Explaining Explainability: Early Performance Prediction with Student Programming Pattern Profiling
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Published
Dec 4, 2024
Muntasir Hoq
Peter Brusilovsky
Bita Akram
https://orcid.org/0000-0003-2591-0476
Abstract
The ability to predict student performance in introductory programming courses is important to help struggling students and enhance their persistence. However, for this prediction to be impactful, it is crucial that it remains transparent and accessible for both instructors and students, ensuring effective utilization of the predicted results. Machine learning models with explainable features provide an effective means for students and instructors to comprehend students' diverse programming behaviors and problem-solving strategies, elucidating the factors contributing to both successful and suboptimal performance. This study develops an explainable model that predicts student performance based on programming assignment submission information in different stages of the course to enable early explainable predictions. We extract data-driven features from student programming submissions and utilize a stacked ensemble model for predicting final exam grades. The experimental results suggest that our model successfully predicts student performance based on their programming submissions earlier in the semester. Employing SHAP, a game-theory-based framework, we explain the model's predictions, aiding stakeholders in understanding the influence of diverse programming behaviors on students' success. Additionally, we analyze crucial features, employing a mix of descriptive statistics and mixture models to identify distinct student profiles based on their problem-solving patterns, enhancing overall explainability. Furthermore, we dive deeper and analyze the profiles using different programming patterns of the students to elucidate the characteristics of different students where SHAP explanations are not comprehensible. Our explainable early prediction model elucidates common problem-solving patterns in students relative to their expertise, facilitating effective intervention and adaptive support.
How to Cite
Hoq, M., Brusilovsky, P., & Akram, B. (2024). Explaining Explainability: Early Performance Prediction with Student Programming Pattern Profiling. Journal of Educational Data Mining, 16(2), 115–148. https://doi.org/10.5281/zenodo.14246435
##plugins.themes.bootstrap3.article.details##
Keywords
explainable student modeling, student programming analysis, early performance prediction, student profiling, student programming pattern
References
Afzaal, M., Nouri, J., Zia, A., Papapetrou, P., Fors, U., Wu, Y., Li, X., and Weegar, R. 2021. Explainable ai for data-driven feedback and intelligent action recommendations to support students self-regulation. Frontiers in Artificial Intelligence 4, 723447.
Ahadi, A., Lister, R., Haapala, H., and Vihavainen, A. 2015. Exploring machine learning methods to automatically identify students in need of assistance. In Proceedings of the 11th Annual International Conference on International Computing Education Research. Association for Computing Machinery, Omaha, Nebraska, USA, 121–130.
Ahmed, I., Jeon, G., and Piccialli, F. 2022. From artificial intelligence to explainable artificial intelligence in industry 4.0: a survey on what, how, and where. IEEE Transactions on Industrial Informatics 18, 8, 5031–5042.
Akram, B., Min, W., Wiebe, E., Mott, B., Boyer, K. E., and Lester, J. 2018. Improving stealth assessment in game-based learning with lstm-based analytics. In Proceedings of the 11th International Conference on Educational Data Mining (EDM), K. E. Boyer and M. Yudelson, Eds. International Educational Data Mining Society, Buffalo, NY, USA, 208–218.
Akram, B., Min, W., Wiebe, E., Mott, B., Boyer, K. E., and Lester, J. 2019. Assessing middle school students’ computational thinking through programming trajectory analysis. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education (SIGCSE). Association for Computing Machinery, Minneapolis, MN, USA, 1269–1269.
Alam, N., Acosta, H., Gao, K., and Mostafavi, B. 2022. Early prediction of student performance in a programming class using prior code submissions and metadata. In Proceedings of the 6th Educational Data Mining in Computer Science Education (CSEDM) Workshop, B. Akram, T. W. Price, Y. Shi, P. Brusilovsky, and S. I. Han Hsiao, Eds. Durham, UK, 30–39. https://doi.org/10.5281/zenodo.6983408.
Alon, U., Zilberstein, M., Levy, O., and Yahav, E. 2019. code2vec: Learning distributed representations of code. Proceedings of the ACM on Programming Languages 3, POPL, 1–29.
Baranyi, M., Nagy, M., and Molontay, R. 2020. Interpretable deep learning for university dropout prediction. In Proceedings of the 21st Annual Conference on Information Technology Education. Association for Computing Machinery, Virtual, 13–19.
Boroujeni, M. S. and Dillenbourg, P. 2018. Discovery and temporal analysis of latent study patterns in mooc interaction sequences. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge. Association for Computing Machinery, Sydney, Australia, 206–215.
Boubekki, A., Jain, S., and Brefeld, U. 2018. Mining user trajectories in electronic text books. In Proceedings of the 11th International Conference on Educational Data Mining (EDM), K. E. Boyer and M. Yudelson, Eds. International Educational Data Mining Society, Buffalo, NY, USA, 147–156.
Carter, A., Hundhausen, C., and Olivares, D. 2019. Leveraging the Integrated Development Environment for Learning Analytics. Cambridge University Press, 679–706.
Castro-Wunsch, K., Ahadi, A., and Petersen, A. 2017. Evaluating neural networks as a method for identifying students in need of assistance. In Proceedings of the ACM Technical Symposium on Computer Science Education (SIGCSE). Association for Computing Machinery, Seattle, WA, US, 111–116.
Chen, H.-C., Prasetyo, E., Tseng, S.-S., Putra, K. T., Kusumawardani, S. S., and Weng, C.-E. 2022. Week-wise student performance early prediction in virtual learning environment using a deep explainable artificial intelligence. Applied Sciences 12, 4, 1885.
Costa, E. B., Fonseca, B., Santana, M. A., de Araújo, F. F., and Rego, J. 2017. Evaluating the effectiveness of educational data mining techniques for early prediction of students’ academic failure in introductory programming courses. Computers in Human Behavior 73, C, 247–256.
de Raadt, M., Hamilton, M., Lister, R., Tutty, J., Baker, B., Box, I., Cutts, Q., Fincher, S., Hamer, J., Haden, P., et al. 2005. Approaches to learning in computer programming students and their effect on success. In Proceedings of the 28th Annual Conference of the Higher Education Research and Development Society of Australasia. Higher Education in a Changing World, M. Weisz and Smith, Eds. Higher Education Research & Development Society of Australia, Sydney, Australia, 408–414.
