New Scientific Research Systems in a Changing Asia

The aim of this chapter is to compare the two worlds of science and learning in the perspective to find commonalties that could be used to develop new methods and technologies to better support collaborative and on-line scientific research. In this perspective we claim the existence of a convergence of these two domains and highlight similarities in on-line tools that support such activities. At the same time, we bring attention to the fact that a largely overlooked aspect of existing on-line scientific collaborative research systems, which is instead well represented in learning systems, is communication among partners. To address this issue we build a collaborative on-line software tool that allows to make some interesting early observations. Further on, we report on the introduction of a discourse structuring facility that could be used, on one hand, to ease the use of communication tools and on the other as a boundary object: an artifact that allows to bridge different paradigms and backgrounds.

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Statistical analysis of classification

Symposium - International Astronomical Union ◽

10.1017/s0074180900053420 ◽

1966 ◽

Vol 24 ◽

pp. 188-189

Author(s):

T. J. Deeming

Keyword(s):

Multivariate Analysis ◽

Statistical Analysis ◽

Classification Scheme ◽

Analytical Procedure ◽

Narrow Band ◽

Scientific Research ◽

Maximum Amount ◽

Amount Of Information

If we make a set of measurements, such as narrow-band or multicolour photo-electric measurements, which are designed to improve a scheme of classification, and in particular if they are designed to extend the number of dimensions of classification, i.e. the number of classification parameters, then some important problems of analytical procedure arise. First, it is important not to reproduce the errors of the classification scheme which we are trying to improve. Second, when trying to extend the number of dimensions of classification we have little or nothing with which to test the validity of the new parameters.Problems similar to these have occurred in other areas of scientific research (notably psychology and education) and the branch of Statistics called Multivariate Analysis has been developed to deal with them. The techniques of this subject are largely unknown to astronomers, but, if carefully applied, they should at the very least ensure that the astronomer gets the maximum amount of information out of his data and does not waste his time looking for information which is not there. More optimistically, these techniques are potentially capable of indicating the number of classification parameters necessary and giving specific formulas for computing them, as well as pinpointing those particular measurements which are most crucial for determining the classification parameters.

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Building a Successful Career in Scientific Research

10.1017/cbo9781139165471 ◽

2006 ◽

Cited By ~ 2

Author(s):

Phil Dee

Keyword(s):

Scientific Research ◽

Successful Career

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Social-Scientific Research Competency

European Journal of Psychological Assessment ◽

10.1027/1015-5759/a000451 ◽

2019 ◽

Vol 35 (5) ◽

pp. 737-750 ◽

Cited By ~ 3

Author(s):

Christopher Gess ◽

Christoph Geiger ◽

Matthias Ziegler

Keyword(s):

Higher Education ◽

Test Scores ◽

Research Performance ◽

Scientific Research ◽

Research Education ◽

Crystallized Intelligence ◽

Research Competency ◽

Social Scientific Research ◽

Social Scientific ◽

Education Courses

Abstract. Although the development of research competency is an important goal of higher education in social sciences, instruments to measure this outcome often depend on the students’ self-ratings. To provide empirical evidence for the utility of a newly developed instrument for the objective measurement of social-scientific research competency, two validation studies across two independent samples were conducted. Study 1 ( n = 675) provided evidence for unidimensionality, expected differences in test scores between differently advanced groups of students as well as incremental validities over and above self-perceived research self-efficacy. In Study 2 ( n = 82) it was demonstrated that the competency measured indeed is social-scientific and relations to facets of fluid and crystallized intelligence were analyzed. Overall, the results indicate that the test scores reflected a trainable, social-scientific, knowledge-related construct relevant to research performance. These are promising results for the application of the instrument in the evaluation of research education courses in higher education.

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