Skip to main content

Assessment of learning in environments interactive through fuzzy cognitive maps

Soft Computing volume 19, pages 1037–1050 (2015)Cite this article

Abstract

Fuzzy cognitive maps have been applied in different areas to express the dynamic behavior of a set of related concepts. These maps are powerful tools for analysis and generate simulations in dynamic systems. Nowadays, individuals use a lot of different technological media: smart TV, Internet, video games, mobile phones, etc. Some of these media present new ways to play, express, learn and communicate. The use of multimedia has become popular in the education field, these multimedia techniques can allow students to get more entertainment, immersion, and interactivity. An interactive learning environment can provide useful information to analyze the student learning process. Information such as: when the student initiates and completes a task, time between interactions, number of attempts, sequence of decisions taken, results related to the aggregate of the group, etc. This research work presents a learning assessment system that uses multivariate analysis based on structural equation modeling and fuzzy cognitive maps as a tool. The main aim of the proposed system is to facilitate assessment of learning on interactive environments. To validate the proposal, we have performed an empirical evaluation. As a part of the evaluation, we apply a sequence of scenarios to stimulate cognitive function of planning in a child population. In this evaluation, it was observed that most practical tasks obtain better results, confirming the Hebb rule on learning and cellular neurophysiological relationship.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

References

  • Aleven V, Vincent I, Roll B, Mclaren K, Koedinger K (2010) Automated, unobtrusive, action-by-action assessment of self-regulation during learning with an intelligent tutoring system. Educ Psychol 45(4):224–233

    Article  Google Scholar 

  • Alonso S, Pérez IJ, Cabrerizo FJ, Herrera-Viedma E (2013) A linguistic consensus model for Web 2.0 communities. Appl Soft Comput 13(1):149–157

    Article  Google Scholar 

  • Ausubel DP (1968) Educational psychology: a cognitive view. Holt, Rinehart & Winston, New York

    Google Scholar 

  • Baker F, Kim S (2004) Item response theory: parameter estimation techniques. Marcel Dedder, New York (p 503)

    Google Scholar 

  • Barab S, Thomas M, Dodge T, Carteaux R, Tuzun H (2005) Making learning fun: quest Atlantis, a game without guns. Educ Technol Res Dev 53(1):86–107

    Article  Google Scholar 

  • Byrd D, Case K, Berg W (2011) Planning: fixed-foreperiod event-related potentials during the Tower of London task. Neuropsychologia 49(5):1024–1032

    Article  Google Scholar 

  • Chen C, Chen M (2009) Mobile formative assessment tool based on data mining techniques for supporting web-based learning. Computers Educón 52(1):256–273

    Article  Google Scholar 

  • Cheng C, Tzeng G (2011) Creating the aspired intelligent assessment systems for teaching materials. Expert Syst Appl 38(10):12168–12179

    Article  Google Scholar 

  • Choi B, Baek Y (2011) Exploring factors of media characteristic influencing flow in learning through virtual worlds. Proc Computers Educ 57(4):2382–2394

    Article  Google Scholar 

  • Faiola A, Newlon C, Pfaff M, Smyslova O (2012) Correlating the effects of flow and telepresence in virtual worlds: enhancing our understanding of user behavior in game-based learning. Computers Hum Behav 29(3):1113–1121

    Article  Google Scholar 

  • Ferreira-Satler M, Romero F, Menendez-Dominguez V, Zapata A, Prieto M (2012) Fuzzy ontologies-based user profiles applied to enhance e-learning activities. Soft Comput 16(7):1129–1141

    Article  Google Scholar 

  • Freitas S, Oliver M (2006) How can exploratory learning with games and simulations within the curriculum be most effectively evaluated? Compters Educ 46(3):249–264

    Article  Google Scholar 

  • Gartner Inc (2009) Gartner Hype cycle for education 2009. In: Proceedings of http://www.gartner.com/DisplayDocument?id=1095713

  • Hair J, Black W, Babin B, Anderson R (2009) Multivariate data analysis. Prentice Hall, London, p 816

  • Havnes A, Smith K, Dysthe O, Ludvigsen K (2012) Formative assessment and feedback: making learning visible. Sutd Educ Eval 38(1):21–27

    Article  Google Scholar 

  • Huang Y, Backman S, Backman K, Moore D (2013) Exploring user acceptance of 3D virtual worlds in travel and tourism marketing. Proc Tourism Manage 36:490–501