Edwards, S. H. and Murali, K. P. 2017. Codeworkout: short programming exercises with built-in data collection. In Proceedings of the 2017 ACM Conference on Innovation and Technology in Computer Science Education. Association for Computing Machinery, Bologna, Italy, 188–193.
Edwards, S. H., Snyder, J., Pérez-Quiñones, M. A., Allevato, A., Kim, D., and Tretola, B. 2009. Comparing effective and ineffective behaviors of student programmers. In Proceedings of the 5th International Workshop on Computing Education Research Workshop. Association for Computing Machinery, Berkeley, CA, USA, 3–14.
Effenberger, T., Cechák, J., and Pelánek, R. 2019. Difficulty and complexity of introductory programming problems. In Proceedings of the Educational Data Mining in Computer Science Education Workshop, D. Azcona, Y. V. Paredes, S. I.-H. Hsiao, and T. Price, Eds. Tempe, AZ, USA, –. https://www.fi.muni.cz/˜xpelanek/publications/difficulty-complexity-programming.pdf.
Embarak, O. 2021. Explainable artificial intelligence for services exchange in smart cities. In Explainable Artificial Intelligence for Smart Cities, M. Lahby, U. Kose, and A. K. Bhoi, Eds. Vol. 1. Taylor & Francis, Boca Raton, 13–30.
Estey, A. and Coady, Y. 2016. Can interaction patterns with supplemental study tools predict outcomes in cs1? In Proceedings of the ACM Conference on Innovation and Technology in Computer Science Education. Association for Computing Machinery, Arequipa, Peru, 236–241.
Ettles, A., Luxton-Reilly, A., and Denny, P. 2018. Common logic errors made by novice programmers. In Proceedings of the 20th Australasian Computing Education Conference. Association for Computing Machinery, Brisbane, Queensland, Australia, 83–89.
Gitinabard, N., Heckman, S., Barnes, T., and Lynch, C. F. 2019. What will you do next? a sequence analysis on the student transitions between online platforms in blended courses. In Proceedings of the 12th International Conference on Educational Data Mining (EDM), C. F. Lynch, A. Merceron, M. Desmarais, and R. Nkambou, Eds. International Educational Data Mining Society, Montréal, Canada, 59–68.
Hasib, K. M., Rahman, F., Hasnat, R., and Alam, M. G. R. 2022. A machine learning and explainable ai approach for predicting secondary school student performance. In Proceedings of the 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, Virtual, 0399–0405.
Henry, J. and Dumas, B. 2020. Developing an assessment to profile students based on their understanding of the variable programming concept. In Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education. Association for Computing Machinery, Trondheim, Norway, 33–39.
Hoq, M., Brusilovsky, P., and Akram, B. 2023. Analysis of an explainable student performance prediction model in an introductory programming course. In Proceedings of the 16th International Conference on Educational Data Mining (EDM), M. Feng, T. Käser, and P. Talukdar, Eds. International Educational Data Mining Society, Bengaluru, India, 79–90.
Hoq, M., Chilla, S. R., Ahmadi Ranjbar, M., Brusilovsky, P., and Akram, B. 2023. Sann: Programming code representation using attention neural network with optimized subtree extraction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, Birmingham, UK, 783–792.
Hoq, M., Patil, A., Akhuseyinoglu, K., Brusilovsky, P., and Akram, B. 2025. An automated approach to recommending relevant worked examples for programming problems. In Proceedings of the 56th ACM Technical Symposium on Computer Science Education (SIGCSE) V. 1. Association for Computing Machinery, Pittsburgh, PA, USA, –. (To be appeared).
Hoq, M., Shi, Y., Leinonen, J., Babalola, D., Lynch, C., Price, T., and Akram, B. 2024. Detecting chatgpt-generated code submissions in a cs1 course using machine learning models. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education (SIGCSE) V. 1. Association for Computing Machinery, Portland, OR, USA, 526–532.
Hoq, M., Vandenberg, J., Mott, B., Lester, J., Norouzi, N., and Akram, B. 2024. Towards attention-based automatic misconception identification in introductory programming courses. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education (SIGCSE) V. 2. Association for Computing Machinery, Portland, OR, USA, 1680–1681.
Hosseini, M., Powell, M., Collins, J., Callahan-Flintoft, C., Jones, W., Bowman, H., and Wyble, B. 2020. I tried a bunch of things: The dangers of unexpected overfitting in classification of brain data. Neuroscience & Biobehavioral Reviews 119, 456–467.
Ihantola, P., Vihavainen, A., Ahadi, A., Butler, M., Börstler, J., Edwards, S. H., Isohanni, E., Korhonen, A., Petersen, A., Rivers, K., et al. 2015. Educational data mining and learning analytics in programming: Literature review and case studies. In Proceedings of the 2015 ITiCSE on Working Group Reports. Association for Computing Machinery, Vilnius, Lithuania, 41–63.
Jadud, M. C. 2006. Methods and tools for exploring novice compilation behaviour. In Proceedings of the 2nd International Workshop on Computing Education Research. Association for Computing Machinery, Canterbury, UK, 73–84.
Jamjoom, M., Alabdulkreem, E., Hadjouni, M., Karim, F., and Qarh, M. 2021. Early prediction for at-risk students in an introductory programming course based on student self-efficacy. Informatica 45, 6, 1–9.
Joseph, A. 2019. Shapley regressions: A framework for statistical inference on machine learning models. arXiv preprint arXiv:1903.04209.
Kamal, M. S., Northcote, A., Chowdhury, L., Dey, N., Crespo, R. G., and Herrera-Viedma, E. 2021. Alzheimer’s patient analysis using image and gene expression data and explainable-ai to present associated genes. IEEE Transactions on Instrumentation and Measurement 70, 1–7.
Karimi, H., Derr, T., Huang, J., and Tang, J. 2020. Online academic course performance prediction using relational graph convolutional neural network. In Proceedings of the 13th International Conference on Educational Data Mining (EDM), A. N. Rafferty, J. Whitehill, C. Romero, and V. Cavalli-Sforza, Eds. International Educational Data Mining Society, –, 444–450.
