UTILIZING CHERNOFF FACES IN MODELING RESPONSES IN THE EVALUATION OF TRIMESTER SCHEME IMPLEMENTATION
DOI:
https://doi.org/10.37569/DalatUniversity.12.1.920(2022)Keywords:
Chernoff’s faces, Modeling responses, Trimester Scheme evaluation.Abstract
This study uses Chernoff faces to model the responses of students, faculty, and administration staff of a teacher education institution in Manila, Philippines, to the implementation of an Outcomes-Based Teacher Education Curriculum (OBTEC) trimester scheme. Chernoff faces provide a valuable representation to model responses because people are used to studying and reacting to faces. This study used a quantitative research method by analyzing cross-sectional data from the study of the OBTEC trimester scheme. A total of 322 participants were selected through convenience sampling and given a 15-item survey in which possible responses ranged from 1 (strongly disagree) to 6 (strongly agree). The administrators were found to give a generally favorable rating (overall mean = 4.56 agree; overall SD = 0.45) to the OBTEC trimester scheme. The statements most highly rated by the administrators pertain to the success of OBTEC in integrating pedagogical content knowledge training with outcomes-based education, preparation of the students for the teaching profession, and consistency with the K to 12 curriculum. These responses are characterized by the structure of the face, the width of the mouth, and the height of the face, respectively. The most negative aspects of the OBTEC trimester scheme, according to the students, are characterized by hair height, nose width, and a hair style of thin hair that points downward. Chernoff faces were found to be a simple, yet powerful tool to model responses in the evaluation of the OBTEC trimester scheme.
Downloads
References
Chen, H. M. (2017). Information visualization principles, techniques, and software. Library Technology Reports, 53(3), 8-16.
Chernoff, H. (1973). The use of faces to represent points in k-dimensional space graphically. Journal of the American Statistical Association, 68(342), 361-368. https://doi.org/10.1080/01621459.1973.10482434
Guha, A. H., & Assaf, G. J. (2019). Representing collected road condition data with Chernoff faces for evaluation of pavement conditions. In K. Zhang & S.-H. Chen (Eds.), Testing and Characterization of Asphalt Materials and Pavement Structures: Proceedings of the 5th GeoChina International Conference 2018 – Civil Infrastructures Confronting Severe Weathers and Climate Changes: From Failure to Sustainability (pp. 136-150). Springer. https://doi.org/10.1007/978-3-319-95789-0_13
Mateos, E. Y. M., Garrido, M. A. L., Hernandez, J.-A., Ortiz, C. A. O., & González, O. (2018). Multivariate analysis of university student engagement through visualization techniques. Research in Computing Science, 147(2), 77-87. https://doi.org/10.13053/rcs-147-2-6
Moiz, S. A., & Chillarige, R. R. (2020). Method Level Code Smells: Chernoff Face Visualization. In S. C. Satapathy, K. S. Raju, K. Shyamala, D. R. Krishna, & M. N. Favorskaya (Eds), Advances in Decision Sciences, Image Processing, Security and Computer Vision (pp. 520-527). Springer, Cham. https://doi.org/10.1007/978-3-030-24322-7_63
Nuñez, J. J. R. (2013). Survey on Chernoff Faces in Hungary and Austria: Further research and experiments, agreements on scientific and technological cooperation of the National Office for Research and Technology of Hungary and Federal Ministry of Science and Research. National Office for Research and Technology, Budapest, Hungary, and Federal Ministry of Education, Science and Research, Vienna, Austria.
Pitt, L. E., Mills, A. J., Chan, A., Menguc, B., & Plangger, K. (2011, June 9-11). Using Chernoff Faces to Portray Social Media Wine Brand Images. 6th AWBR International Conference, Bordeaux Management School, BEM, France.
Soininvaara, K. (2013). The battle of the game consoles: Utilizing Chernoff faces in modelling competitive dynamics between MNCs. Master’s thesis, Aalto University School of Business, Aalto University, Greater Helsinki, Finland.
Teke, E. Ç., Koşkan, Ö., & Köknaroğlu, H. (2020). Chernoff faces application in livestock. Ziraat Fakültesi Dergisi-Süleyman Demirel Üniversitesi, 15(1), 64-71.
VanHoudnos, N., Casey, W., French, D., Lindauer, B., Kanal, E., Wright, E., Woods, B., Moon, S., Jansen, J., & Carbonell, J. (2017). This malware looks familiar: Laymen identify malware run-time similarity with Chernoff faces and stick figures. In T. Nakano & A. Compagnoni (Eds.), Proceedings of the 10th EAI International Conference on Bio-inspired Information and Communications Technologies, (pp. 152-159). European Union Digital Library. https://doi.org/10.4108/eai.22-3-2017.152417
Zhang, H., Hou, Y., Zhao, J., Wang, L., Xi, T., & Li, Y. (2017). Automatic welding quality classification for the spot welding based on the Hopfield associative memory neural network and Chernoff face description of the electrode displacement signal features. Mechanical Systems and Signal Processing, 85, 1035-1043. https://doi.org/10.1016/j.ymssp.2016.06.036
Downloads
Published
Volume and Issues
Section
Copyright & License
Copyright (c) 2022 Rosie C. Lopez-Conde, Jenina N. Nalipay, Inero V. Ancho, Edna Luz R. Abulon, Teresita T. Rungduin, Ma. Antoinette C. Montealegre, Jonathan A. Madronero.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
