Published

2014-07-01

Exploring the Mobile Structural Assessment Tool: Concept Maps for Learning Website

Exploración de la herramienta de aseguramiento estructural móvil: mapas conceptuales para websites de aprendizaje

DOI:

https://doi.org/10.15446/rce.v37n2spe.47939

Keywords:

Concept Maps, Effective Feedback, Pathfinder Network, Structural Assessment (en)
Aseguramiento estructural, Mapas conceptuales, Rredes de búsqueda de ruta, Retroalimentación efectiva. (es)

Authors

  • Mehmet Filiz University of Ottawa
  • David Trumpower University of Ottawa, Canada,
  • Arun Vanapalli University of Ottawa, Canada

In this paper, we describe how the pathfinder algorithm converts relatedness ratings of concept pairs to concept maps; we also present how this algorithm has been used to develop the Concept Maps for Learning website (www.conceptmapsforlearning.com) based on the principles of effective formative assessment. The pathfinder networks, one of the network representation tools, claim to help more students memorize and recall the relations between concepts than spatial representation tools (such as Multi- Dimensional Scaling). Therefore, the pathfinder networks have been used in various studies on knowledge structures, including identifying students’ misconceptions. To accomplish this, each student’s knowledge map and the expert knowledge map are compared via the pathfinder software, and the differences between these maps are highlighted. After misconceptions are identified, the pathfinder software fails to provide any feedback on these misconceptions. To overcome this weakness, we have been developing a mobile-based concept mapping tool providing visual, textual and remedial feedback (ex. videos, website links and applets) on the concept relations. This information is then placed on the expert concept map, but not on the student’s concept map. Additionally, students are asked to note what they understand from given feedback, and given the opportunity to revise their knowledge maps after receiving various types of feedback.

En este artículo se describe cómo el algoritmo de búsqueda de ruta convierte puntajes de conceptos pareados en mapas conceptuales. También se presenta cómo este algoritmo ha sido utilizado para desarrollar estos mapas conceptuales para aprendizaje (www.conceptmapsforlearning.com) basados en los principios del aseguramiento formativo efectivo. Las redes de búsqueda de ruta, una de las herramientas de representación de redes, ayudan a memorizar a los estudiantes y enunciar las relaciones entre mapas más que las herramientas de expresión espacial (tales como el escalonamiento multidimensional). Por tanto, las redes de búsqueda de rutas han sido usadas en varios estudios de estructura del conocimiento incluyendo la identificación de malos conceptos usados por los estudiantes. Para lograr esto, cada mapa de conocimiento tanto del estudiante como del experto son comparados vía el software de búsqueda de ruta y se remarcan las diferencias entre éstos. Después que los malos conceptos son identificados, el software de búsqueda falla en entregar una retroalimentación en estos nodos conceptuales. Para superar esta debilidad, se desarrolla una herramienta de mapa conceptual móvil que manda retroalimentaciones visuales, textuales y remediales (e.g. vídeos, enlaces a páginas web y applets) en las relaciones de los conceptos. Adicionalmente, los estudiantes son preguntados acerca de qué entienden de la retroalimentación brindada y se les da la oportunidad de revisar sus mapas de conocimiento después de recibir varios tipos de retroalimentación.

https://doi.org/10.15446/rce.v37n2spe.47939

Exploring the Mobile Structural Assessment Tool: Concept Maps for Learning Website

Exploración de la herramienta de aseguramiento estructural móvil: mapas conceptuales para websites de aprendizaje

MEHMET FILIZ1, DAVID TRUMPOWER2, ARUN VANAPALLI3

1University of Ottawa, Faculty of Education, Measurement Evaluation and Assessment Research Unit, Canada. PhD. Student. Email: mehmetfiliz52@hotmail.com
2University of Ottawa, Faculty of Education, Measurement Evaluation and Assessment Research Unit, Canada. Associate Professor. Email: david.trumpower@uOttawa.ca
3University of Ottawa, Faculty of Education, Measurement Evaluation and Assessment Research Unit, Canada. M.A. Student. Email: vanapalliarun@gmail.com


