Automatic Assessment of Mathematical Creativity using Natural Language Processing

References

• Amabile, T. M. (1982). Social psychology of creativity: A consensual assessment technique. Journal of Personality and Social Psychology, 43(5), 997. doi:10.1037/0022-3514.43.5.997
   Web of Science ®Google Scholar
• Amabile, T. M. (1983). The social psychology of creativity: A componential conceptualization. Journal of Personality and Social Psychology, 45(2), 357. doi:10.1037/0022-3514.45.2.357
   Web of Science ®Google Scholar
• Baer, J. (2011). How divergent thinking tests mislead us: Are the Torrance Tests still relevant in the 21st century? The Division 10 debate. Psychology of Aesthetics, Creativity, and the Arts, 5(4), 309–313. doi:10.1037/a0025210
   Web of Science ®Google Scholar
• Bahdanau, D., Cho, K., & Bengio, Y. (2015). Neural machine translation by jointly learning to align and translate (pp. 1-15). arXiv preprint arXiv:1409.0473.
   Google Scholar
• Barbot, B., Hass, R. W., & Reiter-Palmon, R. (2019). Creativity assessment in psychological research:(Re) setting the standards. Psychology of Aesthetics, Creativity, and the Arts, 13(2), 233–239. doi:10.1037/aca0000233
   Web of Science ®Google Scholar
• Beaty, R. E., & Johnson, D. R. (2021). Automating creativity assessment with SemDis: An open platform for computing semantic distance. Behavior Research Methods, 53(2), 757–780. doi:10.3758/s13428-020-01453-w
   PubMed Web of Science ®Google Scholar
• Biggs, J., & Tang, C. (2011). Teaching for quality learning at university. Maidenhead, UK: McGraw-Hill International.
   Google Scholar
• Boden, M. A. (1998). Creativity and artificial intelligence. Artificial Intelligence 40 Years Later, 103(1), 347–356. doi:10.1016/S0004-3702(98)
   Web of Science ®Google Scholar
• Bruner, J. S. (1957). Contemporary approaches to cognition: A symposium held at the University of Colorado. Harvard University Press.
   Google Scholar
• Burke, K. (2010). Balanced assessment: From formative to summative. Bloomington, IN: Solution Tree Press.
   Google Scholar
• Cheng, J., Dong, L., & Lapata, M. (2016). Long short-term memory-networks for machine reading (pp. 551-561). arXiv preprint arXiv:1601.06733.
   Google Scholar
• Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46. doi:10.1177/001316446002000104
   Web of Science ®Google Scholar
• Colton, S., & Wiggins, G. A. (2012, August). Computational creativity: The final frontier? In In Proceedings of the 20th European Conference on Artificial Intelligence (Vol. 12, pp. 21–26).
   Google Scholar
• Cropley, D. H., & Marrone, R. L. (2022). Automated scoring of figural creativity using a convolutional neural network. Psychology of Aesthetics, Creativity, and the Arts. doi:10.1037/aca0000510
   Web of Science ®Google Scholar
• Cropley, D. H., Medeiros, K. E., & Damadzic, A. (2021). The intersection of human and artificial creativity. doi:10.31234/osf.io/t7nv2
   Google Scholar
• Dollinger, S. J., Urban, K. K., & James, T. A. (2004). Creativity and openness: Further validation of two creative product measures. Creativity Research Journal, 16(1), 35–47. doi:10.1207/s15326934crj1601_4
   Web of Science ®Google Scholar
• Dumas, D., & Dunbar, K. N. (2014). Understanding fluency and originality: A latent variable perspective. Thinking Skills and Creativity, 14, 56–67. doi:10.1016/j.tsc.2014.09.003
   Web of Science ®Google Scholar
• El-Mandouh, A. M., Mahmoud, H. A., Abd-Elmegid, L. A., & Haggag, M. H. (2019). Optimized K-means clustering model based on gap statistic. International Journal Advanced Computer Science, 10(1), 183–188.