    Article  Google Scholar 

  • Ibañez M, Morillo D, Delgado C, Pérez D, Santos P, Hernández-Leo D (2010) Assessment in 3D virtual Worlds: QTI in Wonderland. In: Congreso Iberoamericano de Informática Educativa, Santiago de Chile, vol 1, pp 410–417

  • Jarmon L, Traphagan T, Mayrath M, Trivedi A (2009) Virtual world teaching, experiential learning, and assessment: an interdisciplinary communication course in Second Life. Proc Computers Educ 53(1):169–182

    Article  Google Scholar 

  • Kaller C, Unterrainer J, Rahm B, Halsband U (2004) The impact of problem structure on planning: insights from the Tower of London task. Cognitive Brain Res 20(3):462–472

    Article  Google Scholar 

  • Konar A, Jain L (2005) Cognitive engineering a distributed approach to machine intelligence. Springer, New York, p 353

  • Kosko B (1986) Fuzzy cognitive maps. Int J Man Mach Stud 24(1):66–75

    Article  Google Scholar 

  • Luo X, And Wei X (2010) Guided game-based learning using fuzzy cognitive maps. IEEE Trans Learn Technol 3(4):344–357

    Article  Google Scholar 

  • Miyake A, Naomi P, Friedman M (2000) The unity and diversity of executive functions and their contributions to complex “Frontal Lobe” tasks: a latent. Variable Anal Original Res Article Cogn Psychol 41(1):49–100

    Google Scholar 

  • Naim S, Hagras H (2013) A type 2-hesitation fuzzy logic based multi-criteria group decision making system for intelligent shared environments. Soft Comput 13(1):1–15

    Article  Google Scholar 

  • OECD (2011) Measuring improvements in learning outcomes: best practices to assess the value-added of schools. OECD Publishing, p 222

  • Peláez E, Bowles J (1996) Using fuzzy cognitive maps as a system model for failure modes and effects analysis. Inf Sci 88(1–4):177–199

    Google Scholar 

  • Pérez IJ, Mezei J, Wikström R, Carlsson C, Herrera-Viedma E (2014) A new consensus model for GDM problems based on a fuzzy ontology. Soft Comput 17(9):1617–1627

    Article  Google Scholar 

  • Reimann P, Kickmeier-Rust M, Albert D (2013) Problem solving learning environments and assessment: a knowledge space theory approach. Computers Educ 64:183–193

    Article  Google Scholar 

  • Rutter M, Maughan B (2002) School effectiveness findings 1979–2002. Proc J School Psychol 40(6):451–475

    Article  Google Scholar 

  • Shadish W (2001) Quasi-experimental designs. Int Encycl Soc Behav Sci 18:12655–12659

  • Stach W, Kurgan L, Pedrycz W, Reformat M (2005) Genetic learning of fuzzy cognitive maps. J Fuzzy Sets Syst 153(3):371–401

    Article  MATH  MathSciNet  Google Scholar 

  • Siu-Lung J, Chi-Wai R, Fang Y (2012) The effects of peer intrinsic and extrinsic motivation on MMOG game-based collaborative learning. Computers Hum Behav 28(5):1663–1669

  • Tugba S (2012) Place presence, social presence, co-presence, and satisfaction in virtual worlds. Computers Educ 18(1):154–161

    Google Scholar 

  • Wang Y (2003) Assessment of learner satisfaction with asynchronous electronic learning systems. Proc Inf Manage 41(1):75–86

    Article  Google Scholar 

  • Wiliam D (2011) What is assessment for learning? Stud Educ Eval 37:3–14

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Engineering Faculty, Catholic University of Colombia, Bogotá D.C, Colombia

    Holman Bolívar Barón

  2. College of Engineering, Universidad Internacional de La Rioja, Madrid, Spain

    Rubén González Crespo, Jordán Pascual Espada & Oscar Sanjuán Martínez

Authors
  1. Holman Bolívar Barón
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rubén González Crespo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jordán Pascual Espada
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Oscar Sanjuán Martínez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rubén González Crespo.

Additional information

Communicated by V. Loia.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Barón, H.B., Crespo, R.G., Pascual Espada, J. et al. Assessment of learning in environments interactive through fuzzy cognitive maps. Soft Comput 19, 1037–1050 (2015). https://doi.org/10.1007/s00500-014-1313-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-014-1313-x

Keywords

  • Intelligent assessment system
  • Fuzzy cognitive map
  • Interactive learning environments
  • Game-based assessment