Towards a cultural historical theory of knowledge mapping: collaboration and activity in the zone of proximal development

This chapter locates knowledge mapping within the theoretical framework of cultural historical activity theory. Cultural historical activity theory provides an analytic tool for understanding how knowledge maps can act as “stimuli-means”: a cultural artefact that can mediate the performance of subjects (Vygotsky, 1978 ). Knowledge maps possess Vygotsky’s double nature: they not only enable students to enact academic practice but also allow refl ection on that practice. They enable students to build an “internal cognitive schematisation of that practice” (Guile, 2005 , p.127). Further, cultural historical activity theory gives the tools to analyse the social context of our use of knowledge maps and thus consider the mediating rules (tacit and explicit) and division of labour that mediate our use of knowledge maps. Knowledge maps can be viewed as acting within Brandom’s ( 2000 ) space of reasons , which allows learners to use reasons to develop and exchange judgements based on shareable, theoretically articulated concepts and collectively develop the ability to restructure their knowledge and enact these judgements (Guile, 2011 ). In particular multimodal collaborative knowledge maps can act as Vygotsky’s (Vygotsky, 1978 ) zone of proximal development , where teacher and peer-to-peer interaction allow students to solve problems and learn concepts and skills that they would be otherwise unable to tackle.

Cluster identification and separation in the growing self-organizing map: application in protein sequence classification

Growing self-organizing map (GSOM) has been introduced as an improvement to the self-organizing map (SOM) algorithm in clustering and knowledge discovery. Unlike the traditional SOM, GSOM has a dynamic structure which allows nodes to grow reflecting the knowledge discovered from the input data as learning progresses. The spread factor parameter (SF) in GSOM can be utilized to control the spread of the map, thus giving an analyst a flexibility to examine the clusters at different granularities. Although GSOM has been applied in various areas and has been proven effective in knowledge discovery tasks, no comprehensive study has been done on the effect of the spread factor parameter value to the cluster formation and separation. Therefore, the aim of this paper is to investigate the effect of the spread factor value towards cluster separation in the GSOM. We used simple k-means algorithm as a method to identify clusters in the GSOM. By using Davies–Bouldin index, clusters formed by different values of spread factor are obtained and the resulting clusters are analyzed. In this work, we show that clusters can be more separated when the spread factor value is increased. Hierarchical clusters can then be constructed by mapping the GSOM clusters at different spread factor values.