Survey of image-based representations and compression techniques

In this paper, we survey the techniques for image-based rendering (IBR) and for compressing image-based representations. Unlike traditional three-dimensional (3-D) computer graphics, in which 3-D geometry of the scene is known, IBR techniques render novel views directly from input images. IBR techniques can be classified into three categories according to how much geometric information is used: rendering without geometry, rendering with implicit geometry (i.e., correspondence), and rendering with explicit geometry (either with approximate or accurate geometry). We discuss the characteristics of these categories and their representative techniques. IBR techniques demonstrate a surprising diverse range in their extent of use of images and geometry in representing 3-D scenes. We explore the issues in trading off the use of images and geometry by revisiting plenoptic-sampling analysis and the notions of view dependency and geometric proxies. Finally, we highlight compression techniques specifically designed for image-based representations. Such compression techniques are important in making IBR techniques practical.published_or_final_versio

Embracing imperfection in learning analytics

© 2018 Copyright held by the owner/author(s). Learning Analytics (LA) sits at the confluence of many contributing disciplines, which brings the risk of hidden assumptions inherited from those fields. Here, we consider a hidden assumption derived from computer science, namely, that improving computational accuracy in classification is always a worthy goal. We demonstrate that this assumption is unlikely to hold in some important educational contexts, and argue that embracing computational “imperfection” can improve outcomes for those scenarios. Specifically, we show that learner-facing approaches aimed at “learning how to learn” require more holistic validation strategies. We consider what information must be provided in order to reasonably evaluate algorithmic tools in LA, to facilitate transparency and realistic performance comparisons

On the data compression and transmission aspects of panoramic video

This paper proposes efficient data compression and transmission techniques for panoramic video. Panoramic videos have been used as a means for representing dynamic scenes or paths along a static environment. They allow the user to change viewpoints interactively at a point in time or space. High-resolution panoramic videos, while desirable, consume a significant amount of storage and bandwidth for transmission, and make real-time decoding very compute-intensive. A high performance MPEG-like compression algorithm, which takes into account the random access requirements and the redundancies of the panoramic video, is presented. The transmission aspects of panoramic video over cable network, LAN and Internet are also briefly discussed.published_or_final_versio

Framing Professional Learning Analytics as Reframing Oneself

Central to imagining the future of technology-enhanced professional learning is the question of how data are gathered, analyzed, and fed back to stakeholders. The field of learning analytics (LA) has emerged over the last decade at the intersection of data science, learning sciences, human-centered and instructional design, and organizational change, and so could in principle inform how data can be gathered and analyzed in ways that support professional learning. However, in contrast to formal education where most research in LA has been conducted, much work-integrated learning is experiential, social, situated, and practice-bound. Supporting such learning exposes a significant weakness in LA research, and to make sense of this gap, this article proposes an adaptation of the Knowledge-Agency Window framework. It draws attention to how different forms of professional learning locate on the dimensions of learner agency and knowledge creation. Specifically, we argue that the concept of “reframing oneself” holds particular relevance for informal, work-integrated learning. To illustrate how this insight translates into LA design for professionals, three examples are provided: first, analyzing personal and team skills profiles (skills analytics); second, making sense of challenging workplace experiences (reflective writing analytics); and third, reflecting on orientation to learning (dispositional analytics). We foreground professional agency as a key requirement for such techniques to be used effectively and ethically

Mapping learner-data journeys: Evolution of a visual co-design tool

© 2018 Copyright held by the owner/author(s). In this paper we present a three-phase process for crafting Learner-Data Journey maps and using them as communication tools to involve other stakeholders in the co-design of a data-intensive educational tool. The three phases in this process are i) scaffolding groups of learners to collaboratively co-create a Learner-Data Journey based on their own experience, ii) distilling key insights from these journey maps, and iii) providing the means for multiple stakeholders to integrate and synthesise key insights from these journey maps to suggest design requirements. We illustrate the process and the kind of tools that can support the co-creation of Learner-Data Journeys in two educational scenarios where learners have become partners of their own 'surveillance'

<i>“We’re Seeking Relevance”</i>: Qualitative Perspectives on the Impact of Learning Analytics on Teaching and Learning

