title: Performativity and affectivity: Lesson observations in England's Further Education colleges

authors: Edgington, Ursula

subjects: Further Education

abstract: Teaching and learning observations (TLOs) are used in educational environments worldwide to measure and improve quality and support professional development. TLOs can be positive, for teachers who enjoy opportunities to ‘perform’ their craft and/or engage in professional dialogue. However, if this crucial, collaborative developmental element is missing, a TLO becomes intrinsically evaluative in nature and creates complex emotions – within and beyond the classroom. For some teachers, affective reactions to perceived managerial intrusion into their professional space has a negative impact on them, and in turn, their students’ learning.
 International research on TLOs has focused on schools or universities. My research centres specifically on England’s Further Education colleges (FE). Through Interpretive Interactionism, I investigate the different expectations, relationships and identities of teachers and (mis)conceptions of ‘authenticity’ in TLOs. Teaching involves our unique (dis)embodied ‘performativity’ (Butler, 2004) or ‘emotional practice’ which is interpreted and judged by others (Denzin, 1989). Using the concept of ‘aesthetic labour’ (Witz, et al., 2003), I argue that rather than promoting positive transformation through reflection, TLOs promote a rejection of emotional ‘genuineness’ that causes anxiety through a fracturing of personal and professional identities.
 Improving the effectiveness of TLOs should perhaps encompass explicit dialogue about the affectivity involved in the process?

text: Performativity and affectivity: Lesson observations in England's Further Education colleges Teaching and learning observations (TLOs) are used in educational environments worldwide to measure and improve quality and support professional development. TLOs can be positive, for teachers who enjoy opportunities to ‘perform’ their craft and/or engage in professional dialogue. However, if this crucial, collaborative developmental element is missing, a TLO becomes intrinsically evaluative in nature and creates complex emotions – within and beyond the classroom. For some teachers, affective reactions to perceived managerial intrusion into their professional space has a negative impact on them, and in turn, their students’ learning.
 International research on TLOs has focused on schools or universities. My research centres specifically on England’s Further Education colleges (FE). Through Interpretive Interactionism, I investigate the different expectations, relationships and identities of teachers and (mis)conceptions of ‘authenticity’ in TLOs. Teaching involves our unique (dis)embodied ‘performativity’ (Butler, 2004) or ‘emotional practice’ which is interpreted and judged by others (Denzin, 1989). Using the concept of ‘aesthetic labour’ (Witz, et al., 2003), I argue that rather than promoting positive transformation through reflection, TLOs promote a rejection of emotional ‘genuineness’ that causes anxiety through a fracturing of personal and professional identities.
 Improving the effectiveness of TLOs should perhaps encompass explicit dialogue about the affectivity involved in the process?

year: 2013

title: Capturing Context in Collaborative Profiles

authors: Jung, Doris

subjects: computer science

abstract: In various application areas for alerting systems, the context and knowledge of several parties affect profile definition and filtering. For example, in healthcare nurses, doctors and patient influence the treatment process. Thus, profiles for alerting systems have to be generated by the explicit collaboration of several parties who may not know each other directly. 
We propose the new concept of collaborative profiles to capture these different conditions and contexts. These profiles exploit each single party’s expert-knowledge for defining the context under which (health-related) alerting is required. Challenges include the definition and refinement of profiles as well as conflict detection in context definitions.

text: Capturing Context in Collaborative Profiles In various application areas for alerting systems, the context and knowledge of several parties affect profile definition and filtering. For example, in healthcare nurses, doctors and patient influence the treatment process. Thus, profiles for alerting systems have to be generated by the explicit collaboration of several parties who may not know each other directly. 
We propose the new concept of collaborative profiles to capture these different conditions and contexts. These profiles exploit each single party’s expert-knowledge for defining the context under which (health-related) alerting is required. Challenges include the definition and refinement of profiles as well as conflict detection in context definitions.

year: 2005

title: Can large taonga bivalves speed up recovery in degraded estuaries?

authors: Prinz, Natalie

abstract: THE QUESTION:
Will there introduction of adult, long-lived bivalves with differing functional traits speed up recovery of degraded soft sediment habitats in estuaries?
RESULTS:
Average bivalve survival after one year was 18% in + tuangi plot sand 59% in + hanikura plots. Sediment oxygen consumption (SOC) after one month was enhanced by 116% in treatments containing tuangi compared to control treatments. After one year those were similar to ambient levels, still showed higher SOC than control plots.

text: Can large taonga bivalves speed up recovery in degraded estuaries? THE QUESTION:
Will there introduction of adult, long-lived bivalves with differing functional traits speed up recovery of degraded soft sediment habitats in estuaries?
RESULTS:
Average bivalve survival after one year was 18% in + tuangi plot sand 59% in + hanikura plots. Sediment oxygen consumption (SOC) after one month was enhanced by 116% in treatments containing tuangi compared to control treatments. After one year those were similar to ambient levels, still showed higher SOC than control plots.

year: 2023