56,516 research outputs found
Analyzing collaborative learning processes automatically
In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners’ interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multidimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in
The Borrowers: Researching the cognitive aspects of translation
The paper considers the interdisciplinary interaction of research on the cognitive aspects of translation. Examples of influence from linguistics, psychology, neuroscience, cognitive science, reading and writing research and language technology are given, with examples from specific sub-disciplines within each one. The breadth of borrowing by researchers in cognitive translatology is made apparent, but the minimal influence of cognitive translatology on the respective disciplines themselves is also highlighted. Suggestions for future developments are made, including ways in which the domain of cognitive translatology might exert greater influence on other disciplines
COGNITIVE LINGUISTICS AS A METHODOLOGICAL PARADIGM
A general direction in which cognitive linguistics is heading at the turn of the century is outlined and a revised understanding of cognitive linguistics as a methodological paradigm is suggest. The goal of cognitive linguistics is defined as understanding what language is and what language does to ensure the predominance of homo sapiens as a biological species. This makes cognitive linguistics a biologically oriented empirical science
The structure of the Arts & Humanities Citation Index: A mapping on the basis of aggregated citations among 1,157 journals
Using the Arts & Humanities Citation Index (A&HCI) 2008, we apply mapping
techniques previously developed for mapping journal structures in the Science
and Social Science Citation Indices. Citation relations among the 110,718
records were aggregated at the level of 1,157 journals specific to the A&HCI,
and the journal structures are questioned on whether a cognitive structure can
be reconstructed and visualized. Both cosine-normalization (bottom up) and
factor analysis (top down) suggest a division into approximately twelve
subsets. The relations among these subsets are explored using various
visualization techniques. However, we were not able to retrieve this structure
using the ISI Subject Categories, including the 25 categories which are
specific to the A&HCI. We discuss options for validation such as against the
categories of the Humanities Indicators of the American Academy of Arts and
Sciences, the panel structure of the European Reference Index for the
Humanities (ERIH), and compare our results with the curriculum organization of
the Humanities Section of the College of Letters and Sciences of UCLA as an
example of institutional organization
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