| 參考文獻 |
Abe, S., Tago, S., Yokoyama, K., Ogawa, M., Takei, T., Imoto, S., & Fuji, M. (2023). Explainable AI for Estimating Pathogenicity of Genetic Variants Using Large-Scale Knowledge Graphs. Cancers, 15(4), 1118.
Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., & Anadkat, S. (2023). Gpt-4 technical report. arXiv preprint arXiv:2303.08774.
Adnan, M., Uddin, M. I., Khan, E., Alharithi, F. S., Amin, S., & Alzahrani, A. A. (2022). Earliest Possible Global and Local Interpretation of Students’ Performance in Virtual Learning Environment by Leveraging Explainable AI. IEEE Access, 10, 129843-129864.
Ali, S., Abuhmed, T., El-Sappagh, S., Muhammad, K., Alonso-Moral, J. M., Confalonieri, R., Guidotti, R., Del Ser, J., Díaz-Rodríguez, N., & Herrera, F. (2023). Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence. Information Fusion, 99. https://doi.org/10.1016/j.inffus.2023.101805
Amin, F., & Mahmoud, M. (2022). Confusion Matrix in Binary Classification Problems: A Step-by-Step Tutorial. Journal of Engineering Research, 6(5), 0-0.
Ani, A. (2019). Positive feedback improves students’ psychological and physical learning outcomes. Indonesian Journal of Educational Studies Vol, 22(2).
Apley, D. W., & Zhu, J. (2020). Visualizing the effects of predictor variables in black box supervised learning models. Journal of the Royal Statistical Society Series B: Statistical Methodology, 82(4), 1059-1086.
Arachchi, S., Dias, K., Madanayake, R., Chong, E., & Gunawardana, K. (2014). A comparison between Evaluation of computer based testing and paper based testing for subjects in Computer Programming. International Journal of Software Engineering & Applications (IJSEA), 5(1).
Begosso, L. C., Begosso, L. R., Gonçalves, E. M., & Gonçalves, J. R. (2012). An approach for teaching algorithms and computer programming using Greenfoot and Python. 2012 Frontiers in Education Conference Proceedings,
Bergin, S., Reilly, R., & Traynor, D. (2005). Examining the role of self-regulated learning on introductory programming performance. Proceedings of the first international workshop on Computing education research,
Bradley, A. P. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern recognition, 30(7), 1145-1159.
Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32.
Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees.
Brodley, C. E., & Utgoff, P. E. (1995). Multivariate decision trees. Machine Learning, 19, 45-77.
Brooke, J. (1996). SUS-A quick and dirty usability scale. Usability evaluation in industry, 189(194), 4-7.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., & Askell, A. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877-1901.
Carless, D., Salter, D., Yang, M., & Lam, J. (2011). Developing sustainable feedback practices. Studies in higher education, 36(4), 395-407.
Carpenter, R., & Alloway, T. (2019). Computer versus paper-based testing: are they equivalent when it comes to working memory? Journal of psychoeducational assessment, 37(3), 382-394.
Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., & Lopez, A. (2020). A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing, 408, 189-215.
Chen, C.-H., Yang, S. J., Weng, J.-X., Ogata, H., & Su, C.-Y. (2021). Predicting at-risk university students based on their e-book reading behaviours by using machine learning classifiers. Australasian Journal of Educational Technology, 37(4), 130-144.
Chiu, T. K., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4, 100118.
Chou, C.-Y., Lai, K. R., Chao, P.-Y., Tseng, S.-F., & Liao, T.-Y. (2018). A negotiation-based adaptive learning system for regulating help-seeking behaviors. Computers & Education, 126, 115-128.
Chou, Y.-L., Moreira, C., Bruza, P., Ouyang, C., & Jorge, J. (2022). Counterfactuals and causability in explainable artificial intelligence: Theory, algorithms, and applications. Information Fusion, 81, 59-83.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273-297.
Danko, M., & Dečman, M. (2019). The strategy inventory for second language learning: Tested, adapted, and validated in the slovenian higher education context. ESP Today, Journal of English for Specific Purposes At Tertiary Level, 7(2), 207-230.
Das, A., & Rad, P. (2020). Opportunities and challenges in explainable artificial intelligence (xai): A survey. arXiv preprint arXiv:2006.11371.
