From Sensor Data to Educational Insights

Ruipérez‐Valiente, José A.; Martínez-Maldonado, Roberto; Mitri, Daniele Di; Schneider, Ján

doi:10.3390/s22218556

Cited by 5 publications

(1 citation statement)

References 35 publications

(40 reference statements)

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“…Colleges and universities are introducing artificial intelligence in education (AIED), educational data mining (EDM), and learning analytics (LA) to enhance students' capabilities in dealing with numerous types of sensor data. (2) Subjects related to these topics include the training of students to exercise decision-making ability (3) based on game theory, (4) social learning theory, (5) and computational thinking, (6) as these are required to understand the diverse applications of sensor data. Therefore, it is necessary to discuss how to apply such subjects to higher education by developing an interesting and interdisciplinary curriculum in which the following capabilities are developed: critical thinking, (7) problem-solving, and decision-making.…”

Section: Introductionmentioning

confidence: 99%

Important Elements of Sensor Technology and Data Management and Related Education

Chan¹,

Lai²,

Hsieh³

et al. 2023

Sensors and Materials

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The demand for both sensors and a dedicated workforce has been increasing rapidly; an appropriate curriculum is needed to train this workforce. This study was conducted to identify elements that need to be included in the curriculum to teach sensor technology and the associated data management effectively. In this study, interviews with experts and a survey by questionnaire were conducted. The data were analyzed with an analytical network process (ANP), and statistical analysis including ANOVA, regression analysis, and factor analysis. As a result, four criteria (problem analysis, pattern recognition, abstraction of problems, and finding solutions) were defined with 12 subcriteria including faculty education in the curriculum, self-directed learning in the curriculum, committees and working groups to structure the curriculum, planning, organizing and managing the curriculum, learning outcomes, feedback from students and teaching staff, advice of experts, training assistants, appropriate assessment, appropriate education, appropriate evaluation, and recruiting students. Analytical skills, problem-solving, and decision-making were found to be alternatives (elements in education) in the analysis. The results of ANP and other statical analyses indicated that among the criteria, problem analysis is more important than the others in education for sensor technology and data management. Among alternatives (educational elements), analytical skills are more important than problem-solving and decision-making. Therefore, education in sensor-related subjects needs to be more focused on analyzing and detecting problems in sensor data. These results provide the basis for creating a curriculum for education in sensor-related technologies.

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