يعرض 1 - 20 نتائج من 50 نتيجة بحث عن '"curso de programación"', وقت الاستعلام: 0.82s تنقيح النتائج
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    المصدر: Revista Científica; Vol. 46 No. 1 (2023): January-April 2023; 134-146 ; Revista Científica; Vol. 46 Núm. 1 (2023): Enero-Abril 2023; 134-146 ; 2344-8350 ; 0124-2253

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    Relation: https://revistas.udistrital.edu.co/index.php/revcie/article/view/20095/18834; https://revistas.udistrital.edu.co/index.php/revcie/article/view/20095/19145; Alhazbi, S., Hassanh, M. (2013). Fostering self-regulated learning in introductory computer programming course. En WCCCE 2013: Western Canadian Conference on Computing Education.; Allen, S., Knight, J. (2009). A method for collaboratively developing and validating a rubric. International Journal for the Scholarship of Teaching and Learning, 3(2), e210. https://doi.org/10.20429/ijsotl.2009.030210; Allen, D., Tanner, K. (2006). Rubrics: Tools for making learning goals and evaluation criteria explicit for both teachers and learners. CBE—Life Sciences Education, 5(3), 197-203. https://doi.org/10.1187/cbe.06-06-0168; Ayala, G., Ortiz, M., Osorio, M. (2005). Agent modelling for CSCL environments using answer sets programming. En Sixth Mexican International Conference on Computer Science (ENC’05), 214-221. https://doi.org/10.1109/ENC.2005.9; Bhuyan, M. H., Tamir, A. (2020). Evaluating COs of computer programming course for OBE-based BSc in EEE program. International Journal of Learning and Teaching, 12(2), 86-99. https://doi.org/10.18844/ijlt.v12i2.4576; Blikstein, P., Worsley, M., Piech, C., Sahami, M., Cooper, S., Koller, D. (2014). Programming pluralism: Using learning analytics to detect patterns in the learning of computer programming. Journal of the Learning Sciences, 23(4), 561-599. https://doi.org/10.1080/10508406.2014.954750; Böhne, A., Faltin, N., Wagner, B. (2007). Distributed group work in a remote programming laboratory—A comparative study. International Journal of Engineering Education, 23(1), 162-170; Carbonaro, A. (2019). Good practices to influence engagement and learning outcomes on a traditional introductory programming course. Interactive Learning Environments, 27(7), 919-926. https://doi.org/10.1080/10494820.2018.1504307; Carvajal-Ortiz, L., Florian-Gaviria, B., Díaz, J. F. (s.f.). Modelos, métodos y prototipo de software para el apoyo del diseño, evaluación y análisis de aprendizajes en gestión curricular de la educación superior basada en competencias.; Cateté, V., Snider, E., Barnes, T. (2016). Developing a rubric for a creative CS principles lab. En Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education, 290-295. https://doi.org/10.1145/2899415.2899449; Chen, J., Wang, M., Kirschner, P. A., Tsai, C.-C. (2018). The role of collaboration, computer use, learning environments, and supporting strategies in CSCL: A meta-analysis. Review of Educational Research, 88(6), 799-843. https://doi.org/10.3102/0034654318791584; Chen, Y.-H., Lee, W.-C., Tseng, C.-H., Deng, L. Y., Chang, C.-Y., Lee, L.-H. (2020). Cognitive learning performance assessment and analysis with CSCL applied on the NetGuru platform and CSPL applied on the TAoD platform for the network experiment class. Journal of Supercomputing, 76(1), 16-46. https://doi.org/10.1007/s11227-019-02836-3; Clancy, M., Titterton, N., Ryan, C., Slotta, J., Linn, M. (2003). New roles for students, instructors, and computers in a lab-based introductory programming course. ACM SIGCSE Bulletin, 35(1), 132-136. https://doi.org/10.1145/792548.611951; Collins, D., Weber, J., Zambrano, R. (2014). Teaching business ethics online: Perspectives on course design, delivery, student