Demotivation: Understanding Resistance to English Language Learning - The Case of Vietnamese Students

Demotivation in English language learning was investigated, using Vietnam as a case study, with three main foci: (i) the reasons (i.e., the demotives) underlying demotivation; (ii) the degree of influence of different demotives; and (iii) students’ experiences in overcoming demotivation. Using stimulated recall essays from 100 university students of their foreign language learning experiences, the findings indicated that demotivation was a significant issue for EFL learning, and a framework for discussing the different sources of demotives was developed. While some categories of demotives occurred more frequent than others, no category appeared to be more or less difficult to overcome. Rather, students’ awareness of the role of English language and their determination to succeed were critical factors in overcoming demotivation.

Special issue on advances in data mining and its applications

Data mining is the process to identify valid, implicit, previously unknown, potentially useful and understandable information from large databases. It is an important step in the process of knowledge discovery in databases, (Olaru & Wehenkel, 1999). In a data mining process, input data can be structured, seme-structured, or unstructured. Data can be in text, categorical or numerical values. One of the important characteristics of data mining is its ability to deal data with large volume, distributed, time variant, noisy, and high dimensionality. A large number of data mining algorithms have been developed for different applications. For example, association rules mining can be useful for market basket problems, clustering algorithms can be used to discover trends in unsupervised learning problems, classification algorithms can be applied in decision-making problems, and sequential and time series mining algorithms can be used in predicting events, fault detection, and other supervised learning problems (Vapnik, 1999). Classification is among the most important tasks in the data mining, particularly for data mining applications into engineering fields. Together with regression, classification is mainly for predictive modelling. So far, there have been a number of classification algorithms in practice. According to (Sebastiani, 2002), the main classification algorithms can be categorized as: decision tree and rule based approach such as C4.5 (Quinlan, 1996); probability methods such as Bayesian classifier (Lewis, 1998); on-line methods such as Winnow (Littlestone, 1988) and CVFDT (Hulten 2001), neural networks methods (Rumelhart, Hinton & Wiliams, 1986); example-based methods such as k-nearest neighbors (Duda & Hart, 1973), and SVM (Cortes & Vapnik, 1995). Other important techniques for classification tasks include Associative Classification (Liu et al, 1998) and Ensemble Classification (Tumer, 1996).

Triple objective team mentoring: achieving learning objectives with chemical engineering students

A sophisticated style of mentoring has been found to be essential to support engineering student teams undertaking technically demanding, real-world problems as part of a Project-Centred Curriculum (PCC) at The University of Queensland. The term ‘triple-objective’ mentoring was coined to define mentoring that addresses not only the student’s technical goal achievement but also their time and team management. This is achieved through a number of formal mentor meetings that are informed by a confidential instrument which requires students to individually reflect on team processes prior to the meeting, and a checklist of technical requirements against which the interim student team progress and achievements are assessed. Triple-objective mentoring requires significant time input and coordination by the academic but has been shown to ensure effective student team work and learning undiminished by team dysfunction. Student feedback shows they value the process and agree that the tools developed to support the process are effective in developing and assessing team work and skills with average scores mostly above 3 on a four point scale.

Project to investigate improving literacy and numeracy outcomes of distance education students in the early years of schooling

The project was commissioned to investigate and analyse the issue of effective support for distance education students in the early years of school to maximise literacy and numeracy outcomes. The scope of this project was limited to students living in rural and remote areas who are undertaking education at home and who are in their early years of schooling. For the purpose of this project, the early years are conceptualised as the first three years of formal compulsory schooling in each of the States and Territories. There were a number of key tasks for the project which included: 1. Examining of the role of home tutors/supervisors This included interviewing personnel from the State and Territory distance education providers as well as the principals, teachers, home tutors and children. 2. Describing literacy and numeracy teaching and learning, and the use of information and communication technologies (ICT) in distance education This aspect of the project involved a critical review and analysis of relevant literature and reports in the last five years, and a consideration of the new initiatives that had been implemented in the States and Territories in the last two years. 3. The development of resources Through examination of the role of home tutors/supervisors, and an examination of literacy and numeracy and the use of technology in distance education, three resources were developed: ● A guide for home tutors/supervisors and schools of distance education about effective intervention and assessment strategies to support students’ learning and to assist the home tutors/supervisors in implementing ICT to support the development of literacy and numeracy in the early years. ● A calendar of activities for literacy and numeracy that would act as a stimulus for integrated and authentic activity for young children. ● An embryonic website of resources for the stakeholders in rural and distance education that might act as a catalyst for future resource building and sharing. In this way the final key task of the project, which was to create a context for a strategic dissemination plan, was realised when a strategy to address effective dissemination of the findings of the project so as to maximise their usefulness for the relevant groups was achieved.

The clinical utility of the Hopkins Verbal Learning Test as a screening test for mild dementia

Objective. The purpose of this study was to determine whether the Hopkins Verbal Learning Test (HVLT) could be used as a valid and reliable screening test for mild dementia in older people, and to compare its performance to that of the Mini-Mental State Examination (MMSE). Method. Using a cross-sectional design, we studied three groups of older subjects recruited from a district geriatric psychiatry service: (1) 26 patients with DSM-IV dementia and MMSE scores of 18 or better; (2) 15 patients with psychiatric diagnoses other than dementia; and (3) 15 normal controls. The relationship of each potential cutting point on the HVLT and the MMSE was examined against the independently ascertained DSM-IV diagnoses of dementia using a Receiver Operating Characteristic (ROC) analysis. Results. The subjects consisted of 21 (37.5%) males and 35 (62.5%) females with a mean age of 74.7 (SD 6.1) years and a mean of 8.5 (SD 1.8) years of formal education. ROC analysis indicated that the optimal cutting point for detecting mild dementia in this group of subjects using the HVLT was 18/19 (sensitivity = 0.96, specificity = 0.80) and using the MMSE was 25/26 (sensitivity = 0.88, specificity = 0.93). Conclusions. The HVLT can be recommended as a valid and reliable screening test for mild dementia and as an adjunct in the clinical assessment of older people. The HVLT had better sensitivity than the MMSE in detecting patients with mild dementia, whereas the MMSE had better specificity. Copyright (C) 2000 John Wiley & Sons, Ltd.

Progress in integrated assessment and modelling

Environmental processes have been modelled for decades. However. the need for integrated assessment and modeling (IAM) has,town as the extent and severity of environmental problems in the 21st Century worsens. The scale of IAM is not restricted to the global level as in climate change models, but includes local and regional models of environmental problems. This paper discusses various definitions of IAM and identifies five different types of integration that Lire needed for the effective solution of environmental problems. The future is then depicted in the form of two brief scenarios: one optimistic and one pessimistic. The current state of IAM is then briefly reviewed. The issues of complexity and validation in IAM are recognised as more complex than in traditional disciplinary approaches. Communication is identified as a central issue both internally among team members and externally with decision-makers. stakeholders and other scientists. Finally it is concluded that the process of integrated assessment and modelling is considered as important as the product for any particular project. By learning to work together and recognise the contribution of all team members and participants, it is believed that we will have a strong scientific and social basis to address the environmental problems of the 21st Century. (C) 2002 Elsevier Science Ltd. All rights reserved.