Publications

Note: In the following sections, WPI students, who are coauthors, have their names in italics. Here is my DBLP page. My Google Scholar page is here. This page has all my publications, including many data mining papers. If you are looking for 20+  randomized controlled experiments, see here instead. 

Most Cited Papers (and Why)

I am well know for ASSISTments.  If you a looking for a general paper to cite for ASSISTments, this new paper is a good one.  
I am known for the fact that the ASSISTments platform when paired with good content can raise students learning rates compared to a condition representing traditional paper and pencil methods.
  • There are other similar studies showing ASSISTments is effective.  But the most rigorous study to date is listed below done by SRI (I am not an author)
I help host ASSISTmentstestBed.org as a shared scientific instrument.

ASSISTments has a long history, having started back in 2005.
  • CP11   Razzaq, L., Feng, M., Nuzzo-Jones, G., Heffernan, N.T., Koedinger, K. R., Junker, B., Ritter, S., Knight, A., Aniszczyk, C., Choksey, S., Livak, T., Mercado, E., Turner, T.E., Upalekar. R, Walonoski, J.A., Macasek. M.A. & Rasmussen, K.P. (2005). The Assistment project: Blending assessment and assisting. In C.K. Looi, G. McCalla, B. Bredeweg, & J. Breuker (Eds.) Proceedings of the 12th Artificial Intelligence in Education, Amsterdam: ISO Press. pp. 555-562.
If you are looking to cite something for how ASSISTments can adapt to users, this one is good.
  • CP25   Razzaq, L. & Heffernan, N. (2009). To Tutor or Not to Tutor: That is the Question. In Dimitrova, Mizoguchi, du Boulay & Graesser (Eds.) Proceedings of the 2009 Artificial Intelligence in Education Conference. IOS Press. pp. 457-464. Honorable Mention for Best Paper First Authored by a Student.
I am known for my work using Bayes Nets in Student Modeling.
I am known for the fact that ASSISTments is a good assessor of student knowledge and can predicts state test scored better because we use things like how many hints and attempts a student makes.

I am known for my role in helping create CTAT.

I am known for work on detecting "gaming" and why students do it.

Book Chapters

BC5     Heffernan, N., Militello, M, Heffernan, C., & Decoteau, M. (2012). Effective and meaningful use of educational technology: three cases from the classroom.  In C Dede& J. Richards (Eds.).  Digital Teaching Platforms. pp. 88-102. Columbia, NY: Teachers College Press.

BC4     Razzaq, L. & Heffernan, N.  (2010). Open content Authoring Tools.  In Nkambou, Bourdeau & Mizoguchi (Eds.)  Advanced in Intelligent Tutoring Systems.pp 425-439. Berlin: Springer Verlag.

BC3     Pardos, Z. A., Heffernan, N. T., Anderson, B., Heffernan, L. C. (2010). Using Fine-Grained Skill Models to Fit Student Performance with Bayesian Networks.Chapter in C. Romero, S. Ventura, S. R. Viola, M. Pechenizkiy and R. S. J. Baker. Handbook of Educational Data Mining. Boca Raton, Florida: Chapman & Hall/CRC Press.

BC2     Feng, M., Heffernan, N.T., & Koedinger, K.R. (2010). Student Modeling in an Intelligent Tutoring System. In Stankov, Glavinc, and Rosic. (Eds.) Intelligent Tutoring Systems in E-learning Environments: Design, Implementation and Evaluation. pp. 208-236. Hershey, PA: Information Science Reference. (Based on W10W11 and W12).

BC1     Razzaq, Feng, Heffernan, Koedinger, Nuzzo-Jones, Junker, Macasek, Rasmussen, Turner & Walonoski. (2007). A Web-based authoring tool for intelligent tutors: Assessment and instructional assistance. In Nadia Nedjah, Luiza deMacedo Mourelle, Mario Neto Borges and Nival Nunesde Almeida (Eds). Intelligent Educational MachinesIntelligent Systems Engineering Book Series. pp.23-49. Berlin: Springer Verlag. http://www.springerlink.com/content/m2g23834641m858n/fulltext.pdf.

