Real – Time License Plate Recognition and Validation System Through Image Processing and KNN

1. AlaMutka KM (2005), ‘A survey of automated assessment approaches for programming Assignments’ in Computer Science Education
on Taylor francisVol.No 12 pp83–102.
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 ofLearningScience on Taylor Francis; Vol.No23pp561– 99,
3. CharlotteVanPetegem, RienMaertens, NikoStrijbol, JorgVanRenterghem, FelixVanderJeugt, RademeyerDawyndt, BartMesuere
(2023),’ Dodona: Learn to cope with a virtual coteacher that supports active learning’ insoftwareX on Science DirectVol no24,
pp101578.
4. Chow S, Yacef K, Koprinska I, Curran J (2017),‘Automated datadriven 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’inComputers in Human Behavior on science
direct, Vol.No73, pp247– 256.
6. Edwards SH, PerezQuinones MA (2008), ‘WebCAT: 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 Int Journal of Technology Enhanced Learning,
pp304–17.
8. Fonseca I, Martins NC, Lopes F (2023). ‘A webbased platform and a methodology to teach programming languages in electrical
engineering education – evolution and student feedback’. In: 32nd annual conference of the European association or education in
electrical and information engineering., pp. 1–3.