Javier Bilbao, Olatz García, Carolina Rebollar, Eugenio Bravo, Concepción Varela



Concepts characterization and competence assessment for Computational Thinking

pdf PDF


Computational Thinking can be considered as a whole where three important parts are associated in order to complete a transversal competence. These parts are the own competences of Computational Thinking, the attitudes or dispositions that are essential dimensions of Computational Thinking and that support it, and the concepts that are used and developed when you work the Computational Thinking. The transversal competence is the own Computational Thinking. But all competences must be measured in some way if we want to apply them properly and obtain conclusions in order to improve the teaching-learning process. In this paper we present Computational Thinking and its three fundamental parts and we discuss some methods to assess all the process


Computational Thinking, assessment, education, teaching, learning, transversal competence, concepts.


[1] G. Kozhuharova, Development of technology for teacher training in mathematics and natural sciences based on European best practices, Trakia Journal of Sciences, vol. 9, no 4, 2011, pp. 92-96.

[2] C. Echebarria, J.M. Barrutia, I. Aguado, Innovation in universities: Collective intelligence systems and collaborative learning, International Journbal for Knowledge, Science and Technology, no. 4, vol. 1, April 2012, pp. 48-54.

[3] B. Montero-Fleta, Looking beyond linguistic outcomes: active learning and professional competencies in higher education, Procedia - Social and Behavioral Sciences, vol. 46, 2012, pp. 1812-1819.

[4] M.J. Pérez-Peñalver, L.E. Aznar-Mas, Students’ feedback from co-assessment of group work in Civil Engineering, International Journbal for Knowledge, Science and Technology, no. 4, vol. 1, April 2012, pp. 17- 24.

[5] European Parliament Council. Recommendations (Recommendation of the European Parliament and of the Council of 23 April 2008 on the establishment of the European Qualifications Framework for lifelong learning). Official Journal C 111, April 2008, pp. 1-7. Available: http://eurlex. europa.eu/LexUriServ/LexUriServ.do?uri= OJ:C:2008:111:0001:0007:EN:PDF

[6] J. Savery and T. M. Duffy, Problem based learning: An instructional model and its constructivist framework, in Designing constructivist learning environments, B.G. Wilson, Ed. Englewood Cliffs, NJ: Educational Technology Publications, 1996.

[7] E. Markopoulos, J. Bilbao, J.C. Panayiotopoulos, Selecting software Project implementation process models based on software project goals, ENMA International Conference 2007, pp. 373-380, ISBN 978-84- 95809-29-2, Bilbao, July 2007.

[8] European Parliament and the Council. Recommendation of the European Parliament and of the Council of 18 December 2006 on key competences for lifelong learning. Official Journal of the European Union, L394/310, 2006.

[9] European Commission. Europe 2020: A strategy for smart, sustainable and inclusive growth, COM (2010) 2020, 2010.

[10] European Commission. A Digital Agenda for Europe, COM (2010) 245 final, 2010.

[11] K. Aleksic-Maslac, B. Sinkovic, P. Vranesic, Influence of gamification on student engagement on a course - Information and Communication Technologies, WSEAS Transactions on Advances in Engineering Education, vol. 14, 2017, pp. 113-122.

[12] R. Vuorikari, Y. Punie, S. Carretero Gomez, G. Van den Brande, DigComp 2.0: The Digital Competence Framework for Citizens. Update Phase 1: The Conceptual Reference Model. Luxembourg Publication Office of the European Union. EUR 27948 EN. doi:10.2791/11517, 2016.

[13] J. Bilbao, E. Bravo, O. García, C. Varela, C. Rebollar, Assessment of Computational Thinking notions in Secondary School, Baltic Journal of Modern Computing, Vol. 5, No. 4, 2017, pp. 391-397. http://dx.doi.org/10.22364/ bjmc.2017.5.4.05

[14] S. Papert, An exploration in the space of Mathematics Education, International Journal of Computers for Mathematics, Vol. 1, No. 1, 1996, pp. 95-123.

[15] J.M. Wing, Computational thinking. Communications of the ACM, Vol. 49, No. 3, 2006, pp. 33-35.

