K.A.3: Unterschied zwischen den Versionen
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{{Veranstaltung | {{Veranstaltung | ||
| − | |Titel DE= | + | |Titel DE=Design and Application of Quantified Self Approaches to Support Reflective Learning |
| − | |Titel EN= | + | |Titel EN=Design and Application of Quantified Self Approaches to Support Reflective Learning |
| − | |Beschreibung DE= | + | |Beschreibung DE=Reflective learning means returning to and evaluating past personal experiences and work performance |
| − | |Beschreibung EN= | + | in order to promote continuous learning and improve behaviour. This form of informal learning is driven by theories that are evolving since the 19th century and traditional pen and paper diaries are usually the only considered tools. Therefore, they do not rely on the technical advances done in the recent years, especially regarding mobile and ubiquitous technologies. |
| + | On the pragmatic side, the cheap availability of sensor-enabled devices gave rise to the Quantified | ||
| + | Self movement. Quantified Self (QS) is a collaboration of users and tool makers who share an interest in self-knowledge through self-tracking. This interest results in a variety of tools to collect personally relevant information with the purpose of gaining self-knowledge about one's behaviours, habits and thoughts. Hence, QS approaches offer a rich source of data and new interfaces for quantification of abstracts measures | ||
| + | (e.g. emotions) that have 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. | ||
| + | In this presentation we show how these two strands are brought together and how new mobile and web self-tracking applications inspired by QS approaches can support reflective learning processes. | ||
| + | From a theoretical perspective, the Integrated Model of Reflective Learning and Quantified Self served | ||
| + | as basis for this research by categorising and defining which dimensions can be supported by QS applica-tions. We explored three different use cases for each of which a prototype was iteratively designed, imple-mented and evaluated. These use cases encompass i) mood tracking in the telecommunications sector, ii) mood tracking in trading, iii) and capturing of feedback in lectures and professional presentations. The | ||
| + | results of the conducted design studies and evaluations lead to the validation of the | ||
| + | defined theoretical model and overall approach. | ||
| + | |||
| + | |Beschreibung EN=Reflective learning means returning to and evaluating past personal experiences and work performance | ||
| + | in order to promote continuous learning and improve behaviour. This form of informal learning is driven by theories that are evolving since the 19th century and traditional pen and paper diaries are usually the only considered tools. Therefore, they do not rely on the technical advances done in the recent years, especially regarding mobile and ubiquitous technologies. | ||
| + | On the pragmatic side, the cheap availability of sensor-enabled devices gave rise to the Quantified | ||
| + | Self movement. Quantified Self (QS) is a collaboration of users and tool makers who share an interest in self-knowledge through self-tracking. This interest results in a variety of tools to collect personally relevant information with the purpose of gaining self-knowledge about one's behaviours, habits and thoughts. Hence, QS approaches offer a rich source of data and new interfaces for quantification of abstracts measures | ||
| + | (e.g. emotions) that have 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. | ||
| + | In this presentation we show how these two strands are brought together and how new mobile and web self-tracking applications inspired by QS approaches can support reflective learning processes. | ||
| + | From a theoretical perspective, the Integrated Model of Reflective Learning and Quantified Self served | ||
| + | as basis for this research by categorising and defining which dimensions can be supported by QS applica-tions. We explored three different use cases for each of which a prototype was iteratively designed, imple-mented and evaluated. These use cases encompass i) mood tracking in the telecommunications sector, ii) mood tracking in trading, iii) and capturing of feedback in lectures and professional presentations. The | ||
| + | results of the conducted design studies and evaluations lead to the validation of the | ||
| + | defined theoretical model and overall approach. | ||
| + | |||
|Veranstaltungsart=Graduiertenkolloquium | |Veranstaltungsart=Graduiertenkolloquium | ||
|Start=2014/11/07 14:00:00 | |Start=2014/11/07 14:00:00 | ||
| Zeile 11: | Zeile 33: | ||
|Vortragender=Veronica Rivera | |Vortragender=Veronica Rivera | ||
|Eingeladen durch=Rudi Studer | |Eingeladen durch=Rudi Studer | ||
| + | |PDF=Rivera.7.11.14docx.pdf | ||
|Forschungsgruppe=Wissensmanagement | |Forschungsgruppe=Wissensmanagement | ||
|In News anzeigen=True | |In News anzeigen=True | ||
}} | }} | ||
Aktuelle Version vom 29. Oktober 2014, 09:50 Uhr
Design and Application of Quantified Self Approaches to Support Reflective Learning
Veranstaltungsart:
Graduiertenkolloquium
Reflective learning means returning to and evaluating past personal experiences and work performance
in order to promote continuous learning and improve behaviour. This form of informal learning is driven by theories that are evolving since the 19th century and traditional pen and paper diaries are usually the only considered tools. Therefore, they do not rely on the technical advances done in the recent years, especially regarding mobile and ubiquitous technologies.
On the pragmatic side, the cheap availability of sensor-enabled devices gave rise to the Quantified
Self movement. Quantified Self (QS) is a collaboration of users and tool makers who share an interest in self-knowledge through self-tracking. This interest results in a variety of tools to collect personally relevant information with the purpose of gaining self-knowledge about one's behaviours, habits and thoughts. Hence, QS approaches offer a rich source of data and new interfaces for quantification of abstracts measures
(e.g. emotions) that have 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.
In this presentation we show how these two strands are brought together and how new mobile and web self-tracking applications inspired by QS approaches can support reflective learning processes.
From a theoretical perspective, the Integrated Model of Reflective Learning and Quantified Self served
as basis for this research by categorising and defining which dimensions can be supported by QS applica-tions. We explored three different use cases for each of which a prototype was iteratively designed, imple-mented and evaluated. These use cases encompass i) mood tracking in the telecommunications sector, ii) mood tracking in trading, iii) and capturing of feedback in lectures and professional presentations. The
results of the conducted design studies and evaluations lead to the validation of the
defined theoretical model and overall approach.
(Veronica Rivera)
Start: 07. November 2014 um 14:00
Ende: 07. November 2014 um 15:00
Im Gebäude 11.40, Raum: 231
Veranstaltung vormerken: (iCal)
Veranstalter: Forschungsgruppe(n) Wissensmanagement
Information: Media:Rivera.7.11.14docx.pdf