A PROPOSAL TO REFINE CONCEPT MAPPING FOR EFFECTIVE SCIENCE LEARNING

Concept maps are found to be useful in eliciting knowledge, meaningful learning, evaluation of understanding and in studying the nature of changes taking place during cognitive development, particularly in the classroom. Several experts have claimed the effectiveness of this tool for learning science. We agree with the claim, but the effectiveness will improve only if we gradually introduce a certain amount of discipline in constructing the maps. The discipline is warranted, we argue, because science thrives to be an unambiguous and rigorously structured body of knowledge. Since learning science may be seen as a process where a novice is expected to be transformed into an expert, we use the context of learning science for making the proposal. Further, we identify certain anomalies in the evaluation of concept maps, and suggest that the evaluation should be based on semantics of the linking words (relation types) and not on graphical criteria alone.\u

User producer interaction in context: a classification

Science, Technology and Innovation Studies show that intensified user producer interaction (UPI) increases chances for successful innovations, especially in the case of emerging technology. It is not always clear, however, what type of interaction is necessary in a particular context. This paper proposes a conceptualization of contexts in terms of three dimensions – the phase of technology development, the flexibility of the technology, and the heterogeneity of user populations – resulting in a classification scheme with eight different contextual situations. The paper identifies and classifies types of interaction, like demand articulation, interactive learning, learning by using and domestication. It appears that each contextual situation demands a different set of UPI types. To illustrate the potential value of the classification scheme, four examples of innovations with varying technological and user characteristics are explored: the refrigerator, clinical anaesthesia, video cassette recording, and the bicycle. For each example the relevant UPI types are discussed and it is shown how these types highlight certain activities and interactions during key events of innovation processes. Finally, some directions for further research are suggested alongside a number of comments on the utility of the classification

Teaching metalinguistic awareness and reading comprehension with riddles

The article reports on multiple meanings in sentences and metalinguistic awareness in education. Comprehension of lexical ambiguity and structural ambiguity are presented as key components of reading education. The author explores the use of riddles in teaching language comprehension and having students develop their own riddles. The author concludes that riddles can encourage metalinguistic skill development and awareness. Other topics include homonyms, ambiguous sentences, riddle books, and brainstorming

Geometrical Product Specification and Verification as toolbox to meet up-to-date technical requirements

The ISO standards for the Geometrical Product Specification and Verification (GPS) define an internationally uniform description language, that allows expressing unambiguously and completely all requirements for the geometry of a product with the corresponding requirements for the inspection process in technical drawings, taking into account current possibilities of measurement and testing technology. The practice shows that the university curricula of the mechanical engineering faculties often include only limited classes on the GPS, mostly as part of curriculum of subjects like Metrology or Fundamentals of Machine Design. This does not allow students to gain enough knowledge on the subject. Currently there is no coherent EU-wide provision for vocational training (VET) in this area. Consortium, members of which are the authors of this paper, is preparing a proposal of an EU project aiming to develop appropriate course

Discovering missing Wikipedia inter-language links by means of cross-lingual word sense disambiguation

Wikipedia is a very popular online multilingual encyclopedia that contains millions of articles covering most written languages. Wikipedia pages contain monolingual hypertext links to other pages, as well as inter-language links to the corresponding pages in other languages. These inter-language links, however, are not always complete. We present a prototype for a cross-lingual link discovery tool that discovers missing Wikipedia inter-language links to corresponding pages in other languages for ambiguous nouns. Although the framework of our approach is language-independent, we built a prototype for our application using Dutch as an input language and Spanish, Italian, English, French and German as target languages. The input for our system is a set of Dutch pages for a given ambiguous noun, and the output of the system is a set of links to the corresponding pages in our five target languages. Our link discovery application contains two submodules. In a first step all pages are retrieved that contain a translation (in our five target languages) of the ambiguous word in the page title (Greedy crawler module), whereas in a second step all corresponding pages are linked between the focus language (being Dutch in our case) and the five target languages (Cross-lingual web page linker module). We consider this second step as a disambiguation task and apply a cross-lingual Word Sense Disambiguation framework to determine whether two pages refer to the same content or not

Acquiring Word-Meaning Mappings for Natural Language Interfaces

This paper focuses on a system, WOLFIE (WOrd Learning From Interpreted Examples), that acquires a semantic lexicon from a corpus of sentences paired with semantic representations. The lexicon learned consists of phrases paired with meaning representations. WOLFIE is part of an integrated system that learns to transform sentences into representations such as logical database queries. Experimental results are presented demonstrating WOLFIE's ability to learn useful lexicons for a database interface in four different natural languages. The usefulness of the lexicons learned by WOLFIE are compared to those acquired by a similar system, with results favorable to WOLFIE. A second set of experiments demonstrates WOLFIE's ability to scale to larger and more difficult, albeit artificially generated, corpora. In natural language acquisition, it is difficult to gather the annotated data needed for supervised learning; however, unannotated data is fairly plentiful. Active learning methods attempt to select for annotation and training only the most informative examples, and therefore are potentially very useful in natural language applications. However, most results to date for active learning have only considered standard classification tasks. To reduce annotation effort while maintaining accuracy, we apply active learning to semantic lexicons. We show that active learning can significantly reduce the number of annotated examples required to achieve a given level of performance

Weakly supervised segment annotation via expectation kernel density estimation

Since the labelling for the positive images/videos is ambiguous in weakly supervised segment annotation, negative mining based methods that only use the intra-class information emerge. In these methods, negative instances are utilized to penalize unknown instances to rank their likelihood of being an object, which can be considered as a voting in terms of similarity. However, these methods 1) ignore the information contained in positive bags, 2) only rank the likelihood but cannot generate an explicit decision function. In this paper, we propose a voting scheme involving not only the definite negative instances but also the ambiguous positive instances to make use of the extra useful information in the weakly labelled positive bags. In the scheme, each instance votes for its label with a magnitude arising from the similarity, and the ambiguous positive instances are assigned soft labels that are iteratively updated during the voting. It overcomes the limitations of voting using only the negative bags. We also propose an expectation kernel density estimation (eKDE) algorithm to gain further insight into the voting mechanism. Experimental results demonstrate the superiority of our scheme beyond the baselines.Comment: 9 pages, 2 figure