Abstract

In this paper, we describe how the pathfinder algorithm converts relatedness ratings of concept pairs to concept maps; we also present how this algorithm has been used to develop the Concept Maps for Learning website (\url{http://www.conceptmapsforlearning.com}) based on the principles of effective formative assessment. The pathfinder networks, one of the network representation tools, claim to help more students memorize and recall the relations between concepts than spatial representation tools (such as Multi-Dimensional Scaling). Therefore, the pathfinder networks have been used in various studies on knowledge structures, including identifying students misconceptions. To accomplish this, each students knowledge map and the expert knowledge map are compared via the pathfinder software, and the differences between these maps are highlighted. After misconceptions are identified, the pathfinder software fails to provide any feedback on these misconceptions. To overcome this weakness, we have been developing a mobile-based concept mapping tool providing visual, textual and remedial feedback (ex. videos, website links and applets) on the concept relations. This information is then placed on the expert concept map, but not on the students concept map. Additionally, students are asked to note what they understand from given feedback, and given the opportunity to revise their knowledge maps after receiving various types of feedback.

Key words: Concept Maps, Effective Feedback, Pathfinder Network, Structural Assessment.


Resumen

En este artículo se describe cómo el algoritmo de búsqueda de ruta convierte puntajes de conceptos pareados en mapas conceptuales. También se presenta cómo este algoritmo ha sido utilizado para desarrollar estos mapas conceptuales para aprendizaje (\url{http://www.conceptmapsforlearning.com}) basados en los principios del aseguramiento formativo efectivo.
Las redes de búsqueda de ruta, una de las herramientas de representación de redes, ayudan a memorizar a los estudiantes y enunciar las relaciones entre mapas más que las herramientas de expresión espacial (tales como el escalonamiento multidimensional). Por tanto, las redes de búsqueda de rutas han sido usadas en varios estudios de estructura del conocimiento incluyendo la identificación de malos conceptos usados por los estudiantes. Para lograr esto, cada mapa de conocimiento tanto del estudiante como del experto son comparados vía el software de búsqueda de ruta y se remarcan las diferencias entre éstos. Después que los malos conceptos son identificados, el software de búsqueda falla en entregar una retroalimentación en estos nodos conceptuales. Para superar esta debilidad, se desarrolla una herramienta de mapa conceptual móvil que manda retroalimentaciones visuales, textuales y remediales (e.g. vídeos, enlaces a páginas web y applets) en las relaciones de los conceptos. Adicionalmente, los estudiantes son preguntados acerca de qué entienden de la retroalimentación brindada y se les da la oportunidad de revisar sus mapas de conocimiento después de recibir varios tipos de retroalimentación.

Palabras clave: aseguramiento estructural, mapas conceptuales, redes de búsqueda de ruta, retroalimentación efectiva.


Texto completo disponible en PDF


References

1. Acton, W. H., Johnson, P. J. & Goldsmith, T. E. (1994), 'Structural knowledge assessment: Comparison of referent structures', Journal of Educational Psychology 86(2), 303-311.

2. Ausubel, D. P. (1978), , 2 edn, Holt McDougal, New York.

3. Boldt, M. (2001), 'Assessing students' accounting knowledge: a structural approach', Journal of Education for Business 76(5), 262-269. doi: 10.1080/08832320109599646.

4. Boring, R. L. (2005), The validity of human and computerized writing assessment, 'Proceedings of the Human Factors and Ergonomics Society Annual Meeting', Vol. 49, Sage Publications, p. 759-763.

5. Casas-García, L. M. & Luengo-González, R. (2013), 'The study of the pupil's cognitive structure: The concept of angle', European Journal of Psychology of Education 28(2), 373-398.

6. Chen, C. (1998), 'Generalised similarity analysis and pathfinder network scaling', Interacting with Computers 10(2), 107-128.

7. Chen, C. (2004), 'Searching for intellectual turning points: Progressive knowledge domain visualization', 101(14), 5303-5310.

8. Chen, L. H. (2011), 'Enhancement of student learning performance using personalized diagnosis and remedial learning system', Computers & Education 56(1), 289-299. doi: 10.1016/j.compedu.2010.07.015.

9. Clariana, R. B. (2010), Deriving individual and group knowledge structure from network diagrams and from essays, 'Computer-Based Diagnostics and Systematic Analysis of Knowledge', Springer, p. 117-130.

