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Enhancing Programming Education with an Active Learning Plan and Artificial Intelligence Integration

Abstract

Artificial Intelligence (AI) is transforming higher education, bringing challenges and opportunities. Thus, instilling skills to leverage AI effectively and equip students for the future workforce is crucial. This requires a shift from traditional pedagogical methods toward active learning emphasizing problem-solving, collaborative work, and the integration of AI tools. The proposed Active Learning Plan (ALP) addresses these imperatives. This plan encompasses research, problem-solving, pseudocode validation using AI tools such as ChatGPT, thorough code documentation, group collaboration, and evaluation. The ALP delves into the interplay between AI and higher education, and it challenges students to derive manual solutions, draft pseudocode, document it, and subsequently validate it through ChatGPT. Moreover, it fosters collaboration by encouraging the formation of groups of five to craft questionnaires on the core topic, which are later used for learning assessment. It is paramount for students to reflect on their experience, pinpointing strengths, weaknesses, and areas for improvement. The adopted methodology emphasizes problem-solving and collaboration and allows students to engage with cutting-edge technologies, acquiring pivotal skills. The assessment of this approach was based on the Cronbach's alpha coefficient, yielding a value of 1.99016, indicating significant internal consistency. Upon analyzing the outcomes, 74% surpassed the score of 4.1, 18% passed, and only 8% failed to meet the minimum requirement. The deployment of this ALP proves to be an efficient instrument in readying students for an ever-evolving job market.

Keywords

Programming learning, Hybrid learning, Artificial intelligence and programming, learning and artificial intelligence, active programming learning

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Author Biography

Ariel-Adolfo Rodríguez-Hernández

Profesional en Ingeniería de Sistemas, Experto en Formación en Tutoría Virtual, Magister en Software Libre con énfasis en administración Web  y sistemas de comercio electrónico. Magister en Gestión Pedagógica.

 

Investigador Junior reconocido por Colciencias; con experiencia de cinco años en docencia y dirección de trabajos de grado a nivel posgradual en maestrías y especializaciones en áreas de informática, TIC y tecnología.  

 

Experiencia en docencia en pregrado en áreas de ingeniera de sistemas y TIC en programas presenciales, en e-learning y a distancia. Habilidades en manejo y administración de plataformas y entornos aprendizaje en línea.  Diseño de rubricas y acompañamiento tutorial en línea.

 

Experiencia en coordinación de proyectos de investigación y en consultoría en modernización tecnológica e incorporación de TIC en educación superior; en diseño y desarrollo de software.

 

Experiencia en dirección y administración de programas de investigación, extensión y docencia, coordinando equipos de trabajo orientados al cumplimiento de objetivos, direccionando estratégicamente centros de investigación y escuelas de pregrado. Dirigiendo procesos de diseño de programas de educación superior.

 

Par evaluador de Colciencias, Ministerio de Educación Nacional y de revistas científicas. Director del grupo de investigación  TICA: Tecnología, Investigación y Ciencia Aplicada. Conferencista a nivel nacional e internacional en temas de TIC e  e-learning.


References

  1. Ministerio de Educación Nacional, Resumen de indicadores de educación superior, 2020. https://snies.mineducacion.gov.co/portal/Informes-e-indicadores/Resumen-indicadores-Educacion-Superior/
  2. Ministerio de Educación Nacional, Encuesta de percepción del MEN v.2, 2021. https://www.mineducacion.gov.co/1780/articles-351276_recurso_20.pdf
  3. Y. González, O. Manzano, M. Torres, “Tecnologías disruptivas en Educación virtual,” Revista Boletín Redipe, vol. 10, no. 7, pp. 185-200. https://doi.org/10.36260/rbr.v10i7.1357
  4. B. J. Garner, E. Tsui, D. Lukose, “Artificial intelligence (AI) in blended learning,” Knowledge-Based Systems, vol. 22, no. 4, pp 247-248, 2009. https://doi.org/10.1016/j.knosys.2009.03.001
  5. J. H. Berssanette, A. C. de Francisco, “Active learning in the context of the teaching/learning of computer programming: A systematic review,” Journal of Information Technology Education: Research, vol. 20, pp. 201-220. https://doi.org/10.28945/4767
  6. W. Yang, “Artificial Intelligence education for young children: Why, what, and how in curriculum design and implementation," Computer and education: Artificial Intelligence, vol. 3, pp. 555-575, 2022. https://doi.org/10.1016/j.caeai.2022.100061
  7. J. C. Arroyo, D. M. Díaz, D. S. González, “Inteligencia artificial en educación: un panorama de la situación actual,” Educación XXI, vol. 23, no. 1, pp. 13-34, 2020.
  8. Y. Ocaña-Fernández, L. A. Valenzuela-Fernández, L. L. Garro-Aburto, “Inteligencia artificial y sus implicaciones en la educación superior,” Revista de psicología educativa Propósitos y Representaciones, vol. 7, no. 2, pp. 536-568, 2019. https://doi.org/10.20511/pyr2019.v7n2.274
  9. R. M. Clark, S. J. Dickerson, “A Case Study of Post-Workshop Use of Simple Active Learning in an Introductory Computing Sequence," IEEE Transactions on Education, vol. 61, no. 3, pp. 167-176, 2018. https://doi.org/10.1109/TE.2018.2808274
  10. E. Eaton, S. Koenig, C. Schulz, F. Maurelli, J. Lee, J. Eckroth, T. Williams, “Blue sky ideas in artificial intelligence education from the EAAI 2017 new and future AI educator program,” AI Matters, vol. 3, no. 4, pp. 23-31, 2018. https://doi.org/10.1145/3175502.3175509
  11. J. Ma, H. G. Okuno, “A personalized active learning framework for programming education,” Journal Computer Education, vol. 116, pp. 122-136, 2018.
  12. P. Shekhar, M. Borrego, M. DeMonbrun, C. Finelli, C. Crockett, K. Nguyen, “Negative Student Response to Active Learning in STEM Classrooms: A Systematic Review of Underlying Reasons,” Journal of College Science Teaching, vol. 49, no. 6, pp. 45-54, 2020.
  13. J. H., Berssanette, A. C. de Francisco, "Cognitive load theory in the context of teaching and learning computer programming; A systematic review,” IEEE Transaction on education, vol. 65, no. 3, pp. 440-449, 2022. https://doi.org/10.1109/TE.2021.3127215
  14. R. Moreno Padilla, “La llegada de la inteligencia artificial a la educación,” Revista de investigación en tecnologías de la información RITI, vol. 7, no. 14, e22, 2019. https://doi.org/10.36825/riti.07.14.022
  15. J. Bergmann, A. SamsLa, “Flip Your Classroom. Reach Every Student in Every Class Every,” in International Society for Technology in Education (ISTE), 2012.

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