TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 2: Small World Problem

We will discuss several examples and research efforts related to the small world problem and set the ground for our discussion of network theory and social network analysis. Readings: An Experimental Study of the Small World Problem, J. Travers and S. Milgram Sociometry 32 425-443 (1969) [Protected Access] Optional: The Strength of Weak Ties, M.S. Granovetter The American Journal of Sociology 78 1360--1380 (1973) [Protected Access] Optional: Worldwide Buzz: Planetary-Scale Views on an Instant-Messaging Network, J. Leskovec and E. Horvitz MSR-TR-2006-186. Microsoft Research, June 2007. [Web Link, the most recent and comprehensive study on the subject!] Originally from: http://kmi.tugraz.at/staff/markus/courses/SS2008/707.000_web-science/

TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 7: Link Analysis and Search

What are ways of searching in graphs? In this class, we will discuss basics of link analysis, including Google's PageRank algorithm as an example. Readings: The PageRank Citation Ranking: Bringing Order to the Web, L. Page and S. Brin and R. Motwani and T. Winograd (1998) Stanford Tecnical Report

TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 8: Web Technologies 1

This class focuses on a selected subset of web technologies that are of interest to the topics of this course. Readings: Chapter 5 "Representational State Transfer (REST)", in "Architectural Styles and the Design of Network-based Software Architecture", Roy Fielding, Dissertation, University of California Irvine, 2000 Optional: Chapter "Representational State Transfer (REST)" in "Pro PHP XML and Web Services", R. Richards 633--672, 2006

TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 10: Text Mining

This class introduces basics of web mining and information retrieval including, for example, an introduction to the Vector Space Model and Text Mining. Guest Lecturer: Dr. Michael Granitzer Optional: Modeling the Internet and the Web: Probabilistic Methods and Algorithms, Pierre Baldi, Paolo Frasconi, Padhraic Smyth, Wiley, 2003 (Chapter 4, Text Analysis)

An introduction to Technology Enhanced Learning at the University of Southampton

This short (10 minute) video provides students with an overview of the ways in which computers and the internet are used to support their learning. It introduces some really useful resources and shows you where to find help if you need it.

What’s the Difference between Web Technology and Web Science? Opportunities for study and research at the University of Southampton

The University of Southampton has a long history of pursuing research, development and social change with the Web This document guides you through the opportunities for Web-related study and research that we offer: an MSc in Web Technology; a 3-year PhD in Web Technology; an MSc in Web Science or a 4-year PhD in Web Science

Technology Enhanced Learning for Health Sciences

A presentation given in the Health Sciences E-Learning Enhnacnement Academy away-day, June 2010

Technology Enhanced Learning Guide

Technology is changing how students learn and how we research. Perhaps you want to use technology to enhance communication or improve student support. You may want create a distance learning activity, a flexibly delivered module or indeed a whole course. You may simply want to find out where to find authoritative information, or to see what support exists for this type of work. The University is committed to delivering high quality learning and teaching, using technology where appropriate, in order to offer a distinctive Southampton educational experience. Technology Enhanced Learning (TEL), also known as e‑learning, is becoming increasingly important to students, teaching staff and the institution. This guide highlights some of the most important matters to consider. It is intended to help you to tackle the key issues that determine the success of TEL projects and to work on those projects in a considered way. Written with the input of colleagues from around the University, it prompts you to ask important questions and points you to sources of up-to-date knowledge and advice. Technology changes rapidly. This guide is about managing the work in a practical way. The University supports the use of a variety of TEL approaches for teaching and learning and colleagues are ready to offer their experience and advice. Each person has distinctive skills and specific experiences. No single person will have all the answers you are looking for. Be ready to investigate alternative approaches that suit you and your students’ needs in different ways. - Madeline Paterson, University of Southampton

Technology Enhanced Learning at Southampton

A short overview of TEL intended for a short PCAP workshop

An Introduction to Learning Technology

A few slides used in 2013 class to introduce the subject of learning technology

COMP6037 Seminar 23 10 13: Science, knowledge and technology

Set readings 1. Sismondo S. (2009). The Kuhnian revolution. In An introduction to science and technology studies. p12-22 2. Ben-David J, Sullivan T. (1975) Sociology of science. Annual Review of Sociology p203-21 3. Clarke A, Star SL. (2008) The social worlds framework: a theory/methods package. In Hackett EJ et al. The handbook of science and technology studies. Cambridge MA: MIT Press p113-137 Bonus paper (read if you have time) 4. Mitroff I. (1974). Norms and Counternorms in a Select Group of Apollo Moon Scientists. American Sociological Review 39:79-95 • Aim to ensure that you understand the core arguments of each paper • Look up/note any new terminology (and questions you want to ask) • Think about your critical appraisal of the paper (what are the merits/demerits of the argument, evidence etc) In the seminar we will spend about 5 minutes talking about each paper, and then - building on the two lectures - discuss how these ideas might be used to think about the Web and Web Science. At the end there will be some time for questions and a chance to note your key learning points.