Neil T. Heffernan, Ph.D.
Institution:
Carnegie Mellon University, Pittsburgh, PAArea:
memory, cognitive modeling, ACT-RGoogle:
"Neil Heffernan"Mean distance: 16 (cluster 38) | S | N | B | C | P |
Parents
Sign in to add mentor| John Anderson | grad student | 2001 | Carnegie Mellon | |
| (Intelligent tutoring systems have forgotten the tutor: Adding a cognitive model of human tutors.) | ||||
Children
Sign in to add trainee| Shubhendu Trivedi | grad student | Worcester Polytechnic Institute | |
| Kirk P Vanacore | grad student | Worcester Polytechnic Institute |
BETA: Related publications
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Publications
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| Graesser AC, Hu X, Nye BD, et al. (2018) ElectronixTutor: an intelligent tutoring system with multiple learning resources for electronics. International Journal of Stem Education. 5: 15 |
| Inventado PS, Scupelli P, Ostrow K, et al. (2018) Contextual factors affecting hint utility. International Journal of Stem Education. 5: 1-13 |
| Ostrow KS, Wang Y, Heffernan NT. (2017) How Flexible Is Your Data? A Comparative Analysis of Scoring Methodologies across Learning Platforms in the Context of Group Differentiation Journal of Learning Analytics. 4: 91-112 |
| Zhong X, Sun Z, Xiong H, et al. (2017) Learning Curve Analysis using Intensive Longitudinal and Cluster-Correlated Data Procedia Computer Science. 114: 250-257 |
| Pedro MOCZS, Baker RS, Heffernan NT. (2017) An Integrated Look at Middle School Engagement and Learning in Digital Environments as Precursors to College Attendance. Technology, Knowledge, and Learning. 22: 243-270 |
| Ocumpaugh J, Baker RS, Gowda SM, et al. (2014) Population Validity for Educational Data Mining Models: A Case Study in Affect Detection. British Journal of Educational Technology. 45: 487-501 |
| San Pedro MOZ, Baker RSJD, Gowda SM, et al. (2013) Towards an understanding of affect and knowledge from student interaction with an intelligent tutoring system Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 7926: 41-50 |
| Song F, Trivedi S, Wang Y, et al. (2013) Applying clustering to the problem of predicting retention within an ITS: Comparing regularity clustering with traditional methods Flairs 2013 - Proceedings of the 26th International Florida Artificial Intelligence Research Society Conference. 527-532 |
| A PZ, Gowda SM, Baker RSJd, et al. (2012) The sum is greater than the parts: ensembling models of student knowledge in educational software Sigkdd Explorations. 13: 37-44 |
| Pardos ZA, Trivedi S, Heffernan NT, et al. (2012) Clustered knowledge tracing Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 7315: 405-410 |