10. Clariana, R. B. & Koul, R. (2004), A computer-based approach for translating text into concept map-like representations, 'Proceedings of the First International Conference on Concept Mapping', p. 14-17.

11. Clariana, R. B. & Wallace, P. (2007), 'A computer-based approach for deriving and measuring individual and team knowledge structure from essay questions', Journal of Educational Computing Research 37(3), 211-227.

12. Cooke, N. J. (1992a), 'Predicting judgment time from measures of psychological proximity', Journal of Experimental Psychology: Learning, Memory, and Cognition 18(3), 640-653.

13. Cooke, N. J. (1992b), 'Eliciting semantic relations for empirically derived networks', International Journal of Man-Machine Studies 37(6), 721-750.

14. Cooke, N. J. (1999), Knowledge elicitation, 'Handbook of Applied Cognition', Wiley, New York, p. 479-510.

15. Cooke, N. J., Neville, K. J. & Rowe, A. L. (1996), 'Procedural network representations of sequential data', Human Computer Interaction 11(1), 29-68.

16. Cooke, N. J. & Schvaneveldt, R. W. (1988), 'Effects of computer programming experience on network representations of abstract programming concepts', International Journal of Man-Machine Studies 29(4), 407-427.

17. Cooke, N. M., Durso, F. T. & Schvaneveldt, R. W. (1986), 'Recall and measures of memory organization', Journal of Experimental Psychology: Learning, Memory, and Cognition 12(4), 538-549. doi: 10.1037/0278-7393.12.4.538.

18. Davis, M. A., Curtis, M. B. & Tschetter, J. D. (2003), 'Evaluating cognitive training outcomes: validity and utility of structural knowledge assessment', Journal of Business and Psychology 18(2), 191-206.

19. Day, E. A., Arthur Jr, W. & Gettman, D. (2001), 'Knowledge structures and the acquisition of a complex skill', Journal of Applied Psychology 86(5), 1022-1033.

20. DeChurch, L. A. & Mesmer-Magnus, J. R. (2010), 'Measuring shared team mental models: a meta-analysis', Group Dynamics: Theory, Research, and Practice 14(1), 1-14.

21. Dicerbo, K. E. (2007), 'Knowledge structures of entering computer networking students and their instructors', Journal of Information Technology Education 6, 263-277.

22. Filiz, M., Trumpower, D. & Atas, S. (2012), Analysis of how well a concept mapping website conforms to principles of effective assessment for learning, 'Concept Maps: Theory, Methodology, Technology. Proceedings of the 5th International Conference on Concept Mapping', Malta, p. 169-172.

23. Filiz, M., Trumpower, D. & Atas, S. (2013a), The contributions of digital concept maps to assessment for learning practices, 'Cognition and Exploratory Learning in Digital Age', Texas, USA.

24. Filiz, M., Trumpower, D. & Atas, S. (2013b), How a computer-based concept mapping can be used in statistics education: A case of ANOVA, 'United States Conference on Teaching Statistics 2013', North Carolina, USA.

25. Goldsmith, T. E., Johnson, P. J. & Acton, W. H. (1991), 'Assessing structural knowledge', Journal of Educational Psychology 83(1), 88-96.

26. Gomez, R. L., Hadfield, O. D. & Housner, L. D. (1996), 'Conceptual maps and simulated teaching episodes as indicators of competence in teaching elementary mathematics', Journal of Educational Psychology 88(3), 572-585. doi: 10.1037/0022-0663.88.3.572.

27. Guerrero-Bote, V., Zapico-Alonso, F., Espinosa-Calvo, M., Gomez-Crisostomo, R. & Moya-Anegon, F. (2006), 'Binary pathfinder: an improvement to the pathfinder algorithm', Information Processing and Management 42(6), 1484-1490. doi: 10.1016/j.ipm.2006.03.015.

28. Ichino, M. & Yaguchi, H. (1994), 'Generalized Minkowski metrics for mixed feature-type data analysis', Systems, Man and Cybernetics, IEEE Transactions on 24(4), 698-708.