Khan, I., Al Sadiri, A., Ahmad, A. R., and Jabeur, N. 2019. Tracking student performance in introductory programming by means of machine learning. In Proceedings of the 4th MEC International Conference on Big Data and Smart City (ICBDSC). IEEE, Muscat, Oman, 1–6.
Lauría, E. J., Baron, J. D., Devireddy, M., Sundararaju, V., and Jayaprakash, S. M. 2012. Mining academic data to improve college student retention: An open source perspective. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge. Association for Computing Machinery, Vancouver, British Columbia, Canada, 139–142.
Liu, E., Koprinska, I., and Yacef, K. 2023. Early prediction of student performance in online programming courses. In Proceedings of the 24th International Conference on Artificial Intelligence in Education (AIED), N. Wang, G. Rebolledo-Mendez, N. Matsuda, O. C. Santos, and V. Dimitrova, Eds. Springer, Springer Nature Switzerland, Tokyo, Japan, 365–371.
Llanos, J., Bucheli, V. A., and Restrepo-Calle, F. 2023. Early prediction of student performance in cs1 programming courses. PeerJ Computer Science 9, e1655.
Loginova, E. and Benoit, D. F. 2021. Embedding navigation patterns for student performance prediction. In Proceedings of the 14th International Conference on Educational Data Mining (EDM), S. S. Hsiao, I-Han (Sharon)and Sahebi, F. Bouchet, and J.-J. Vie, Eds. International Educational Data Mining Society, Virtual, 391–399.
Lorenzen, S., Hjuler, N., and Alstrup, S. 2018. Tracking behavioral patterns among students in an online educational system. In Proceedings of the 11th International Conference on Educational Data Mining (EDM), K. E. Boyer and M. Yudelson, Eds. International Educational Data Mining Society, Buffalo, NY, USA, 280–285.
Lu, O. H., Huang, J. C., Huang, A. Y., and Yang, S. J. 2017. Applying learning analytics for improving students engagement and learning outcomes in an moocs enabled collaborative programming course. Interactive Learning Environments 25, 2, 220–234.
Lu, Y., Wang, D., Meng, Q., and Chen, P. 2020. Towards interpretable deep learning models for knowledge tracing. In Proceedings of the 21st International Conference on Artificial Intelligence in Education (AIED), I. I. Bittencourt, M. Cukurova, K. Muldner, R. Luckin, and E. Millán, Eds. Springer International Publishing, Palermo, Italy, 185–190.
Lundberg, S. M., Erion, G. G., and Lee, S.-I. 2018. Consistent individualized feature attribution for tree ensembles. arXiv preprint arXiv:1802.03888.
Lundberg, S. M. and Lee, S.-I. 2017. A unified approach to interpreting model predictions. In Proceedings of the 31st International Conference on Neural Information Processing Systems, U. v. Luxburg, I. Guyon, S. Bengio, H. Wallach, and R. Fergus, Eds. Vol. 30. Curran Associates Inc., Long Beach, CA, USA, 4768–4777.
Mao, Y. 2019. One minute is enough: Early prediction of student success and event-level difficulty during novice programming tasks. In Proceedings of the 12th International Conference on Educational Data Mining (EDM), C. F. Lynch, A. Merceron, M. Desmarais, and R. Nkambou, Eds. International Educational Data Mining Society, Montréal, Canada, 119–128.
Mao, Y., Khoshnevisan, F., Price, T., Barnes, T., and Chi, M. 2022. Cross-lingual adversarial domain adaptation for novice programming. In Proceedings of the AAAI Conference on Artificial Intelligence. AAAI Press, Philadelphia, PA, US, 7682–7690.
Marsden, J., Yoder, S., and Akram, B. 2022. Predicting Student Performance with Control-flow Graph Embeddings. In 6th Educational Data Mining in Computer Science Education (CSEDM) Workshop, B. Akram, T. W. Price, Y. Shi, P. Brusilovsky, and S. I. Han Hsiao, Eds. Durham, UK, 32–40. https://doi.org/10.5281/zenodo.6983402.
Mouri, K., Shimada, A., Yin, C., and Kaneko, K. 2018. Discovering hidden browsing patterns using non-negative matrix factorization. In Proceedings of the 11th International Conference on Educational Data Mining (EDM), K. E. Boyer and M. Yudelson, Eds. International Educational Data Mining Society, Buffalo, NY, USA, 568–571.
Mu, T., Jetten, A., and Brunskill, E. 2020. Towards suggesting actionable interventions for wheel-spinning students. In Proceedings of The 13th International Conference on Educational Data Mining (EDM), A. N. Rafferty, J. Whitehill, C. Romero, and V. Cavalli-Sforza, Eds. International Educational Data Mining Society, Virtual, 183–193.
Muddamsetty, S. M., Jahromi, M. N., and Moeslund, T. B. 2021. Expert level evaluations for explainable ai (xai) methods in the medical domain. In Proceedings of the International Conference on Pattern Recognition, A. Del Bimbo, R. Cucchiara, S. Sclaroff, G. M. Farinella, T. Mei, M. Bertini, H. J. Escalante, and R. Vezzani, Eds. Springer, Springer International Publishing, Virtual, 35–46.
Ng, A. Y. et al. 1997. Preventing” overfitting” of cross-validation data. In Proceedings of the Fourteenth International Conference on Machine Learning, D. H. Fisher, Ed. Vol. 97. Morgan Kaufmann Publishers Inc., San Francisco, CA, US, 245–253.
Pei, B. and Xing, W. 2022. An interpretable pipeline for identifying at-risk students. Journal of Educational Computing Research 60, 2, 380–405.
Pereira, F. D., Fonseca, S. C., Oliveira, E. H., Cristea, A. I., Bellhäuser, H., Rodrigues, L., Oliveira, D. B., Isotani, S., and Carvalho, L. S. 2021. Explaining individual and collective programming students’ behavior by interpreting a black-box predictive model. IEEE Access 9, 117097–117119.
Pereira, F. D., Oliveira, E., Cristea, A., Fernandes, D., Silva, L., Aguiar, G., Alamri, A., and Alshehri, M. 2019. Early dropout prediction for programming courses supported by online judges. In Proceedings of the 20th International Conference on Artificial Intelligence in Education (AIED), S. Isotani, E. Millán, A. Ogan, P. Hastings, B. McLaren, and R. Luckin, Eds. Springer, Springer International Publishing, Chicago, IL, USA, 67–72.
