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.

Effects of environmental colour on mood: a wearable LifeColour capture device

Colour is everywhere in our daily lives and impacts things like our mood, yet we rarely take notice of it. One method of capturing and analysing the predominant colours that we encounter is through visual lifelogging devices such as the SenseCam. However an issue related to these devices is the privacy concerns of capturing image level detail. Therefore in this work we demonstrate a hardware prototype wearable camera that captures only one pixel - of the dominant colour prevelant in front of the user, thus circumnavigating the privacy concerns raised in relation to lifelogging. To simulate whether the capture of dominant colour would be sufficient we report on a simulation carried out on 1.2 million SenseCam images captured by a group of 20 individuals. We compare the dominant colours that different groups of people are exposed to and show that useful inferences can be made from this data. We believe our prototype may be valuable in future experiments to capture colour correlated associated with an individual's mood.Colour is everywhere in our daily lives and impacts things like our mood, yet we rarely take notice of it. One method of capturing and analysing the predominant colours that we encounter is through visual lifelogging devices such as the SenseCam. However an issue related to these devices is the privacy concerns of capturing image level detail. Therefore in this work we demonstrate a hardware prototype wearable camera that captures only one pixel - of the dominant colour prevelant in front of the user, thus circumnavigating the privacy concerns raised in relation to lifelogging. To simulate whether the capture of dominant colour would be sufficient we report on a simulation carried out on 1.2 million SenseCam images captured by a group of 20 individuals. We compare the dominant colours that different groups of people are exposed to and show that useful inferences can be made from this data. We believe our prototype may be valuable in future experiments to capture colour correlated associated with an individual's mood.