engagement, and assessment. Journal of Business Ethics, 125, 513-529. https://doi.org/10.1007/s10551-013-1932-7; Coto, M., Mora, S., Collazos, C. (2014). Evaluation of the collaboration process from an individual and collaborative perspective. En Proceedings of the XV International Conference on Human Computer Interaction, 1-9. https://doi.org/10.1145/2662253.2662342; Demaidi, M. N., Qamhieh, M., Afeefi, A. (2019). Applying blended learning in programming courses. IEEE Access, 7, 156824-156833. https://doi.org/10.1109/ACCESS.2019.2949927; Dümmel, N., Westfechtel, B., Ehmann, M. (2018). Effects of a preliminary programming course on students’ performance. ECSEE’18: Proceedings of the 3rd European Conference of Software Engineering Education, 77-86. https://doi.org/10.1145/3209087.3209088; Fiesler, C., Garrett, N., Beard, N. (2020). What do we teach when we teach tech ethics? A syllabi analysis. En Proceedings of the 51st ACM Technical Symposium on Computer Science Education, 289-295. https://doi.org/10.1145/3328778.3366825; Fleming, D. L. (2008). Using best practices in online discussion and assessment to enhance collaborative learning. College Teaching Methods & Styles Journal, 4(10), e5573. https://doi.org/10.19030/ctms.v4i10.5573; García Retana, J. Á. (2011). Modelo educativo basado en competencias: importancia y necesidad. Actualidades Investigativas en Educación, 11(3), e10225. https://doi.org/10.15517/aie.v11i3.10225; Goss, H. (2022). Student learning outcomes assessment in higher education and in academic libraries: A review of the literature. The Journal of Academic Librarianship, 48(2). https://doi.org/10.1016/j.acalib.2021.102485; Hidalgo, C., Bucheli, V. (2019). Modelo soportado en inteligencia artificial para el desarrollo de actividades de aprendizaje activo basadas en colaboración asistida por computador (M-IDEA). En 17th LACCEI International Multi-Conference for Engineering, Education, and Technology.; Hidalgo, C. G., Bucheli, V. A., Restrepo, F., González, F. A. (2021). Estrategia de enseñanza basada en la colaboración y la evaluación automática de código fuente en un curso de programación CS1. Investigación e Innovación en Ingenierías, 9(1), e4185. https://doi.org/10.17081/invinno.9.1.4185; Kardan, A., Sadeghi, H. (2015). Modeling the learner group formation problem in computer-supported collaborative learning using mathematical programming. En 8th National and 5th International Conference on E-Learning and e-Teaching. https://doi.org/10.1109/ICELET.2014.7040616; Kilgour, P., Northcote, M., Williams, A., Kilgour, A. (2020). A plan for the co-construction and collaborative use of rubrics for student learning. Assessment & Evaluation in Higher Education, 45(1), 140-153. https://doi.org/10.1080/02602938.2019.1614523; Lakas, A., Belkacem, A. N. (2021). A framework for course-embedded assessment for evaluating learning outcomes of a network programming course. En IEEE Global Engineering Education Conference, 989-995. https://doi.org/10.1109/EDUCON46332.2021.9454129; Lämsä, J., Uribe, P., Jiménez, A., Caballero, D., Hämäläinen, R., Araya, R. (2021). Deep networks for collaboration analytics: Promoting automatic analysis of face-to-face interaction in the context of inquiry-based learning. Journal of Learning Analytics, 8(1), e7118. https://doi.org/10.18608/jla.2021.7118; Lee, G., Fong, W. W., Gordon, J. (2013). Blended learning: The view is different from student, teacher, or institution perspective. En Lecture Notes in Computer Science (LNCS, 8038, pp. 356-363). https://doi.org/10.1007/978-3-642-39750-9_33; Llanos, J. M., Hidalgo, C. G., Bucheli, V. A. (2022). Strategy based on computer-supported collaborative learning to form workgroups automatically in an introductory