Quille, K. and Bergin, S. 2019. Cs1: how will they do? how can we help? a decade of research and practice. Computer Science Education 29, 2-3, 254–282.
Ribeiro, M. T., Singh, S., and Guestrin, C. 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, San Francisco, CA, USA, 1135–1144.
Ryan, A. M. and Shim, S. S. 2012. Changes in help seeking from peers during early adolescence: Associations with changes in achievement and perceptions of teachers. Journal of Educational Psychology 104, 4, 1122.
Sahami, M. and Piech, C. 2016. As cs enrollments grow, are we attracting weaker students? In Proceedings of the 47th ACM Technical Symposium on Computing Science Education (SIGCSE). Association for Computing Machinery, Memphis, TN, US, 54–59.
Scheers, H. and De Laet, T. 2021. Interactive and explainable advising dashboard opens the black box of student success prediction. In Proceedings of the 16th European Conference on Technology Enhanced Learning (EC-TEL), T. De Laet, R. Klemke, C. Alario-Hoyos, I. Hilliger, and A. Ortega-Arranz, Eds. Springer, Springer International Publishing, Bozen-Bolzano, Italy, 52–66.
Serradilla, O., Zugasti, E., Cernuda, C., Aranburu, A., de Okariz, J. R., and Zurutuza, U. 2020. Interpreting remaining useful life estimations combining explainable artificial intelligence and domain knowledge in industrial machinery. In Proceedings of the 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, Glasgow, UK, 1–8.
Shahiri, A. M., Husain, W., et al. 2015. A review on predicting student’s performance using data mining techniques. Procedia Computer Science 72, 414–422.
Shapley, L. S. 1952. Quota solutions of n-person games. Tech. rep., RAND CORP SANTA MONICA CA.
Sheard, J., Markham, S., and Dick, M. 2003. Investigating differences in cheating behaviours of it undergraduate and graduate students: The maturity and motivation factors. Higher Education Research & Development 22, 1, 91–108.
Sheshadri, A., Gitinabard, N., Lynch, C. F., Barnes, T., and Heckman, S. 2018. Predicting student performance based on online study habits: A study of blended courses. In Proceedings of the 11th International Conference on Educational Data Mining (EDM), K. E. Boyer and M. Yudelson, Eds. International Educational Data Mining Society, Buffalo, NY, USA, 87–96.
Shi, Y., Schmucker, R., Chi, M., Barnes, T., and Price, T. 2023. KC-Finder: Automated knowledge component discovery for programming problems. In Proceedings of the 16th International Conference on Educational Data Mining (EDM), M. Feng, T. Käser, and P. Talukdar, Eds. International Educational Data Mining Society, Bengaluru, India, 28–39.
Singh, A., Sengupta, S., Rasheed, M. A., Jayakumar, V., and Lakshminarayanan, V. 2021. Uncertainty aware and explainable diagnosis of retinal disease. In Proceedings of the Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications, T. M. Deserno and B. J. Park, Eds. Vol. 11601. SPIE, Virtual, 116–125.
Sun, Q., Wu, J., and Liu, K. 2020. Toward understanding students’ learning performance in an object-oriented programming course: The perspective of program quality. IEEE Access 8, 37505–37517.
Sweeney, M., Lester, J., Rangwala, H., Johri, A., et al. 2016. Next-term student performance prediction: A recommender systems approach. Journal of Educational Data Mining 8, 1, 22–51.
Thakker, D., Mishra, B. K., Abdullatif, A., Mazumdar, S., and Simpson, S. 2020. Explainable artificial intelligence for developing smart cities solutions. Smart Cities 3, 4, 1353–1382.
Ting, K. M. and Witten, I. H. 1997. Stacked generalization: when does it work? In Poceedings of the 15th International Joint Conference on Artificial Intelligence. IJCAI, Buenos Aires, Argentina, 866–871.
Trifoni, A. and Shahini, M. 2011. How does exam anxiety affect the performance of university students. Mediterranean Journal of Social Sciences 2, 2, 93–100.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. 2017. Attention is all you need. In Proceedings of the 31st Conference on Neural Information Processing Systems, U. v. Luxburg, I. Guyon, S. Bengio, H. Wallach, and R. Fergus, Eds. Curran Associates Inc., Long Beach, CA, USA, 6000–6010.
Veerasamy, A. K., D’Souza, D., Apiola, M.-V., Laakso, M.-J., and Salakoski, T. 2020. Using early assessment performance as early warning signs to identify at-risk students in programming courses. In Proceedings of the 2020 IEEE Frontiers in Education Conference (FIE). IEEE, Uppsala, Sweden, 1–9.
Vultureanu-Albişi, A. and Bădică, C. 2021. Improving students’ performance by interpretable explanations using ensemble tree-based approaches. In Proceedings of the 2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI). IEEE, Virtual, 215–220.
Watson, C. and Li, F. W. 2014. Failure rates in introductory programming revisited. In Proceedings of the 2014 Conference on Innovation & Technology in Computer Science Education. Association for Computing Machinery, Uppsala Sweden, 39–44.
Wolpert, D. H. 1992. Stacked generalization. Neural Networks 5, 2, 241–259.
Yoder, S., Hoq, M., Brusilovsky, P., and Akram, B. 2022. Exploring sequential code embeddings for predicting student success in an introductory programming course. In 6th Educational Data Mining in Computer Science Education (CSEDM) Workshop, B. Akram, T. W. Price, Y. Shi, P. Brusilovsky, and S. I. Han Hsiao, Eds. Durham, UK, 2–9. https://doi.org/10.5281/zenodo.6983195.
Yudelson, M., Hosseini, R., Vihavainen, A., and Brusilovsky, P. 2014. Investigating automated student modeling in a java MOOC. In Proceedings of the 7th International Conference on Educational Data Mining (EDM), K. E. Boyer and M. Yudelson, Eds. International Educational Data Mining Society, London, United Kingdom, 261–264.
Zhang, J., Wang, X., Zhang, H., Sun, H., Wang, K., and Liu, X. 2019. A novel neural source code representation based on abstract syntax tree. In 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE). IEEE, Montral, Canada, 783–794.
