Learning Factors Analysis – A General Method for Cognitive Model Evaluation and Improvement

Learning time of student is precious, over-practice of target knowledge component wastes student’s time, however, under-practice may mean the student may not grasp target knowledge component properly. To any student, it is helpful if intelligent tutoring system can determine how many practice opportunities needed for mastery of knowledge component. In this paper, to improve student’s learning efficiency, a method of predicted learning curve based on grey models is proposed to determine the counts of practice op-portunity for mastery of knowledge component. The experimental results show that the predicted value on error rate of practice opportunity over knowledge component with the proposed method is much closer to the value of real learning curve than the predicted learning curve produced by learning factors analysis. It implies the proposed prediction method is potential to present reasonable practices for personalized tutoring.

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A General Method for Spectrometer Design in the Scoff Approximation

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100092657 ◽

1976 ◽

Vol 34 ◽

pp. 538-539

Author(s):

J. R. Fields

Keyword(s):

Electron Microscope ◽

Transmission Electron Microscope ◽

Energy Analysis ◽

Incident Beam ◽

Scanning Transmission Electron Microscope ◽

Chemical Identification ◽

Scanning Transmission Electron ◽

Transmission Electron ◽

Scanning Transmission ◽

General Method

The energy analysis of electrons scattered by a specimen in a scanning transmission electron microscope can improve contrast as well as aid in chemical identification. In so far as energy analysis is useful, one would like to be able to design a spectrometer which is tailored to his particular needs. In our own case, we require a spectrometer which will accept a parallel incident beam and which will focus the electrons in both the median and perpendicular planes. In addition, since we intend to follow the spectrometer by a detector array rather than a single energy selecting slit, we need as great a dispersion as possible. Therefore, we would like to follow our spectrometer by a magnifying lens. Consequently, the line along which electrons of varying energy are dispersed must be normal to the direction of the central ray at the spectrometer exit.

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Phase behavior of cationic and anionic surfactants at low concentrations

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100123878 ◽

1992 ◽

Vol 50 (1) ◽

pp. 694-695

Author(s):

E. Naranjo

Keyword(s):

Phase Behavior ◽

Structural Characterization ◽

Dynamic Systems ◽

Anionic Surfactants ◽

Intermolecular Forces ◽

Stable Form ◽

Surfactant Mixtures ◽

Low Concentrations ◽

Cationic And Anionic Surfactants ◽

General Method

Equilibrium vesicles, those which are the stable form of aggregation and form spontaneously on mixing surfactant with water, have never been demonstrated in single component bilayers and only rarely in lipid or surfactant mixtures. Designing a simple and general method for producing spontaneous and stable vesicles depends on a better understanding of the thermodynamics of aggregation, the interplay of intermolecular forces in surfactants, and an efficient way of doing structural characterization in dynamic systems.

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An Application of the HFACS Method to Aviation Accidents in Africa

Aviation Psychology and Applied Human Factors ◽

10.1027/2192-0923/a000093 ◽

2016 ◽

Vol 6 (1) ◽

pp. 33-38 ◽

Cited By ~ 4

Author(s):

Isaac Munene

Keyword(s):

Human Factors ◽

Physical Environment ◽

Human Factor ◽

Flight Training ◽

African Countries ◽

Causal Factors ◽

Aviation Accidents ◽

Factors Analysis ◽

Adverse Weather ◽

Decision Errors

Abstract. The Human Factors Analysis and Classification System (HFACS) methodology was applied to accident reports from three African countries: Kenya, Nigeria, and South Africa. In all, 55 of 72 finalized reports for accidents occurring between 2000 and 2014 were analyzed. In most of the accidents, one or more human factors contributed to the accident. Skill-based errors (56.4%), the physical environment (36.4%), and violations (20%) were the most common causal factors in the accidents. Decision errors comprised 18.2%, while perceptual errors and crew resource management accounted for 10.9%. The results were consistent with previous industry observations: Over 70% of aviation accidents have human factor causes. Adverse weather was seen to be a common secondary casual factor. Changes in flight training and risk management methods may alleviate the high number of accidents in Africa.

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