        

Journal Articles

J25 Soffer, D., Goldman, S., Pellegrino, J., Heffernan, C., & Heffernan, N. (about to be submitted) The Effect of Automatic Reassessment and Relearning on the Retention of Mathematical Knowledge and Skills.  Submitted to Journal of Applied Research in Memory and Cognition (JARMAC). Elsevier. 

J24 McGuire, P., Tu, S., Logue., M., Mason, C., Ostrow, K. (2017) Counterintuitive effects of online feedback in middle school math: results from a randomized controlled trial in ASSISTments. Educational Media International.  Taylor Francis.  Vol Pages 1-14.  [Heffernan was not an author but he helped these scientists use his platform.]

J23 Inventado, P., Scupelli, P.,Ostrow, K., Heffernan, N., Ocumpaugh, J., Almeda, V., & Slater, S. (In press) Contextual factors affecting hint utility.  International Journal of STEM Education. Pages TBD

J22 Ostrow, K.S., Wang, Y., & Heffernan, N.T. (2017). How Flexible Is Your Data? A Comparative Analysis of Scoring Methodologies Across Learning Platforms in the Context of Group Differentiation. To appear in The Journal of Learning Analytics,VOL 4, NO 2, 91-112.

J21 Ostrow, K.S., Heffernan, N.T., & Williams, J.J. (2017). Tomorrow’s EdTech Today: Establishing a Learning Platform as a Collaborative Research Tool for Sound Science. Teachers College Record, Volume 119 Number 3, 2017, p. 1-36.

J20 Heffernan, N.T., Ostrow, K.S., Kelly, K., Selent, D., Van Inwegen, E.G., Xiong, X., & Williams, J.J. (2016). The Future of Adaptive Learning: Does the Crowd Hold the Key? International Journal of Artificial Intelligence in Education. Springer New York. Vol 26(2), 615-644.

DOI: 10.1007/s40593-016-0094-z 
J19  Heffernan, N. & Heffernan, C. (2014). The ASSISTments Ecosystem: Building a Platform that Brings Scientists and Teachers Together for Minimally Invasive Research on Human Learning and Teaching. International Journal of Artificial Intelligence in Education. 24 (4), 470-497. Link to the Springer version DOI 10.1007/s40593-014-0024-x .  The Special Issue focused on landmark systems.

J18  Ocumpaugh, J., Baker, R., Gowda, S., Heffernan, N., Heffernan, C. (2014). Population validity for Educational Data Mining models: A case study in affect detection. British Journal of Educational Technology, 45(3), 487-501. DOI: 10.1111/bjet.12156

J17  Pardos, Z.A., Gowda, S. M., Baker, R. S.J.D., Heffernan, N. T., (2012). The Sum is Greater than the Parts: Ensembling Models of Student Knowledge in Educational Software.  ACM's Knowledge Discovery and Datamining Explorations, 13(2), 37-44.

J16  Gong, Y, Beck, J. E., Heffernan, N. T. (2011).  How to Construct More Accurate Student Models:  Comparing and Optimizing Knowledge Tracing and Performance Factor Analysis. International Journal of Artificial Intelligence in Education. 21, 27-46.

J15  Pardos, Z., Dailey, M. & Heffernan, N. (2011). Learning what works in ITS from non-traditional randomized controlled trial data. The International Journal of Artificial Intelligence in Education. 21, 47-63.

J14  Koedinger, K., McLaughlin, E. & Heffernan, N. (2010). A Quasi-Experimental Evaluation of an On-line Formative Assessment and Tutoring System.   Journal of Educational Computing Research.  Baywood Publishing. 4, 489 - 510  (See also)

J13  Baker, R., Goldstein, A., Heffernan, N. (2011). Detecting the Moment of Learning. International Journal of Artificial Intelligence in Education, 21(1-2), 5-25. (Get the data here)

J12  Broderick, Z., O’Connor, C., Mulcahy, C., Heffernan, N. & Heffernan, C. (2011). Increasing Parent Engagement in Student Learning Using an Intelligent Tutoring System.  Journal of Interactive Learning Research, 22(4), 523-550.  Longer version available as WPI CS Technical Report Number 2010 #08.  Chesapeake, VA: AACE. Retrieved August 15, 2013, from http://www.editlib.org/p/34133.

