Prediction of Confusion Attempting Algebra Homework in an Intelligent Tutoring System through Machine Learning Techniques for Educational Sustainable Development
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Wu Zhang | Yonglin Xu | Syed Muhammad Raza Abidi | Mushtaq Hussain | Yonglin Xu | Wu Zhang | S. Abidi | M. Hussain
[1] Vernon C. Smith,et al. Predictive Modeling to Forecast Student Outcomes and Drive Effective Interventions in Online Community College Courses , 2012 .
[2] Emily R. Fyfe,et al. Providing feedback on computer-based algebra homework in middle-school classrooms , 2016, Comput. Hum. Behav..
[3] V. Aleven,et al. Rapid Authoring of Intelligent Tutors for Real-World and Experimental Use , 2006, Sixth IEEE International Conference on Advanced Learning Technologies (ICALT'06).
[4] Reva Freedman. Atlas: A Plan Manager for Mixed-Initiative, Multimodal Dialogue , 1999 .
[5] Neil T. Heffernan,et al. Towards an Understanding of Affect and Knowledge from Student Interaction with an Intelligent Tutoring System , 2013, AIED.
[6] Robert Gilmore Pontius,et al. The total operating characteristic to measure diagnostic ability for multiple thresholds , 2014, Int. J. Geogr. Inf. Sci..
[7] E. Zaluska,et al. Predicting attrition from Massive Open Online Courses in FutureLearn and edX , 2017 .
[8] Mingyu Feng,et al. Online Mathematics Homework Increases Student Achievement , 2016 .
[9] Ryan Shaun Joazeiro de Baker,et al. Exploring the Relationship between Novice Programmer Confusion and Achievement , 2011, ACII.
[10] Cristina Conati,et al. Procedural Help in Andes: Generating Hints Using a Bayesian Network Student Model , 1998, AAAI/IAAI.
[11] Wu Zhang,et al. Using machine learning to predict student difficulties from learning session data , 2018, Artificial Intelligence Review.
[12] Ming Yang,et al. Detection of Left-Sided and Right-Sided Hearing Loss via Fractional Fourier Transform , 2016, Entropy.
[13] Zachary A. Pardos,et al. Affective states and state tests: investigating how affect throughout the school year predicts end of year learning outcomes , 2013, LAK '13.
[14] Vincent Aleven,et al. Towards Sensor-Free Affect Detection in Cognitive Tutor Algebra. , 2012, EDM 2012.
[15] Keeley A. Crockett,et al. On predicting learning styles in conversational intelligent tutoring systems using fuzzy decision trees , 2017, Int. J. Hum. Comput. Stud..
[16] Neil T. Heffernan,et al. Addressing the assessment challenge with an online system that tutors as it assesses , 2009, User Modeling and User-Adapted Interaction.
[17] Wanli Xing,et al. Dropout Prediction in MOOCs: Using Deep Learning for Personalized Intervention , 2019 .
[18] Xinyu Zhang,et al. Cloud Model Approach for Lateral Control of Intelligent Vehicle Systems , 2016, Sci. Program..
[19] N. Heffernan,et al. Tutor Modeling vs . Student Modeling , 2012 .
[20] Jianqiang Wang,et al. Object Classification Using CNN-Based Fusion of Vision and LIDAR in Autonomous Vehicle Environment , 2018, IEEE Transactions on Industrial Informatics.
[21] Kinshuk,et al. Computer aided learning for entry level accountancy students , 1996 .
[22] C. Y. Peng,et al. An Introduction to Logistic Regression Analysis and Reporting , 2002 .
[23] D. H. Vu,et al. A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables , 2015 .
[24] Hua Li,et al. Dropout prediction in MOOCs using behavior features and multi-view semi-supervised learning , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).
[25] Yudong Zhang,et al. Dual-Tree Complex Wavelet Transform and Twin Support Vector Machine for Pathological Brain Detection , 2016 .
[26] Cristina Conati,et al. Empirically building and evaluating a probabilistic model of user affect , 2009, User Modeling and User-Adapted Interaction.
[27] Arthur C. Graesser,et al. Interventions to Regulate Confusion during Learning , 2012, ITS.
[28] C. Metz. Basic principles of ROC analysis. , 1978, Seminars in nuclear medicine.
[29] Deyi Li,et al. A Hardware Platform Framework for an Intelligent Vehicle Based on a Driving Brain , 2018, Engineering.
[30] Neil T. Heffernan,et al. The ASSISTments Ecosystem: Building a Platform that Brings Scientists and Teachers Together for Minimally Invasive Research on Human Learning and Teaching , 2014, International Journal of Artificial Intelligence in Education.
[31] Matthew D. Dailey,et al. Feedback during Web-Based Homework: The Role of Hints , 2011, AIED.
[32] Kurt VanLehn,et al. The Andes Physics Tutoring System: Lessons Learned , 2005, Int. J. Artif. Intell. Educ..
[33] Naman K. Gupta,et al. Understanding Instructional Support Needs of Emerging Internet Users for Web-based Information Seeking , 2010, EDM 2010.
[34] A. Graesser,et al. Confusion can be beneficial for learning. , 2014 .
[35] Neil T. Heffernan,et al. Estimating the Effect of Web-Based Homework , 2013, AIED Workshops.
[36] Naomi J. Aldrich,et al. Does Discovery-Based Instruction Enhance Learning?. , 2011 .
[37] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[38] Ryan Shaun Joazeiro de Baker,et al. Classroom activities and off-task behavior in elementary school children , 2013, CogSci.
[39] T. Goetz,et al. Academic Emotions in Students' Self-Regulated Learning and Achievement: A Program of Qualitative and Quantitative Research , 2002 .
[40] J. Hattie,et al. The Power of Feedback , 2007 .
[41] Olusola O. Adesope,et al. Intelligent tutoring systems and learning outcomes: A meta-analysis , 2014 .
[42] Ingo Mierswa,et al. YALE: rapid prototyping for complex data mining tasks , 2006, KDD '06.
[43] Robert A. Cribbie,et al. The gamma generalized linear model, log transformation, and the robust Yuen-Welch test for analyzing group means with skewed and heteroscedastic data , 2019, Commun. Stat. Simul. Comput..
[44] R. S. Bichkar,et al. Performance Prediction of Engineering Students using Decision Trees , 2011 .
[45] Mary A. Mark,et al. An Interview Reflection on “Intelligent Tutoring Goes to School in the Big City” , 2015, International Journal of Artificial Intelligence in Education.