Model of a Recommendation System Based on the Context from the Analysis of Static Code for the Development of Computational Thinking: A Web Programming Case
Abstract This project proposes a recommendation model for educational content based on the context of a user, which uses a context model that incorporates the role, tasks, programming exercises and their application to the problem of recommendation. The recommendations are made on the basis of the estimate of the difference between the current level of knowledge of a user in front of the skills required in their work context. The experiments developed in the context of the student, show that, using a model of probabilistic reasoning helps to get better recommendations of educational content, according to the missing competences of a student on an issue that needs to learn, which seeks to standardization for recommendation systems.
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Altuna C. E. J., & Estrada, L. G. (2013). Generación de pistas durante el aprendizaje de la programación para concursos usando el análisis estático y dinámico de las soluciones. Ingeniare. Revista chilena de ingeniería, 21(2), 205-217. http://dx.doi.org/10.4067/S0718-33052013000200005
Baojiang, C., Jiansong, L., Tao, G., Jianxin, W., & Ding, M. (2010). Code Comparison System based on Abstract Syntax Tree 2010 3rd IEEE International Conference on Broadband Network and Multimedia Technology (IC-BNMT) (Beijing, China, 26-28 Oct. 2010) (pp. 668-673). USA: IEEE. http://dx.doi.org/10.1109/ICBNMT.2010.5705174
Batallanos, C. A., Poccohuanca, O. Q., & Sayco, S. C. C. (2017). Modelo de sistema de recomendación de objetos para incentivar el desarrollo del pensamiento computacional. Revista Referencia Pedagógica, 5(1), 96-108.
Carrillo, G., & Ochoa, X. (2013). Recomendación de objetos de aprendizaje basado en el perfil del usuario y la información de atención contextualizada. Paper presented at the 8th Latin American Conference on Learning Objects and Technologies-LACLO, Valdivia, Chile.
Cazella, S. C., Nunes, M. A. S. N., & Reategui, E. (2010). A Ciência da Opinião: Estado da arte em Sistemas de Recomendação. Paper presented at the Jornada de Atualização de Informática, JAI 2010 - CSBC2010, Rio de Janeiro, Brazil.
Chen, M., Tong, M., Liu, C., Han, M., & Xia, Y. (2017). Recommendation of Learning Path Using an Improved ACO Based on Novel Coordinate System 2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI) (Hamamatsu, Japan, 9-13 July 2017) (pp. 747-753). USA: IEEE. http://dx.doi.org/10.1109/IIAI-AAI.2017.90
D’Abreu, J. V. V., & Villalba-Condori, K. O. (2017). Educación y Robótica Educativa. Revista de Educación a Distancia, (54). http://dx.doi.org/10.6018/red/54/11
Dey, A. K. (2001). Understanding and using context. Personal and Ubiquitous Computing, 5(1), 4-7. http://dx.doi.org/10.1007/s007790170019
Fidalgo-Blanco, Á., Sein-Echaluce, M. L., & García-Peñalvo, F. J. (2014). Knowledge spirals in higher education teaching innovation. International Journal of Knowledge Management, 10(4), 16-37. http://dx.doi.org/10.4018/ijkm.2014100102
Fidalgo-Blanco, Á., Sein-Echaluce, M. L., & García-Peñalvo, F. J. (2015). Epistemological and ontological spirals: From individual experience in educational innovation to the organisational knowledge in the university sector. Program: Electronic library and information systems, 49(3), 266-288. http://dx.doi.org/10.1108/PROG-06-2014-0033
Frango Silveira, I., & Villalba-Condori, K. O. (2018). An Open Perspective for Educational Games. Journal of Information Technology Research, 11(1), 18-28. http://dx.doi.org/10.4018/JITR.2018010102
García-Peñalvo, F. J. (2016). A brief introduction to TACCLE 3 – Coding European Project.
In F. J. García-Peñalvo & J. A. Mendes (Eds.), 2016 International Symposium on Computers in Education (SIIE 16). USA: IEEE. http://dx.doi.org/10.1109/SIIE.2016.7751876
García-Peñalvo, F. J., & Mendes, J. A. (2018). Exploring the computational thinking effects in pre-university education. Computers in Human Behavior, 80, 407-411. http://dx.doi.org/10.1016/j.chb.2017.12.005
García-Peñalvo, F. J., Reimann, D., Tuul, M., Rees, A., & Jormanainen, I. (2016). An overview of the most relevant literature on coding and computational thinking with emphasis on the relevant issues for teachers. Belgium: TACCLE3 Consortium. http://doi.org/10.5281/zenodo.165123
Laboratoria (2015). Code that transforms. Retrieved from http://laboratoria.la.
Lavonen, J., Korhonen, T., & Villalba-Condori, K. O. (2018). Descripción de las estrategias colaborativas frente a los retos de la educación finlandesa / Description of collaborative strategies to meet the challenges of Finnish education. Revista Referencia Pedagógica, 6(1), 86-100.
Mahmood, T., & Ricci, F. (2009). Improving recommender systems with adaptive conversational strategies. Proceedings of the 20th ACM conference on Hypertext and hypermedia, HT ‘09 (Torino, Italy — June 29 - July 01, 2009) (pp. 73-82). New York, NY, USA: ACM. http://dx.doi.org/10.1145/1557914.1557930
Motelet, O., & Baloian, N. (2006). Hybrid System for Generating Learning Object Metadata. Sixth IEEE International Conference on Advanced Learning Technologies (ICALT’06) (Kerkrade, Netherlands, 5-7 July 2006) (pp. 563-567). USA: IEEE. http://dx.doi.org/10.1109/ICALT.2006.1652504
Newcomb, P. (2005). Abstract Syntax Tree Metamodel Standard ASTM Tutorial 1.0. Retrieved from https://goo.gl/SdF9ky.
Niemann, K., Scheffel, M., Friedrich, M., Kirschenmann, U., Schmitz, H.-C., & Wolpers, M. (2010). Usage-based object similarity. Journal of Universal Computer Science, 16(16), 2272–2290. http://dx.doi.org/10.3217/jucs-016-16-2272
Nist (2013). Common evaluation measures. The Twenty-Second Text REtrieval Conference (TREC 2013) Proceedings. https://goo.gl/Hjp9MM.
Owens, D., & Anderson, M. (2013). A generic framework for automated Quality Assurance of software models - Application of an Abstract Syntax Tree 2013 Science and Information Conference, SAI, (London, UK, 7-9 Oct. 2013) (pp. 207-211). USA: IEEE.
Parr, T. (2014). ANTLR. Retrieved from http://www.antlr.org/.
Rodríguez, P. A., Duque, N. D., & Ovalle, D. A. (2016). Modelo híbrido de recomendación Adaptativa de Objetos de Aprendizaje basado en Perfiles de Usuario. Formación universitaria, 9(4), 83–94. http://dx.doi.org/10.4067/S0718-50062016000400010
Saraswat, N., Ghosh, H., Agrawal, M., & Narayanan, U. (2015). Contextual Recommendation of Educational Contents. In C. Conati, N. Heffernan, A. Mitrovic, & M. F. Verdejo (Eds.), Artificial Intelligence in Education. Proceedings of the 17th International Conference, AIED 2015, Madrid, Spain, June 22-26, 2015 (pp. 439-448). Cham: Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-19773-9_44
Shankar, U., Talwar, K., Foster, J. S., & Wagner, D. (2001). Detecting format string vulnerabilities with type qualifiers. Proceedings of the 10th conference on USENIX Security Symposium, SSYM’01 (Washington, D.C. — August 13 - 17, 2001) (Vol. 10, Article No. 16). Washington, D.C.: USENIX Association.
Shroff, G., Dey, L., & Ghosh, H. (2014). Enterprise Contextual Intelligence. Proceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), WI-IAT ‘14 (Vol. 2, pp. 202-209). USA: IEEE Computer Society. http://dx.doi.org/10.1109/wi-iat.2014.99
TACCLE 3 Consortium. (2017). TACCLE 3: Coding Erasmus + Project website. Retrieved from https://goo.gl/f4QZUA
Tripp, A. (2006). Manual Tree Walking Is Better Than Tree Grammars. Retrieved from https://goo.gl/6vyFKn.
Vera, J., Mamani, A. O., & Villalba-Condori, K. O. (2015). Modelo de sistema de recomendación de Objetos de Aprendizaje en dispositivos móviles, caso: Desarrollo del pensamiento computacional. XX Congreso Internacional de Informática Educativa, TISE 2015. Nuevas Ideas en Informática Educativa (Santiago, Chile, 1-3 de diciembre de 2015) (pp. 730-734).
Villalba-Condori, K. O. (2018). Teaching Formation to develop Computational thinking. In F. J. García-Peñalvo (Ed.), Global Implications of Emerging Technology Trends (pp. 59-72). Hershey PA, USA: IGI Global. http://dx.doi.org/10.4018/978-1-5225-4944-4.ch004
Villalba-Condori, K. O., Castro Cuba, S., Deco, C., Bender, C., & García-Peñalvo, F. J. (2017). A Recommender System of Open Educational Resources based on the Purpose of Learning. In A. Díaz, A. Casali, M. Chacón Rivas, & A. Silva Sprock (Eds.), Proceedings of 2017 Twelfth Latin American Conference on Learning Technologies - LACLO 2017 (9-12 October 2017, La Plata, Argentina) (pp. 104-107). EEUU: IEEE. http://dx.doi.org/10.1109/LACLO.2017.8120899
Wassermann, G., & Su, Z. (2007). Sound and precise analysis of web applications for injection vulnerabilities. ACM SIGPLAN Notices, 42(6), 32-41. http://dx.doi.org/10.1145/1273442.1250739
Wiley, D. A. (2002). Connecting learning objects to instructional design theory: A definition, a metaphor, and a taxonomy. In D. A. Wiley (Ed.), The Instructional Use of Learning Objects: Agency for Instructional Technology.
Worsley, M., & Blikstein, P. (2013). Programming Pathways: A Technique for Analyzing Novice Programmers’ Learning Trajectories. In H. C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.), Artificial Intelligence in Education. Proceedings of the 16th International Conference, AIED 2013, Memphis, TN, USA, July 9-13, 2013 (pp. 844-847). Berlin, Heidelberg: Springer. http://dx.doi.org/10.1007/978-3-642-39112-5_127
Baojiang, C., Jiansong, L., Tao, G., Jianxin, W., & Ding, M. (2010). Code Comparison System based on Abstract Syntax Tree 2010 3rd IEEE International Conference on Broadband Network and Multimedia Technology (IC-BNMT) (Beijing, China, 26-28 Oct. 2010) (pp. 668-673). USA: IEEE. http://dx.doi.org/10.1109/ICBNMT.2010.5705174
Batallanos, C. A., Poccohuanca, O. Q., & Sayco, S. C. C. (2017). Modelo de sistema de recomendación de objetos para incentivar el desarrollo del pensamiento computacional. Revista Referencia Pedagógica, 5(1), 96-108.
Carrillo, G., & Ochoa, X. (2013). Recomendación de objetos de aprendizaje basado en el perfil del usuario y la información de atención contextualizada. Paper presented at the 8th Latin American Conference on Learning Objects and Technologies-LACLO, Valdivia, Chile.
Cazella, S. C., Nunes, M. A. S. N., & Reategui, E. (2010). A Ciência da Opinião: Estado da arte em Sistemas de Recomendação. Paper presented at the Jornada de Atualização de Informática, JAI 2010 - CSBC2010, Rio de Janeiro, Brazil.
