Dear learner: Participatory visualisation of learning data for sensemaking

© 2017 ACM. We discuss the application of a hand-drawn self-visualization approach to learner-data, to draw attention to the space of representational possibilities, the power of representation interactions, and the performativity of information representation

Certainty as a Provocation: The Design and Analysis of 2 Quant-Qual Tool Dyads for a Qualified Self Technology Project

This paper takes its starting point in recognising that the Quantified Self Movement can go beyond its existing purely quantitative nature and develop a second degree of meaning, so that the individual achieves self knowledge through human insights. We designed a research methodology to explore an individuals current and past relationship with ‘Activity Levels’ and ‘Balance Health’ using two Quant-Qual dyads. For the first Dyad, quantitative data was gathered about the number of steps taken by participants, and compared to the Qual Tool of Energy Diaries. For the second Dyad, quantitative data about postural sway was gathered through an application and qualitative data about the perceptions of balance was gathered through a personal diary. Quantitative data provided grounds for sensitising the participants to the idea of ‘Balance Health’ and ‘Activity Levels’ and the Qual tools revealed the lack of an actionable vocabulary on the one hand for Balance Health and rich narratives for activity levels on the other. Therefore, there exists an opportunity for research through design, to understand an individuals perception of their activity and to compare this existing self knowledge (or the lack of) to factual quantitative data in-order to design Qualified Self technology devices

What\u27s Happening in the Quantified Self Movement?

Rapid adoption of wearable tracking devices and motion sensitive apps has led to the development of the “Quantified Self” movement (QS). Some in the learning sciences community have begun to take notice and incorporate ideas from QS into the research and design of new learning environments. Yet the QS movement is still new enough that very little is known about it, and there are many open questions about how QS might be of value to the learning sciences. This paper provides some history of the movement and through a qualitative analysis of a public video corpus of QS presentations, identifies the variety of participants, the reported motivations driving individuals to self-quantify, and the data analysis activities of some individuals active in the movement. Opportunities for future research and design efforts in the learning sciences are also discussed

Self-control? Students' quantified self in the digital university

Discussions of ‘big data’ in Higher Education have focused on what the institution can know about its students, and its ability to act on their behalf. These discussions appear learner-centred, but continue to attribute agency to the institution. An alternative way of framing the use of data is offered by the quantified self movement. This focuses attention on the user, asking how technology creates new representations of the self, what these mean to them, and how it changes their relationships with others. This paper will drawn on this alternative framing to raise questions about the use of students’ data, and about who could and should be expected to know about and to act on learners’ experiences

Enhancing learning with technology

Specht, M., & Klemke, R. (2013, 26-27 September). Enhancing Learning with Technology. In D. Milosevic (Ed.), Proceedings of the fourth international conference on eLearning (eLearning 2013) (pp. 37-45). Belgrade Metropolitan University, Belgrade, Serbia. http://econference.metropolitan.ac.rs/We are living in a technology-enhanced world. Also learning is affected by recent, upcoming, and foreseen technological changes. This paper gives a bird’s eye view to technological trends and reflects how learning can benefit from them

Personal Analytics Explorations to Support Youth Learning

While personalized learning environments often include systems that automatically adapt to inferred learner needs, other forms of personalized learning exist. One form involves the use of personal analytics in which the learner obtains and analyzes data about himself/herself. More known in informatics communities, there is potential for use of personal analytics for design of instruction. This chapter provides two cases of personal analytics learning explorations to demonstrate their range and potential. One case is of a high school student examining how sleep influences her mood. The other case is of a sixth-grade class of students examining how deviations from typical walking behavior change distributional shape in plotted step data. Both cases show how social support and direct experience with data correction are intimately involved in how youth can learn through personal analytics activities

Living in a Simulation? An Empirical Investigation of a Smart Driving-Simulation Testing System

The internet of things (IoT) generally refers to the embedding of computing and communication devices in various types of physical objects (e.g., automobiles) used in people’s daily lives. This paper draws on feedback intervention theory to investigate the impact of IoT-enabled immediate feedback interventions on individual task performance. Our research context is a smart test-simulation service based on internet-of-vehicles (IoV) technology that was implemented by a large driver-training service provider in China. This system captures and analyzes data streams from onboard sensors and cameras installed in vehicles in real time and immediately provides individual students with information about errors made during simulation tests. We postulate that the focal smart service functions as a feedback intervention (FI) that can improve task performance. We also hypothesize that student training schedules moderate this effect and propose an interaction effect on student performance based on feedback timing and the number of FI cues. We collected data about students’ demographics, their training session records, and information about their simulation test(s) and/or their official driving skills field tests and used a quasi-experimental method along with propensity score matching to empirically validate our research model. Difference-in-difference analysis and multiple regression results support the significant impact of the simulation test as an FI on student performance on the official driving skills field test. Our results also supported the interaction effect between feedback timing and the number of corrective FI cues on official test performance. This paper concludes with a discussion of the theoretical contributions and practical significance of our research