programming course (CS1). Revista Facultad de Ingeniería, 31(61), e14368. https://doi.org/10.19053/01211129.v31.n61.2022.14368; Means, B., Toyama, Y., Murphy, R., Bakia, M., Jones, K. (2009). Evaluation of Evidence-Based Practices in Online Learning: A Meta-Analysis and Review of Online Learning Studies [Grade Thesis]. Centre for Learning Technology, Reino Unido. https://repository.alt.ac.uk/629/; Mehennaoui, Z., Lafifi, Y., Seridi, H., Boudria, A. (2014). A new approach for grouping learners in CSCL systems. En International Conference on Multimedia Computing and Systems, 628-632. https://doi.org/10.1109/ICMCS.2014.6911143; Michel, N., Cater, J. J., Varela, O. (2009). Active versus passive teaching styles: An empirical study of student learning outcomes. Human Resource Development Quarterly, 20(4), 397-418. https://doi.org/10.1002/hrdq.20025; Mustapha, A., Samsudin, N. A., Arbaiy, N., Mohammed, R., Hamid, I. R. (2016). Generic assessment rubrics for computer programming courses. Turkish Online Journal of Educational Technology - TOJET, 15(1), 53-68; OECD iLibrary. (s.f.). Assessment of learning outcomes in higher education: A comparative review of selected practices. OECD Education Working Papers. https://www.oecd-ilibrary.org/content/paper/244257272573; Palmer, M. S., Bach, D. J., Streifer, A. C. (2014). Measuring the promise: A learning-focused syllabus rubric. To Improve the Academy, 33(1), 14-36. https://doi.org/10.1002/tia2.20004; Pereira, F. D., Oliveira, E., Cristea, A., Fernandes, D., Silva, L., Aguiar, G., Alamri, A., Alshehri, M. (2019). Early dropout prediction for programming courses supported by online judges. In S. Isotani, E. Millán, A. Ogan, P. Hastings, B. McLaren, & R. Luckin (Eds.), Artificial Intelligence in Education (vol. 11626, pp. 67-72). Springer. https://doi.org/10.1007/978-3-030-23207-8_13; Powell, L. M., Wimmer, H. (2015). Evaluating the effectiveness of self-created student screencasts as a tool to increase student learning outcomes in a hands-on computer programming course. Information Systems Education Journal, 13(5), 106-111; Sadler, D. R. (2016). Three in-course assessment reforms to improve higher education learning outcomes. Assessment & Evaluation in Higher Education, 41(7), 1081-1099. https://doi.org/10.1080/02602938.2015.1064858; Saito, D., Yajima, R., Washizaki, H., Fukazawa, Y. (2021). Validation of rubric evaluation for programming education. Education Sciences, 11(10), e656. https://doi.org/10.3390/educsci11100656; Saunders, L. (2012). Faculty perspectives on information literacy as a student learning outcome. The Journal of Academic Librarianship, 38(4), 226-236. https://doi.org/10.1016/j.acalib.2012.06.001; Stahl, G., Koschmann, T., Suthers, D. (2006). Computer-Supported Collaborative Learning: An Historical Perspective; Tiantong, M., Teemuangsai, S. (2013). Student team achievement divisions (STAD) technique through the moodle to enhance learning achievement. International Education Studies, 6(4), 85-92. https://doi.org/10.5539/ies.v6n4p85; TrainCom. (s.f.). Indicators of achievement. http://train-com.de/traincom/english/competency/achievement.rsys; Wei, X., Saab, N., Admiraal, W. (2021). Assessment of cognitive, behavioral, and affective learning outcomes in massive open online courses: A systematic literature review. Computers & Education, 163, e104097. https://doi.org/10.1016/j.compedu.2020.104097; Zlatkin-Troitschanskaia, O., Pant, H. A., Coates, H. (2016). Assessing student learning outcomes in higher education: Challenges and international perspectives. Assessment & Evaluation in Higher Education, 41(5), 655-661. https://doi.org/10.1080/02602938.2016.1169501; https://revistas.udistrital.edu.co/index.php/revcie/article/view/20095

