), Wellington, pp 147–156

    Google Scholar 

  30. Sheard T (2001) Accomplishments and research challenges in meta-programming. In: Proceedings of 2nd international workshop on semantics, application, and implementation of program generation (SAIG’2001), Florence. Lecture notes in computer science, vol 2196. Springer, pp 2–44

    Google Scholar 

  31. Taha W (2004) A gentle introduction to multi-stage programming. Domain-specific program generation. Lecture notes in computer science, vol 3016, pp 30–50

    Google Scholar 

  32. Taha W (1999) Multi-stage programming: its theory and applications. Ph.D. thesis, Oregon Graduate Institute of Science and Technology

    Google Scholar 

  33. Thum T, Batory D, Kastner C (2009) Reasoning about edits to feature models. In: Software engineering. ICSE 2009. IEEE 31st international conference on (pp 254–264). IEEE

    Google Scholar 

  34. Thomas D (2005) Refactoring as meta programming? J Object Technol 4(1):7–12

    Article  Google Scholar 

  35. Tourwe T, Mens T (2003) Identifying refactoring opportunities using logic meta programming. In: 7th European conference software maintenance and reengineering, IEEE, Benevento, Italy

    Google Scholar 

  36. Ullrich C (2008) Pedagogically founded courseware generation for web-based learning: an HTN-planning-based approach implemented in PAIGOS. Springer, Berlin

    Google Scholar 

  37. Vesin B, Ivanović M, Klašnja-Milićević A, Budimac Z (2013) Ontology-based architecture with recommendation strategy in java tutoring system. Comput Sci Inf Syst 10(1):237–261

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Software Engineering, Kaunas University of Technology, Kaunas, Lithuania

    Vytautas Štuikys

Authors
  1. Vytautas Štuikys
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Štuikys, V. (2015). Enhanced Features of SLOs: Focus on Specialization. In: Smart Learning Objects for Smart Education in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-16913-2_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-16913-2_7

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16912-5

  • Online ISBN: 978-3-319-16913-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation