K. Brennan and M. Resnick, New frameworks for studying and assessing the development of computational thinking, in Proc. 2012 Ann. Meeting of the American Educational Research Association, Vancouver, Canada, 2012, p. 25.
L. Seiter and B. Foreman, Modeling the learning progressions of computational thinking of primary grade students, in Proc. Ninth Ann. Int. ACM Conf. Int. Computing Education Research, San Diego, CA, USA, 2013, pp. 59–66.
J. M. Wing, Computational thinking and thinking about computing, Philosoph. Trans. Roy. Soc. A Math. Phys. Eng. Sci., vol. 366, no. 1881, pp. 3717–3725, 2008.
X. Chai, Y. Sun, H. Luo, and M. Guizani, DWES: A dynamic weighted evaluation system for scratch based on computational thinking, IEEE Trans. Emerg. Top. Comput., vol. 10, no. 2, pp. 917–932, 2022.
X. Lu, Z. Lin, H. Jin, J. Yang, and J. Z. Wang, RAPID: Rating pictorial aesthetics using deep learning, in Proc. 22nd ACM Int. Conf. Multimedia, Orlando, FL, USA, 2014, pp. 457–466.
X. Lu, Z. Lin, X. Shen, R. Mech, and J. Z. Wang, Deep multi-patch aggregation network for image style, aesthetics, and quality estimation, in Proc. 2015 IEEE Int. Conf. Computer Vision, Santiago, Chile, 2015, pp. 990–998.
S. Ma, J. Liu, and C. W. Chen, A-Lamp: Adaptive layout-aware multi-patch deep convolutional neural network for photo aesthetic assessment, in Proc. 2017 IEEE Conf. Computer Vision and Pattern Recognition, Honolulu, HI, USA, 2017, pp. 722–731.
K. Sheng, W. Dong, C. Ma, X. Mei, F. Huang, and B. G. Hu, Attention-based multi-patch aggregation for image aesthetic assessment, in Proc. 26th ACM Int. Conf. Multimedia, Seoul, Republic of Korea, 2018, pp. 879–886.
Z. Wang, S. Chang, F. Dolcos, D. Beck, D. Liu, and T. S. Huang, Brain-inspired deep networks for image aesthetics assessment, arXiv preprint arXiv: 1601.04155, 2016.
J. Moreno-León, G. Robles, and M. Román-González, Dr. Scratch: Automatic analysis of scratch projects to assess and foster computational thinking, RED. Revist. Edu. Distan, vol. 46, no. 10, pp. 1–23, 2015.
Z. Chang, Y. Sun, T. Y. Wu, and M. Guizani, Scratch analysis tool (SAT): A modern scratch project analysis tool based on ANTLR to assess computational thinking skills, in Proc 14th Int. Wireless Communications & Mobile Computing Conf. (IWCMC), Limassol, Cyprus, 2018, pp. 950–955.
S. Kong, X. Shen, Z. Lin, R. Mech, and C. Fowlkes, Photo aesthetics ranking network with attributes and content adaptation, in Proc. 14th European Conf. Computer Vision, Amsterdam, The Netherlands, 2016, pp. 662–679.
K. Schwarz, P. Wieschollek, and H. P. A. Lensch, Will people like your image? Learning the aesthetic space, in Proc. 2018 IEEE Winter Conf. Applications of Computer Vision (WACV), Lake Tahoe, NV, USA, 2018, pp. 2048–2057.
N. Murray, L. Marchesotti, and F. Perronnin, AVA: A large-scale database for aesthetic visual analysis, in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Providence, RI, USA, 2012, pp. 2408–2415.
X. Lu, Z. Lin, H. Jin, J. Yang, and J. Z. Wang, Rating image aesthetics using deep learning, IEEE Trans. Multimedia, vol. 17, no. 11, pp. 2021–2034, 2015.
V. Hosu, B. Goldlucke, and D. Saupe, Effective aesthetics prediction with multi-level spatially pooled features, in Proc. 2019 IEEE/CVF Conf. Computer Vision and Pattern Recognition, Long Beach, CA, USA, 2019, pp. 9375–9383.
H. Talebi and P. Milanfar, NIMA: Neural image assessment, IEEE Trans. Image Process., vol. 27, no. 8, pp. 3998–4011, 2018.
X. Chai, Y. Sun, H. Luo, and M. Guizani, An artistic analysis model based on sequence cartoon images for scratch, Int.J. Intellig. Syst., vol. 37, no. 11, pp. 9598–9619, 2022.
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi, Inception-v4, inception-ResNet and the impact of residual connections on learning, in Proc. Thirty-First AAAI Conf. Artificial Intelligence, San Francisco, CA, USA, 2017, pp. 4278–4284.
P. Techapalokul and E. Tilevich, Quality hound—an online code smell analyzer for scratch programs, in Proc. 2017 IEEE Symp. Visual Languages and Human-Centric Computing (VL/HCC), Raleigh, NC, USA, 2017, pp. 337–338.
G. Fraser, U. Heuer, N. Körber, F. Obermüller, and E. Wasmeier, LitterBox: A linter for scratch programs, in Proc. IEEE/ACM 43rd Int. Conf. Software Engineering: Software Engineering Education and Training (ICSE-SEET), Madrid, Spain, 2021, pp. 183–188.
F. J. M. Jr, The Kolmogorov-Smirnov test for goodness of fit, J. Am. Stat. Assoc., vol. 46, no. 253, pp. 68–78, 1951.
J. R. Quinlan, Induction of decision trees, Mach. Learn., vol. 1, no. 1, pp. 81–106, 1986.
L. Breiman, Random forests, Mach. Learn., vol. 45, no. 1, pp. 5–32, 2001.
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, Learning representations by back-propagating errors, Nature, vol. 323, no. 6088, pp. 533–536, 1986.
J. H. Friedman, Greedy function approximation: A gradient boosting machine, Ann. Stat., vol. 29, no. 5, pp. 1189–1232, 2001.
T. Q. Chen and C. Guestrin, XGBoost: A scalable tree boosting system, in Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, San Francisco, CA, USA, 2016, pp. 785–794.
G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T. Y. Liu, LightGBM: A highly efficient gradient boosting decision tree, in Proc. 31st Int. Conf. Neural Information Processing Systems, Long Beach, CA, USA, 2017, pp. 3149–3157.