Chen, M., Tong, M., Liu, C., Han, M., & Xia, Y. (2017). Recommendation of Learning Path Using an Improved ACO Based on Novel Coordinate System 2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI) (Hamamatsu, Japan, 9-13 July 2017) (pp. 747-753). USA: IEEE. http://dx.doi.org/10.1109/IIAI-AAI.2017.90
D’Abreu, J. V. V., & Villalba-Condori, K. O. (2017). Educación y Robótica Educativa. Revista de Educación a Distancia, (54). http://dx.doi.org/10.6018/red/54/11
Dey, A. K. (2001). Understanding and using context. Personal and Ubiquitous Computing, 5(1), 4-7. http://dx.doi.org/10.1007/s007790170019
Fidalgo-Blanco, Á., Sein-Echaluce, M. L., & García-Peñalvo, F. J. (2014). Knowledge spirals in higher education teaching innovation. International Journal of Knowledge Management, 10(4), 16-37. http://dx.doi.org/10.4018/ijkm.2014100102
Fidalgo-Blanco, Á., Sein-Echaluce, M. L., & García-Peñalvo, F. J. (2015). Epistemological and ontological spirals: From individual experience in educational innovation to the organisational knowledge in the university sector. Program: Electronic library and information systems, 49(3), 266-288. http://dx.doi.org/10.1108/PROG-06-2014-0033
Frango Silveira, I., & Villalba-Condori, K. O. (2018). An Open Perspective for Educational Games. Journal of Information Technology Research, 11(1), 18-28. http://dx.doi.org/10.4018/JITR.2018010102
García-Peñalvo, F. J. (2016). A brief introduction to TACCLE 3 – Coding European Project.
In F. J. García-Peñalvo & J. A. Mendes (Eds.), 2016 International Symposium on Computers in Education (SIIE 16). USA: IEEE. http://dx.doi.org/10.1109/SIIE.2016.7751876
García-Peñalvo, F. J., & Mendes, J. A. (2018). Exploring the computational thinking effects in pre-university education. Computers in Human Behavior, 80, 407-411. http://dx.doi.org/10.1016/j.chb.2017.12.005
García-Peñalvo, F. J., Reimann, D., Tuul, M., Rees, A., & Jormanainen, I. (2016). An overview of the most relevant literature on coding and computational thinking with emphasis on the relevant issues for teachers. Belgium: TACCLE3 Consortium. http://doi.org/10.5281/zenodo.165123
Laboratoria (2015). Code that transforms. Retrieved from http://laboratoria.la.
Lavonen, J., Korhonen, T., & Villalba-Condori, K. O. (2018). Descripción de las estrategias colaborativas frente a los retos de la educación finlandesa / Description of collaborative strategies to meet the challenges of Finnish education. Revista Referencia Pedagógica, 6(1), 86-100.
Mahmood, T., & Ricci, F. (2009). Improving recommender systems with adaptive conversational strategies. Proceedings of the 20th ACM conference on Hypertext and hypermedia, HT ‘09 (Torino, Italy — June 29 - July 01, 2009) (pp. 73-82). New York, NY, USA: ACM. http://dx.doi.org/10.1145/1557914.1557930
Motelet, O., & Baloian, N. (2006). Hybrid System for Generating Learning Object Metadata. Sixth IEEE International Conference on Advanced Learning Technologies (ICALT’06) (Kerkrade, Netherlands, 5-7 July 2006) (pp. 563-567). USA: IEEE. http://dx.doi.org/10.1109/ICALT.2006.1652504
Newcomb, P. (2005). Abstract Syntax Tree Metamodel Standard ASTM Tutorial 1.0. Retrieved from https://goo.gl/SdF9ky.
Niemann, K., Scheffel, M., Friedrich, M., Kirschenmann, U., Schmitz, H.-C., & Wolpers, M. (2010). Usage-based object similarity. Journal of Universal Computer Science, 16(16), 2272–2290. http://dx.doi.org/10.3217/jucs-016-16-2272
Nist (2013). Common evaluation measures. The Twenty-Second Text REtrieval Conference (TREC 2013) Proceedings. https://goo.gl/Hjp9MM.
Owens, D., & Anderson, M. (2013). A generic framework for automated Quality Assurance of software models - Application of an Abstract Syntax Tree 2013 Science and Information Conference, SAI, (London, UK, 7-9 Oct. 2013) (pp. 207-211). USA: IEEE.
Parr, T. (2014). ANTLR. Retrieved from http://www.antlr.org/.