29. Kim, M. K. (2012), 'Cross-validation study of methods and technologies to assess mental models in a complex problem solving situation', Computers in Human Behavior 28(2), 703-717.

30. Kim, M. (2013), 'Concept map engineering: Methods and tools based on the semantic relation approach', Educational Technology Research and Development 61(6), 951-978.

31. Kivlighan, D. M. & Tibbits, B. M. (2012), 'Silence is mean and other misconceptions of group counseling trainees: Identifying errors of commission and omission in trainees' knowledge structures', Group Dymanmics: Theory, Research and Practice 16(1), 14-34.

32. Koul, R., Clariana, R. B. & Salehi, R. (2005), 'Comparing several human and computer-based methods for scoring concept maps and essays', Journal of Educational Computing Research 32(3), 227-239.

33. Lau, W. W. & Yuen, A. H. (2010), 'Promoting conceptual change of learning sorting algorithm through the diagnosis of mental models: The effects of gender and learning styles', Computers & Education 54(1), 275-288.

34. McGaghie, W. C., McCrimmon, D. R., Mitchell, G., Thompson, J. A. & Ravitch, M. M. (2000), 'Quantitative concept mapping in pulmonary physiology: comparison of student and faculty knowledge structures', American Journal of Physiology - Advances in Physiology Education 23(1), 72-81.

35. Moni, R. W., Beswick, E. & Moni, K. B. (2005), 'Using student feedback to construct an assessment rubric for a concept map in physiology', American Journal of Physiology - Advances in Physiology Education 29(4), 197-203. doi: 10.1152/advan.00066.2004.

36. Nash, J. G. & Nash, J. M. (2003), 'A structural representation of migraine diagnostic criteria: the experts' view', Headache: The Journal of Head and Face Pain 43(4), 322-329.

37. Rowe, A. L., Cooke, N. J., Hall, E. P. & Halgren, T. L. (1996), 'Toward an on-line knowledge assessment methodology: building on the relationship between knowing and doing', Journal of Experimental Psychology: Applied 2(1), 31-47. doi: 10.1037/1076-898X.2.1.31.

38. Sarwar, G. S. (2012), Comparing the Effect of Reflections, Written Exercises, and Multimedia Instruction to Address Learners' Misconceptions Using Structural Assessment of Knowledge, PhD thesis, University of Ottawa.

39. Schvaneveldt, R. W., ed. (1990), Pathfinder Associative Networks: Studies in Knowledge Organization, Ablex Pub, Norwood, N.J.

40. Taricani, E. & Clariana, R. (2006), 'A technique for automatically scoring open-ended concept maps', Educational Technology Research and Development 54(1), 65-82.

41. Trumpower, D. L. & Sarwar, G. S. (2010), 'Effectiveness of structural feedback provided by pathfinder networks', Journal of Educational Computing Research 43(1), 7-24.

42. Trumpower, D. L., Filiz, M. & Sarwar, G. S. (2014), Assessment for learning using digital knowledge maps, 'Digital Knowledge Maps in Education', Springer, p. 221-237.

43. Von Minden, A. M., Walls, R. T. & Nardi, A. H. (1998), 'Charting the links between mathematics content and pedagogy concepts: cartographies of cognition', Journal of Experimental Education 66(4), 339-358.

44. Wilson, J. M. (1998), 'Differences in knowledge networks about acids and bases of year-12, undergraduate and postgraduate chemistry students', Research in Science Education 28(4), 429-446.


[Recibido en agosto de 2014. Aceptado en noviembre de 2014]

Este artículo se puede citar en LaTeX utilizando la siguiente referencia bibliográfica de BibTeX:

@ARTICLE{RCEv37n2a04,
    AUTHOR  = {Filiz, Mehmet and Trumpower, David and Vanapalli, Arun},
    TITLE   = {{Exploring the Mobile Structural Assessment Tool: Concept Maps for Learning Website}},
    JOURNAL = {Revista Colombiana de Estadística},
    YEAR    = {2014},
    volume  = {37},
    number  = {2},
    pages   = {297-317}
}

References

Acton, W. H., Johnson, P. J. & Goldsmith, T. E. (1994), ‘Structural knowledge assessment: Comparison of referent structures’, Journal of Educational Psychology 86(2), 303–311.