Ahadi, A., Lister, R., Haapala, H., and Vihavainen, A. 2015. Exploring machine learning methods to automatically identify students in need of assistance. In Proceedings of the 11th Annual International Conference on International Computing Education Research. Association for Computing Machinery, Omaha, Nebraska, USA, 121–130.
Ahmed, I., Jeon, G., and Piccialli, F. 2022. From artificial intelligence to explainable artificial intelligence in industry 4.0: a survey on what, how, and where. IEEE Transactions on Industrial Informatics 18, 8, 5031–5042.
Akram, B., Min, W., Wiebe, E., Mott, B., Boyer, K. E., and Lester, J. 2018. Improving stealth assessment in game-based learning with lstm-based analytics. In Proceedings of the 11th International Conference on Educational Data Mining (EDM), K. E. Boyer and M. Yudelson, Eds. International Educational Data Mining Society, Buffalo, NY, USA, 208–218.
Akram, B., Min, W., Wiebe, E., Mott, B., Boyer, K. E., and Lester, J. 2019. Assessing middle school students’ computational thinking through programming trajectory analysis. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education (SIGCSE). Association for Computing Machinery, Minneapolis, MN, USA, 1269–1269.
Alam, N., Acosta, H., Gao, K., and Mostafavi, B. 2022. Early prediction of student performance in a programming class using prior code submissions and metadata. In Proceedings of the 6th Educational Data Mining in Computer Science Education (CSEDM) Workshop, B. Akram, T. W. Price, Y. Shi, P. Brusilovsky, and S. I. Han Hsiao, Eds. Durham, UK, 30–39. https://doi.org/10.5281/zenodo.6983408.
Alon, U., Zilberstein, M., Levy, O., and Yahav, E. 2019. code2vec: Learning distributed representations of code. Proceedings of the ACM on Programming Languages 3, POPL, 1–29.
Baranyi, M., Nagy, M., and Molontay, R. 2020. Interpretable deep learning for university dropout prediction. In Proceedings of the 21st Annual Conference on Information Technology Education. Association for Computing Machinery, Virtual, 13–19.
Boroujeni, M. S. and Dillenbourg, P. 2018. Discovery and temporal analysis of latent study patterns in mooc interaction sequences. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge. Association for Computing Machinery, Sydney, Australia, 206–215.
Boubekki, A., Jain, S., and Brefeld, U. 2018. Mining user trajectories in electronic text books. In Proceedings of the 11th International Conference on Educational Data Mining (EDM), K. E. Boyer and M. Yudelson, Eds. International Educational Data Mining Society, Buffalo, NY, USA, 147–156.
Carter, A., Hundhausen, C., and Olivares, D. 2019. Leveraging the Integrated Development Environment for Learning Analytics. Cambridge University Press, 679–706.
Castro-Wunsch, K., Ahadi, A., and Petersen, A. 2017. Evaluating neural networks as a method for identifying students in need of assistance. In Proceedings of the ACM Technical Symposium on Computer Science Education (SIGCSE). Association for Computing Machinery, Seattle, WA, US, 111–116.
Chen, H.-C., Prasetyo, E., Tseng, S.-S., Putra, K. T., Kusumawardani, S. S., and Weng, C.-E. 2022. Week-wise student performance early prediction in virtual learning environment using a deep explainable artificial intelligence. Applied Sciences 12, 4, 1885.
Costa, E. B., Fonseca, B., Santana, M. A., de Araújo, F. F., and Rego, J. 2017. Evaluating the effectiveness of educational data mining techniques for early prediction of students’ academic failure in introductory programming courses. Computers in Human Behavior 73, C, 247–256.
de Raadt, M., Hamilton, M., Lister, R., Tutty, J., Baker, B., Box, I., Cutts, Q., Fincher, S., Hamer, J., Haden, P., et al. 2005. Approaches to learning in computer programming students and their effect on success. In Proceedings of the 28th Annual Conference of the Higher Education Research and Development Society of Australasia. Higher Education in a Changing World, M. Weisz and Smith, Eds. Higher Education Research & Development Society of Australia, Sydney, Australia, 408–414.
Edwards, S. H. and Murali, K. P. 2017. Codeworkout: short programming exercises with built-in data collection. In Proceedings of the 2017 ACM Conference on Innovation and Technology in Computer Science Education. Association for Computing Machinery, Bologna, Italy, 188–193.
Edwards, S. H., Snyder, J., Pérez-Quiñones, M. A., Allevato, A., Kim, D., and Tretola, B. 2009. Comparing effective and ineffective behaviors of student programmers. In Proceedings of the 5th International Workshop on Computing Education Research Workshop. Association for Computing Machinery, Berkeley, CA, USA, 3–14.
Effenberger, T., Cechák, J., and Pelánek, R. 2019. Difficulty and complexity of introductory programming problems. In Proceedings of the Educational Data Mining in Computer Science Education Workshop, D. Azcona, Y. V. Paredes, S. I.-H. Hsiao, and T. Price, Eds. Tempe, AZ, USA, –. https://www.fi.muni.cz/˜xpelanek/publications/difficulty-complexity-programming.pdf.
Embarak, O. 2021. Explainable artificial intelligence for services exchange in smart cities. In Explainable Artificial Intelligence for Smart Cities, M. Lahby, U. Kose, and A. K. Bhoi, Eds. Vol. 1. Taylor & Francis, Boca Raton, 13–30.
Estey, A. and Coady, Y. 2016. Can interaction patterns with supplemental study tools predict outcomes in cs1? In Proceedings of the ACM Conference on Innovation and Technology in Computer Science Education. Association for Computing Machinery, Arequipa, Peru, 236–241.
Ettles, A., Luxton-Reilly, A., and Denny, P. 2018. Common logic errors made by novice programmers. In Proceedings of the 20th Australasian Computing Education Conference. Association for Computing Machinery, Brisbane, Queensland, Australia, 83–89.
Gitinabard, N., Heckman, S., Barnes, T., and Lynch, C. F. 2019. What will you do next? a sequence analysis on the student transitions between online platforms in blended courses. In Proceedings of the 12th International Conference on Educational Data Mining (EDM), C. F. Lynch, A. Merceron, M. Desmarais, and R. Nkambou, Eds. International Educational Data Mining Society, Montréal, Canada, 59–68.
