Neural Computing and Applications

, Volume 31, Supplement 1, pp 671–681 | Cite as

A hybrid AHP-GA method for metadata-based learning object evaluation

  • Murat İnceEmail author
  • Tuncay Yiğit
  • Ali Hakan Işık
Original Article


A wide variety of demand in e-learning and web-based learning caused a new approach in e-content presentation. In order to accomplish these demands, learning object repositories (LORs) were developed. LORs have many learning objects (LOs) that are used to produce different types of e-content. When there are many LOs in LORs, the evaluation and selection of them become a subjective and time-consuming process. Thus, selecting the most suitable and best qualified LO is considered as a multi-criteria decision-making (MCDM) problem. In this study, a hybrid analytic hierarchy process genetic algorithm (AHP-GA) method was developed for the evaluation of LOs from web-based Intelligent Learning Object Framework (Zonesa) LOR. This proposed hybrid system was used in a real case study and the results demonstrated that the proposed system can be used effectively by both users and machines to produce content by the help of LO metadata.


Analytic hierarchy process Learning object selection Metadata Repository Genetic algorithm Recommendation system 



The authors wish to thank the Scientific and Technological Research Council of Turkey (TUBITAK) that supported this project financially with project number EEEAG 115E600.

Compliance with ethical standards

This study was funded by Scientific and Technological Research Council of Turkey (TUBITAK) (grant number EEEAG 115E600).

