Resumen
his work presents an innovative approach to engage student participation through active learning, using the popular online word-guessing game, named Wordle. Wordle is a word-guessing game where players have six attempts to guess a five-letter word. With each guess, the letters turn the background color to indicate correctness: green for correct letters in the right position, yellow for correct letters in the wrong position, and gray for incorrect letters. Players aim to deduce the mystery word using logic and word association within the given attempts, making it both challenging and rewarding. The method proposed here involves students through the challenge of solving a set of personalized Wordle puzzles, at the end of the class, which include key concepts studied on the previous lecture. In this case, the words might have a different length. Students compete in a Wordle league, that is active during the whole semester accumulating points for each word guessed, fostering competition and motivation. The standings of the league are publicly available for all students. In addition, students are asked, at the end of the course, to create a concept map with all the keywords found, following the Unified Modeling Language (UML) standard, which is a fundamental topic of the subject. This activity aids in consolidating acquired knowledge and developing synthesis and information organization skills. The proposal was validated in the academic year 2022/2023, in the Engineering of Requirements (ER) course, which is a subject within the Software Engineering degree at Universidad Rey Juan Carlos (Madrid, Spain). The results showed a positive correlation between Wordle participation and academic performance. Furthermore, they suggested that students who actively engaged in the activity demonstrated a greater commitment to the subject and a better understanding of the key concepts. The benefits of this active learning proposal are manifold. It encourages class attendance, improves attention in class, and increases students’ motivation. It also aids in consolidating acquired knowledge and developing synthesis and information organization skills. Further research is needed to understand the impact of this strategy, but preliminary results are encouraging and suggest a promising path towards innovation in digital education.
Sergio Cavero
Doctor en Inteligencia Artificial
Sergio Cavero nació en Madrid (España) el 24 de septiembre de 1997. Se graduó en Ingeniería del Software por la Universidad Politécnica de Madrid en 2019. Durante sus estudios de grado realizó una estancia en la Universidad de Bradford (Reino Unido). Además, fue galardonado en dos ocasiones con la Beca de Excelencia de la Comunidad de Madrid, así como con el premio al Mejor Proyecto Fin de Carrera. Posteriormente, realizó un Máster en Inteligencia Artificial en la misma universidad (UPM) obteniendo los premios al Mejor Expediente Académico (‘Premio José Cuena’) y al Mejor Trabajo Fin de Máster. Sus resultados académicos le permitieron ser beneficiario de una de las ‘Ayudas para la Formación de Profesorado Universitario (FPU)’, financiadas por el Gobierno español. Actualmente realiza su tesis doctoral en la Universidad Rey Juan Carlos, dirigida por los profesores Abraham Duarte y Eduardo G. Pardo. Sus principales intereses de investigación se centran en la interfaz entre las Ciencias de la Computación, la Inteligencia Artificial y la Investigación Operativa. La mayoría de sus publicaciones tratan sobre el desarrollo de procedimientos metaheurísticos para problemas de optimización modelados por grafos.