J11  Militello, M., & Heffernan, N. (2009). Which one is "just right"? What educators should know about formative assessment systems. International Journal of Educational Leadership Preparation, 4(3), 1-8.

J10  Feng, M., Heffernan, N.T., Heffernan, & C., Mani, M. (2009). Using Mixed-Effects Modeling to Analyze Different Grain-Sized Skill Models. IEEE Transactions on Learning Technologies, 2(2), 79-92. Featured Best Article of 8 Article in the edition (Based on PP8 and WP15)

J9    Razzaq, L., Patvarczki, J., Almeida, S.F., Vartak, M., Feng, M., Heffernan, N.T. and Koedinger, K. (2009). The ASSISTment builder: Supporting the Life-cycle of ITS Content Creation. IEEE Transactions on Learning Technologies Special Issue on Real-World Applications of Intelligent Tutoring Systems. 2(2) 157-166 (Based on YRP5)

J8    Feng, M., Heffernan, N.T., & Koedinger, K.R. (2009). Addressing the assessment challenge in an Intelligent Tutoring System that tutors as it assesses. The Journal of User Modeling and User-Adapted Interaction.19, 243-266. (Based on CP15) Best Paper of the Year (See Award #20 above). Mentioned in National Ed. Tech Plan (See Award 19).

J7    Mendicino, M., Razzaq, L. & Heffernan, N. T. (2009). Improving Learning from Homework Using Intelligent Tutoring Systems. Journal of Research on Technology in Education (JRTE). 41(3), 331-346. PDF

J6    Baker, R., Walonoski, J., Heffernan, T., Roll, I., Corbett, A. & Koedinger, K. (2008). Why students engage in "Gaming the System" behavior in interactive learning environments Journal of Interactive Learning Research (JILR).19(2), 185-224 (Based on CP12 and PP5)

J5    Razzaq, L., Heffernan, N., Feng, M., & Pardos Z. (2007). Developing Fine-Grained Transfer Models in the ASSISTment System. Journal of Technology, Instruction, Cognition, and Learning. 5(3), 289-304.

J4    Feng, M., & Heffernan, N.T. (2007). Towards live informing and automatic analyzing of student learning: Reporting in the Assistment system. Journal of Interactive Learning Research (JILR) 18(2), 207-230. (Based on W12)

J3    Feng, M., & Heffernan, N.T. (2006). Informing teachers live about student learning: Reporting in the Assistment system Technology, Instruction, Cognition, and Learning Journal. 3(1-2), 63. (Based on W12)

J2    Heffernan, N. T., Koedinger, K. & Razzaq, L. (2008). Expanding the model-tracing architecture: A 3rd generation intelligent tutor for Algebra symbolization.The International Journal of Artificial Intelligence in Education. 18(2), 153-178 (Builds upon CP8 and CP1-4) .

J1    Heffernan, N. T., & Koedinger, K. R. (2002). Results from a Web-Based Tutor for Writing Algebra Expressions for Word-Problems Sciences et Techniques Educatives. 9(1-2), 11-35. (This French language journal has translated our paper into French. My translation of this Journal's title is "Educational Sciences and Technology.") http://www.inrp.fr/atief/ste/ste-res-vol9.htm (Based on D1)

Dissertation

D1    Heffernan, N. T (2001). Intelligent tutoring systems have forgotten the tutor: Adding a cognitive model of human tutors Dissertation. Computer Science Department, School of Computer Science, Carnegie Mellon University. Technical Report CMU-CS-01-127. (Pieces published as J1, CP8, CP5, CP4, and CP3)

Strictly Reviewed Conferences (Acceptance rates on the 30%s or below)

Note: Unlike most other disciplines where journal papers are more prestigious than conference papers, in Computer Science as a discipline, conference publications are often more difficult to get accepted, therefore, more prestigious than most journal publications. These conference proceedings are stringently peer-reviewed, with at least three reviewers. The acceptance rate is usually in the 30%-39% range. (EDM2010 was unusual in that they accepted 42% of the papers, but that is non-standard).  Note that ITS2014 started to make a distinction between long and short papers.  For instance, the acceptance rate for long papers (typically 8-10 pages) was 17.5 % and 42% for short (6 pages).  I have started labeling the acceptance rates on new papers to make that easier to understand.



