COMPUTER VISION TO SUPPORT PRE-SERVICE TEACHER MASTERY OF STUDENT CENTRE APPROACH
Abstract
Various researcher state that to ensure student had the 4Cs competence is to use student-centered approach in the classroom. This further recognize the skills to create dynamic classroom as the core-skills of 21st teacher. To create such classroom, a teacher must be able to master the systematic and dynamic aspects of learning. It requires teacher to fully aware of every student activity. However, based on preliminary investigation, there two things that become obstacles, namely: (1) teacher had hard time giving full attention to whole classes due to non-coercion manner of student-centre approach, and (2) mastery of dynamic aspects in learning requires real experience which will certainly spend a lot of time. They need practical solutions that can help them master the core skill of 21st teacher. Therefore, we designed an application with image processing features (face detection, face recognition, and pose extraction) in order to help teachers to give full attention and provide positive reinforcement for every classroom participant. The research is done by R&D Method, whereas application design uses SDLC method. The results are product in the form of software applications that are expected to accurately measure percentage student attention in the classroom. Product also present information as a basis for decision making to assist teacher in creating a dynamic classroom. In addition, we certainly hopeful that by using the product, prospective teachers can quickly adapt and apply the dynamic classroom.
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Akker, Jan Van den. (1999). Design approaches and tools in education and training. Publication: Design Approaches And Tools In Education And Training, hlm. 1-14.
Canedo, D., Trifan, A., & Neves, A. J. (2018, June). Monitoring students’ attention in a classroom through computer vision. In International Conference on Practical Applications of Agents and Multi-Agent Systems (pp. 371-378). Springer, Cham.
Chai, X., Shan, S., Gao, W., 2003, "Pose Normalization for Robust Face Recognition Based on Statistical Affine Transformation", in Proceedings of the ICICS-PCM 2003, paper 3A4.1
Cotton, K. (2001). “Schoolwide and Classroom Discipline.” School Improvement Research Series. (SIRS). Northwest Regional Educational Laboratory.
Dodit, S. dkk. “Sistem Pengenalan Wajah Secara Real-Time dengan Adaboost, Eigenface PCA & MySQL” Jurnal EECCIS Vol. 7, No. 2. 2013
Flachsbart, J., Franklin, D., & Hammond, K. (2000, January). Improving human computer interaction in a classroom environment using computer vision. In Proceedings of the 5th international conference on Intelligent user interfaces (pp. 86-93).
Han, J., Shao, L., Xu, D., & Shotton, J. (2013). Enhanced computer vision with microsoft kinect sensor: A review. IEEE transactions on cybernetics, 43(5), 1318-1334.
Hikmawan, R., Sari, D. P., Majid, N. A., Ridwan, T., Nuriyah, W., Aprilia, L., & Diani, D. (2019, October). Development of Ikigai instructional method to cultivate computational thinking of millennial generations. In Journal of Physics: Conference Series (Vol. 1318, No. 1, p. 012007). IOP Publishing.
Jensen, O. H. (2008). Implementing the Viola-Jones face detection algorithm (Master's thesis, Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark).
Jones, M., & Viola, P. (2003). Fast multi-view face detection. Mitsubishi Electric Research Lab TR-20003-96, 3(14), 2.
Lim, J. H., Teh, E. Y., Geh, M. H., & Lim, C. H. (2017, December). Automated classroom monitoring with connected visioning system. In 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) (pp. 386-393). IEEE.
Marlowe, J. (2000). “Learning Alone.” American School. Board Journal 187(12): 56–57, 62
Mulyadi, N. 2018. Manajemen Kelas Dalam Meningkatkan Proses Pembelajaran. Jurnal Keilmuan Manajemen Pendidikan. Volume 4 No 1. (Dapat diakses di http://www.jurnal.uinbanten.ac.id/index.php/tarbawi/article/view/1769) (Diakses pada 11 Februari 2020)
Orlich, D. C., Harder, R. J., Callahan, R. C., Trevisan, M. S., & Brown, A. H. (2012). Teaching strategies: A guide to effective instruction. Cengage Learning.
Putro, M.D., Adji, T.B. dan Winduratna, B., 2012. Sistem Deteksi Wajah dengan Menggunakan Metode Viola-Jones.
Sacchetti, R., Teixeira, T., Barbosa, B., Neves, A. J., Soares, S. C., & Dimas, I. D. (2018). Human body posture detection in context: the case of teaching and learning environments. SIGNAL 2018 Editors, 87, 79-84.
Schussler, D. L., Jennings, P. A., Sharp, J. E., & Frank, J. L. (2016). Improving teacher awareness and well-being through CARE: A qualitative analysis of the underlying mechanisms. Mindfulness, 7(1), 130-142.
Sharif M., "Face Recognition using Gabor Filters", Journal of Applied Computer Science & Mathematics, no. 11, Suceava, May. 2011
Sivalingam, R., Cherian, A., Fasching, J., Walczak, N., Bird, N., Morellas, V., ... & Papanikolopoulos, N. (2012, May). A multi-sensor visual tracking system for behavior monitoring of at-risk children. In 2012 IEEE International Conference on Robotics and Automation (pp. 1345-1350). IEEE.
Stiefelhagen, R., Zhu, J.: Head orientation and gaze direction in meetings. In: CHI 2002 Extended Abstracts on Human Factors in Computing Systems. ACM (2002)
Triatmoko, A.H., Pramono, S.H. dan Dachlan, H.S., 2014. Penggunaan Metode Viola-Jones dan Algoritma Eigen Eyes dalam Sistem Kehadiran Pegawai. Jurnal Eeccis, 8(1), pp.41-46.
Undang-Undang Republik Indonesia nomor 20 Tahun 2003 tentang Sistem Pendidikan Nasional
Viola, P., & Jones, M. (2001, December). Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001 (Vol. 1, pp. I-I). IEEE.
Wang, Y. Q. (2014). An analysis of the Viola-Jones face detection algorithm. Image Processing On Line, 4, 128-148.
Zaletelj, J., & Košir, A. (2017). Predicting students’ attention in the classroom from Kinect facial and body features. EURASIP journal on image and video processing, 2017(1), 80.
Zhang, K., Zhang, Z., Li, Z., & Qiao, Y. (2016). Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Processing Letters, 23(10), 1499-1503.
DOI: https://doi.org/10.17509/e.v20i3.40750
DOI (PDF): https://doi.org/10.17509/e.v20i3.40750.g17080
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