Hasib, K. M., Rahman, F., Hasnat, R., and Alam, M. G. R. 2022. A machine learning and explainable ai approach for predicting secondary school student performance. In Proceedings of the 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, Virtual, 0399–0405.
Henry, J. and Dumas, B. 2020. Developing an assessment to profile students based on their understanding of the variable programming concept. In Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education. Association for Computing Machinery, Trondheim, Norway, 33–39.
Hoq, M., Brusilovsky, P., and Akram, B. 2023. Analysis of an explainable student performance prediction model in an introductory programming course. In Proceedings of the 16th International Conference on Educational Data Mining (EDM), M. Feng, T. Käser, and P. Talukdar, Eds. International Educational Data Mining Society, Bengaluru, India, 79–90.
Hoq, M., Chilla, S. R., Ahmadi Ranjbar, M., Brusilovsky, P., and Akram, B. 2023. Sann: Programming code representation using attention neural network with optimized subtree extraction. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, Birmingham, UK, 783–792.
Hoq, M., Patil, A., Akhuseyinoglu, K., Brusilovsky, P., and Akram, B. 2025. An automated approach to recommending relevant worked examples for programming problems. In Proceedings of the 56th ACM Technical Symposium on Computer Science Education (SIGCSE) V. 1. Association for Computing Machinery, Pittsburgh, PA, USA, –. (To be appeared).
Hoq, M., Shi, Y., Leinonen, J., Babalola, D., Lynch, C., Price, T., and Akram, B. 2024. Detecting chatgpt-generated code submissions in a cs1 course using machine learning models. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education (SIGCSE) V. 1. Association for Computing Machinery, Portland, OR, USA, 526–532.
Hoq, M., Vandenberg, J., Mott, B., Lester, J., Norouzi, N., and Akram, B. 2024. Towards attention-based automatic misconception identification in introductory programming courses. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education (SIGCSE) V. 2. Association for Computing Machinery, Portland, OR, USA, 1680–1681.
Hosseini, M., Powell, M., Collins, J., Callahan-Flintoft, C., Jones, W., Bowman, H., and Wyble, B. 2020. I tried a bunch of things: The dangers of unexpected overfitting in classification of brain data. Neuroscience & Biobehavioral Reviews 119, 456–467.
Ihantola, P., Vihavainen, A., Ahadi, A., Butler, M., Börstler, J., Edwards, S. H., Isohanni, E., Korhonen, A., Petersen, A., Rivers, K., et al. 2015. Educational data mining and learning analytics in programming: Literature review and case studies. In Proceedings of the 2015 ITiCSE on Working Group Reports. Association for Computing Machinery, Vilnius, Lithuania, 41–63.
Jadud, M. C. 2006. Methods and tools for exploring novice compilation behaviour. In Proceedings of the 2nd International Workshop on Computing Education Research. Association for Computing Machinery, Canterbury, UK, 73–84.
Jamjoom, M., Alabdulkreem, E., Hadjouni, M., Karim, F., and Qarh, M. 2021. Early prediction for at-risk students in an introductory programming course based on student self-efficacy. Informatica 45, 6, 1–9.
Joseph, A. 2019. Shapley regressions: A framework for statistical inference on machine learning models. arXiv preprint arXiv:1903.04209.
Kamal, M. S., Northcote, A., Chowdhury, L., Dey, N., Crespo, R. G., and Herrera-Viedma, E. 2021. Alzheimer’s patient analysis using image and gene expression data and explainable-ai to present associated genes. IEEE Transactions on Instrumentation and Measurement 70, 1–7.
Karimi, H., Derr, T., Huang, J., and Tang, J. 2020. Online academic course performance prediction using relational graph convolutional neural network. In Proceedings of the 13th International Conference on Educational Data Mining (EDM), A. N. Rafferty, J. Whitehill, C. Romero, and V. Cavalli-Sforza, Eds. International Educational Data Mining Society, –, 444–450.
Khan, I., Al Sadiri, A., Ahmad, A. R., and Jabeur, N. 2019. Tracking student performance in introductory programming by means of machine learning. In Proceedings of the 4th MEC International Conference on Big Data and Smart City (ICBDSC). IEEE, Muscat, Oman, 1–6.
Lauría, E. J., Baron, J. D., Devireddy, M., Sundararaju, V., and Jayaprakash, S. M. 2012. Mining academic data to improve college student retention: An open source perspective. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge. Association for Computing Machinery, Vancouver, British Columbia, Canada, 139–142.
Liu, E., Koprinska, I., and Yacef, K. 2023. Early prediction of student performance in online programming courses. In Proceedings of the 24th International Conference on Artificial Intelligence in Education (AIED), N. Wang, G. Rebolledo-Mendez, N. Matsuda, O. C. Santos, and V. Dimitrova, Eds. Springer, Springer Nature Switzerland, Tokyo, Japan, 365–371.
Llanos, J., Bucheli, V. A., and Restrepo-Calle, F. 2023. Early prediction of student performance in cs1 programming courses. PeerJ Computer Science 9, e1655.
Loginova, E. and Benoit, D. F. 2021. Embedding navigation patterns for student performance prediction. In Proceedings of the 14th International Conference on Educational Data Mining (EDM), S. S. Hsiao, I-Han (Sharon)and Sahebi, F. Bouchet, and J.-J. Vie, Eds. International Educational Data Mining Society, Virtual, 391–399.
Lorenzen, S., Hjuler, N., and Alstrup, S. 2018. Tracking behavioral patterns among students in an online educational system. In Proceedings of the 11th International Conference on Educational Data Mining (EDM), K. E. Boyer and M. Yudelson, Eds. International Educational Data Mining Society, Buffalo, NY, USA, 280–285.
Lu, O. H., Huang, J. C., Huang, A. Y., and Yang, S. J. 2017. Applying learning analytics for improving students engagement and learning outcomes in an moocs enabled collaborative programming course. Interactive Learning Environments 25, 2, 220–234.
Lu, Y., Wang, D., Meng, Q., and Chen, P. 2020. Towards interpretable deep learning models for knowledge tracing. In Proceedings of the 21st International Conference on Artificial Intelligence in Education (AIED), I. I. Bittencourt, M. Cukurova, K. Muldner, R. Luckin, and E. Millán, Eds. Springer International Publishing, Palermo, Italy, 185–190.