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    المصدر: Revista Científica; Vol. 45 No. 3 (2022): September-December 2022; 358-368 ; Revista Científica; Vol. 45 Núm. 3 (2022): Septiembre-Diciembre 2022; 358-368 ; 2344-8350 ; 0124-2253

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    Relation: https://revistas.udistrital.edu.co/index.php/revcie/article/view/19662/18553; https://revistas.udistrital.edu.co/index.php/revcie/article/view/19662/18610; Akram, B., Azizolsoltani, H., Min, W., Wiebe, E., Navied, A., Mott, B. W., Boyer, K., Lester, J. C. (2020). A Data-Driven Approach to Automatically Assessing Concept-Level CS Competencies Based on Student Programs. En CSEDM@EDM; Angelone, A. M., Vittorini, P. (2020). The Automated Grading of R Code Snippets: Preliminary Results in a Course of Health Informatics. En 9th International Conference (pp. 19-27). Springer. https://doi.org/10.1007/978-3-030-23990-9_3; Annamaa, A., Suviste, R., Vene, V. (2017). Comparing different styles of automated feedback for programming exercises. En Proceedings of the 17th Koli Calling International Conference on Computing Education Research (pp. 183-184). https://doi.org/10.1145/3141880.3141909; Ardimento, P., Bernardi, M. L., Cimitile, M. (2020). Software Analytics to Support Students in Object-Oriented Programming Tasks: An Empirical Study. IEEE Access, 8, 132171-132187. https://doi.org/10.1109/ACCESS.2020.3010172; Beck, P., Mohammadi-Aragh, M. J., Archibald, C. (2019). An Initial Exploration of Machine Learning Techniques to Classify Source Code Comments in Real-time. En ASEE Annual Conference & Exposition; Dominguez, A. K., Yacef, K., Curran, J. (2010). Data mining to generate individualised feedback. En Proceedings of the 10th international conference on Intelligent Tutoring Systems (pp. 303-305). https://doi.org/10.1007/978-3-642-13437-1_52; Edwards, S. H. (2014). Work-in-progress: Program grading and feedback generation with Web-CAT. En Proceedings of the first ACM conference on Learning @ scale conference (pp. 215-216). https://doi.org/10.1145/2556325.2567888; English, J., English, T. (2019). Combining Summative and Formative Evaluation Using Automated Assessment. Issues in Informing Science and Information Technology, 16, 143-151. https://doi.org/10.28945/4293; Funabiki, N., Mohri, T., Yamaguchi, S. (2016). Toward personalized learning in JPLAS: Generating and scoring functions for debugging questions. En IEEE 5th Global Conference on Consumer Electronics (pp. 1-4). https://doi.org/10.1109/GCCE.2016.7800392; Gerdes, A., Heeren, B., Jeuring, J., van Binsbergen, L. T. (2017). Ask-Elle: An Adaptable Programming Tutor for Haskell Giving Automated Feedback. International Journal of Artificial Intelligence in Education, 27(1), 65-100. https://doi.org/10.1007/s40593-015-0080-x; Hidalgo, C., G, V. A. B., Calle, F. R., Osorio, F. A. G. (2021). Estrategia de enseñanza basada en la colaboración y la evaluación automática de código fuente en un curso de programación CS1. Investigación e Innovación en Ingenierías, 9(1), 50-60; Jeuring, J., Gerdes, A., Heeren, B. (2012). Ask-Elle: A Haskell Tutor. En 21st Century Learning for 21st Century Skills (pp. 453-458). Springer. https://doi.org/10.1007/978-3-642-33263-0_42 Jupyter Development Team. (s. f.). nbgrader—Nbgrader 0.7.1 documentation. https://nbgrader.readthedocs.io/en/stable/; Jupyter, P., Blank, D., Bourgin, D., Brown, A., Bussonnier, M., Frederic, J., Granger, B., Griffiths, T., Hamrick, J., Kelley, K., Pacer, M., Page, L., Perez, F., Ragan-Kelley, B., Suchow, J., Willing, C. (2019). nbgrader: A Tool for Creating and Grading Assignments in the Jupyter Notebook. Journal of Open Source Education, 2, e32. https://doi.org/10.21105/jose.00032; Keuning, H., Jeuring, J., Heeren, B. (2016). Towards