A BPM-based approach for ensuring an agile and adaptive learning process
Tóm tắt
Agility is a contemporary approach to IT project management, which we can also use in education. Students learn through the gradual implementation of iterative projects with information exchange between team members. Agility is above all a mindset. Being agile is quite simply being able to adapt to an environment that changes. Furthermore, various research works focused on the assessment of innovative teaching methods to promote the acquisition of new professional skills (e.g. project-based learning, active and collaborative learning, smart learning, etc.). In addition, adaptive learning is a pedagogical method favoring tailor-made e-learning to respond to the acquisition of certain skills, through the adaptation of pedagogical resources according to the learners’ needs. Therefore, to establish a model based on these different methods to benefit from their advantages, we based our work on the Business Process Management approach, which constitutes the means of implementing thedesired agility in the learning process. Thanks to such a cyclical and continuous improvement approach, the learning process will evolve and take into account both the needs and the specificities of the involved actors (learners or teachers). We implemented our learning process and applied Process Mining techniques to foster the adoption of “Smart Education”. We also attempted to ensure learning process adaptability based on the scrutiny of the log files obtained throughprevious executions of our learning process.
Tài liệu tham khảo
Ayech, H. B. H., Ghannouchi, S. A., & Amor, E. A. E. H. (2021). Extension of the BPM lifecycle to promote the maintainability of BPMN models. Procedia Computer Science, 181, 852–860.
Badakhshan, P. et al. (202). Celonis process repository: A bridge between business process management and process mining. BPM (PhD/Demos), 2020.
Bai, H., & Zhang, Q. (2020). English smart classroom teaching system based on 5 network and internet of things. In Microprocessors and microsystems, p. 103421.
Bannert, M., Reimann, P., & Sonnenberg, C. (2014). Process mining techniques for analysing patterns and strategies in students’ self-regulated learning. Metacognition and Learning, 9(2), 161–185.
Batagan, L., Boja, C., & Cristian, I. (2011). Intelligent educational systems, support for an education cluster. In Proceedings of the 5th European conference on European computing conference, 2011.
Bergaoui, N., & Ghannouchi, S. A. (2021). A new model for an agile adaptive learning process: A questionnaire for evaluating its added value. In 2021 international conference on innovations in intelligent systems and applications (INISTA). IEEE, 2021.
Bergaoui, N., & Ghannouchi, S. (2021). A BPM-based agile approach to ensure adaptive learning. https://doi.org/10.3233/FAIA210056
Bergener, K., et al. (2012). On the importance of agile communication skills in BPM education: Design principles for international seminars. Knowledge Management & E-Learning, 4(4), 415.
Beuchat, P. N., Bradford, G. J., & Buskes, G. (2022). Challenges and opportunities of using differential-drive robots with project-based learning pedagogies. IFAC-PapersOnLine, 55(17), 186–193.
Brüß, C., et al. (2019). Process mining and natural language.
Bruter, A. (2013). Le cours magistral dans l’enseignement secondaire. Nature, histoire, representations (1802–1902). Histoire@ Politique, 3, 22–38.
Capraro, R. M., Capraro, M. M., & Morgan, J. R. (2013). STEM project-based learning. An integrated science, technology, engineering, and mathematics (STEM) approach (Vol. 2).
Castillo, P. A. A. (2011) BONITA SOFT: Gestor de procesos de negocios BPM. Universidad Nacional de Colombia (2011).
Chedrawi, C., & Howayeck, P. (2019). Artificial intelligence a disruptive innovation in higher education accreditation programs: Expert systems and AACSB. In ICT for a better life and a better world (pp. 115–129). Cham: Springer.
Ciolacu, M., & Beer, R. (2016). Adaptive user interface for higher education based on web technology. In 2016 IEEE 22nd international symposium for design and technology in electronic packaging (SIITME). IEEE, 2016.
