Comparing user experience between fuzzy logic and exact feedback systems in an e-learning environment
Sanz Villafruela, Diego (2018)
Turun ammattikorkeakoulu
2018
Creative Commons Attribution-NonCommercial 1.0 Finland
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-201805076796
https://urn.fi/URN:NBN:fi:amk-201805076796
Tiivistelmä
Neurophysiology students, including nursing students, must complete a course on electroencephalogram (EEG) sensor placement as part of their third-year studies. Currently, students attend and observe an EEG placement demonstration by experienced EEG professionals at the beginning of a semester and at the end of the semester they receive hands-on training. The lecturers have suggested building an e-learning environment that will help to bridge the gap between the observation and practical training sessions.
This thesis presents the design, development, and implementation of such an e-learning environment that provides feedback to the students about the accuracy of EEG electrode placement. The learning environment contains two different feedback systems. One that provides fuzzy (more human) guidance to the students and another giving exact value error feedback. The purpose of this thesis was to determine which of the two systems the students enjoyed more and which one they thought would provide the best learning.
The learning environment bases its evaluation of the virtual EEG placement on the 10-20 system—an international standard for the placement of EEG electrodes. Students were asked to spend two weeks with the system after their observation training. After their experience with the learning environment, students were invited to fill in a questionnaire and have a group discussion about their experiences with the virtual EEG placement system. The questionnaire measured student perceptions over three error categories, namely: short, medium and long distances between virtual placement and ideal positioning.
The results showed that the students preferred the fuzzy logic over the exact feedback system. Although the students noted that the exact feedback system provided overall a more precise error feedback, the fuzzy logic was generally better-received for short and medium errors. For long errors, the exact and fuzzy feedback systems received similar results. Group discussions also indicated that the students welcomed the additional learning opportunity between their observation and practical training sessions and felt it would be beneficial to their learning.
From this user experience test, in conclusion, the system warrants further development and possibly future formal integration into the lesson plan for neurophysiology students.
This thesis presents the design, development, and implementation of such an e-learning environment that provides feedback to the students about the accuracy of EEG electrode placement. The learning environment contains two different feedback systems. One that provides fuzzy (more human) guidance to the students and another giving exact value error feedback. The purpose of this thesis was to determine which of the two systems the students enjoyed more and which one they thought would provide the best learning.
The learning environment bases its evaluation of the virtual EEG placement on the 10-20 system—an international standard for the placement of EEG electrodes. Students were asked to spend two weeks with the system after their observation training. After their experience with the learning environment, students were invited to fill in a questionnaire and have a group discussion about their experiences with the virtual EEG placement system. The questionnaire measured student perceptions over three error categories, namely: short, medium and long distances between virtual placement and ideal positioning.
The results showed that the students preferred the fuzzy logic over the exact feedback system. Although the students noted that the exact feedback system provided overall a more precise error feedback, the fuzzy logic was generally better-received for short and medium errors. For long errors, the exact and fuzzy feedback systems received similar results. Group discussions also indicated that the students welcomed the additional learning opportunity between their observation and practical training sessions and felt it would be beneficial to their learning.
From this user experience test, in conclusion, the system warrants further development and possibly future formal integration into the lesson plan for neurophysiology students.