Ausubel, D. P. (1978), 2 edn, Holt McDougal, New York.

Boldt, M. (2001), ‘Assessing students’ accounting knowledge: A structural approach’, Journal of Education for Business 76(5), 262–269. doi: 10.1080/08832320109599646.

Boring, R. L. (2005), The validity of human and computerized writing assessment, in ‘Proceedings of the Human Factors and Ergonomics Society Annual Meeting’, Vol. 49, Sage Publications, pp. 759–763.

Casas-García, L. M. & Luengo-González, R. (2013), ‘The study of the pupil’s cognitive structure: The concept of angle’, European Journal of Psychology of Education 28(2), 373–398.

Chen, C. (1998), ‘Generalised similarity analysis and pathfinder network scaling’, Interacting with Computers 10(2), 107–128.

Chen, C. (2004), ‘Searching for intellectual turning points: Progressive knowledge domain visualization’, 101(14), 5303–5310.

Chen, L. H. (2011), ‘Enhancement of student learning performance using personalized diagnosis and remedial learning system’, Computers & Education 56(1), 289–299. doi: 10.1016/j.compedu.2010.07.015.

Clariana, R. B. (2010), Deriving individual and group knowledge structure from network diagrams and from essays, in ‘Computer-Based Diagnostics and Systematic Analysis of Knowledge’, Springer, pp. 117–130.

Clariana, R. B. & Koul, R. (2004), A computer-based approach for translating text into concept map-like representations, in ‘Proceedings of the First International Conference on Concept Mapping’, pp. 14–17.

Clariana, R. B. &Wallace, P. (2007), ‘A computer-based approach for deriving and measuring individual and team knowledge structure from essay questions’, Journal of Educational Computing Research 37(3), 211–227.

Cooke, N. J. (1992a), ‘Eliciting semantic relations for empirically derived networks’, International Journal of Man-Machine Studies 37(6), 721–750.

Cooke, N. J. (1992b), ‘Predicting judgment time from measures of psychological proximity’, Journal of Experimental Psychology: Learning, Memory, and Cognition 18(3), 640–653.

Cooke, N. J. (1999), Knowledge elicitation, in ‘Handbook of Applied Cognition’, Wiley, New York, pp. 479–510.

Cooke, N. J., Neville, K. J. & Rowe, A. L. (1996), ‘Procedural network representations of sequential data’, Human Computer Interaction 11(1), 29–68.

Cooke, N. J. & Schvaneveldt, R. W. (1988), ‘Effects of computer programming experience on network representations of abstract programming concepts’, International Journal of Man-Machine Studies 29(4), 407–427.

Cooke, N. M., Durso, F. T. & Schvaneveldt, R. W. (1986), ‘Recall and measures of memory organization’, Journal of Experimental Psychology: Learning, Memory, and Cognition 12(4), 538–549. doi: 10.1037/0278-7393.12.4.538.

Davis, M. A., Curtis, M. B. & Tschetter, J. D. (2003), ‘Evaluating cognitive training outcomes: Validity and utility of structural knowledge assessment’, Journal of Business and Psychology 18(2), 191–206.

Day, E. A., Arthur Jr, W. & Gettman, D. (2001), ‘Knowledge structures and the acquisition of a complex skill’, Journal of Applied Psychology 86(5), 1022–1033.

DeChurch, L. A. & Mesmer-Magnus, J. R. (2010), ‘Measuring shared team mental models: A meta-analysis’, Group Dynamics: Theory, Research, and Practice 14(1), 1–14.

Dicerbo, K. E. (2007), ‘Knowledge structures of entering computer networking students and their instructors’, Journal of Information Technology Education 6, 263–277.

Filiz, M., Trumpower, D. & Atas, S. (2012), Analysis of how well a concept mapping website conforms to principles of effective assessment for learning, in ‘Concept Maps: Theory, Methodology, Technology. Proceedings of the 5th International Conference on Concept Mapping’, Malta, pp. 169–172.

Filiz, M., Trumpower, D. & Atas, S. (2013a), The contributions of digital concept maps to assessment for learning practices, in ‘Cognition and Exploratory Learning in Digital Age’, Texas, USA.