Workshops

Short Papers

The Educational Data Mining Conference created a new category of paper, called a short paper, in addition to the 12 page "Full Papers" and the two page "Poster" category. In 2011 the acceptance rates for short papers was 46%, which is considerably lower than the 33% acceptance rate for "Full Papers." 

SP22 Zhao, S .  Wuhan... 

SP21 Zhang, L., Xiong, X., Zhao, S., Botelho, A. & Heffernan, N. (2017)  Incorporating Rich Features into Deep Knowledge Tracing. In the Proceedings of the Forth (2017) ACM Conference on Learning @ Scale.(L@S2017)   Cambridge, MA. Pages Pages 169-172.  (Acceptance Rate 44%) PDF  (I longer version is available here)

SP20 Yin, B., Patikorn, T., Botelho, A., Heffernan, N. (2017) Observing Personalizations in Learning: Identifying Heterogeneous Treatment Effects Using Causal Trees. In the Proceedings of the Forth (2017) ACM Conference on Learning @ Scale.(L@S2017)  Pages 299-302.  Cambridge, MA.  (Acceptance Rate 44%) PDF

SP19 Zhao, S., Zhang, Y., Xiong, X., Botelho, A. F., & Heffernan, N. T.  (in press) A Memory-Augmented Neural Model for Automated Grading. In the Proceedings of the Forth (2017) ACM Conference on Learning @ Scale.(L@S2017)   Cambridge, MA. Pages 189-172 (Acceptance Rate 44%) 

SP18 Lu, Xiwen, Xiong, X. & Heffernan, N. (2017) Experimenting Choices of Video and Text Feedbacks in Authentic Foreign Language Assignments at Scale.  In the Proceedings of the Forth (2017) ACM Conference on Learning @ Scale.(L@S2017)  Pages 335-338.  (Acceptance Rate 44%)  PDF

SP17 Inventado, P. S., VanInwegen, E., Ostrow, K., Scupelli, P., Heffernan, N., Baker, R., Slater, S. & Ocumpaugh, J. (2016) Design Subtleties Driving Differential Attrition. The 6th International Learning Analytics & Knowledge Conference pp 284-289. 

SP16 Ostrow, K.S. & Heffernan, N. T. (2016) Studying Learning at Scale with the ASSISTments TestBed Proceedings of the Third (2016) ACM Conference on Learning @Scale pp. 333-334.

SP15 Kelly, K. M. & Heffernan, N. T. (2016) Optimizing the Amount of Practice in an On-Line Platform Proceedings of the Third (2016) ACM Conference on Learning @Scale pp. 145-148.

SP14  Inventado, P., Scupelli, P., Van Inwegen, E., Ostrow, K., Heffernan, N., Ocumpaugh, J., Baker, R., Slater, S. & Almeda, M. (2016) Availability Slows Completion Times in Summer Work.  In Barnes, Chi & Feng (eds) The 9th International Conference on Educational Data Mining.  pp 388-394. (Acceptance Rate of 50%)

SP13 Botelho, A., Adjei, S. & Heffernan, N. (2016) Modeling Interactions Across Skills: A Method to Construct and Compare Models Predicting the Existence of Skill Relationships In Barnes, Chi & Feng (eds) The 9th International Conference on Educational Data Mining. Pages 292-297.

SP12 Wang, Y., Ostrow, K., Beck, J. & Heffernan, N. T. (2016)  Enhancing the efficiency and reliability of group differentiation through partial credit.  In the Proceedings of the Sixth International Conference on Learning Analytics & Knowledge LAK2016 Pages 454-458  (Acceptance Rate = Not released but maybe 50%). Materials from the study.

