Developing information architecture through records management classification techniques

Purpose – This work aims to draw attention to information retrieval philosophies and techniques allied to the records management profession, advocating a wider professional consideration of a functional approach to information management, in this instance in the development of information architecture. Design/methodology/approach – The paper draws from a hypothesis originally presented by the author that advocated a viewpoint whereby the application of records management techniques, traditionally applied to develop business classification schemes, was offered as an additional solution to organising information resources and services (within a university intranet), where earlier approaches, notably subject- and administrative-based arrangements, were found to be lacking. The hypothesis was tested via work-based action learning and is presented here as an extended case study. The paper also draws on evidence submitted to the Joint Information Systems Committee in support of the Abertay University's application for consideration for the JISC award for innovation in records and information management. Findings – The original hypothesis has been tested in the workplace. Information retrieval techniques, allied to records management (functional classification), were the main influence in the development of pre- and post-coordinate information retrieval systems to support a wider information architecture, where the subject approach was found to be lacking. Their use within the workplace has since been extended. Originality/value – The paper advocates that the development of information retrieval as a discipline should include a wider consideration of functional classification, as this alternative to the subject approach is largely ignored in mainstream IR works

Reviews

Alan Clarke, Designing Computer‐Based Learning Materials, Aldershot: Gower, 2001. ISBN: 0–566–08320–5. Hardback, xviii+196 pages, £45.00

Interpreting Lived Experience through Writing Online in a Graduate Seminar

Participants in an online doctoral seminar participated in the use of a writing strategy to explore the sociocultural contexts of their lived experience. Creating literary texts in three forms was an effective strategy in mediating participants’ understanding. Each form provided a new lens through which to interpret experience. Participants functioned as an interpretive community. The final papers, autobiographical narratives, illuminated the complex relations among prediscursive experience, reflection on experience, distancing, and the iterative transformational quality of time. The online format embodied a virtual interpretive location which allowed participants to revisit texts and postings over time. Des participants dans un cours de doctorat en ligne, ont utilisé une stratégie de rédaction leur permettant d’explorer les contextes socioculturels de leurs expériences de vie. La création de trois formes de textes littéraires s’est avèrée une stratégie efficace pour faciliter la compréhension des participants. Chaque forme littéraire a offert de nouvelle perspectives aux étudiants pour interpréter leurs expériences. Les participants dans cette communauté ont pu interpréter collectivement leurs expériences de vie. Les textes finaux, qui ont pris la forme de narratifs autobiographiques ont illustré la complexité des relations qui existent entre une expérience pré-réfléchie, la réflexion sur l’expérience, la capacité de prendre de la distance face a l’expérience. Dans ces mêmes textes on y trouve la nature réitérée et la qualitée transformative du temps. Le format en ligne a crée un endroit virtuel interprétatif qui a permis aux participants de revisiter les textes et les messages affichés dans le temps

TRADES: A new software to derive orbital parameters from observed transit times and radial velocities. Revisiting Kepler-11 and Kepler-9

Aims. With the purpose of determining the orbital parameters of exoplanetary systems from observational data, we have developed a software, named TRADES (TRAnsits and Dynamics of Exoplanetary Systems), to simultaneously fit observed radial velocities and transit times data. Methods. We implemented a dynamical simulator for N-body systems, which also fits the available data during the orbital integration and determines the best combination of the orbital parameters using grid search, $\chi^2$ minimization, genetic algorithms, particle swarm optimization, and bootstrap analysis. Results. To validate TRADES, we tested the code on a synthetic three-body system and on two real systems discovered by the Kepler mission: Kepler-9 and Kepler-11. These systems are good benchmarks to test multiple exoplanet systems showing transit time variations (TTVs) due to the gravitational interaction among planets. We have found that orbital parameters of Kepler-11 planets agree well with the values proposed in the discovery paper and with a a recent work from the same authors. We analyzed the first three quarters of Kepler-9 system and found parameters in partial agreement with discovery paper. Analyzing transit times (T0s) covering 12 quarters of Kepler data, that we have found a new best-fit solution. This solution outputs masses that are about 55% of the values proposed in the discovery paper; this leads to a reduced semi-amplitude of the radial velocities of about 12.80 m/s.Comment: 14 pages, 13 figures, 6 tables; accepted for publication in Astronomy & Astrophysics, and corrected by the Language Edito

Collaborative trails in e-learning environments

This deliverable focuses on collaboration within groups of learners, and hence collaborative trails. We begin by reviewing the theoretical background to collaborative learning and looking at the kinds of support that computers can give to groups of learners working collaboratively, and then look more deeply at some of the issues in designing environments to support collaborative learning trails and at tools and techniques, including collaborative filtering, that can be used for analysing collaborative trails. We then review the state-of-the-art in supporting collaborative learning in three different areas – experimental academic systems, systems using mobile technology (which are also generally academic), and commercially available systems. The final part of the deliverable presents three scenarios that show where technology that supports groups working collaboratively and producing collaborative trails may be heading in the near future

Why People Search for Images using Web Search Engines

What are the intents or goals behind human interactions with image search engines? Knowing why people search for images is of major concern to Web image search engines because user satisfaction may vary as intent varies. Previous analyses of image search behavior have mostly been query-based, focusing on what images people search for, rather than intent-based, that is, why people search for images. To date, there is no thorough investigation of how different image search intents affect users' search behavior. In this paper, we address the following questions: (1)Why do people search for images in text-based Web image search systems? (2)How does image search behavior change with user intent? (3)Can we predict user intent effectively from interactions during the early stages of a search session? To this end, we conduct both a lab-based user study and a commercial search log analysis. We show that user intents in image search can be grouped into three classes: Explore/Learn, Entertain, and Locate/Acquire. Our lab-based user study reveals different user behavior patterns under these three intents, such as first click time, query reformulation, dwell time and mouse movement on the result page. Based on user interaction features during the early stages of an image search session, that is, before mouse scroll, we develop an intent classifier that is able to achieve promising results for classifying intents into our three intent classes. Given that all features can be obtained online and unobtrusively, the predicted intents can provide guidance for choosing ranking methods immediately after scrolling