Filiz, M., Trumpower, D. & Atas, S. (2013b), How a computer-based concept mapping can be used in statistics education: A case of ANOVA, in ‘United States Conference on Teaching Statistics 2013’, North Carolina, USA.

Goldsmith, T. E., Johnson, P. J. & Acton, W. H. (1991), ‘Assessing structural knowledge’, Journal of Educational Psychology 83(1), 88–96.

Gomez, R. L., Hadfield, O. D. & Housner, L. D. (1996), ‘Conceptual maps and simulated teaching episodes as indicators of competence in teaching elementary mathematics’, Journal of Educational Psychology 88(3), 572–585. doi: 10.1037/0022-0663.88.3.572.

Guerrero-Bote, V., Zapico-Alonso, F., Espinosa-Calvo, M., Gomez-Crisostomo, R. & Moya-Anegon, F. (2006), ‘Binary pathfinder: An improvement to the pathfinder algorithm’, Information Processing and Management 42(6), 1484– 1490. doi: 10.1016/j.ipm.2006.03.015.

Ichino, M. & Yaguchi, H. (1994), ‘Generalized Minkowski metrics for mixed feature-type data analysis’, Systems, Man and Cybernetics, IEEE Transactions on 24(4), 698–708.

Kim, M. (2013), ‘Concept map engineering: Methods and tools based on the semantic relation approach’, Educational Technology Research and Development 61(6), 951–978.

Kim, M. K. (2012), ‘Cross-validation study of methods and technologies to assess mental models in a complex problem solving situation’, Computers in Human Behavior 28(2), 703–717.

Kivlighan, D. M. & Tibbits, B. M. (2012), ‘Silence is mean and other misconceptions of group counseling trainees: Identifying errors of commission and omission in trainees’ knowledge structures’, Group Dymanmics: Theory, Research and Practice 16(1), 14–34.

Koul, R., Clariana, R. B. & Salehi, R. (2005), ‘Comparing several human and computer-based methods for scoring concept maps and essays’, Journal of Educational Computing Research 32(3), 227–239.

Lau, W. W. & Yuen, A. H. (2010), ‘Promoting conceptual change of learning sorting algorithm through the diagnosis of mental models: The effects of gender and learning styles’, Computers & Education 54(1), 275–288.

McGaghie, W. C., McCrimmon, D. R., Mitchell, G., Thompson, J. A. & Ravitch, M. M. (2000), ‘Quantitative concept mapping in pulmonary physiology: Comparison of student and faculty knowledge structures’, American Journal of Physiology - Advances in Physiology Education 23(1), 72–81.

Moni, R. W., Beswick, E. & Moni, K. B. (2005), ‘Using student feedback to construct an assessment rubric for a concept map in physiology’, American Journal of Physiology - Advances in Physiology Education 29(4), 197–203.

doi: 10.1152/advan.00066.2004.

Nash, J. G. & Nash, J. M. (2003), ‘A structural representation of migraine diagnostic criteria: The experts’ view’, Headache: The Journal of Head and Face Pain 43(4), 322–329.

Rowe, A. L., Cooke, N. J., Hall, E. P. & Halgren, T. L. (1996), ‘Toward an on-line knowledge assessment methodology: Building on the relationship between knowing and doing’, Journal of Experimental Psychology: Applied 2(1), 31–47. doi: 10.1037/1076-898X.2.1.31.

Sarwar, G. S. (2012), Comparing the Effect of Reflections, Written Exercises, and Multimedia Instruction to Address Learners’ Misconceptions Using Structural Assessment of Knowledge, PhD thesis, University of Ottawa.

Schvaneveldt, R. W., ed. (1990), Pathfinder Associative Networks: Studies in Knowledge Organization, Ablex Pub, Norwood, N.J.

Taricani, E. & Clariana, R. (2006), ‘A technique for automatically scoring openended concept maps’, Educational Technology Research and Development 54(1), 65–82.

Trumpower, D. L. & Sarwar, G. S. (2010), ‘Effectiveness of structural feedback provided by pathfinder networks’, Journal of Educational Computing Research 43(1), 7–24.

Trumpower, D. L., Filiz, M. & Sarwar, G. S. (2014), Assessment for learning using digital knowledge maps, in ‘Digital Knowledge Maps in Education’, Springer, pp. 221–237.