SP11 Adjei, S., Boethello, A. & Heffernan, N. (2016)  Predicting student performance on post-requisite skills using prerequisite skill data: an alternative method for refining prerequisite skill structures In the Proceedings of the Sixth International Conference on Learning Analytics & Knowledge LAK2016 Pages 469-473 (Acceptance Rate = Not released but maybe 50%).

SP10 Ostrow, K,  Donnelly, C. & Heffernan, N.  (2015) Optimizing Partial Credit Algorithms to Predict Student Performance. In the Proceedings of the 8th International Conference on Educational Data Mining EDM2015, Madrid, Spain. ISBN: 978-84-606-9425-0. pp 404-407. (Acceptance Rate =50%)

SP9 Castro, F., Adjei, S.,  Colombo, T., & Heffernan, N.T. (2015) Building Models to Predict Hint-or-Attempt Actions of  Students  In the Proceedings of the 8th International Conference on Educational Data Mining EDM2015, Madrid, Spain. ISBN: 978-84-606-9425-0. pp 476-479.(Acceptance Rate =50%)

SP8 Wang, Y., Heffernan, N, & Heffernan, C. (2015). Towards better affect detectors: effect of missing skills, class features and common wrong answersProceedings of the Fifth International Conference on Learning Analytics And Knowledge.  pp 31-35.   

SP7 Van Inwegen,E.,  Adjei, S., Wang, Y., & Heffernan, N. (2015)  An analysis of the impact of action order on future performance: the fine-grain action modelProceedings of the Fifth International Conference on Learning Analytics And Knowledge.  pp 320-324. 

SP6 San Pedro, M.O., Baker, R., Heffernan, N., Ocumpaugh, J. (2015) Exploring College Major Choice and Middle School Student Behavior, Affect and Learning: What Happens to Students Who Game the System?  Proceedings of the 5th International Learning Analytics and Knowledge Conference. pp 36-40. 

SP5     Adjei, S., Selent, D., Heffernan, N., Pardos, Z., Broaddus, A., Kingston, N. (2014). Refining Learning Maps with Data Fitting Techniques: Searching for Better Fitting Learning Maps.  In John Stamper et al. (Eds) Proceedings of the 7th International Conference on Educational Data Mining. pp. 413-414.

SP4     Selent, D. & Heffernan, N. (2014). Reducing Student Hint Use by Creating Buggy Messages from machine Learned Incorrect ProcessesIn Stefan Trausan-Matu, et al. (Eds) International Conference on Intelligent Tutoring 2014.  LNCS 8474. (66% acceptance rate)

SP3     Gu, J., Wang, Y. & Heffernan, N. (2014). Personalizing Knowledge Tracing: Should We Individualize Slip, Guess, Prior or Learn rate? In Stefan Trausan-Matu, et al. (Eds) International Conference on Intelligent Tutoring 2014.  LNCS 8474. (66% acceptance rate)

SP2  Kelly, K.,  Heffernan, N., Heffernan, C.,  Goldman, S., Pellegrino, G. & Soffer, D. (2013). Estimating the Effect of Web-Based Homework. In Lane, Yacef, Mostow & Pavlik (Eds) The Artificial Intelligence in Education Conference. pp. 824-827. (Watch the videos and related archived material here: http://web.cs.wpi.edu/~nth/PublicScienceArchive/Kelly.htm and permanently archived at http://www.webcitation.org/6E6lv54G8  ) A longer version is here.

SP1  Feng, M., Heffernan, N., Pardos, Z. & Heffernan, C.(2011). Establishing the value of dynamic assessment in an online tutoring system. In Pechenizkiy, M., Calders, T., Conati, C., Ventura, S., Romero , C., and Stamper, J. (Eds.) Proceedings of the 4th International Conference on Educational Data Mining. pp. 295-300.

Published 3-4 Page Papers (aka "Poster") in Prestigious Conferences (Acceptance rates of  50-60%).