Whilst a significant body of learning analytics research tends to focus on impact from the perspective of usability or improved learning outcomes, this paper proposes an approach based on Affordance Theory to describe awareness and intention as a bridge between usability and impact. 10 educators at 3 European institutions participated in detailed interviews on the affordances they perceive in using learning analytics to support practice in education. Evidence illuminates connections between an educator’s epistemic beliefs about learning and the purpose of education, their perception of threats or resources in delivering a successful learning experience, and the types of data they would consider as evidence in recognising or regulating learning. This evidence can support the learning analytics community in considering the proximity to the student, the role of the educator, and their personal belief structure in developing robust analytics tools that educators may be more likely to use

Large scale predictive process mining and analytics of university degree course data

© 2017 ACM. For students, in particular freshmen, the degree pathway from semester to semester is not that transparent, although students have a reasonable idea what courses are expected to be taken each semester. An often-pondered question by students is: "what can I expect in the next semester?" More precisely, given the commitment and engagement I presented in this particular course and the respective performance I achieved, can I expect a similar outcome in the next semester in the particular course I selected? Are the demands and expectations in this course much higher so that I need to adjust my commitment and engagement and overall workload if I expect a similar outcome? Is it better to drop a course to manage expectations rather than to (predictably) fail, and perhaps have to leave the degree altogether? Degree and course advisors and student support units find it challenging to provide evidence based advise to students. This paper presents research into educational process mining and student data analytics in a whole university scale approach with the aim of providing insight into the degree pathway questions raised above. The beta-version of our course level degree pathway tool has been used to shed light for university staff and students alike into our university's 1,300 degrees and associated 6 million course enrolments over the past 20 years

Reflective writing analytics for actionable feedback

© 2017 ACM. Reflective writing can provide a powerful way for students to integrate professional experience and academic learning. However, writing reflectively requires high quality actionable feedback, which is time-consuming to provide at scale. This paper reports progress on the design, implementation, and validation of a Reflective Writing Analytics platform to provide actionable feedback within a tertiary authentic assessment context. The contributions are: (1) a new conceptual framework for reflective writing; (2) a computational approach to modelling reflective writing, deriving analytics, and providing feedback; (3) the pedagogical and user experience rationale for platform design decisions; and (4) a pilot in a student learning context, with preliminary data on educator and student acceptance, and the extent to which we can evidence that the software provided actionable feedback for reflective writing

Implementing AcaWriter as a Novel Strategy to Support Pharmacy Students’ Reflective Practice in Scientific Research

Objective. To explore pharmacy students’ perceptions of a novel web application tool (AcaWriter) implemented in a Master of Pharmacy curriculum to support reflective thinking in scientific research. Methods. A qualitative research design involving a 50-minute focus group (n=12) was used. The focus group session was audio-taped, transcribed verbatim, and analyzed thematically using the Braun and Clarke framework. Results. Analysis generated four themes related to AcaWriter’s utility in enhancing students’ research thinking and capacity. The themes identified included: ease of use to prompt reflection, tangible tool with non-judgmental capacity; benefits for enhancing self and peer reflection on research techniques and group dynamics; benefits of the reflective writing process to enhance research capacity compared with engaging in reflective dialogue; and benefits beyond the writing process: cultivating self-improvement and self-confidence. Conclusion. The findings of this study show that a novel web application implemented within a pharmacy curriculum can assist students’ self and peer reflection on a research task. Further research is needed to explore the impact of using this tool and its relationship with academic performance and outcomes

Ethics of AI in Education: Towards a Community-Wide Framework

While Artificial Intelligence in Education (AIED) research has at its core the desire to support student learning, experience from other AI domains suggest that such ethical intentions are not by themselves sufficient. There is also the need to consider explicitly issues such as fairness, accountability, transparency, bias, autonomy, agency, and inclusion. At a more general level, there is also a need to differentiate between doing ethical things and doing things ethically, to understand and to make pedagogical choices that are ethical, and to account for the ever-present possibility of unintended consequences. However, addressing these and related questions is far from trivial. As a first step towards addressing this critical gap, we invited 60 of the AIED community’s leading researchers to respond to a survey of questions about ethics and the application of AI in educational contexts. In this paper, we first introduce issues around the ethics of AI in education. Next, we summarise the contributions of the 17 respondents, and discuss the complex issues that they raised. Specific outcomes include the recognition that most AIED researchers are not trained to tackle the emerging ethical questions. A well-designed framework for engaging with ethics of AIED that combined a multidisciplinary approach and a set of robust guidelines seems vital in this context