Lundberg, S. M., Erion, G. G., and Lee, S.-I. 2018. Consistent individualized feature attribution for tree ensembles. arXiv preprint arXiv:1802.03888.
Lundberg, S. M. and Lee, S.-I. 2017. A unified approach to interpreting model predictions. In Proceedings of the 31st International Conference on Neural Information Processing Systems, U. v. Luxburg, I. Guyon, S. Bengio, H. Wallach, and R. Fergus, Eds. Vol. 30. Curran Associates Inc., Long Beach, CA, USA, 4768–4777.
Mao, Y. 2019. One minute is enough: Early prediction of student success and event-level difficulty during novice programming tasks. In Proceedings of the 12th International Conference on Educational Data Mining (EDM), C. F. Lynch, A. Merceron, M. Desmarais, and R. Nkambou, Eds. International Educational Data Mining Society, Montréal, Canada, 119–128.
Mao, Y., Khoshnevisan, F., Price, T., Barnes, T., and Chi, M. 2022. Cross-lingual adversarial domain adaptation for novice programming. In Proceedings of the AAAI Conference on Artificial Intelligence. AAAI Press, Philadelphia, PA, US, 7682–7690.
Marsden, J., Yoder, S., and Akram, B. 2022. Predicting Student Performance with Control-flow Graph Embeddings. In 6th Educational Data Mining in Computer Science Education (CSEDM) Workshop, B. Akram, T. W. Price, Y. Shi, P. Brusilovsky, and S. I. Han Hsiao, Eds. Durham, UK, 32–40. https://doi.org/10.5281/zenodo.6983402.
Mouri, K., Shimada, A., Yin, C., and Kaneko, K. 2018. Discovering hidden browsing patterns using non-negative matrix factorization. In Proceedings of the 11th International Conference on Educational Data Mining (EDM), K. E. Boyer and M. Yudelson, Eds. International Educational Data Mining Society, Buffalo, NY, USA, 568–571.
Mu, T., Jetten, A., and Brunskill, E. 2020. Towards suggesting actionable interventions for wheel-spinning students. In Proceedings of The 13th International Conference on Educational Data Mining (EDM), A. N. Rafferty, J. Whitehill, C. Romero, and V. Cavalli-Sforza, Eds. International Educational Data Mining Society, Virtual, 183–193.
Muddamsetty, S. M., Jahromi, M. N., and Moeslund, T. B. 2021. Expert level evaluations for explainable ai (xai) methods in the medical domain. In Proceedings of the International Conference on Pattern Recognition, A. Del Bimbo, R. Cucchiara, S. Sclaroff, G. M. Farinella, T. Mei, M. Bertini, H. J. Escalante, and R. Vezzani, Eds. Springer, Springer International Publishing, Virtual, 35–46.
Ng, A. Y. et al. 1997. Preventing” overfitting” of cross-validation data. In Proceedings of the Fourteenth International Conference on Machine Learning, D. H. Fisher, Ed. Vol. 97. Morgan Kaufmann Publishers Inc., San Francisco, CA, US, 245–253.
Pei, B. and Xing, W. 2022. An interpretable pipeline for identifying at-risk students. Journal of Educational Computing Research 60, 2, 380–405.
Pereira, F. D., Fonseca, S. C., Oliveira, E. H., Cristea, A. I., Bellhäuser, H., Rodrigues, L., Oliveira, D. B., Isotani, S., and Carvalho, L. S. 2021. Explaining individual and collective programming students’ behavior by interpreting a black-box predictive model. IEEE Access 9, 117097–117119.
Pereira, F. D., Oliveira, E., Cristea, A., Fernandes, D., Silva, L., Aguiar, G., Alamri, A., and Alshehri, M. 2019. Early dropout prediction for programming courses supported by online judges. In Proceedings of the 20th International Conference on Artificial Intelligence in Education (AIED), S. Isotani, E. Millán, A. Ogan, P. Hastings, B. McLaren, and R. Luckin, Eds. Springer, Springer International Publishing, Chicago, IL, USA, 67–72.
Quille, K. and Bergin, S. 2019. Cs1: how will they do? how can we help? a decade of research and practice. Computer Science Education 29, 2-3, 254–282.
Ribeiro, M. T., Singh, S., and Guestrin, C. 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, San Francisco, CA, USA, 1135–1144.
Ryan, A. M. and Shim, S. S. 2012. Changes in help seeking from peers during early adolescence: Associations with changes in achievement and perceptions of teachers. Journal of Educational Psychology 104, 4, 1122.
Sahami, M. and Piech, C. 2016. As cs enrollments grow, are we attracting weaker students? In Proceedings of the 47th ACM Technical Symposium on Computing Science Education (SIGCSE). Association for Computing Machinery, Memphis, TN, US, 54–59.
Scheers, H. and De Laet, T. 2021. Interactive and explainable advising dashboard opens the black box of student success prediction. In Proceedings of the 16th European Conference on Technology Enhanced Learning (EC-TEL), T. De Laet, R. Klemke, C. Alario-Hoyos, I. Hilliger, and A. Ortega-Arranz, Eds. Springer, Springer International Publishing, Bozen-Bolzano, Italy, 52–66.
Serradilla, O., Zugasti, E., Cernuda, C., Aranburu, A., de Okariz, J. R., and Zurutuza, U. 2020. Interpreting remaining useful life estimations combining explainable artificial intelligence and domain knowledge in industrial machinery. In Proceedings of the 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, Glasgow, UK, 1–8.
Shahiri, A. M., Husain, W., et al. 2015. A review on predicting student’s performance using data mining techniques. Procedia Computer Science 72, 414–422.
Shapley, L. S. 1952. Quota solutions of n-person games. Tech. rep., RAND CORP SANTA MONICA CA.
Sheard, J., Markham, S., and Dick, M. 2003. Investigating differences in cheating behaviours of it undergraduate and graduate students: The maturity and motivation factors. Higher Education Research & Development 22, 1, 91–108.
Sheshadri, A., Gitinabard, N., Lynch, C. F., Barnes, T., and Heckman, S. 2018. Predicting student performance based on online study habits: A study of blended courses. In Proceedings of the 11th International Conference on Educational Data Mining (EDM), K. E. Boyer and M. Yudelson, Eds. International Educational Data Mining Society, Buffalo, NY, USA, 87–96.