Work in this category was usually not good enough for a "full" conference paper like above but was instead choose to be published in the proceedings but was given less space. This work is always presented at the conference via a poster presentation.



Less Stringently Reviewed Venues

Work in the category was usually selected to be presented at a workshop.  Such work was sometime reviewed by just the coordinator of the workshop. This category is not usually completely up to date.

WP23  Wilson, K., Xiong, X., Khajah, M., Lindsey, R. V.,  Zhao, S., Karklin, K., Van Inwegen, E.,  Han, B.,  Ekanadham, C.,   Beck, J.,   Heffernan, N., & Mozer, M., (submitted) Estimating student proficiency: Deep learning is not the panacea    Submission to the NIPS 2016 Workshop on Machine Learning for Education

WP22 Williams, J. J., Ostrow, K., Xiong, X., Glassman, E., Kim, J., Maldonado, S. G., Reich, J., & Heffernan, N. (2015). Using and Designing Platforms for In Vivo Educational Experiments. Proceedings of the Second ACM Conference on Learning@Scale. [Extended Abstract]

WP21 Williams, J. J.,  Schultz, S., & Heffernan, N. T. (2015) Adaptively Personalizing Instruction through Collaborative Development of MOOClets by Instructors, Education, Psychology and Machine Learning Researchers.   Learning with MOOCs II workshop that will be held at Teachers College, Columbia University on October 2-3, 2015.  There were two peer reviews but an unpublished acceptance rates so I listed down in this section.

