Interdisciplinary experience in the Teacher Training College of Vitoria-Gasteiz: teaching profession module

[EN] The higher education regulation process in Europe, known as the Bologna Process, has involved many changes, mainly in relation to methodology and assessment. The paper given below relates to implementing the new EU study plans into the Teacher Training College of Vitoria-Gasteiz; it is the first interdisciplinary paper written involving teaching staff and related to the Teaching Profession module, the first contained in the structure of the new plans. The coordination of teaching staff is one of the main lines of work in the Bologna Process, which is also essential to develop the right skills and maximise the role of students as an active learning component. The use of active, interdisciplinary methodologies has opened up a new dimension in universities, requiring the elimination of the once componential, individual structure, making us look for new areas of exchange that make it possible for students' training to be developed jointly.

Contributions to unsupervised and supervised learning with applications in digital image processing

311 p. : il.

Using the system dynamics paradigm in teaching and learning technological university subjets

2nd International Conference on Education and New Learning Technologies

Indication of pollution in a teaching and research farm reservoir

The production and productivity of a water body is largely dependent on its quality. One major source of water pollution is from the agrochemicals from nearby farmlands. The quality of water in the Obafemi Awolowo University Teaching and Research Farm Reservoir (Ile-Ife, Nigeria) was monitored between October, 1993 and March, 1994. Structured questionnaires were administered to obtain information on the types of agrochemicals in use on the farm. Water samples were collected fortnightly for analyses of the physico-chemical parameters and ionic content of the water. Investigation revealed that 21 agrochemicals had been in use on the farm. The physico-chemical parameters of the water showed that the water was very poor in nutrient. The high concentration of ammonium ion contents of the water shows an indication that the residues of certain agrochemicals got into the water to pollute it. Agrochemicals should be used with great caution on farmlands especially in areas close to water bodies from which man obtains fish and other proteinous foods. This paper also suggests a regular monitoring of water quality of reservoirs in order to pick the earliest signs of pollution

Teaching Change: Information Literacy Collaboration at Texas A&M University at Galveston

While college students use a wide array of technologies to access information, their skills at determining what is relevant, in a university setting and in life, are poor. Many of these skills have to be taught in college courses. Instruction must be performed by a collaborative team using technologies that effectively reach students. This team must be ready to go into the classroom when needed and be able to address the problem whenever the student needs assistance. The results will be better writing and better research skills that will not only benefit the faculty but will lead to lifelong learning.

Teaching Students to Evaluate E-Reference Sources with Moodle Forums

This presentation will describe the use of online forums in Moodle, a course management system, to teach students to evaluate both print and electronic reference sources. For two semesters I have created an online forum called the Fishing Hole Scouting Report for an in-class exercise. Students are given 2-3 background questions and told to find the answers using both a library-approved reference source and Wikipedia. Students then evaluate the reference source for effectiveness and ease of use, compared to Wikipedia, and post their comments in the forum to share with the class. I will highlight the educational benefits of using Moodle forums for this purpose and discuss best practices for selecting the most effective sources and questions.

On the optimal usage of labelled examples in semi-supervised multi-class classification problems

In recent years, the performance of semi-supervised learning has been theoretically investigated. However, most of this theoretical development has focussed on binary classification problems. In this paper, we take it a step further by extending the work of Castelli and Cover [1] [2] to the multi-class paradigm. Particularly, we consider the key problem in semi-supervised learning of classifying an unseen instance x into one of K different classes, using a training dataset sampled from a mixture density distribution and composed of l labelled records and u unlabelled examples. Even under the assumption of identifiability of the mixture and having infinite unlabelled examples, labelled records are needed to determine the K decision regions. Therefore, in this paper, we first investigate the minimum number of labelled examples needed to accomplish that task. Then, we propose an optimal multi-class learning algorithm which is a generalisation of the optimal procedure proposed in the literature for binary problems. Finally, we make use of this generalisation to study the probability of error when the binary class constraint is relaxed.

BAMS: A Tool for Supervised Burned Area Mapping Using Landsat Data

A new supervised burned area mapping software named BAMS (Burned Area Mapping Software) is presented in this paper. The tool was built from standard ArcGIS (TM) libraries. It computes several of the spectral indexes most commonly used in burned area detection and implements a two-phase supervised strategy to map areas burned between two Landsat multitemporal images. The only input required from the user is the visual delimitation of a few burned areas, from which burned perimeters are extracted. After the discrimination of burned patches, the user can visually assess the results, and iteratively select additional sampling burned areas to improve the extent of the burned patches. The final result of the BAMS program is a polygon vector layer containing three categories: (a) burned perimeters, (b) unburned areas, and (c) non-observed areas. The latter refer to clouds or sensor observation errors. Outputs of the BAMS code meet the requirements of file formats and structure of standard validation protocols. This paper presents the tool's structure and technical basis. The program has been tested in six areas located in the United States, for various ecosystems and land covers, and then compared against the National Monitoring Trends in Burn Severity (MTBS) Burned Area Boundaries Dataset.