a Systematic Review of Automated Feedback Generation for Programming Exercises. En Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education (pp. 41-46). https://doi.org/10.1145/2899415.2899422; Keuning, H., Jeuring, J., Heeren, B. (2018). A Systematic Literature Review of Automated Feedback Generation for Programming Exercises. ACM Transactions on Computing Education, 19(1), 1-43. https://doi.org/10.1145/3231711; Kim, D., Kwon, Y., Liu, P., Kim, I. L., Perry, D. M., Zhang, X., Rodriguez-Rivera, G. (2016). Apex: Automatic programming assignment error explanation. En Proceedings of the 2016 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications (pp. 311-327). https://doi.org/10.1145/2983990.2984031; Manzoor, H., Naik, A., Shaffer, C. A., North, C., Edwards, S. H. (2020). Auto-Grading Jupyter Notebooks. En Proceedings of the 51st ACM Technical Symposium on Computer Science Education (pp. 1139-1144). https://doi.org/10.1145/3328778.3366947; Marin, V. J., Pereira, T., Sridharan, S., Rivero, C. R. (2017). Automated Personalized Feedback in Introductory Java Programming MOOCs. En IEEE 33rd International Conference on Data Engineering (pp. 1259-1270). https://doi.org/10.1109/ICDE.2017.169; McBroom, J., Yacef, K., Koprinska, I. (2020). Scalability in Online Computer Programming Education: Automated Techniques for Feedback, Evaluation and Equity. En Proceedings of the 13th International Conference on Educational Data Mining. https://educationaldatamining.org/files/conferences/EDM2020/papers/paper\_252.pdf; Narciss, S. (2008). Feedback Strategies for Interactive Learning Tasks (pp. 125-144) Ramirez-Echeverry, J. J., Restrepo-Calle, F., González, F. (2018). Uncode: Interactive System for Learning and Automatic Evaluation of Computer Programming Skills. https://doi.org/10.21125/edulearn.2018.1632; Razeeth, M., Kariapper, R. K. A. R., Pirapuraj, P., Nafrees, A., Rishan, U. M., Ali, S. (2019). E-learning at home vs traditional learning among higher education students: A survey based analysis. https://www.semanticscholar.org/paper/E-learning-at-home-vs-traditional-learning-among-a-Razeeth-Kariapper/546b671af8f0542edb932a7819b62e80a0c3010b; Shermis, M. D. (2014). State-of-the-art automated essay scoring: Competition, results, and future directions from a United States demonstration. Assessing Writing, 20, 53-76. https://doi.org/10.1016/j.asw.2013.04.001; Srikant, S., Aggarwal, V. (2014). A system to grade computer programming skills using machine learning. En Proceedings of the 20th International Conference on Knowledge Discovery and Data Mining (pp. 1887-1896). https://doi.org/10.1145/2623330.2623377; Universidad del Valle. (s. f.). Course list - INGInious M-iDEA. http://ingin.ddns.net/courselist Universidad Nacional de Colombia. (2022). UNCode [Python]. UNCode. https://github.com/JuezUN/INGInious; Université catholique de Louvain. (s. f.). What is INGInious? —INGInious 0.7 documentation. https://docs.inginious.org/en/v0.7/what_is_inginious.html; Universiteit Utrecht, Open Universiteit. (s. f.-a). Ask-Elle. https://ideas.science.uu.nl/AskElle/ Universiteit Utrecht, Open Universiteit. (s. f.-b). Ideas tutorial. https://ideas.science.uu.nl/tutorial/; Vittorini, P., Menini, S., Tonelli, S. (2021). An AI-Based System for Formative and Summative Assessment in Data Science Courses. International Journal of Artificial Intelligence in Education, 31(2), 159-185. https://doi.org/10.1007/s40593-020-00230-2; Yong Castillo, E., Bedoya Ortiz, D. H. (2022). De la educación tradicional a la educación mediada por TIC. https://pdf4pro.com/amp/view/de-la-educaci-243-n-tradicional-a-la-educaci-243-n-mediada-por-tic-733bf7.html; https://revistas.udistrital.edu.co/index.php/revcie/article/view/19662