Clark, D. B., D’Angelo, C. M., & Menekse, M. (2009). Initial structuring of online discussions to improve learning and argumentation: Incorporating students’ own explanations as seed comments versus an augmented-preset approach to seeding discussions. Journal of Science Education and Technology, 18(4), 321–333.
Collins, J. A., & Chiaramonte, M. W. (2017). Project-based learning and design thinking: Fomenting agility and innovation in government. In: 2017 IEEE international professional communication conference (ProComm). IEEE, 2017.
Cordeiro, R. F., et al. (2017). Agile all-digital RF transceiver implemented in FPGA. IEEE Transactions on Microwave Theory and Techniques, 65(11), 4229–4240.
Cruz, J. R. J. (2019). Aprendizaje por proyectos apoyado por el diseño instruccional 4C/ID y el diseño ágil scrum en un curso de sistemas embebidos biomédicos (project-based learning supported by the instructional design 4C/ID and the agile scrum design in a course of biomedical embedded systems). Pistas Educativas, 41(133).
de Moura, F. L., de Sá-Soares, F., Kubis, H. M., Kawashita, I., Mota, J. S., & Takagi, N. (2021). IT-CMF and BPM critical capability: Improving software development lab on academic context. Procedia Computer Science, 181, 325–332.
Delhij, A., van Solingen, R., & Wijnands, W. (2015). The eduScrum guide. The rules of the Game.
dos Santos Garcia, C., et al. (2019). Process mining techniques and applications—A systematic mapping study. Expert Systems with Applications, 133, 260–295.
Essalmi, F., et al. (2010). A fully personalization strategy of E-learning scenarios. Computers in Human Behavior, 26(4), 581–591.
Fischer, G. (2001). User modeling in human–computer interaction. User Modeling and User-Adapted Interaction, 11(1), 65–86.
Gomez-del Rio, T., & Rodriguez, J. (2022). Design and assessment of a project-based learning in a laboratory for integrating knowledge and improving engineering design skills. Education for Chemical Engineers, 40, 17–28.
Grimheden, M. E. (2013). Can agile methods enhance mechatronics design education? Mechatronics, 23(8), 967–973.
Hammer, M., & Champy, J. (1993). Reengineering the corporation: A manifesto for business revolution. New York: Harper Collins.
Han, J., Kim, K. H., Rhee, W., & Cho, Y. H. (2021). Learning analytics dashboards for adaptive support in face-to-face collaborative argumentation. Computers & Education, 163, 104041.
Irmert, F., et al. (2009). The adaptation model of a runtime adaptable DBMS. Dataspace: The Final Frontier: 26th British National Conference on Databases, BNCOD 26, Birmingham, UK, July 7-9, 2009. Proceedings 26. Springer Berlin Heidelberg.
IVIIT Capability Maturity Framework (IT-CMF). (2016). The body of knowledge guide, Innovation Value Institute, Maynooth (2016).
Katalnikova, S., et al. (2017). Intelligent collaborative educational systems and knowledge representation. Procedia Computer Science, 104, 166–173.
Kingston, J. (2015). Doing twice as much maths in half the time: Implementing scrum methodology in a Year 7 mathematics classroom. TEACH Journal of Christian Education, 9(2), 2.
Koper, R. (2014). Conditions for effective smart learning environments. Smart Learning Environments, 1(1), 1–17.
Krajcik, J. S., & Phyllis, C. (2006) Blumenfeld. Project-based learning. na, 2006.
Lin, J., et al. (2018). Intelligent recommendation system for course selection in smart education. Procedia Computer Science, 129, 449–453.
Magnisalis, I., Demetriadis, S., & Karakostas, A. (2011). Adaptive and intelligent systems for collaborative learning support: A review of the field. IEEE Transactions on Learning Technologies, 4(1), 5–20.
Magnuson, P., et al. (2021). Teacherless observations: Supporting student agency. International Education Theory and Practice, 7(1), 44–57.
