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Research Article
Age and Gender Detection Using Deep Learning in Open CV
R. Nivethitha1
T.S.P. Ksheerabdinath2
A. Mohammed Navas3
T.S. Muneesh Wara Venkatesh4
1Assistant Professor, Department of Computer Science and Engineering, K.L.N. College of Engineering, Sivagangai, Tamil Nadu, India. 234Final Year Students, Department of Computer Science and Engineering, K.L.N. College of Engineering, Sivagangai, Tamil Nadu, India.
Published Online: September-October 2024
Pages: 49-51
Cite this article
No DOIReferences
1. Ala-Mutka KM (2005), ‘A survey of automated assessment approaches for programming assignments’ in Computer Science
Education on Taylor Francis, Vol. 12, pp. 83–102.
2. Blikstein P, Worsley M, Piech C, Sahami M, Cooper S, Koller D (2014). ‘Programming pluralism: Using learning analytics to detect
patterns in the learning of computer programming’ in Journal of Learning Sciences on Taylor Francis, Vol. 23, pp. 561–99.
3. Charlotte Van Petegem, Rien Maertens, Niko Strijbol, Jorg Van Renterghem, Felix Vander Jeugt, Rademeyer Dawyndt, Bart Mesuere
(2023), ‘Dodona: Learn to code with a virtual co-teacher that supports active learning’ in SoftwareX on Science Direct, Vol. 24, pp.
101-578.
4. Chow S, Yacef K, Koprinska I, Curran J (2017), ‘Automated data-driven hints for computer programming students.’ In: Adjunct
publication of the 25th conference on user modeling, adaptation and personalization. New York, NY, USA: Association for Computing
Machinery, pp. 5–10.
5. Costa EB, Fonseca B, Santana MA, de Araújo FF, Rego J (2017), ‘Evaluating the effectiveness of educational data mining techniques
for early prediction of students’ academic failure in introductory programming courses’ in Computers in Human Behavior on Science
Direct, Vol. 73, pp. 247–256.
6. Edwards SH, Perez-Quinones MA (2008). ‘Web-CAT: Automatically grading programming assignments’. In: Proceedings of the
13th annual conference on innovation and technology in computer science education. New York, NY, USA: Association for
Computing Machinery; p. 328.
7. Ferguson R (2012). ‘Learning analytics: Drivers, developments and challenges.’ in International Journal of Technology Enhanced
Learning, pp. 304–17.
8. Fonseca I, Martins NC, Lopes F (2023). ‘A web-based platform and a methodology to teach programming languages in electrical
engineering education – evolution and student feedback’. In: 32nd annual conference of the European Association for Education in
Electrical and Information Engineering, pp. 1–3.
9. Hsi-min Chen, Bao - an Nguyen, Yi-Xiang Yan, and Chyi-Ren Dow (2020), ‘Analysis of Learning Behavior in an Automated
Programming Assessment Environment: A Code Quality Perspective’ in Computer Science and Education on IEEE.
10. Ihantola P, Ahoniemi T, Karavirta V, Seppälä O (2010). ‘Review of recent systems for automatic assessment of programming
assignments’. In: Proceedings of the 10th Koli Calling International Conference on Computing Education Research. New York, NY,
USA: Association for Computing Machinery; pp. 86–93.
Education on Taylor Francis, Vol. 12, pp. 83–102.
2. Blikstein P, Worsley M, Piech C, Sahami M, Cooper S, Koller D (2014). ‘Programming pluralism: Using learning analytics to detect
patterns in the learning of computer programming’ in Journal of Learning Sciences on Taylor Francis, Vol. 23, pp. 561–99.
3. Charlotte Van Petegem, Rien Maertens, Niko Strijbol, Jorg Van Renterghem, Felix Vander Jeugt, Rademeyer Dawyndt, Bart Mesuere
(2023), ‘Dodona: Learn to code with a virtual co-teacher that supports active learning’ in SoftwareX on Science Direct, Vol. 24, pp.
101-578.
4. Chow S, Yacef K, Koprinska I, Curran J (2017), ‘Automated data-driven hints for computer programming students.’ In: Adjunct
publication of the 25th conference on user modeling, adaptation and personalization. New York, NY, USA: Association for Computing
Machinery, pp. 5–10.
5. Costa EB, Fonseca B, Santana MA, de Araújo FF, Rego J (2017), ‘Evaluating the effectiveness of educational data mining techniques
for early prediction of students’ academic failure in introductory programming courses’ in Computers in Human Behavior on Science
Direct, Vol. 73, pp. 247–256.
6. Edwards SH, Perez-Quinones MA (2008). ‘Web-CAT: Automatically grading programming assignments’. In: Proceedings of the
13th annual conference on innovation and technology in computer science education. New York, NY, USA: Association for
Computing Machinery; p. 328.
7. Ferguson R (2012). ‘Learning analytics: Drivers, developments and challenges.’ in International Journal of Technology Enhanced
Learning, pp. 304–17.
8. Fonseca I, Martins NC, Lopes F (2023). ‘A web-based platform and a methodology to teach programming languages in electrical
engineering education – evolution and student feedback’. In: 32nd annual conference of the European Association for Education in
Electrical and Information Engineering, pp. 1–3.
9. Hsi-min Chen, Bao - an Nguyen, Yi-Xiang Yan, and Chyi-Ren Dow (2020), ‘Analysis of Learning Behavior in an Automated
Programming Assessment Environment: A Code Quality Perspective’ in Computer Science and Education on IEEE.
10. Ihantola P, Ahoniemi T, Karavirta V, Seppälä O (2010). ‘Review of recent systems for automatic assessment of programming
assignments’. In: Proceedings of the 10th Koli Calling International Conference on Computing Education Research. New York, NY,
USA: Association for Computing Machinery; pp. 86–93.
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