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Title:
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A FRAMEWORK FOR APPLYING QUANTIFIED SELF APPROACHES TO SUPPORT REFLECTIVE LEARNING |
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Author(s):
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Verónica Rivera-Pelayo, Valentin Zacharias, Lars Müller, Simone Braun |
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ISBN:
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978-972-8939-66-3 |
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Editors:
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Inmaculada Arnedillo Sánchez and Pedro Isaías |
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Year:
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2012 |
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Edition:
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Single |
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Keywords:
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Reflective learning, Quantified Self, Mobile applications, Framework. |
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Type:
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Full Paper |
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First Page:
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123 |
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Last Page:
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131 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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This paper presents a framework for technical support of reflective learning. This is derived from a unification of theory on reflective learning with a conceptual framework of Quantified Self tools. Reflective learning means returning to and evaluating past work performances and personal experiences in order to promote continuous learning and improve future experiences. However, theories of reflective learning do not sufficiently consider technical support. Quantified Self (QS) is a collaboration of users who use and develop a variety of tools to collect personally relevant information with the purpose of gaining self-knowledge about one's behaviors, habits and thoughts. Hence, QS approaches, including mobile devices, sensors and social applications, offer a rich source of data that has not been available for learning processes before. However, these are rather experimental approaches and currently there is no unifying framework that clusters and connects these many emergent tools with the goals and benefits of their use. This paper brings these two strands into one unified framework that shows how QS approaches can support reflective learning processes on the one hand and how reflective learning can inform the design of new QS tools for informal learning purposes on the other hand. |
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