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Latchman HA, Salzmann C, Gillet D, Bouzekri H (1999) Information technology enhanced learning in distance and conventional education. IEEE Trans Educ 42(4):247–254Google Scholar
  2. 2.
    Li X, Tian Y, Smarandache F, Alex R (2015) An extension collaborative innovation model in the context of big data. Int J Inf Technol Decis Mak 14(01):69–91Google Scholar
  3. 3.
    Khalifa M, Lam R (2002) Web-based learning effects on learning process and outcome. IEEE Trans Educ 45(4):350–356Google Scholar
  4. 4.
    Bhaskar M, Das MM, Chithralekha T, Sivasatya S (2010) Genetic algorithm based adaptive learning scheme generation for context aware E-learning. Int J Comput Sci Eng 2(4):1271–1279Google Scholar
  5. 5.
    Vargo J, Nesbit JC, Belfer K, Archambault A (2003) Learning object evaluation computer mediated collaboration and inter-rater reliability. Int J Comput Appl 25(3):1–8Google Scholar
  6. 6.
    Yigit T, Isik AH, Ince M (2014) Web-based learning object selection software using analytical hierarchy process. IET Softw 8(4):174–183Google Scholar
  7. 7.
    IEEE LTSC (2016) Final draft standard for learning object metadata. Accessed 3 April 2016
  8. 8.
    Chang PT, Lo YT (2001) Modelling of job-shop scheduling with multiple quantitative and qualitative objectives and a GA/TS mixture approach. Int J Comput Integr Manuf 14(4):367–384Google Scholar
  9. 9.
    Tyagi R, Das C (1997) A methodology for cost versus service trade-offs in wholesale location-distribution using mathematical programming and analytic hierarchy process. J Bus Logist 18(2):77–99Google Scholar
  10. 10.
    Lin C, Wang W, Yu W (2008) Improving AHP for construction with an adaptive AHP approach (A3). Autom Constr 17(2):180–187Google Scholar
  11. 11.
    Pendharkar P (2003) Characterization of aggregate fuzzy membership functions using Saaty’s eigen value approach. Comput Oper Res 30(2):199–212zbMATHGoogle Scholar
  12. 12.
    Yeh J, Kreng B, Lin C (2001) A consensus approach for synthesizing the elements of comparison matrix in the analytic hierarchy process. Int J Syst Sci 32(11):1353–1363zbMATHGoogle Scholar
  13. 13.
    Terano T, Ishino Y (1996) Knowledge acquisition from questionnaire data using simulated breeding and inductive learning methods. Expert Syst Appl 11(4):507–518Google Scholar
  14. 14.
    Ding L, Yue Y, Ahmet K, Jackson M, Parkin R (2005) Global optimization of a feature-based process sequence using GA and ANN techniques. Int J Prod Res 43(15):3247–3272zbMATHGoogle Scholar
  15. 15.
    Chan FTS, Chung SH (2004) Multi-criteria genetic optimization for distribution network problems. Int J Adv Manuf Technol 24(7–8):517–532Google Scholar
  16. 16.
    Chan FTS, Chung SH (2004) A multi-criterion genetic algorithm for order distribution in demand driven supply chain. Int J Comput Integr Manuf 17(4):339–351Google Scholar
  17. 17.
    Chan FTS, Chung SH, Choy KL (2006) Optimization of order fulfillment in distribution network problems. J Intell Manuf 17(3):307–319Google Scholar
  18. 18.
    Chan FTS, Chung SH, Wadhwa S (2004) A heuristic methodology for order distribution in a demand driven collaborative supply chain. Int J Prod Res 42:1–19zbMATHGoogle Scholar
  19. 19.
    Chan FTS, Chung SH, Wadhwa S (2005) A hybrid genetic algorithm for production and distribution. Omega 33(4):345–355Google Scholar
  20. 20.
    Aguilar-Lasserre AA, Bautista MAB, Ponsich A, Huerta MAG (2009) An AHP-based decision-making tool for the solution of multiproduct batch plant design problem under imprecise demand. Comput Oper Res 36(3):711–736zbMATHGoogle Scholar
  21. 21.
    Dehghanian F, Mansour S (2009) Designing sustainable recovery network of end-of-life products using genetic algorithm. Resour Conserv Recycl 53(10):559–570Google Scholar
  22. 22.
    Guan X, Wang Y, Tao L (2009) Machining scheme selection of digital manufacturing based on genetic algorithm and AHP. J Intell Manuf 20(6):661–669Google Scholar
  23. 23.
    Wiley DA (2002) Connecting learning objects to instructional design theory: a definition, a metaphor, and a taxonomy. Accessed 3 April 2016
  24. 24.
    Sinclair J, Joy M, Yau YK, Hagan S (2013) A practice-oriented review of learning objects. IEEE Trans Learn Technol 6:177–192Google Scholar
  25. 25.
    Sabitha AS, Mehrotra D (2013) A push strategy for delivering of learning objects using metadata based association analysis (fp-tree). In: International conference on computer communication and informatics, pp 1–4Google Scholar
  26. 26.
    Liu J, Greer J (2004) Individualized selection of learning object. In: Workshop on applications of semantic web technologies for e-learning, pp 29–34Google Scholar
  27. 27.
    Sabitha AS, Mehrotra D (2012) User centric retrieval of learning objects in LMS. In: Third international conference on computer and communication technology, pp 14–19Google Scholar
