A BPM-based approach for ensuring an agile and adaptive learning process

Smart Learning Environments

Review

Nisseb Bergaoui¹  Sonia Ayachi Ghannouchi² 

[1] High Institute of Computer Science and Communication Technologies of Hammam Sousse, University of Sousse, Sousse, Tunisia;Tunisia Laboratary RIADI-GDL, University of Manouba, Manouba, Tunisia;High Institute of Management of Sousse, University of Sousse, Sousse, Tunisia;

关键词: Agile learning;    Adaptive learning;    BPM;    Process mining;    Smart education;   

DOI  :  10.1186/s40561-023-00259-5

 received in 2022-10-03, accepted in 2023-08-03,  发布年份 2023

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© Springer Nature Singapore Pte Ltd., part of Springer Nature 2023

【 图 表 】

13570_2023_282_Article_IEq6.gif

13570_2023_282_Article_IEq3.gif

Fig. 3

Fig. 6

13690_2023_1151_Article_IEq22.gif

13690_2023_1151_Article_IEq17.gif

13690_2023_1151_Article_IEq15.gif

13690_2023_1151_Article_IEq11.gif

13690_2023_1151_Article_IEq24.gif

Fig. 3

40798_2023_599_Article_IEq18.gif

Fig. 3