The Teacher: another Variable in the Use of Foreign Language Learning Strategies?

The Bologna process aims to create the European Higher Education Framework (EHEF) by making academic degree and quality assurance standards more comparable and compatible throughout Europe. The EHEF has different implications for university students, representing a change in emphasis from ‘teaching’ to ‘learning’, from a teacher-centred approach to a student-centred approach. In the last thirty years, researchers have discussed the role of teachers and students in the language learning-teaching process. Until then, the acquisition of a foreign language was focused on the teacher’s methodology. In the 80s and 90s, a series of student-centred approaches emerged, with the aim of making students more autonomous and independent in their learning. Language learning strategies are part of the tools used to improve language learning. There are different definitions and taxonomies of language learning strategies (Chamot (2001), Cohen (1998), Oxford (1990), O'Malley (1990) and Wenden & Rubin (1987) and there have been extensive descriptive studies on the different variables affecting the use of learning strategies including gender, previous linguistic knowledge, motivation, learning styles and/or second language versus foreign language acquisition. This paper aims to explore the instructor’s conscious or unconscious influence students’ use of learning strategies. To undertake this study, a group of teachers was asked to assess the 50 strategies presented in an adapted version of the Strategies Inventory Language Learning (Oxford 1990) according to their suitability and practicality for their students. The participants were lecturers from the French and English Department at Cádiz University. The languages included in the study were English, French and German for specific and general purposes

Taxonomies for Development

{Excerpt} Organizations spend millions of dollars on management systems without commensurate investments in the categorization needed to organize the information they rest on. Taxonomy work is strategic work: it enables efficient and interoperable retrieval and sharing of data, information, and knowledge by building needs and natural workflows in intuitive structures. Bible readers think that taxonomy is the world’s oldest profession. Whatever the case, the word is now synonymous with any hierarchical system of classification that orders domains of inquiry into groups and signifies natural relationships among these. (A taxonomic scheme is often depicted as a “tree” and individual taxonomic units as “branches” in the tree.) Almost anything can be classified according to some taxonomic scheme. Resulting catalogs provide conceptual frameworks for miscellaneous purposes including knowledge identification, creation, storage, sharing, and use, including related decision making

Evaluating cost taxonomies for information systems management

The consideration of costs, benefits and risks underpin many Information System (IS) evaluation decisions. Yet, vendors and project-champions alike tend to identify and focus much of their effort on the benefits achievable from the adoption of new technology, as it is often not in the interest of key stakeholders to spend too much time considering the wider cost and risk implications of enterprise-wide technology adoptions. In identifying a void in the literature, the authors of the paper present a critical analysis of IS-cost taxonomies. In doing so, the authors establish that such cost taxonomies tend to be esoteric and difficult to operationalize, as they lack specifics in detail. Therefore, in developing a deeper understanding of IS-related costs, the authors position the need to identify, control and reduce IS-related costs within the information systems evaluation domain, through culminating and then synthesizing the literature into a frame of reference that supports the evaluation of information systems through a deeper understanding of IS-cost taxonomies. The paper then concludes by emphasizing that the total costs associated with IS-adoption can only be determined after having considered the multi-faceted dimensions of information system investments

A quantitative taxonomy of human hand grasps

Background: A proper modeling of human grasping and of hand movements is fundamental for robotics, prosthetics, physiology and rehabilitation. The taxonomies of hand grasps that have been proposed in scientific literature so far are based on qualitative analyses of the movements and thus they are usually not quantitatively justified. Methods: This paper presents to the best of our knowledge the first quantitative taxonomy of hand grasps based on biomedical data measurements. The taxonomy is based on electromyography and kinematic data recorded from 40 healthy subjects performing 20 unique hand grasps. For each subject, a set of hierarchical trees are computed for several signal features. Afterwards, the trees are combined, first into modality-specific (i.e. muscular and kinematic) taxonomies of hand grasps and then into a general quantitative taxonomy of hand movements. The modality-specific taxonomies provide similar results despite describing different parameters of hand movements, one being muscular and the other kinematic. Results: The general taxonomy merges the kinematic and muscular description into a comprehensive hierarchical structure. The obtained results clarify what has been proposed in the literature so far and they partially confirm the qualitative parameters used to create previous taxonomies of hand grasps. According to the results, hand movements can be divided into five movement categories defined based on the overall grasp shape, finger positioning and muscular activation. Part of the results appears qualitatively in accordance with previous results describing kinematic hand grasping synergies. Conclusions: The taxonomy of hand grasps proposed in this paper clarifies with quantitative measurements what has been proposed in the field on a qualitative basis, thus having a potential impact on several scientific fields

The propagation of technology management taxonomies for evaluating investments in information systems

To provide managers with a critical insight into the management of new technology, this paper uses a case study research strategy to examine the technology management experiences of a leading UK manufacturing organization during its adoption of a vendor-supplied Manufacturing Resource Planning information system.<br /

TiFi: Taxonomy Induction for Fictional Domains [Extended version]

Taxonomies are important building blocks of structured knowledge bases, and their construction from text sources and Wikipedia has received much attention. In this paper we focus on the construction of taxonomies for fictional domains, using noisy category systems from fan wikis or text extraction as input. Such fictional domains are archetypes of entity universes that are poorly covered by Wikipedia, such as also enterprise-specific knowledge bases or highly specialized verticals. Our fiction-targeted approach, called TiFi, consists of three phases: (i) category cleaning, by identifying candidate categories that truly represent classes in the domain of interest, (ii) edge cleaning, by selecting subcategory relationships that correspond to class subsumption, and (iii) top-level construction, by mapping classes onto a subset of high-level WordNet categories. A comprehensive evaluation shows that TiFi is able to construct taxonomies for a diverse range of fictional domains such as Lord of the Rings, The Simpsons or Greek Mythology with very high precision and that it outperforms state-of-the-art baselines for taxonomy induction by a substantial margin

Ways of Applying Artificial Intelligence in Software Engineering

As Artificial Intelligence (AI) techniques have become more powerful and easier to use they are increasingly deployed as key components of modern software systems. While this enables new functionality and often allows better adaptation to user needs it also creates additional problems for software engineers and exposes companies to new risks. Some work has been done to better understand the interaction between Software Engineering and AI but we lack methods to classify ways of applying AI in software systems and to analyse and understand the risks this poses. Only by doing so can we devise tools and solutions to help mitigate them. This paper presents the AI in SE Application Levels (AI-SEAL) taxonomy that categorises applications according to their point of AI application, the type of AI technology used and the automation level allowed. We show the usefulness of this taxonomy by classifying 15 papers from previous editions of the RAISE workshop. Results show that the taxonomy allows classification of distinct AI applications and provides insights concerning the risks associated with them. We argue that this will be important for companies in deciding how to apply AI in their software applications and to create strategies for its use