  28. 28.
    Chellatamilan T, Suresh RM (2012) Automatic classification of learning objects through dimensionality reduction and feature subset selections in an e-learning system. In: IEEE international conference on technology enhanced education, pp 1–6Google Scholar
  29. 29.
    Limongelli C, Miola A, Sciarrone F, Temperini M (2012) Supporting teachers to retrieve and select learning objects for personalized courses in the Moodle_LS environment. In: IEEE 12th international conference on advanced learning technologies, pp 518–520Google Scholar
  30. 30.
    Kurilovas E (2009) Evaluation and optimization of e-learning software packages: learning object repositories, In: Fourth international conference on software engineering advances, pp 477–483Google Scholar
  31. 31.
    Mohammed FA, Hagag MAE (2013) Integrating AHP and genetic algorithm model adopted for personal selection. Int J Eng Trend Technol 6(5):247–256Google Scholar
  32. 32.
    Yu CS (2002) A GP-AHP method for solving group decision-making fuzzy AHP problems. Comput Oper Res 29(14):1969–2001zbMATHGoogle Scholar
  33. 33.
    Saaty TL (1980) The analytic hierarchy process: planning, priority setting, resources allocation. McGraw, New YorkzbMATHGoogle Scholar
  34. 34.
    Raut RD, Mahajan VC (2015) A new strategic approach of fuzzy-quality function deployment and analytical hierarchy process in construction industry. Int J Logist Syst Manag 20(2):260–290Google Scholar
  35. 35.
    Singh RP, Nachtnebel HP (2016) Analytical hierarchy process (AHP) application for reinforcement of hydropower strategy in Nepal. Renew Sust Energ Rev 55:43–58Google Scholar
  36. 36.
    Che ZH, Wang HS, Sha DY (2007) A multi-criterion interaction-oriented model with proportional rule for designing supply chain networks. Expert Syst Appl 33(4):1042–1053Google Scholar
  37. 37.
    Vidal LA, Sahin E, Martelli N, Berhoune M, Bonan B (2010) Applying AHP to select drugs to be produced by anticipation in a chemotherapy compounding unit. Expert Syst Appl 37(2):1528–1534Google Scholar
  38. 38.
    Dragović I, Turajlić N, Radojević D, Petrović B (2014) Combining boolean consistent fuzzy logic and AHP illustrated on the web service selection problem. Int J Comput Intell Syst 7(1):84–93Google Scholar
  39. 39.
    Dalalah D, Hayajneh M, Batieha F (2011) A fuzzy multi-criteria decision making model for supplier selection. Expert Syst Appl 38(7):8384–8391Google Scholar
  40. 40.
    Fallahpour A, Olugu EU, Musa SN (2015) A hybrid model for supplier selection: integration of AHP and multi expression programming (MEP). Neural Comput Appl. doi: 10.1007/s00521-015-2160-0 Google Scholar
  41. 41.
    Ho W (2008) Integrated analytic hierarchy process and its applications—a literature review. Eur J Oper Res 186(1):211–228MathSciNetzbMATHGoogle Scholar
  42. 42.
    Piltan M, Sowlati T (2016) A multi-criteria decision support model for evaluating the performance of partnerships. Expert Syst Appl 45:373–384Google Scholar
  43. 43.
    Deng H (1999) Multicriteria analysis with fuzzy pairwise comparison. Int J Approx Reason 21(3):215–231Google Scholar
  44. 44.
    Secme NY, Bayrakdaroğlu A, Kahraman C (2009) Fuzzy performance evaluation in Turkish banking sector using analytic hierarchy process and TOPSIS. Expert Syst Appl 36(9):11699–11709Google Scholar
  45. 45.
    Volna E, Kotyrba M (2014) A comparative study to evolutionary algorithms. In: 28th European conference on modelling and simulation, pp 340–345Google Scholar
  46. 46.
    Droste S, Jansen T, Wegener I (2002) On the analysis of the (1+ 1) evolutionary algorithm. Theor Comput Sci 276(1–2):51–81MathSciNetzbMATHGoogle Scholar
  47. 47.
    Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. Mach Learn 3(2):95–99Google Scholar
  48. 48.
    Wang YZ (2003) Using genetic algorithm methods to solve course scheduling problems. Expert Syst Appl 25(1):39–50MathSciNetGoogle Scholar
  49. 49.
    Goldberg DE (1989) Genetic algorithms in search optimization and machine learning. Addison-wesley, Reading Menlo ParkzbMATHGoogle Scholar
  50. 50.
    Lin D, Lee CKM, Wu Z (2012) Integrating analytical hierarchy process to genetic algorithm for re-entrant flow shop scheduling problem. Int J Prod Res 50(7):1813–1824Google Scholar
  51. 51.
    Saaty TL (1990) How to make a decision: the analytic hierarchy process. Eur J Oper Res 48(1):9–26MathSciNetzbMATHGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.Vocational School of Technical SciencesUniversity of Suleyman DemirelIspartaTurkey
  2. 2.Department of Computer EngineeringUniversity of Suleyman DemirelIspartaTurkey
  3. 3.Department of Computer EngineeringUniversity of Mehmet Akif ErsoyBurdurTurkey

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