Mcgill, M., Johnson, C., Atlas, J., et al. (2016) Game development for computer sciences education. In: ACM conference on innovation and technology in computer science education. ACM.
Noguera, I., Guerrero-Roldán, A.-E., & Masó, R. (2018). Collaborative agile learning in online environments: Strategies for improving team regulation and project management. Computers & Education, 116, 110–129. ISSN: 0360-1315.
OMG Document Number: formal/2011–01–03. (2011). Business process model and notation (BPMN).
Palatnik, A. (2022). Didactic situations in project-based learning: The case of numerical patterns and sequences. The Journal of Mathematical Behavior, 66, 100956.
Pan, A.-J., Lai, C.-F., & Kuo, H.-C. (2023). Investigating the impact of a possibility-thinking integrated project-based learning history course on high school students’ creativity, learning motivation, and history knowledge. Thinking Skills and Creativity, 47, 101214.
Papi, C., & Glikman, V. (2015). Les étudiants entre cours magistraux et usage des TIC. In Distances et médiations des savoirs. Distance and Mediation of Knowledge (Vol. 3, No. 9).
Peredo, R., et al. (2011). Intelligent Web-based education system for adaptive learning. Expert Systems with Applications, 38(12), 14690–14702.
Rao, B. P., & Singh, R. K. (2020). Disruptive intelligent system in engineering education for sustainable development. Procedia Computer Science, 172, 1059–1065.
Raval, M. S. (2019). Hybrid project-based learning in computer vision. The International Journal of Electrical Engineering & Education. https://doi.org/10.1177/0020720919857632
Sadowska, M. (2015). An approach to assessing the quality of business process models expressed in BPMN. e-Informatica Software Engineering Journal, 9(1).
Schmiedel, T. H., Recker, J., & vom Brocke, J. (2020). The relation between BPM culture, BPM methods, and process performance: Evidence from quantitative field studies. Information & Management, 57(2), 103175.
Scott, E., et al. (2014). Are learning styles useful indicators to discover how students use Scrum for the first time? Computers in Human Behavior, 36, 56–64.
Shen, Y., Heng, R., & Qian, D. (2020). Smart classroom learning atmosphere monitoring based on FPGA and Convolutional Neural Network. In Microprocessors and microsystems, p. 103488.
Tessier, N., et al. (2021). Élaboration et évaluation de l’utilité, de l’utilisabilité et de l’acceptabilité de ressources éducatives produites en réponse à la crise de la COVID-19. Global Health Promotion. https://doi.org/10.1177/17579759219961
Van Der Aalst, W. (2012). Process mining: Overview and opportunities. ACM Transactions on Management Information Systems (TMIS), 3(2), 1–17.
Vinge, V. (1993). The coming technological singularity, presented at the VISION-21 symposium, NASA Lewis Research Center and the Ohio Aerospace Institute, Washington, DC.
von Rosing, M., von Scheel, J., Gill, A. Q. (2015). Applying agile Principles to BPM. In M. von Rosing, A.-W. Scheer, H. von Scheel (Eds.), The complete business process handbook. Morgan Kaufmann.
Vultaggio, G. (2021). “The most anxious generation”: The relationship between Gen Z students, social media, and anxiety.
Wijnands, W., & Stolze, A. (2019). Transforming education with eduScrum. In Agile and lean concepts for teaching and learning (pp. 95–114). Springer, Singapore.
Yang, J., Zhang, X. L., & Su, P. (2019). Deep-learning-based agile teaching framework of software development courses in computer science education. Procedia Computer Science, 154, 137–145.
Zacarias, M., Martins, P. V., & Gonçalves, A. (2017). An agile business process and practice meta-model. Procedia Computer Science, 121, 170–177.
Zhu, J., et al. (2022). The impact of short videos on student performance in an online-flipped college engineering course. Humanities and Social Sciences Communications, 9(1), 1–10.