Augmented reality (AR) has emerged as a promising technology for enhancing educational experiences, offering opportunities for immersive and interactive learning. This study aims to investigate the use of discrete variables and data science techniques in the context of AR education and explore the impact of researchers’ positionality on the design and implementation of such interventions.
With the advancement of data science, the ability to collect and analyze large amounts of data has become increasingly accessible. This presents an opportunity to understand the complex relationships between discrete variables, such as student engagement, learning outcomes, and instructional design elements, within AR educational settings. By leveraging data science techniques, researchers can extract meaningful insights and inform evidence-based practices in AR education.
However, it is crucial to acknowledge the influence of researchers’ positionality on the design and implementation of AR educational interventions. Positionality refers to the subjective lens through which researchers approach their work, shaped by their experiences, beliefs, and socio-cultural backgrounds. Understanding and reflecting on the influence of positionality is essential for ensuring transparency, validity, and ethical considerations in research.
This study adopts a mixed-methods approach, combining quantitative analysis of student data collected from AR educational interventions and qualitative inquiry into researchers’ positionality. In the quantitative phase, discrete variables will be identified and analyzed to investigate their relationships and impact on student learning outcomes. Data will be gathered through sensors embedded in AR devices, capturing student movements, interactions, and engagement levels. The qualitative phase will involve interviews and reflective practices to elucidate the researchers’ positionality and its potential influence on the design and interpretation of findings.
The findings of this study will contribute to the growing body of literature on the use of data science techniques in AR education. Moreover, by critically examining and reflecting on positionality, this research aims to enhance the rigor and ethical considerations of future AR educational interventions. Ultimately, this work seeks to inform educational stakeholders, policymakers, and practitioners on effective strategies for integrating AR and data science into educational settings.
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