ESMeRA: a computational model to support Experience Sampling Method (ESM) real-time application

Abstract

Context: Technological transformations drive the growth of online classrooms and present both opportunities and challenges for understanding students’ experiences in the learning process. The Experience Sampling Method (ESM) allows capturing these experiences as they occur and can be used to understand better the factors that influence the learning process. Problem: Although the ESM has advantages compared to other collection methods, such as reducing cognitive bias, it is still not widespread in the Education area. In addition, it was observed in the literature the lack of tools to support the application of the ESM. Solution: The article presents a computational model named ESMeRA to aid in applying the ESM. The model includes aspects to provide computational support for the preparation, collection, and analysis of the experiences collected from the participants. IS Theory: The work was carried out following the Design Theory. Also, the foundations of the Unified Theory of Acceptance and Use of Technology (UTAUT) were mainly employed to evaluate the artifact’s user intent. Method: This work adopted the Design Science Research (DSR) method. A literature review on ESM allowed the problem awareness, and a prototype of the artifact was developed and positively evaluated by five experts. Contributions and Impact in the IS area: The contribution of this work is twofold. First, the developed model can feasibly support applying the ESM to collect students’ experiences. The practicality of the model also helps disseminate the method. Second, the work aims to address the IS challenge of understanding the students’ experiences in their learning process.