   Google Scholar
• Feng, S. Y., Gangal, V., Wei, J., Chandar, S., Vosoughi, S., Mitamura, T., & Hovy, E. (2021). A survey of data augmentation approaches for NLP. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (pp. 968-988).
   Google Scholar
• Forster, E. A., & Dunbar, K. N. (2009). Creativity evaluation through latent semantic analysis. In N. A. Taatgen & H. van Rijn (Eds.), Proceedings of the 31th Annual Conference of the Cognitive Science Society (pp. 602–607). Austin: Cognitive Science Society.
   Google Scholar
• Geisinger, K. F. (2016). 21st century skills: What are they and how do we assess them? Applied Measurement in Education, 29(4), 245–249. doi:10.1080/08957347.2016.1209207
   Web of Science ®Google Scholar
• Gordon, A. D. (1996). Null models in cluster validation. In From data to knowledge (pp. 32–44). Berlin, Heidelberg: Springer.
   Google Scholar
• Graves, A., & Schmidhuber, J. (2005). Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks, 18(5–6), 602–610. doi:10.1016/j.neunet.2005.06.042
   PubMed Web of Science ®Google Scholar
• Harbinson, J., & Haarman, H. (2014). Automated scoring of originality using semantic representations. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 36, No. 36).
   Google Scholar
• Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. doi:10.1162/neco.1997.9.8.1735
   PubMed Web of Science ®Google Scholar
• Karwowski, M., & Kaufman, J. C. (Eds.). (2017). The creative self: Effect of beliefs, self-efficacy, mindset, and identity. Academic Press.
   Google Scholar
• Kaufman, J. C., Baer, J., Cropley, D. H., Reiter-Palmon, R., & Sinnett, S. (2013). Furious activity vs. understanding: How much expertise is needed to evaluate creative work? Psychology of Aesthetics, Creativity, and the Arts, 7(4), 332. Wiley & Sons. doi:10.1037/a0034809
   Web of Science ®Google Scholar
• Kettler, T., Lamb, K. N., & Mullet, D. R. (2021). Developing creativity in the classroom: Learning and innovation for 21st-century schools. New York, NY: Routledge.
   Google Scholar
• Kovalkov, A., Paassen, B., Segal, A., Pinkwart, N., & Gal, K. (2022). Automatic creativity measurement in scratch programs across modalities. In IEEE Transactions on Learning Technologies,14(6): 740-753. doi: 10.1109/TLT.2022.3144442.
   Google Scholar
• Krishna, K., & Murty, M. N. (1999). Genetic K-means algorithm. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 29(3), 433–439. doi:10.1109/3477.764879
   PubMed Web of Science ®Google Scholar
• Lucas, B., Claxton, G., & Spencer, E. (2013), Progression in student creativity in school: First steps towards new forms of formative assessments (OECD Education Working Papers, No. 86). OECD Publishing. doi: 10.1787/5k4dp59msdwk-en
   Google Scholar
• Luong, M.-T., Pham, H., & Manning, C. D. (2015). Effective approaches to attention-based neural machine translation. arXiv.org. https://arxiv.org/abs/1508.04025.
   Google Scholar
• Newell, A., Shaw, J. C., & Simon, H. A. (1962). The processes of creative thinking. In Contemporary Approaches to Creative Thinking, 1958, University of Colorado, CO, US; This paper was presented at the aforementioned symposium. Atherton Press, Colorado, USA.
   Google Scholar
• Olson, J. A., Nahas, J., Chmoulevitch, D., Cropper, S. J., & Webb, M. E. (2021). Naming unrelated words predicts creativity. Proceedings of the National Academy of Sciences, 118(25). doi:10.1073/pnas.2022340118
   Web of Science ®Google Scholar
• Patston, T. J., Kaufman, J. C., Cropley, A. J., & Marrone, R. (2021). What is creativity in education? A qualitative study of international curricula. Journal of Advanced Academics, 32(2), 207–230. doi:10.1177/1932202X20978356
   Web of Science ®Google Scholar
• Pennington, J., Socher, R., & Manning, C. D. (2014, October). Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532–1543), Doha, Qatar.
