Vpliv aktivne vizualizacije na sposobnost pomnjenja besedne definicije pri dijakih

The era of visual communication influences the cognitive strategies of the individual. Education, too, must adjust to these changes, which raises questions regarding the use of visualisation in teaching. In the present study, we examine the impact of visualisation on the ability of high school students to memorise text. In the theoretical part of the research, we first clarify the concept of visualisation. We define the concept of active visualisation and visualisation as a means of acquiring and conveying knowledge, and we describe the different kinds of visualisation (appearance-based analogies and form-based analogies), specifically defining appearance-based schemata visualisations (where imagery is articulated in a typical culturally trained manner). In the empirical part of the research, we perform an experiment in which we evaluate the effects of visualisation on students’ ability to memorise a difficult written definition. According to the theoretical findings, we establish two hypotheses. In the first, we assume that the majority of the visualisations that students form will be appearance-based schemata visualisations. This hypothesis is based on the assumption that, in visualisation, people spontaneously use analogies based on imagery and schemas that are typical of their society. In the second hypothesis, we assume that active visualisation will contribute to the students’ ability to memorise text in a statistically significant way. This hypothesis is based on the assumption that the combination of verbal and visual experiences enhances cognitive learning. Both hypotheses were confirmed in the research. As our study only dealt with the impact of the most spontaneous type of appearancebased schemata visualisations, we see further possibilities in researching the influence of visualisations that are more complex formally. (DIPF/Orig.

Visual support for ontology learning: an experience report

Ontology learning methods aim to automate ontology construction. They are complex methods involving several elements such as documents, terms and concepts. During the development of an ontology learning method, as well as during its deployment, several situations occur where understanding the relations between these elements is crucial. Our hypothesis is that visual techniques can be used to aid this understanding. To support this claim, we present a set of such complex situations and describe the visual solutions that we developed to support them

Supporting active database learning and training through interactive multimedia

The learning objectives of a database course include aspects from conceptual and theoretical knowledge to practical development and implementation skills. We present an interactive educational multimedia system based on the virtual apprenticeship model for the knowledge- and skills-oriented Web-based education of database course students. Combining knowledge learning and skills training in an integrated environment is a central aspect of our system. We show that tool-mediated independent learning and training in an authentic setting is an alternative to traditional classroom-based approaches

SpectraClassifier 1.0: a user friendly, automated MRS-based classifier-development system

Background: SpectraClassifier (SC) is a Java solution for designing and implementing Magnetic Resonance Spectroscopy (MRS)-based classifiers. The main goal of SC is to allow users with minimum background knowledge of multivariate statistics to perform a fully automated pattern recognition analysis. SC incorporates feature selection (greedy stepwise approach, either forward or backward), and feature extraction (PCA). Fisher Linear Discriminant Analysis is the method of choice for classification. Classifier evaluation is performed through various methods: display of the confusion matrix of the training and testing datasets; K-fold cross-validation, leave-one-out and bootstrapping as well as Receiver Operating Characteristic (ROC) curves. Results: SC is composed of the following modules: Classifier design, Data exploration, Data visualisation, Classifier evaluation, Reports, and Classifier history. It is able to read low resolution in-vivo MRS (single-voxel and multi-voxel) and high resolution tissue MRS (HRMAS), processed with existing tools (jMRUI, INTERPRET, 3DiCSI or TopSpin). In addition, to facilitate exchanging data between applications, a standard format capable of storing all the information needed for a dataset was developed. Each functionality of SC has been specifically validated with real data with the purpose of bug-testing and methods validation. Data from the INTERPRET project was used. Conclusions: SC is a user-friendly software designed to fulfil the needs of potential users in the MRS community. It accepts all kinds of pre-processed MRS data types and classifies them semi-automatically, allowing spectroscopists to concentrate on interpretation of results with the use of its visualisation tools