WP20 Patvarczki, J., Almeida, S., Beck, J. &  Heffernan, N. (2008). Lessons Learned from Scaling Up a Web-Based Intelligent Tutoring System. Lecture Notes in Computer Science, Intelligent Tutoring Systems, 5091.pp. 766-770. 
WP19 Razzaq, L., Heffernan, N.T. (2008). Towards Designing a User-Adaptive Web-Based E-Learning System. In Mary Czerwinski, Arnold M. Lund, Desney S. Tan (Eds.): Extended Abstracts Proceedings of the 2008 Conference on Human Factors in Computing Systems. pp. 3525-3530. Florence, Italy.
WP18 Pardos, Z., Heffernan, N. T., Anderson, B., & Heffernan-Lindquist, C. (2007). The effect of model granularity on student performance prediction using Bayesian networks. The Educational Data Mining Workshop held at the International User Modeling Conference 2007. Corfu, Greece. (Work later led to CP18)
WP17 Pardos, Z., Feng, M. Heffernan, N. T., Heffernan-Lindquist, C. & Ruiz, C. (2007). Analyzing fine-grained skill models using Bayesian and mixed effect methods. In the Educational Data Mining Workshop held at the 13th Conference on Artificial Intelligence in Education. This is a longer version of CP18.
WP16 Lllyod, N., Heffernan, N. & Ruiz, C. (2007). Predicting student engagement in intelligent tutoring systems using teacher expert knowledge. In the Educational DataMining Workshop held at the 13th Conference on Artificial Intelligence in Education.
WP15 Feng, M., Heffernan, N. T., Mani, M., & Heffernan, C. (2006). Using mixed-effects modeling to compare different grain-sized skill models. In Beck, J., Aimeur, E., & Barnes, T. (Eds). Educational Data Mining: Papers from the AAAI Workshop. Menlo Park, CA: AAAI Press. pp. 57-66. Technical Report WS-06-05. (Work later led to PP8)
WP14 Pardos, Z. A., Heffernan, N. T., Anderson, B., & Heffernan C. (2006). Using fine-grained skill models to fit student performance with Bayesian networks. Workshop in Educational Data Mining held at the Eighth International Conference on Intelligent Tutoring Systems. Taiwan. 2006. (Work later led to PP8)
WP13 Feng, M., Heffernan, N. T., & Koedinger, K. R. (2005). Looking for sources of error in predicting student's knowledge. In Beck. J. (Eds). Educational Data Mining: Papers from the 2005 AAAI Workshop. Menlo Park, California: AAAI Press. pp. 54-61. Technical Report WS-05-02.
WP12 Feng, M., & Heffernan, N. (2005). Informing teachers live about student learning: Reporting in the Assistment system. Workshop on Usage Analysis in Learning Systems held at the 12th International Conference on Artificial Intelligence in Education. Amsterdam. (Work later led to J3 and J5)
WP11 Nuzzo-Jones, G., Walonoski, J.A., Heffernan, N.T. & Livak, T. (2005). The eXtensible tutor architecture: A new foundation for ITS. Workshop on Adaptive Systems for Web-Based Education: Tools and Reusability held at the 12th Annual Conference on Artificial Intelligence in Education. Amsterdam. pp. 1-7. (Work later led to BC1 and PP4)
WP10 Turner, T.E., Macasek, M.A., Nuzzo-Jones, G., Heffernan, N..T & Koedinger, K. (2005). The Assistment builder: A Rapid development tool for ITS. In a workshop on on Adaptive Systems for Web-Based Education: Tools and Reusability, held at the 12th Annual Conference on Artificial Intelligence in Education Amsterdam. (Work later led to BC1, PP4 and CP14)
WP9   Freyberger, J., Heffernan, N., & Ruiz, C. (2004). Using association rules to guide a search for best fitting transfer models of student learning. In Beck, Baker, Corbett, Kay, Litman, Mitrovic & Rigger (Eds.) Workshop on Analyzing Student-Tutor Interaction Logs to Improve Educational Outcomes. Held at the 7th Annual Intelligent Tutoring Systems Conference, Maceio, Brazil. Lecture Notes in Computer Science.
WP8   Livak, T., Heffernan, N. T., Moyer, D. (2004). Using cognitive models for computer generated forces and human tutoring. 13th Annual Conference on (BRIMS) Behavior Representation in Modeling and Simulation. Simulation Interoperability Standards Organization. Arlington, VA.
WP7   Razzaq, L. & Heffernan, N. T (2004). Tutorial dialog in an equation solving intelligent tutoring system. Workshop on “Dialog-based Intelligent Tutoring Systems: State of the art and new research directions” at the 7th Annual Intelligent Tutoring Systems Conference, Maceio, Brazil. pp. 33-42.
WP6   Koedinger, K. R., Aleven, V., & Heffernan, N. T. (2003). Toward a rapid development environment for cognitive tutors. The 12th Annual Conference on Behavior Representation in Modeling and Simulation. Simulation Interoperability Standards Organization. (Work later led to CP7)
WP5   Heffernan, N. T., (2002). Web-based evaluation showing both motivational and cognitive benefits of the Ms. Lindquist tutor. SIGdial endorsed Workshop on Empirical Methods for Tutorial Dialogue Systems which was part of the International Conference on Intelligent Tutoring System 2002. pp. 1-8. Also appeared in a NSF-DFG sponsored workshop on Collaboration between German and American researchers in instructional technology. Tampa, Florida, May 5-7, 2002. (Work later led to CP8)
WP4   Heffernan, N. T, Koedinger, K. (2001). The design and formative analysis of a dialog-based tutor. Workshop on Tutorial Dialogue Systems held as part of the 2001 Artificial Intelligence in Education. pp. 23-34. (Work later led to CP8)
WP3   Heffernan, N. T., & Koedinger, K. R. (2000). Building a 3rd generation ITS for symbolization: Adding a tutorial model with multiple tutorial strategies. Workshop entitled "Learning Algebra with the computer, a transdisciplinary workshop". Held at Intelligent Tutoring Systems 2000 Conference. (pp. 12-22). Lecture Notes in Computer Science 1839, Berlin: Springer. (Work later led to CP8)
WP2   Heffernan, N. T. (2000). Adding a cognitive model of human tutor to an intelligent tutoring systems. Intelligent Tutoring System Conference- Young Researchers Track. (Work later led to D1)
WP1   Heffernan, N. T. (1998). Intelligent tutoring systems have forgotten the tutor: Adding a cognitive model of human tutors. Abstract at the 1998 Computer Human Interaction Conference's Doctoral Consortium. (Work later led to D1)


Unknown Acceptance Rate


Other Less Important Publications