Datensegmentierung mittels Clusteranalyse

Grimmer, Udo; Mucha, Hans‐Joachim

doi:10.1007/978-3-642-86094-2_6

Cited by 7 publications

(1 citation statement)

References 6 publications

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“…Furthermore, it can be used as an initial partition to develop an agglomerative hierarchical clustering algorithm with a reasonable distance measure or metric respectively for CAM. Some expedient measures have been reviewed in (Grimmer and Mucha, 1998;Maimon and Rokach, 2005), but we are not limited to the listed ones. So, on the one hand our framework can be enriched with an unsupervised learning process for CAM observations.…”

Section: Cam Analysismentioning

confidence: 99%

ANALYSING CONTEXTUALIZED ATTENTION METADATA FOR SELF-REGULATED LEARNING - A Supporting Framework for Self-monitoring and Self-reflection

Scheffel

Beer²,

Wolpers

2010

Proceedings of the 2nd International Conference on Computer Supported Education

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In order to successfully learn in a self-regulated way, self-monitoring of the learner and reflection of learning behaviour is required. We therefore introduce a framework that collects usage metadata from application programs and stores them as Contextualized Attention Metadata (CAM). We also present three approaches on how we exploit the collected CAM for further analysis such as object recommendation or learning activity classification in order to help the learner become aware of her learning behaviour, to self-reflect and to support her during her learning processes.

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Section: Cam Analysismentioning

confidence: 99%