Elkins, S., Kochmar, E., Serban, I., & Cheung, J. C. (2023). How useful are educational questions generated by large language models? International Conference on Artificial Intelligence in Education,
Ergen, B., & Kanadlı, S. (2017). The effect of self-regulated learning strategies on academic achievement: A meta-analysis study. Eurasian Journal of Educational Research, 17(69), 55-74.
Evans, C. (2013). Making sense of assessment feedback in higher education. Review of educational research, 83(1), 70-120.
Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics, 1189-1232.
Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2018). A survey of methods for explaining black box models. ACM computing surveys (CSUR), 51(5), 1-42.
Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., Scardapane, S., Spinelli, I., Mahmud, M., & Hussain, A. (2024). Interpreting black-box models: a review on explainable artificial intelligence. Cognitive Computation, 16(1), 45-74.
Herodotou, C., Rienties, B., Boroowa, A., Zdrahal, Z., & Hlosta, M. (2019). A large-scale implementation of predictive learning analytics in higher education: The teachers’ role and perspective. Educational Technology Research and Development, 67, 1273-1306.
Holzinger, A., Carrington, A., & Müller, H. (2020). Measuring the quality of explanations: the system causability scale (SCS) comparing human and machine explanations. KI-Künstliche Intelligenz, 34(2), 193-198.
Holzinger, A., Langs, G., Denk, H., Zatloukal, K., & Müller, H. (2019). Causability and explainability of artificial intelligence in medicine. Wiley Interdisciplinary Reviews: Data mining and knowledge discovery, 9(4), e1312.
Jang, Y., Choi, S., Jung, H., & Kim, H. (2022). Practical early prediction of students’ performance using machine learning and eXplainable AI. Education and Information Technologies, 27(9), 12855-12889.
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., & Hüllermeier, E. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and individual differences, 103, 102274. https://www.sciencedirect.com/science/article/abs/pii/S1041608023000195
https://www.sciencedirect.com/science/article/abs/pii/S1041608023000195?via%3Dihub
Kotsiantis, S. B. (2013). Decision trees: a recent overview. Artificial Intelligence Review, 39, 261-283.
Krening, S., Harrison, B., Feigh, K. M., Isbell, C. L., Riedl, M., & Thomaz, A. (2016). Learning from explanations using sentiment and advice in RL. IEEE Transactions on Cognitive and Developmental Systems, 9(1), 44-55.
López Zambrano, J., Lara Torralbo, J. A., & Romero Morales, C. (2021). Early prediction of student learning performance through data mining: A systematic review. Psicothema.
Li, S., Chen, J., Shen, Y., Chen, Z., Zhang, X., Li, Z., Wang, H., Qian, J., Peng, B., & Mao, Y. (2022). Explanations from large language models make small reasoners better. arXiv preprint arXiv:2210.06726.
Liffiton, M., Sheese, B. E., Savelka, J., & Denny, P. (2023). Codehelp: Using large language models with guardrails for scalable support in programming classes. Proceedings of the 23rd Koli Calling International Conference on Computing Education Research,
Lipton, Z. C. (2018). The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue, 16(3), 31-57.
Lu, O. H., Huang, A. Y., Huang, J. C., Huang, C. S., & Yang, S. J. (2016). Early-Stage Engagement: Applying Big Data Analytics on Collaborative Learning Environment for Measuring Learners′ Engagement Rate. 2016 International Conference on Educational Innovation through Technology (EITT),
Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30.
M, H., & M.N, S. (2015). A Review on Evaluation Metrics for Data Classification Evaluations. International journal of data mining & knowledge management process, 5(2), 01-11. https://doi.org/10.5121/ijdkp.2015.5201
Marquès Puig, J. M., Daradoumis, T., Arguedas, M., & Calvet Liñan, L. (2022). Using a distributed systems laboratory to facilitate students′ cognitive, metacognitive and critical thinking strategy use. Journal of Computer Assisted Learning, 38(1), 209-222.
Minh, D., Wang, H. X., Li, Y. F., & Nguyen, T. N. (2022). Explainable artificial intelligence: a comprehensive review. Artificial Intelligence Review, 1-66.
Nagy, M., & Molontay, R. (2023). Interpretable Dropout Prediction: Towards XAI-Based Personalized Intervention. International Journal of Artificial Intelligence in Education, 1-27.
Namoun, A., & Alshanqiti, A. (2020). Predicting student performance using data mining and learning analytics techniques: A systematic literature review. Applied Sciences, 11(1), 237.
