66,517 research outputs found

    Investigating attributes affecting the performance of WBI users

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    This is the post-print version of the final paper published in Computers and Education. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.Numerous research studies have explored the effect of hypermedia on learners' performance using Web Based Instruction (WBI). A learner's performance is determined by their varying skills and abilities as well as various differences such as gender, cognitive style and prior knowledge. In this paper, we investigate how differences between individuals influenced learner's performance using a hypermedia system to accommodate an individual's preferences. The effect of learning performance is investigated to explore relationships between measurement attributes including gain scores (post-test minus pre-test), number of pages visited in a WBI program, and time spent on such pages. A data mining approach was used to analyze the results by comparing two clustering algorithms (K-Means and Hierarchical) with two different numbers of clusters. Individual differences had a significant impact on learner behavior in our WBI program. Additionally, we found that the relationship between attributes that measure performance played an influential role in exploring performance level; the relationship between such attributes induced rules in measuring level of a learners' performance

    Unraveling the influence of domain knowledge during simulation-based inquiry learning

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    This study investigated whether the mere knowledge of the meaning of variables can facilitate inquiry learning processes and outcomes. Fifty-seven college freshmen were randomly allocated to one of three inquiry tasks. The concrete task had familiar variables from which hypotheses about their underlying relations could be inferred. The intermediate task used familiar variables that did not invoke underlying relations, whereas the abstract task contained unfamiliar variables that did not allow for inference of hypotheses about relations. Results showed that concrete participants performed more successfully and efficiently than intermediate participants, who in turn were equally successful and efficient as abstract participants. From these findings it was concluded that students learning by inquiry benefit little from knowledge of the meaning of variables per se. Some additional understanding of the way these variables are interrelated seems required to enhance inquiry learning processes and outcomes

    Intelligent computational sketching support for conceptual design

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    Sketches, with their flexibility and suggestiveness, are in many ways ideal for expressing emerging design concepts. This can be seen from the fact that the process of representing early designs by free-hand drawings was used as far back as in the early 15th century [1]. On the other hand, CAD systems have become widely accepted as an essential design tool in recent years, not least because they provide a base on which design analysis can be carried out. Efficient transfer of sketches into a CAD representation, therefore, is a powerful addition to the designers' armoury.It has been pointed out by many that a pen-on-paper system is the best tool for sketching. One of the crucial requirements of a computer aided sketching system is its ability to recognise and interpret the elements of sketches. 'Sketch recognition', as it has come to be known, has been widely studied by people working in such fields: as artificial intelligence to human-computer interaction and robotic vision. Despite the continuing efforts to solve the problem of appropriate conceptual design modelling, it is difficult to achieve completely accurate recognition of sketches because usually sketches implicate vague information, and the idiosyncratic expression and understanding differ from each designer

    Discovering academics' key learning connections: An ego-centric network approach to analysing learning about teaching

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    The aim of this exploratory study is to investigate the role of personal networks in supporting academicsā€™ professional learning about teaching. As part of a wider project, the paper focuses on the composition of academicsā€™ networks and possible implications of network tendencies for academicsā€™ learning about teaching. The study adopts a mixed-method approach. Firstly, the composition of academicsā€™ networks is examined using Social Network Analysis. Secondly, the role of these networks in academicsā€™ learning about teaching is analysed through semi-structured interviews. Findings reveal the prevalence of localised and strong-tie connections, which could inhibit opportunities for effective learning and spread of innovations in teaching. The study highlights the need to promote connectivity within and across institutions, creating favourable conditions for effective professional development

    Discovering Links for Metadata Enrichment on Computer Science Papers

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    At the very beginning of compiling a bibliography, usually only basic information, such as title, authors and publication date of an item are known. In order to gather additional information about a specific item, one typically has to search the library catalog or use a web search engine. This look-up procedure implies a manual effort for every single item of a bibliography. In this technical report we present a proof of concept which utilizes Linked Data technology for the simple enrichment of sparse metadata sets. This is done by discovering owl:sameAs links be- tween an initial set of computer science papers and resources from external data sources like DBLP, ACM and the Semantic Web Conference Corpus. In this report, we demonstrate how the link discovery tool Silk is used to detect additional information and to enrich an initial set of records in the computer science domain. The pros and cons of silk as link discovery tool are summarized in the end.Comment: 22 pages, 4 figures, 7 listings, presented at SWIB1

    Shape matching and clustering

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    Generalising knowledge and matching patterns is a basic human trait in re-using past experiences. We often cluster (group) knowledge of similar attributes as a process of learning and or aid to manage the complexity and re-use of experiential knowledge [1, 2]. In conceptual design, an ill-defined shape may be recognised as more than one type. Resulting in shapes possibly being classified differently when different criteria are applied. This paper outlines the work being carried out to develop a new technique for shape clustering. It highlights the current methods for analysing shapes found in computer aided sketching systems, before a method is proposed that addresses shape clustering and pattern matching. Clustering for vague geometric models and multiple viewpoint support are explored

    Jesus Teaching Through Discovery

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    What made Jesusā€™ teaching effective? Jesusā€™ teaching was effective because it resulted in changing the hearersā€™ heart and having the hearer apply his message to their lives. Jesusā€™ teaching amazed listeners, for example, after hearing the Sermon on the Mount the crowds were amazed (Matthew 7:28). He taught ordinary, unschooled, disciples for three years and their teaching changed the entire world of their time and continues to affect our world today. The hearers of his teaching opened their ā€œeyes and earsā€. What made his teaching so successful? His teaching consisted of a set of procedures. Jesus identified the teaching moments; facilitated inquiry by giving inspiring questions, enabled audiences to formulate hypothesizes through insights, and encouraged his audiences to apply their learning to practical situations. Jesus knew that learning was not simply memorizing facts or reciting the Law of Moses. Learning involved organizing new facts to existing schema and applying that new information. His teaching is typically a discovery learning process. The following article will review Jesusā€™ teaching method through the modern lens of discovery learning
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