Practice-based film education for children: teaching and learning for creativity, citizenship and participation

Practice-oriented film education aimed at children has been hailed for various reasons: at a personal level, as a means of providing tools for self-expression, for developing creativity and communication skills. And at a social level, it is argued that children must now become competent producers, in addition to critical consumers, of audiovisual content so they can take part in the global public sphere that is arguably emerging. This chapter discusses how the challenges posed by introducing children to filmmaking (i.e. digital video) are being met at three civil associations in Mexico: La Matatena AC, which seeks to enrich the children’s lives by means of the aesthetic experience filmmaking can bring them. Comunicaciòn Comunitaria, concerned with the impact filmmaking can have on the community, preserving cultural memory and enabling participation. And Juguemos a Grabar, with a focus on urban regeneration through the cultural industries.

Personal learning artefacts: persistence, ownership and privacy

Traditional classrooms have been often regarded as closed spaces within which experimentation, discussion and exploration of ideas occur. Professors have been used to being able to express ideas frankly, and occasionally rashly while discussions are ephemeral and conventional student work is submitted, graded and often shredded. However, digital tools have transformed the nature of privacy. As we move towards the creation of life-long archives of our personal learning, we collect material created in various 'classrooms'. Some of these are public, and open, but others were created within 'circles of trust' with expectations of privacy and anonymity by learners. Taking the Creative Commons license as a starting point, this paper looks at what rights and expectations of privacy exist in learning environments? What methods might we use to define a 'privacy license' for learning? How should the privacy rights of learners be balanced with the need to encourage open learning and with the creation of eportfolios as evidence of learning? How might we define different learning spaces and the privacy rights associated with them? Which class activities are 'private' and closed to the class, which are open and what lies between? A limited set of set of metrics or zones is proposed, along the axes of private-public, anonymous-attributable and non-commercial-commercial to define learning spaces and the digital footprints created within them. The application of these not only to the artefacts which reflect learning, but to the learning spaces, and indeed to digital media more broadly are explored. The possibility that these might inform not only teaching practice but also grading rubrics in disciplines where public engagement is required will also be explored, along with the need for consideration by educational institutions of the data rights of students.

Occupant location prediction in smart buildings using association rule mining

Heating, ventilation, air conditioning (HVAC) systems are significant consumers of energy, however building management systems do not typically operate them in accordance with occupant movements. Due to the delayed response of HVAC systems, prediction of occupant locations is necessary to maximize energy efficiency. We present an approach to occupant location prediction based on association rule mining, allowing prediction based on historical occupant locations. Association rule mining is a machine learning technique designed to find any correlations which exist in a given dataset. Occupant location datasets have a number of properties which differentiate them from the market basket datasets that association rule mining was originally designed for. This thesis adapts the approach to suit such datasets, focusing the rule mining process on patterns which are useful for location prediction. This approach, named OccApriori, allows for the prediction of occupants’ next locations as well as their locations further in the future, and can take into account any available data, for example the day of the week, the recent movements of the occupant, and timetable data. By integrating an existing extension of association rule mining into the approach, it is able to make predictions based on general classes of locations as well as specific locations.