Ogata, H., Yin, C., Oi, M., Okubo, F., Shimada, A., Kojima, K., & Yamada, M. (2015). E-Book-based learning analytics in university education. International conference on computer in education (ICCE 2015),
Öqvist, M., & Nouri, J. (2018). Coding by hand or on the computer? Evaluating the effect of assessment mode on performance of students learning programming. Journal of Computers in Education, 5, 199-219.
Osmanbegović, E., Suljić, M., & Agić, H. (2014). Determining dominant factor for students performance prediction by using data mining classification algorithms. Tranzicija, 16(34), 147-158.
Ouatik, F., Erritali, M., Ouatik, F., & Jourhmane, M. (2022). Predicting student success using big data and machine learning algorithms. International Journal of Emerging Technologies in Learning (Online), 17(12), 236.
Oxford, R. (1990). Language learning strategiesWhat every teacher should know. Heinle & heinle Publishers.;.
Pankiewicz, M., & Baker, R. S. (2023). Large Language Models (GPT) for automating feedback on programming assignments. arXiv preprint arXiv:2307.00150.
Pek, R. Z., Özyer, S. T., Elhage, T., Özyer, T., & Alhajj, R. (2022). The role of machine learning in identifying students at-risk and minimizing failure. IEEE Access, 11, 1224-1243.
Pintrich, P. R. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ).
Porter, B., & Grippa, F. (2020). A platform for AI-enabled real-time feedback to promote digital collaboration. Sustainability, 12(24), 10243.
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). " Why should i trust you?" Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining,
Ronanki, K., Cabrero-Daniel, B., Horkoff, J., & Berger, C. (2024). Requirements engineering using generative ai: Prompts and prompting patterns. In Generative AI for Effective Software Development (pp. 109-127). Springer.
Roussel, C., & Böhm, K. (2023). Geospatial xai: A review. ISPRS International Journal of Geo-Information, 12(9), 355.
Selvaraj, A. M., & Azman, H. (2020). Reframing the Effectiveness of Feedback in Improving Teaching and Learning Achievement. International Journal of Evaluation and Research in Education, 9(4), 1055-1062.
Shapley, L. S. (1953). A value for n-person games.
Singh, D., & Singh, B. (2020). Investigating the impact of data normalization on classification performance. Applied Soft Computing, 97, 105524.
Tan, B., Gan, Z., & Wu, Y. (2023). The measurement and early warning of daily financial stability index based on XGBoost and SHAP: Evidence from China. Expert Systems with Applications, 227, 120375.
Tomasevic, N., Gvozdenovic, N., & Vranes, S. (2020). An overview and comparison of supervised data mining techniques for student exam performance prediction. Computers & Education, 143, 103676.
Van der Velden, B. H., Kuijf, H. J., Gilhuijs, K. G., & Viergever, M. A. (2022). Explainable artificial intelligence (XAI) in deep learning-based medical image analysis. Medical Image Analysis, 79, 102470.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
Wang, X., Zhang, L., & He, T. (2022). Learning performance prediction-based personalized feedback in online learning via machine learning. Sustainability, 14(13), 7654.
Wang, Y. (2021). When artificial intelligence meets educational leaders’ data-informed decision-making: A cautionary tale. Studies in Educational Evaluation, 69, 100872.
White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., & Schmidt, D. C. (2023). A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382.
Wong, J., Baars, M., Davis, D., Van Der Zee, T., Houben, G.-J., & Paas, F. (2019). Supporting self-regulated learning in online learning environments and MOOCs: A systematic review. International Journal of Human–Computer Interaction, 35(4-5), 356-373.
Xu, W., Meng, J., Raja, S. K. S., Priya, M. P., & Kiruthiga Devi, M. (2023). Artificial intelligence in constructing personalized and accurate feedback systems for students. International Journal of Modeling, Simulation, and Scientific Computing, 14(01), 2341001.
Zheng, L. (2016). The effectiveness of self-regulated learning scaffolds on academic performance in computer-based learning environments: A meta-analysis. Asia Pacific Education Review, 17, 187-202. |
[Endnote RIS 格式]
[相關文章]
[文章引用]
[完整記錄]
[館藏目錄]
至系統瀏覽論文 (2029-8-1以後開放)
plurk
funp
live
udn
HD
myshare
netvibes
friend
youpush
delicious
baidu
Google bookmarks
del.icio.us
hemidemi
facebook
twitter
google
reddit