مطالب

This study aimed at describing a causal model of variables influencing preservice EFL teachers΄ technological pedagogical content knowledge (TPACK). A descriptive correlational design was used to meet the objective of the study, therefore, 138 (67 male and 71 female) fourth-year student teachers in Teacher Education University were selected through convenience sampling method. A questionnaire including seven sections each of which measuring an aspect of TPACK was utilized. The sections of instrument were borrowed from those validated by Sahin (2011) for evaluating content knowledge, pedagogical knowledge, pedagogical content knowledge, and technological knowledge, and by Chai, et al. (2011) for measuring technological knowledge, technological pedagogical knowledge and TPACK. Path analysis and Pearson correlation were used for inferential statistical analysis. Results showed that there existed significant positive correlations between the TPACK constructs. Additionally, content knowledge and pedagogical knowledge, unlike technological knowledge, were found to have a direct impact on TPACK. Moreover, it was found that, among the measured variables, content knowledge had the greatest total effect on TPACK whereas that of pedagogical content knowledge was the minimum. Therefore, the results of this study have implications for curriculum design, policy decisions and teacher education planning.

It has been nearly 86 years since the Egyptian bronze ram-headed statuette, known as Harsaphes, was found outside the Treasury in the Garrison Street of Persepolis during the archaeological excavations by the Oriental Institute of the University of Chicago. The iconographic features of this little-known statuette is associated it with an Egyptian deity, which makes its genre unique in the heartland of the Persian Empire. It should be noted that the four ram-headed deities: Heryshef, Khnum, Amun, and Banebdjedet, sometimes bear a striking resemblance in the formal Egyptian art. This paper re-opens the discussion on the statuette’s identity, and offers a detailed comparison between this statuette and other Egyptian ram-headed deities. The results from a comparative analysis are critical to the supposed attribution of Harsaphes to the statuette, and examine the likelihood of being all four ram-headed gods.

The human machine interface research in the light of modern fast computers and advanced sensors is taking new heights. The classification and processing of neural activity in the brain accessed by Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), functional Magnetic Resonance Imaging (fMRI), Electrocorticography (ECoG), EEG Electroencephalogram (EEG) etc., are peeling off new paradigms for pattern recognition in human brain-machine interaction applications. In the present paper, an effective novel scheme based upon a synergetic approach employing the Genetic Algorithm (GA), Support Vector Machine and Wavelet packet transform for motor imagery classification and optimal Channel selection is proposed. GA with SVM acting as the objective function is employed for simultaneous selection of features and channels optimally. The binary population of GA is uniquely represented in three-dimensional structure and a new cross-over operator for GA are introduced. The new modified cross-over operator is proposed for the modified three-dimensional population. The ‘data set I’ of ‘BCI Competition IV’ is taken for evaluation of the efficacy of the proposed scheme. For subject ‘a’ accuracy is 88.9 6.9 with 10 channels, for subject ‘b’ accuracy is 79.20±5.36with 11 channels, for subject ‘f’ accuracy is 90.50±3.56 with 13 channels, and for subject ‘g’ accuracy is 92.23±3.21with 12 channels. The proposed scheme outperforms in terms of classification accuracy for subjects ‘a, b, f, g’ and in terms of number of channels for subject ‘a’ and that for subject ‘b’ is same as reported earlier in literature. Therefore, proposed scheme contributes a significant development in terms of new three-dimensional representation of binary population for GA as well as significant new modification to the GA operators. The efficacy of the scheme is evident from the results presented in the paper for dataset under consideration.