Rodríguez, P. A., Duque, N. D., & Ovalle, D. A. (2016). Modelo híbrido de recomendación Adaptativa de Objetos de Aprendizaje basado en Perfiles de Usuario. Formación universitaria, 9(4), 83–94. http://dx.doi.org/10.4067/S0718-50062016000400010
Saraswat, N., Ghosh, H., Agrawal, M., & Narayanan, U. (2015). Contextual Recommendation of Educational Contents. In C. Conati, N. Heffernan, A. Mitrovic, & M. F. Verdejo (Eds.), Artificial Intelligence in Education. Proceedings of the 17th International Conference, AIED 2015, Madrid, Spain, June 22-26, 2015 (pp. 439-448). Cham: Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-19773-9_44
Shankar, U., Talwar, K., Foster, J. S., & Wagner, D. (2001). Detecting format string vulnerabilities with type qualifiers. Proceedings of the 10th conference on USENIX Security Symposium, SSYM’01 (Washington, D.C. — August 13 - 17, 2001) (Vol. 10, Article No. 16). Washington, D.C.: USENIX Association.
Shroff, G., Dey, L., & Ghosh, H. (2014). Enterprise Contextual Intelligence. Proceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), WI-IAT ‘14 (Vol. 2, pp. 202-209). USA: IEEE Computer Society. http://dx.doi.org/10.1109/wi-iat.2014.99
TACCLE 3 Consortium. (2017). TACCLE 3: Coding Erasmus + Project website. Retrieved from https://goo.gl/f4QZUA
Tripp, A. (2006). Manual Tree Walking Is Better Than Tree Grammars. Retrieved from https://goo.gl/6vyFKn.
Vera, J., Mamani, A. O., & Villalba-Condori, K. O. (2015). Modelo de sistema de recomendación de Objetos de Aprendizaje en dispositivos móviles, caso: Desarrollo del pensamiento computacional. XX Congreso Internacional de Informática Educativa, TISE 2015. Nuevas Ideas en Informática Educativa (Santiago, Chile, 1-3 de diciembre de 2015) (pp. 730-734).
Villalba-Condori, K. O. (2018). Teaching Formation to develop Computational thinking. In F. J. García-Peñalvo (Ed.), Global Implications of Emerging Technology Trends (pp. 59-72). Hershey PA, USA: IGI Global. http://dx.doi.org/10.4018/978-1-5225-4944-4.ch004
Villalba-Condori, K. O., Castro Cuba, S., Deco, C., Bender, C., & García-Peñalvo, F. J. (2017). A Recommender System of Open Educational Resources based on the Purpose of Learning. In A. Díaz, A. Casali, M. Chacón Rivas, & A. Silva Sprock (Eds.), Proceedings of 2017 Twelfth Latin American Conference on Learning Technologies - LACLO 2017 (9-12 October 2017, La Plata, Argentina) (pp. 104-107). EEUU: IEEE. http://dx.doi.org/10.1109/LACLO.2017.8120899
Wassermann, G., & Su, Z. (2007). Sound and precise analysis of web applications for injection vulnerabilities. ACM SIGPLAN Notices, 42(6), 32-41. http://dx.doi.org/10.1145/1273442.1250739
Wiley, D. A. (2002). Connecting learning objects to instructional design theory: A definition, a metaphor, and a taxonomy. In D. A. Wiley (Ed.), The Instructional Use of Learning Objects: Agency for Instructional Technology.
Worsley, M., & Blikstein, P. (2013). Programming Pathways: A Technique for Analyzing Novice Programmers’ Learning Trajectories. In H. C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.), Artificial Intelligence in Education. Proceedings of the 16th International Conference, AIED 2013, Memphis, TN, USA, July 9-13, 2013 (pp. 844-847). Berlin, Heidelberg: Springer. http://dx.doi.org/10.1007/978-3-642-39112-5_127
Vera, J., Villalba-Condori, K., & Castro Cuba-sayco, S. (2018). Model of a Recommendation System Based on the Context from the Analysis of Static Code for the Development of Computational Thinking: A Web Programming Case. Education in The Knowledge Society, 19(2), 103–126. https://doi.org/10.14201/eks2018192103126
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