Utilizing student activity patterns to predict performance
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[1] John Mason,et al. Why the high attrition rate for computer science students: some thoughts and observations , 2005, SGCS.
[2] Raymond Lister. After the gold rush: toward sustainable scholarship in computing , 2008, ACE '08.
[3] Raymond Lister,et al. Exploring Machine Learning Methods to Automatically Identify Students in Need of Assistance , 2015, ICER.
[4] László Györfi,et al. A Probabilistic Theory of Pattern Recognition , 1996, Stochastic Modelling and Applied Probability.
[5] Shane Dawson,et al. Mining LMS data to develop an "early warning system" for educators: A proof of concept , 2010, Comput. Educ..
[6] Kevin Thorn,et al. Should Instructional Designers care about the Tin Can API? , 2013, ELERN.
[7] Judy McKay,et al. Seven factors that influence ICT student achievement , 2007, ITiCSE '07.
[8] Jacob Slonim,et al. Crossroads for Canadian CS enrollment , 2008, Commun. ACM.
[9] Tuba Yilmaz,et al. Student perceptions of computer science: a retention study comparing graduating seniors with cs leavers , 2008, SIGCSE '08.
[10] Christopher Ré,et al. Brainwash: A Data System for Feature Engineering , 2013, CIDR.
[11] Carlos Delgado Kloos,et al. Monitoring student progress using virtual appliances: A case study , 2012, Comput. Educ..
[12] Taylor Martin,et al. Using Learning Analytics to Understand the Learning Pathways of Novice Programmers , 2013 .
[13] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[14] 安藤 寛,et al. Cross-Validation , 1952, Encyclopedia of Machine Learning and Data Mining.
[15] S. Dunne,et al. Initial findings on the impact of an alternative approach to Problem Based Learning in Conputer Science , 2004 .
[16] Matthew D. Pistilli,et al. Course signals at Purdue: using learning analytics to increase student success , 2012, LAK.
[17] Salvatore J. Stolfo,et al. Real-world Data is Dirty: Data Cleansing and The Merge/Purge Problem , 1998, Data Mining and Knowledge Discovery.
[18] J. Manyika. Big data: The next frontier for innovation, competition, and productivity , 2011 .
[19] Ryan Shaun Joazeiro de Baker,et al. Automatically Detecting a Student's Preparation for Future Learning: Help Use is Key , 2011, EDM.
[20] Francisco Herrera,et al. An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes , 2011, Pattern Recognit..
[21] Paulo Blikstein,et al. Using learning analytics to assess students' behavior in open-ended programming tasks , 2011, LAK.
[22] 園田 茂,et al. 5.Classification and Regression Trees(CART)による脳卒中患者の退院時ADL予測(脳卒中-ADL予測) , 1995 .
[23] Aidan Mooney,et al. An Overview of the Integration of Problem Based Learning into an existing Computer Science Programming Module , 2004 .
[24] Nirupama Akella. Macro vs. Micro? Arguing for the Whole and Not the Chunk! , 2013, ELERN.
[25] Richard A. Berk. Classification and Regression Trees (CART) , 2008 .
[26] Kevin Casey,et al. Mining Moodle to understand Student Behaviour , 2010 .
[27] Ryan Shaun Joazeiro de Baker,et al. Towards Predicting Future Transfer of Learning , 2011, AIED.
[28] D. Feitelson,et al. Quantification of Code Regularity Using Preprocessing and Compression , 2014 .
[29] Neil Brown,et al. Blackbox: a large scale repository of novice programmers' activity , 2014, SIGCSE.
[30] J. Ross Quinlan,et al. Bagging, Boosting, and C4.5 , 1996, AAAI/IAAI, Vol. 1.
[31] George Siemens,et al. Penetrating the fog: analytics in learning and education , 2014 .
[32] N. Bashardoost,et al. Kernel Smoothing For ROC Curve And Estimation For Thyroid Stimulating Hormone , 2011 .
[33] Paul Roe,et al. Learning to Program: Going Pair-Shaped , 2007 .