A picture is worth a thousand words : an approach to learning about visuals

Students in the middle years encounter an increasing range of unfamiliar visuals. Visual literacy, the ability to encode and decode visuals and to think visually, is an expectation of all middle years curriculum areas and an expectation of NAPLAN literacy and numeracy tests. This article presents a multidisciplinary approach to teaching visual literacy that links the content of all learning areas and encourages students to transfer skills from familiar to unfamiliar contexts. It proposes a classification of visuals in six parts: one-dimensional; two-dimensional; map; shape; connection; and picture, based on the properties, rather than the purpose, of the visual. By placing a visual in one of these six categories, students learn to transfer the skills used to decode familiar visuals to unfamiliar cases in the same category. The article also discusses a range of other teaching strategies that can be used to complement this multi-disciplinary approach.

Putting an "end" to the motor cortex representations of action words

Language processing is an example of implicit learning of multiple statistical cues that provide probabilistic information regarding word structure and use. Much of the current debate about language embodiment is devoted to how action words are represented in the brain, with motor cortex activity evoked by these words assumed to selectively reflect conceptual content and/or its simulation. We investigated whether motor cortex activity evoked by manual action words (e.g., caress) might reflect sensitivity to probabilistic orthographic-phonological cues to grammatical category embedded within individual words. We first review neuroimaging data demonstrating that nonwords evoke activity much more reliably than action words along the entire motor strip, encompassing regions proposed to be action category specific. Using fMRI, we found that disyllabic words denoting manual actions evoked increased motor cortex activity compared with non-body-part-related words (e.g., canyon), activity which overlaps that evoked by observing and executing hand movements. This result is typically interpreted in support of language embodiment. Crucially, we also found that disyllabic nonwords containing endings with probabilistic cues predictive of verb status (e.g., -eve) evoked increased activity compared with nonwords with endings predictive of noun status (e.g., -age) in the identical motor area. Thus, motor cortex responses to action words cannot be assumed to selectively reflect conceptual content and/or its simulation. Our results clearly demonstrate motor cortex activity reflects implicit processing of ortho-phonological statistical regularities that help to distinguish a word's grammatical class.

Different countries, same science classes: Students’ experiences of school science in their own words

This paper reviews the remarkably similar experiences of school science reported by high school students in Sweden, England, and Australia. It compares student narratives from interpretive studies by Lindahl, by Osborne and Collins, and by Lyons, identifying core themes relating to critical contemporary issues in science education. These themes revolve around the transmissive pedagogy, decontextualized content, and unnecessary difficulty of school science commonly reported by students in the studies. Their collective experiences are used as a framework for examining student conceptions of, and attitudes to, school science more generally, drawing on an extensive range of international literature. The paper argues that the experiences of students in the three studies provide important insights into the widespread declines in interest and enrolments in high school and university science courses.

Towards automated content analysis of discussion transcripts: A cognitive presence case

In this paper, we present the results of an exploratory study that examined the problem of automating content analysis of student online discussion transcripts. We looked at the problem of coding discussion transcripts for the levels of cognitive presence, one of the three main constructs in the Community of Inquiry (CoI) model of distance education. Using Coh-Metrix and LIWC features, together with a set of custom features developed to capture discussion context, we developed a random forest classification system that achieved 70.3% classification accuracy and 0.63 Cohen's kappa, which is significantly higher than values reported in the previous studies. Besides improvement in classification accuracy, the developed system is also less sensitive to overfitting as it uses only 205 classification features, which is around 100 times less features than in similar systems based on bag-of-words features. We also provide an overview of the classification features most indicative of the different phases of cognitive presence that gives an additional insights into the nature of cognitive presence learning cycle. Overall, our results show great potential of the proposed approach, with an added benefit of providing further characterization of the cognitive presence coding scheme.

From the consciousness industry to the creative industries : consumer-created content, social network markets, & the growth of knowledge

In studies of media industries, too much attention has been paid to providers and firms, too little to consumers and markets. But with user-created content, the question first posed more than a generation ago by the uses & gratifications method and taken up by semiotics and the active audience tradition (‘what do audiences do with media?’), has resurfaced with renewed force. What’s new is that where this question (of what the media industries and audiences did with each other) used to be individualist and functionalist, now, with the advent of social networks using Web 2.0 affordances, it can be re-posed at the level of systems and populations as well.

Search engine content analysis

Search engines have forever changed the way people access and discover knowledge, allowing information about almost any subject to be quickly and easily retrieved within seconds. As increasingly more material becomes available electronically the influence of search engines on our lives will continue to grow. This presents the problem of how to find what information is contained in each search engine, what bias a search engine may have, and how to select the best search engine for a particular information need. This research introduces a new method, search engine content analysis, in order to solve the above problem. Search engine content analysis is a new development of traditional information retrieval field called collection selection, which deals with general information repositories. Current research in collection selection relies on full access to the collection or estimations of the size of the collections. Also collection descriptions are often represented as term occurrence statistics. An automatic ontology learning method is developed for the search engine content analysis, which trains an ontology with world knowledge of hundreds of different subjects in a multilevel taxonomy. This ontology is then mined to find important classification rules, and these rules are used to perform an extensive analysis of the content of the largest general purpose Internet search engines in use today. Instead of representing collections as a set of terms, which commonly occurs in collection selection, they are represented as a set of subjects, leading to a more robust representation of information and a decrease of synonymy. The ontology based method was compared with ReDDE (Relevant Document Distribution Estimation method for resource selection) using the standard R-value metric, with encouraging results. ReDDE is the current state of the art collection selection method which relies on collection size estimation. The method was also used to analyse the content of the most popular search engines in use today, including Google and Yahoo. In addition several specialist search engines such as Pubmed and the U.S. Department of Agriculture were analysed. In conclusion, this research shows that the ontology based method mitigates the need for collection size estimation.

