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Computer Science

D-Index
51
Citations
9777
World Ranking
5371
National Ranking
2458

Overview

Neil T. Heffernan is affiliated with Worcester Polytechnic Institute in the United States. Their research primarily focuses on the intersection of computer science and education, with extensive work in areas such as artificial intelligence, computer science applications, education, developmental and educational psychology, and statistics and probability.

Their research topics cover diverse domains including:

  • Online Learning and Analytics
  • Intelligent Tutoring Systems and Adaptive Learning
  • Innovative Teaching and Learning Methods
  • Topic Modeling
  • Student Assessment and Feedback
  • Natural Language Processing Techniques
  • Advanced Causal Inference Techniques

Neil T. Heffernan has contributed to a number of recent papers, which illustrate their engagement with advancing computational methods in educational contexts. Notable publications include:

  • "Leveraging natural language processing to support automated assessment and feedback for student open responses in mathematics," published in 2023 in the Journal of Computer Assisted Learning
  • "MathBERT: A Pre-trained Language Model for General NLP Tasks in Mathematics Education," published in 2021 in arXiv (Cornell University)
  • "Facilitating Student Learning With a Chatbot in an Online Math Learning Platform," published in 2024 in the Journal of Educational Computing Research
  • "Generative AI for Education (GAIED): Advances, Opportunities, and Challenges," published in 2024 in arXiv (Cornell University)
  • "Reinforcement Learning for Education: Opportunities and Challenges," published in 2021 in arXiv (Cornell University)

Their frequent co-authors include:

  • Ethan Prihar
  • Anthony F. Botelho
  • Sami Baral
  • Adam Sales
  • Aaron Haim

Neil T. Heffernan's publications are often found in venues such as:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • Arabixiv (OSF Preprints)
  • Educational Data Mining
  • Journal of Computer Assisted Learning

Their body of work demonstrates a strong emphasis on developing and analyzing intelligent systems that facilitate learning and assessment in mathematics and related fields. This includes leveraging natural language processing and reinforcement learning methods to enhance educational platforms and improve student feedback mechanisms.

Best Publications

  • The ASSISTments Ecosystem: Building a Platform that Brings Scientists and Teachers Together for Minimally Invasive Research on Human Learning and Teaching

    Neil T. Heffernan;Cristina Lindquist Heffernan

  • Addressing the assessment challenge with an online system that tutors as it assesses

    Mingyu Feng;Neil Heffernan;Kenneth Koedinger

  • Context-Aware Attentive Knowledge Tracing

    Aritra Ghosh;Neil Heffernan;Andrew S. Lan

  • Modeling individualization in a bayesian networks implementation of knowledge tracing

    Zachary A. Pardos;Neil T. Heffernan

  • Why Students Engage in “Gaming the System” Behavior in Interactive Learning Environments

    Ryan Baker;Jason Walonoski;Neil Heffernan;Ido Roll

  • A Comparison of Traditional Homework to Computer-Supported Homework.

    Michael Mendicino;Leena Razzaq;Neil T. Heffernan

  • Opening the door to non-programmers: Authoring Intelligent tutor behavior by demonstration

    Kenneth R. Koedinger;Vincent Aleven;Neil Heffernan;Bruce Mclaren

  • Population validity for educational data mining models: A case study in affect detection

    Jaclyn Ocumpaugh;Ryan Shaun Baker;Sujith M. Gowda;Neil T. Heffernan

  • KT-IDEM: introducing item difficulty to the knowledge tracing model

    Zachary A. Pardos;Neil T. Heffernan

  • Detection and analysis of off-task gaming behavior in intelligent tutoring systems

    Jason A. Walonoski;Neil T. Heffernan

  • AXIS: Generating Explanations at Scale with Learnersourcing and Machine Learning

    Joseph Jay Williams;Juho Kim;Anna Rafferty;Samuel Maldonado

  • Comparing knowledge tracing and performance factor analysis by using multiple model fitting procedures

    Yue Gong;Joseph E. Beck;Neil T. Heffernan

  • A QUASI-EXPERIMENTAL EVALUATION OF AN ON-LINE FORMATIVE ASSESSMENT AND TUTORING SYSTEM*

    Kenneth R. Koedinger;Elizabeth A. McLaughlin;Neil T. Heffernan

  • Predicting College Enrollment from Student Interaction with an Intelligent Tutoring System in Middle School.

    Maria Ofelia Clarissa Z. San Pedro;Ryan Shaun Baker;Alex J. Bowers;Neil T. Heffernan

  • An Intelligent Tutoring System Incorporating a Model of an Experienced Human Tutor

    Neil T. Heffernan;Kenneth R. Koedinger

  • Incorporating Rich Features into Deep Knowledge Tracing

    Liang Zhang;Xiaolu Xiong;Siyuan Zhao;Anthony Botelho

  • Improving Sensor-Free Affect Detection Using Deep Learning

    Anthony F. Botelho;Ryan S. Baker;Neil T. Heffernan

  • Predicting state test scores better with intelligent tutoring systems: developing metrics to measure assistance required

    Mingyu Feng;Neil T. Heffernan;Kenneth R. Koedinger

  • Using Fine-Grained Skill Models to Fit Student Performance with Bayesian Networks

    Zachary A. Pardos;Neil T. Heffernan;Brigham Anderson;Cristina L Heffernan

  • What are the Biases in My Word Embedding

    Nathaniel Swinger;Maria De-Arteaga;Neil Thomas Heffernan;Mark Dm Leiserson

Frequent Co-Authors

Ryan S. Baker
Ryan S. Baker University of Pennsylvania
Kenneth R. Koedinger
Kenneth R. Koedinger Carnegie Mellon University
Vincent Aleven
Vincent Aleven Carnegie Mellon University
Carolyn Penstein Rosé
Carolyn Penstein Rosé Carnegie Mellon University
Jeremy Roschelle
Jeremy Roschelle Digital Promise
Antonija Mitrovic
Antonija Mitrovic University of Canterbury
Diane J. Litman
Diane J. Litman University of Pittsburgh
Mykola Pechenizkiy
Mykola Pechenizkiy Eindhoven University of Technology
Arthur C. Graesser
Arthur C. Graesser University of Memphis
Susan R. Goldman
Susan R. Goldman University of Illinois at Chicago

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