FOTE 2008 Conference Report

A report prepared by JA.Net and ULCC about the Future of Technology in Education (FOTE 2008) conference, Imperial College, 3rd October 2008. It covers the main speakers, themes and presentations: Cloud Computing, Second Life, Portability, Personalisation, Shared Services, Campus of the Future, Mobile Technology, Creativity and Media Production, Social Collaboration Tools for Staff and Students

Knowles, Kolb, & Google: Prior Learning Assessment as a Model for 21st-Century Learning

For adult students who have committed anew to completing a four-year bachelor’s degree, prior learning assessment (PLA) can be a surprising bonus that affirms their previous life experiences, shortens the degree completion pathway, and ultimately lowers tuition dollars. What students typically do not realize as they enter the process, however, is that PLA can be much more than simply a road to a diploma: When designed with an intentional framework of andragogical principles and experiential emphases, PLA can provide adult students with a lifelong model for self-assessment and higher-level learning in a 21st-century Google era

The snowflake effect: the future of mashups and learning

Emerging technologies for learning report - Article exploring web mashups and their potential for educatio

Computer Analysis of Architecture Using Automatic Image Understanding

In the past few years, computer vision and pattern recognition systems have been becoming increasingly more powerful, expanding the range of automatic tasks enabled by machine vision. Here we show that computer analysis of building images can perform quantitative analysis of architecture, and quantify similarities between city architectural styles in a quantitative fashion. Images of buildings from 18 cities and three countries were acquired using Google StreetView, and were used to train a machine vision system to automatically identify the location of the imaged building based on the image visual content. Experimental results show that the automatic computer analysis can automatically identify the geographical location of the StreetView image. More importantly, the algorithm was able to group the cities and countries and provide a phylogeny of the similarities between architectural styles as captured by StreetView images. These results demonstrate that computer vision and pattern recognition algorithms can perform the complex cognitive task of analyzing images of buildings, and can be used to measure and quantify visual similarities and differences between different styles of architectures. This experiment provides a new paradigm for studying architecture, based on a quantitative approach that can enhance the traditional manual observation and analysis. The source code used for the analysis is open and publicly available

Slave to the Algorithm? Why a \u27Right to an Explanation\u27 Is Probably Not the Remedy You Are Looking For

Algorithms, particularly machine learning (ML) algorithms, are increasingly important to individuals’ lives, but have caused a range of concerns revolving mainly around unfairness, discrimination and opacity. Transparency in the form of a “right to an explanation” has emerged as a compellingly attractive remedy since it intuitively promises to open the algorithmic “black box” to promote challenge, redress, and hopefully heightened accountability. Amidst the general furore over algorithmic bias we describe, any remedy in a storm has looked attractive. However, we argue that a right to an explanation in the EU General Data Protection Regulation (GDPR) is unlikely to present a complete remedy to algorithmic harms, particularly in some of the core “algorithmic war stories” that have shaped recent attitudes in this domain. Firstly, the law is restrictive, unclear, or even paradoxical concerning when any explanation-related right can be triggered. Secondly, even navigating this, the legal conception of explanations as “meaningful information about the logic of processing” may not be provided by the kind of ML “explanations” computer scientists have developed, partially in response. ML explanations are restricted both by the type of explanation sought, the dimensionality of the domain and the type of user seeking an explanation. However, “subject-centric explanations (SCEs) focussing on particular regions of a model around a query show promise for interactive exploration, as do explanation systems based on learning a model from outside rather than taking it apart (pedagogical versus decompositional explanations) in dodging developers\u27 worries of intellectual property or trade secrets disclosure. Based on our analysis, we fear that the search for a “right to an explanation” in the GDPR may be at best distracting, and at worst nurture a new kind of “transparency fallacy.” But all is not lost. We argue that other parts of the GDPR related (i) to the right to erasure ( right to be forgotten ) and the right to data portability; and (ii) to privacy by design, Data Protection Impact Assessments and certification and privacy seals, may have the seeds we can use to make algorithms more responsible, explicable, and human-centered

Media Ecologies

In this chapter, we frame the media ecologies that contextualize the youth practices we describe in later chapters. By drawing from case studies that are delimited by locality, institutions, networked sites, and interest groups (see appendices), we have been able to map the contours of the varied social, technical, and cultural contexts that structure youth media engagement. This chapter introduces three genres of participation with new media that have emerged as overarching descriptive frameworks for understanding how youth new media practices are defi ned in relation and in opposition to one another. The genres of participation—hanging out, messing around, and geeking out—refl ect and are intertwined with young people’s practices, learning, and identity formation within these varied and dynamic media ecologies