   Google Scholar
• Plucker, J., Makel, M., & Qian, M. (2019). Assessment of Creativity. In The Cambridge handbook of creativity (pp. 44–68). Cambridge University Press.
   Google Scholar
• Race, P. (2014). Making learning happen: A guide for post-compulsory education. London, UK: Sage.
   Google Scholar
• Raganato, A., & Tiedemann, J. (2018). An analysis of encoder representations in transformer-based machine translation. In Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP. The Association for Computational Linguistics, Brussels, Belgium.
   Google Scholar
• Reiter-Palmon, R., Forthmann, B., & Barbot, B. (2019). Scoring divergent thinking tests: A review and systematic framework. Psychology of Aesthetics, Creativity, and the Arts, 13(2), 144. doi:10.1037/aca0000227
   Web of Science ®Google Scholar
• Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985). Learning internal representations by error propagation. California Univ San Diego La Jolla Inst for Cognitive Science.
   Google Scholar
• Simonton, D. K. (1986). Aesthetic success in classical music: A computer analysis of 1935 compositions. Empirical Studies of the Arts, 4(1), 1–17. doi:10.2190/JLAP-JRK1-0TDY-DBRM
   Google Scholar
• Simonton, D. K. (1990). Lexical choices and aesthetic success: A computer content analysis of 154 Shakespeare sonnets. Computers and the Humanities, 24(4), 251–264.
   Google Scholar
• Sternberg, R. J. (2020). What’s wrong with creativity testing? The Journal of Creative Behavior, 54(1), 20–36. doi:10.1002/jocb.237
   Web of Science ®Google Scholar
• STERNBERG, R.J.E. (1988). The nature of of Creativity: Contemporary Psychological Perspectives. United Kingdom: Cambridge University Press , 43–75.
   Google Scholar
• Theurer, C., Berner, N. E., & Lipowsky, F. (2016). Assessing creative potential as student outcome: On the applicability of the TCT-DP in repeated measures. Thinking Skills and Creativity, 20, 74–82. doi:10.1016/j.tsc.2016.03.002
   Web of Science ®Google Scholar
• Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of clusters in a data set via the Gap Statistic. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(2), 411–423. [online]. doi:10.1111/1467-9868.00293
   Web of Science ®Google Scholar
• Torrance, E. P. (1999). Torrance Test of Creative Thinking: Norms and technical manual. Beaconville, IL: Scholastic Testing Services.
   Google Scholar
• Tubb, A. L., Cropley, D. H., Marrone, R. L., Patston, T., & Kaufman, J. C. (2020). The development of mathematical creativity across high school: Increasing, decreasing, or both? Thinking Skills and Creativity, 35, 100634. doi:10.1016/j.tsc.2020.100634
   Web of Science ®Google Scholar
• Urban, K. K., & Jellen, H. G. (1996). Test for creative thinking - drawing production (TCT-DP). Lisse, Netherlands: Swets and Zeitlinger.
   Google Scholar
• Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., and Polosukhin, I. (2017). ‘Attention is all you need.’ Advances in Neural Information Processing Systems, 30: 1-11.
   Google Scholar
• WEF. (2016). The future of jobs: Employment, skills and workforce strategy for the fourth industrial revolution. http://reports.weforum.org/future-of-jobs-2016/
   Google Scholar
• WEF. (2020). The Future of Jobs Report 2020. http://www3.weforum.org/docs/WEF_Future_of_Jobs_2020.pdf
   Google Scholar
• Wei, J., & Zou, K. (2019). ‘Eda: Easy data augmentation techniques for boosting performance on text classification tasks.’ arXiv preprint arXiv:1901.11196.
   Google Scholar
• Wiggins, G. A. (2006). A preliminary framework for description, analysis and comparison of creative systems. Knowledge-Based Systems, 19(7), 449–458. doi:10.1016/j.knosys.2006.04.009
   Web of Science ®Google Scholar