[16] V. Dagienė, G. Stupuriene, Bebras - a Sustainable Community Building Model for the Concept Based Learning of Informatics and Computational Thinking, Informatics in Education, Vol. 15, No. 1, 2016, pp. 25-44.

[17] J. Liu, L. Wang, Computational Thinking in Discrete Mathematics, IEEE 2nd International Workshop on Education Technology and Computer Science, 2010, pp. 413-416.

[18] M. González, M. Fernando, L. C. Herrero, M. A. Martín, I. Mozo, C. Quintano, Análisis de metodologías y métodos de evaluación para el desarrollo de competencia, Actas de las II Jornadas Internacionales UPM sobre Innovación Educativa y Convergencia Europea, Madrid, 2008.

[19] F. Dochy, M. Segers, D. Sluijsmans, The Use of Self-, Peer and Co-assessment in Higher Education: A Review, Studies in Higher Education, vol 24, no 3, 1999, pp. 331-350.

[20] M.A. Andreu-Andrés, Los alumnos como evaluadores en el proceso de enseñanzaaprendizaje, Revista Iberoamericana de Educación, Vol. 50, No. 1, 2009, pp. 1-10.

[21] F. Watts, A. García-Carbonell, A., J. A. Llorens, Introduccion in La evaluación compartida: investigación multidisciplinar, F. Watts and A. García-Carbonell, (ed.) Ed. Universidad Politècnica de Valencia, 2006, pp. 1-9 Available: http://www.upv.es/gie/LinkedDocuments/desca rgar%20libro.pdf

[22] D.W. Johnson, R.T. Johnson, M.B. Stanne, Cooperative learning methods: A metaanalysis, Cooperative Learning Center at the University of Minnesota, 2000. Available: http://www.clcrc.com/pages/cl-methhods.html

[23] T.J. Crooks, The impact of classroom evaluation practice on students, Review of Educational Research, Vol 58, No. 4, 1988, pp. 438-481.

[24] M.J. Pérez Peñalver, Experiencias de evaluación de trabajo en grupo en el área de matemáticas. In La evaluación compartida: investigación multidisciplinar, F. Watts and A. García-Carbonell, (ed.) Ed. Universidad Politècnica de Valencia, 2006, pp. 91-107. Available: http://www.upv.es/gie/LinkedDocuments/desca rgar%20libro.pdf

[25] J.L. Lemke, Investigar para el futuro de la educación científica: nuevas formas de aprender, nuevas formas de vivir, Enseñanza de las Ciencias, Vol. 24, No. 1, 2006, pp. 5-12. Available: http://ensciencias.uab.es/revistes/24- 1/005-012.pdf

[26] K. Struyven, F. Dochy, S. Janssens, Students’ perceptions about evaluation and assessment in higher education: a review, Assessment & Evaluation in Higher Education, Vol. 30, No. 4, 2005, pp. 325–341.

[27] M. Segers, F. Dochy, New assessment forms in problem-based learning: the value-added of the students’ perspective, Studies in Higher Education, Vol. 26, 2001, pp. 327-33.

[28] S.J. Hanrahan, G. Isaacs, Assessing self- and peer- assessment: The students' views, Higher Education Research & Development, Vol. 20, No. 1, 2001, pp. 53-70.

[29] P. Maclaughlin, N. Simpson, Peer Assessment in first year university: how the students feel, Studies in Educational Evaluation, Vol. 30, 2004, pp. 135-149.

[30] T. Gatfield, Examining student satisfaction with group projects and peer assessment, Assessment & Evaluation in Higher Education, Vol. 24, No. 4, 1999, pp. 365-377.

[31] M. Dorling, M. Walker, Computing Progression Pathways, available at https://www.hoddereducation.co.uk/Compute- IT/ProgressionPathwaysGrid, 2014.

[32] K. Brennan, M. Resnick, New frameworks for studying and assessing the development of computational thinking, American Educational Research Association meeting, Vancouver, BC, Canada, 2012.

Cite this paper

Javier Bilbao, Olatz García, Carolina Rebollar, Eugenio Bravo, Concepción Varela. (2018) Concepts characterization and competence assessment for Computational Thinking. International Journal of Computers, 3, 97-104


Copyright © 2018 Author(s) retain the copyright of this article.
This article is published under the terms of the Creative Commons Attribution License 4.0