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    المؤلفون: Pérez, Jesús, Castro, José

    المصدر: COLOMBIAN JOURNAL OF ADVANCED TECHNOLOGIES; Vol. 2 No. 32 (2018): July - December; 68-77 ; REVISTA COLOMBIANA DE TECNOLOGIAS DE AVANZADA (RCTA); Vol. 2 Núm. 32 (2018): Julio – Diciembre; 68-77 ; 2500-8625 ; 1692-7257

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    Relation: Comunidad de Madrid. S2009/TIC-1650/E-MADRID; Gobierno de España. TIN2011-28308-C03-01; 28-30 Octubre 2013; Granada; Jornadas de Innovación Educativa en Ingeniería Telemática (JIE); Díaz Verdejo, Jesús E. et al. (eds.) (2013). Actas de las XI Jornadas de Ingeniería Telemática (JITEL 2013): Granada. 28-30 Octubre 2013. Fundación General Universidad de Granada-Empresa, 563-570.; http://hdl.handle.net/10016/19630; 563; 570; XI Jornadas de Ingeniería Telemática (JITEL 2013). Granada. 28-30 Octubre 2013: Libro de ponencias; CC/0000020112

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    المصدر: Ingeniería e Investigación; Vol. 34 No. 2 (2014); 85-89 ; Ingeniería e Investigación; Vol. 34 Núm. 2 (2014); 85-89 ; 2248-8723 ; 0120-5609

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    Relation: https://revistas.unal.edu.co/index.php/ingeinv/article/view/41276/46147; https://revistas.unal.edu.co/index.php/ingeinv/article/view/41276/47539; Bates, A. W. (2005). Technology, e-learning and distance education (2nd ed.). Routledge, ISBN 0-415-28436-8.; Garrison, D. R., & Anderson, T. (2013). E-learning in the 21st century: a framework for research and practice. Taylor & Francis.; Garcia-Barrios, V. M., Modritscher, F., & Gutl, C. (2005). Personalization versus adaptation a user-centred model approach and its application. In I-KNOW'05 (pp. 120-127).; Gómez, J., León, E., Rodríguez, A., Cubides, C., Mahecha, J., Rubiano, J., & Prado, W. (2012). A Didactic E-learning Platform with Open Content Navigation and Adaptive Exercises. In Proceedings of the ICEELI 2012 Conference (pp. 1-6). IEEE Explore.; Kearsley, G. (2000). Online education: Learning and teaching in Cyberspace. Belmont, CA: Wadsworth Publishing Company.; Lilley, M. (2007). The development and application of computer- adaptive testing in a higher education environment.; Linacre, J. M. (2000). Computer-adaptive testing: A methodology whose time has come. MSA memorandum No. 9. Chicago: MESA psychometric laboratory, University of Chicago.; Salmon, G. (2004). E-activities: The Key to Teaching and Learning Online (2nd ed.). London: Taylor & Francis.; Siemens, G., & Downes, S. (2011). The MOOC Guide. Retrieved from https://sites.google.com/site/themoocguide.; Zhao, X. (2010). Adaptive content delivery based on contextual and situational model. (Ph. D. Thesis). The University of Electro-Communications, Tokyo, Japan.; https://revistas.unal.edu.co/index.php/ingeinv/article/view/41276

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    وصف الملف: application/pdf

    Relation: http://revistas.unal.edu.co/index.php/ingeinv/article/view/41276; Universidad Nacional de Colombia Revistas electrónicas UN Ingeniería e Investigación; Ingeniería e Investigación; Ingeniería e Investigación; Vol. 34, núm. 2 (2014); 85-89 Ingeniería e Investigación; Vol. 34, núm. 2 (2014); 85-89 2248-8723 0120-5609; Perdomo, Jonatan Gomez and Leon, Elizabeth and Cubides Garzón, Edwin Camilo and Maecha D'Maria, Julián Ricardo and Rodriguez, Arles Ernesto and Rubiano Zambrano, Juan Camilo (2014) Evolution of teaching and evaluation methodologies: the experience in the computer programming course at the universidad nacional de colombia. Ingeniería e Investigación; Vol. 34, núm. 2 (2014); 85-89 Ingeniería e Investigación; Vol. 34, núm. 2 (2014); 85-89 2248-8723 0120-5609 .; https://repositorio.unal.edu.co/handle/unal/48983; http://bdigital.unal.edu.co/42440/

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