Exploring the influence of a science content course incorporating explicit nature of science and argumentation instruction on preservice primary teachers' views of nature of science

There exists a general consensus in the science education literature around the goal of enhancing students. and teachers. views of nature of science (NOS). An emerging area of research in science education explores NOS and argumentation, and the aim of this study was to explore the effectiveness of a science content course incorporating explicit NOS and argumentation instruction on preservice primary teachers. views of NOS. A constructivist perspective guided the study, and the research strategy employed was case study research. Five preservice primary teachers were selected for intensive investigation in the study, which incorporated explicit NOS and argumentation instruction, and utilised scientific and socioscientific contexts for argumentation to provide opportunities for participants to apply their NOS understandings to their arguments. Four primary sources of data were used to provide evidence for the interpretations, recommendations, and implications that emerged from the study. These data sources included questionnaires and surveys, interviews, audio- and video-taped class sessions, and written artefacts. Data analysis involved the formation of various assertions that informed the major findings of the study, and a variety of validity and ethical protocols were considered during the analysis to ensure the findings and interpretations emerging from the data were valid. Results indicated that the science content course was effective in enabling four of the five participants. views of NOS to be changed. All of the participants expressed predominantly limited views of the majority of the examined NOS aspects at the commencement of the study. Many positive changes were evident at the end of the study with four of the five participants expressing partially informed and/or informed views of the majority of the examined NOS aspects. A critical analysis of the effectiveness of the various course components designed to facilitate the development of participants‟ views of NOS in the study, led to the identification of three factors that mediated the development of participants‟ NOS views: (a) contextual factors (including context of argumentation, and mode of argumentation), (b) task-specific factors (including argumentation scaffolds, epistemological probes, and consideration of alternative data and explanations), and (c) personal factors (including perceived previous knowledge about NOS, appreciation of the importance and utility value of NOS, and durability and persistence of pre-existing beliefs). A consideration of the above factors informs recommendations for future studies that seek to incorporate explicit NOS and argumentation instruction as a context for learning about NOS.

Effective web service discovery using a combination of a semantic model and a data mining technique

With the advent of Service Oriented Architecture, Web Services have gained tremendous popularity. Due to the availability of a large number of Web services, finding an appropriate Web service according to the requirement of the user is a challenge. This warrants the need to establish an effective and reliable process of Web service discovery. A considerable body of research has emerged to develop methods to improve the accuracy of Web service discovery to match the best service. The process of Web service discovery results in suggesting many individual services that partially fulfil the user’s interest. By considering the semantic relationships of words used in describing the services as well as the use of input and output parameters can lead to accurate Web service discovery. Appropriate linking of individual matched services should fully satisfy the requirements which the user is looking for. This research proposes to integrate a semantic model and a data mining technique to enhance the accuracy of Web service discovery. A novel three-phase Web service discovery methodology has been proposed. The first phase performs match-making to find semantically similar Web services for a user query. In order to perform semantic analysis on the content present in the Web service description language document, the support-based latent semantic kernel is constructed using an innovative concept of binning and merging on the large quantity of text documents covering diverse areas of domain of knowledge. The use of a generic latent semantic kernel constructed with a large number of terms helps to find the hidden meaning of the query terms which otherwise could not be found. Sometimes a single Web service is unable to fully satisfy the requirement of the user. In such cases, a composition of multiple inter-related Web services is presented to the user. The task of checking the possibility of linking multiple Web services is done in the second phase. Once the feasibility of linking Web services is checked, the objective is to provide the user with the best composition of Web services. In the link analysis phase, the Web services are modelled as nodes of a graph and an allpair shortest-path algorithm is applied to find the optimum path at the minimum cost for traversal. The third phase which is the system integration, integrates the results from the preceding two phases by using an original fusion algorithm in the fusion engine. Finally, the recommendation engine which is an integral part of the system integration phase makes the final recommendations including individual and composite Web services to the user. In order to evaluate the performance of the proposed method, extensive experimentation has been performed. Results of the proposed support-based semantic kernel method of Web service discovery are compared with the results of the standard keyword-based information-retrieval method and a clustering-based machine-learning method of Web service discovery. The proposed method outperforms both information-retrieval and machine-learning based methods. Experimental results and statistical analysis also show that the best Web services compositions are obtained by considering 10 to 15 Web services that are found in phase-I for linking. Empirical results also ascertain that the fusion engine boosts the accuracy of Web service discovery by combining the inputs from both the semantic analysis (phase-I) and the link analysis (phase-II) in a systematic fashion. Overall, the accuracy of Web service discovery with the proposed method shows a significant improvement over traditional discovery methods.