Von Minden, A. M., Walls, R. T. & Nardi, A. H. (1998), ‘Charting the links between mathematics content and pedagogy concepts: Cartographies of cognition’, Journal of Experimental Education 66(4), 339–358.

Wilson, J. M. (1998), ‘Differences in knowledge networks about acids and bases of year-12, undergraduate and postgraduate chemistry students’, Research in Science Education 28(4), 429–446.

How to Cite

APA

Filiz, M., Trumpower, D., & Vanapalli, A. (2014). Exploring the Mobile Structural Assessment Tool: Concept Maps for Learning Website. Revista Colombiana de Estadística, 37(2Spe), 297–317. https://doi.org/10.15446/rce.v37n2spe.47939

ACM

[1]
Filiz, M., Trumpower, D. and Vanapalli, A. 2014. Exploring the Mobile Structural Assessment Tool: Concept Maps for Learning Website. Revista Colombiana de Estadística. 37, 2Spe (Jul. 2014), 297–317. DOI:https://doi.org/10.15446/rce.v37n2spe.47939.

ACS

(1)
Filiz, M.; Trumpower, D.; Vanapalli, A. Exploring the Mobile Structural Assessment Tool: Concept Maps for Learning Website. Rev. colomb. estad. 2014, 37, 297-317.

ABNT

FILIZ, M.; TRUMPOWER, D.; VANAPALLI, A. Exploring the Mobile Structural Assessment Tool: Concept Maps for Learning Website. Revista Colombiana de Estadística, [S. l.], v. 37, n. 2Spe, p. 297–317, 2014. DOI: 10.15446/rce.v37n2spe.47939. Disponível em: https://revistas.unal.edu.co/index.php/estad/article/view/47939. Acesso em: 18 may. 2022.

Chicago

Filiz, Mehmet, David Trumpower, and Arun Vanapalli. 2014. “Exploring the Mobile Structural Assessment Tool: Concept Maps for Learning Website”. Revista Colombiana De Estadística 37 (2Spe):297-317. https://doi.org/10.15446/rce.v37n2spe.47939.

Harvard

Filiz, M., Trumpower, D. and Vanapalli, A. (2014) “Exploring the Mobile Structural Assessment Tool: Concept Maps for Learning Website”, Revista Colombiana de Estadística, 37(2Spe), pp. 297–317. doi: 10.15446/rce.v37n2spe.47939.

IEEE

[1]
M. Filiz, D. Trumpower, and A. Vanapalli, “Exploring the Mobile Structural Assessment Tool: Concept Maps for Learning Website”, Rev. colomb. estad., vol. 37, no. 2Spe, pp. 297–317, Jul. 2014.

MLA

Filiz, M., D. Trumpower, and A. Vanapalli. “Exploring the Mobile Structural Assessment Tool: Concept Maps for Learning Website”. Revista Colombiana de Estadística, vol. 37, no. 2Spe, July 2014, pp. 297-1, doi:10.15446/rce.v37n2spe.47939.

Turabian

Filiz, Mehmet, David Trumpower, and Arun Vanapalli. “Exploring the Mobile Structural Assessment Tool: Concept Maps for Learning Website”. Revista Colombiana de Estadística 37, no. 2Spe (July 1, 2014): 297–317. Accessed May 18, 2022. https://revistas.unal.edu.co/index.php/estad/article/view/47939.

Vancouver

1.
Filiz M, Trumpower D, Vanapalli A. Exploring the Mobile Structural Assessment Tool: Concept Maps for Learning Website. Rev. colomb. estad. [Internet]. 2014Jul.1 [cited 2022May18];37(2Spe):297-31. Available from: https://revistas.unal.edu.co/index.php/estad/article/view/47939

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CrossRef citations1

1. Teresa Coma-Rosello, Antonio Aguelo-Arguis, Pedro Alvarez, Cecilia Sanz, Sandra Baldassarri. (2018). Analysis of Innovative Approaches in the Class Using Conceptual Maps and Considering the Learning Styles of Students. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, 13(4), p.120. https://doi.org/10.1109/RITA.2018.2879388.


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