An Evaluation of the Teaching and Learning of Reflective Practice at the Centre for Textile Conservation, University of Glasgow

The value of reflective practice in both professional education and lifelong learning is well established. In conservation the concept is fundamental to our ability to make informed decisions: to develop the cognitive and affective skills necessary to implement appropriate conservation strategies confidently, competently and ethically in wide ranging and dynamic contexts. Beyond the broad understanding of reflective practice as a process of learning through and from experience in order to gain new insights, it can have a considerable diversity of meanings. Its complexity can make it intimidating and, for conservation students, it has been found to be a challenging task. Through an evaluation of the learning, teaching and assessment of reflective practice at the Centre for Textile Conservation and Technical Art History (CTCTAH), University of Glasgow (UoG), this paper aims to peel back the layers of complexity to consider why it is challenging for students and how learning can be developed and assessed effectively

An investigation of gender and age differences in academic motivation and classroom behaviour in adolescents

This study investigated gender- and age-related differences in academic motivation and classroom behaviour in adolescents. Eight hundred and fifty-five students (415 girls and 440 boys) aged 11–16 (M age = 13.96, SD = 1.47) filled in a questionnaire that examined student academic motivation and teachers completed a questionnaire reporting student classroom behaviour. Interestingly, early adolescent boys’ (11–12 years) self-reported academic motivation was significantly more closely associated with reports of student classroom behaviour completed by teachers. However, a surprising result was the significant drop in girls’ adaptive motivation from early to mid-adolescence (13–14 years) and a significant increase in mid-adolescence (13–14 years). Furthermore, teachers reported a significant increase in negative classroom behaviour in mid-adolescent and late adolescent girls (15–16 years). The need to further understand the association between academic motivation and classroom behaviour at different stages in adolescence, and to design interventions to improve classroom behaviour, is deliberated

Gender differences in adolescents' academic motivation and classroom behaviour

© 2013 Taylor & Francis. The present study investigated gender differences in adolescents’ academic motivation and classroom behaviour and gender differences in the extent to which motivation was associated with, and predicted, classroom behaviour. Seven hundred and fifty students (384 boys and 366 girls) aged 11–16 (M age = 14.0, 1.59 SD) completed a questionnaire examining academic motivation and teachers completed assessments of their classroom behaviour. Girls generally reported higher levels of academic motivation, whilst teacher reports of behaviour were poorer for boys. Interestingly, boys’ reported levels of academic motivation were significantly more closely associated with teacher reports of their classroom behaviour. Furthermore, cognitive aspects of boys’ motivation were better predictors of their classroom behaviour than behavioural aspects. On the other hand, behavioural aspects of girls’ motivation were better predictors of their behaviour. Implications for understanding the relationship between motivation and behaviour among adolescent boys and girls are discussed, in addition to interventions aimed at improving adolescents’ classroom behaviour

Differential Anomaly Detection for Analysis of High Cardinality Time-series Data

Analysis of high cardinality time-series data is necessary in several contexts, such as root cause analysis based on logs, when outages occur in large computing infrastructure. Driving down mean time to mitigation (MTTM) depends on timely alerting, rapid root cause analysis, and effective mitigation. This disclosure describes differential anomaly detection to analyze system logs and automatically identify evidence of the likely root cause of a system outage in a way that is understandable to a human. The techniques described in this disclosure overcome the cardinality limitation and can find the most relevant information that is usable by engineers to take their investigation further. The techniques involve normalizing the data and outputting a list of Boolean predicates, sorted in increasing order of likelihood, that identify rows in system logs after an outage that are not in the logs prior to the outage