Shi, Y., Schmucker, R., Chi, M., Barnes, T., and Price, T. 2023. KC-Finder: Automated knowledge component discovery for programming problems. In Proceedings of the 16th International Conference on Educational Data Mining (EDM), M. Feng, T. Käser, and P. Talukdar, Eds. International Educational Data Mining Society, Bengaluru, India, 28–39.
Singh, A., Sengupta, S., Rasheed, M. A., Jayakumar, V., and Lakshminarayanan, V. 2021. Uncertainty aware and explainable diagnosis of retinal disease. In Proceedings of the Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications, T. M. Deserno and B. J. Park, Eds. Vol. 11601. SPIE, Virtual, 116–125.
Sun, Q., Wu, J., and Liu, K. 2020. Toward understanding students’ learning performance in an object-oriented programming course: The perspective of program quality. IEEE Access 8, 37505–37517.
Sweeney, M., Lester, J., Rangwala, H., Johri, A., et al. 2016. Next-term student performance prediction: A recommender systems approach. Journal of Educational Data Mining 8, 1, 22–51.
Thakker, D., Mishra, B. K., Abdullatif, A., Mazumdar, S., and Simpson, S. 2020. Explainable artificial intelligence for developing smart cities solutions. Smart Cities 3, 4, 1353–1382.
Ting, K. M. and Witten, I. H. 1997. Stacked generalization: when does it work? In Poceedings of the 15th International Joint Conference on Artificial Intelligence. IJCAI, Buenos Aires, Argentina, 866–871.
Trifoni, A. and Shahini, M. 2011. How does exam anxiety affect the performance of university students. Mediterranean Journal of Social Sciences 2, 2, 93–100.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. 2017. Attention is all you need. In Proceedings of the 31st Conference on Neural Information Processing Systems, U. v. Luxburg, I. Guyon, S. Bengio, H. Wallach, and R. Fergus, Eds. Curran Associates Inc., Long Beach, CA, USA, 6000–6010.
Veerasamy, A. K., D’Souza, D., Apiola, M.-V., Laakso, M.-J., and Salakoski, T. 2020. Using early assessment performance as early warning signs to identify at-risk students in programming courses. In Proceedings of the 2020 IEEE Frontiers in Education Conference (FIE). IEEE, Uppsala, Sweden, 1–9.
Vultureanu-Albişi, A. and Bădică, C. 2021. Improving students’ performance by interpretable explanations using ensemble tree-based approaches. In Proceedings of the 2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI). IEEE, Virtual, 215–220.
Watson, C. and Li, F. W. 2014. Failure rates in introductory programming revisited. In Proceedings of the 2014 Conference on Innovation & Technology in Computer Science Education. Association for Computing Machinery, Uppsala Sweden, 39–44.
Wolpert, D. H. 1992. Stacked generalization. Neural Networks 5, 2, 241–259.
Yoder, S., Hoq, M., Brusilovsky, P., and Akram, B. 2022. Exploring sequential code embeddings for predicting student success in an introductory programming course. In 6th Educational Data Mining in Computer Science Education (CSEDM) Workshop, B. Akram, T. W. Price, Y. Shi, P. Brusilovsky, and S. I. Han Hsiao, Eds. Durham, UK, 2–9. https://doi.org/10.5281/zenodo.6983195.
Yudelson, M., Hosseini, R., Vihavainen, A., and Brusilovsky, P. 2014. Investigating automated student modeling in a java MOOC. In Proceedings of the 7th International Conference on Educational Data Mining (EDM), K. E. Boyer and M. Yudelson, Eds. International Educational Data Mining Society, London, United Kingdom, 261–264.
Zhang, J., Wang, X., Zhang, H., Sun, H., Wang, K., and Liu, X. 2019. A novel neural source code representation based on abstract syntax tree. In 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE). IEEE, Montral, Canada, 783–794.
Issue
Section
Articles
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
- The Author retains copyright in the Work, where the term “Work” shall include all digital objects that may result in subsequent electronic publication or distribution.
- Upon acceptance of the Work, the author shall grant to the Publisher the right of first publication of the Work.
- The Author shall grant to the Publisher and its agents the nonexclusive perpetual right and license to publish, archive, and make accessible the Work in whole or in part in all forms of media now or hereafter known under a Creative Commons 4.0 License (Attribution-Noncommercial-No Derivatives 4.0 International), or its equivalent, which, for the avoidance of doubt, allows others to copy, distribute, and transmit the Work under the following conditions:
- Attribution—other users must attribute the Work in the manner specified by the author as indicated on the journal Web site;
- Noncommercial—other users (including Publisher) may not use this Work for commercial purposes;
- No Derivative Works—other users (including Publisher) may not alter, transform, or build upon this Work,with the understanding that any of the above conditions can be waived with permission from the Author and that where the Work or any of its elements is in the public domain under applicable law, that status is in no way affected by the license.
- The Author is able to enter into separate, additional contractual arrangements for the nonexclusive distribution of the journal's published version of the Work (e.g., post it to an institutional repository or publish it in a book), as long as there is provided in the document an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post online a pre-publication manuscript (but not the Publisher’s final formatted PDF version of the Work) in institutional repositories or on their Websites prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (see The Effect of Open Access). Any such posting made before acceptance and publication of the Work shall be updated upon publication to include a reference to the Publisher-assigned DOI (Digital Object Identifier) and a link to the online abstract for the final published Work in the Journal.
- Upon Publisher’s request, the Author agrees to furnish promptly to Publisher, at the Author’s own expense, written evidence of the permissions, licenses, and consents for use of third-party material included within the Work, except as determined by Publisher to be covered by the principles of Fair Use.
- The Author represents and warrants that:
- the Work is the Author’s original work;
- the Author has not transferred, and will not transfer, exclusive rights in the Work to any third party;
- the Work is not pending review or under consideration by another publisher;
- the Work has not previously been published;
- the Work contains no misrepresentation or infringement of the Work or property of other authors or third parties; and
- the Work contains no libel, invasion of privacy, or other unlawful matter.
- The Author agrees to indemnify and hold Publisher harmless from Author’s breach of the representations and warranties contained in Paragraph 6 above, as well as any claim or proceeding relating to Publisher’s use and publication of any content contained in the Work, including third-party content.