Thomas Price

Adaptive Immediate Feedback Can Improve Novice Programming Engagement and Intention to Persist in Computer Science

Proceedings of the International Computing Education Research Conference, 1–10.

An Evaluation of Data-Driven Programming Hints in a Classroom Setting

In Lecture Notes in Computer Science (pp. 246–251).

Crescendo : Engaging Students to Self-Paced Programming Practices

Proceedings of the ACM Technical Symposium on Computer Science Education.

Engaging Students with Instructor Solutions in Online Programming Homework

ACM CHI Conference on Human Factors in Computing Systems. Presented at the Honolulu, HI, USA. Honolulu, HI, USA.

Step Tutor: Supporting Students through Step-by-Step Example-Based Feedback

Proceedings of the International Conference on Innovation and Technology in Computer Science Education.

Unproductive Help-seeking in Programming: What it is and How to Address it

Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education.

A Comparison of the Quality of Data-Driven Programming Hint Generation Algorithms

International Journal of Artificial Intelligence in Education, 29(3), 368–395.

An Evaluation of the Impact of Automated Programming Hints on Performance and Learning

Proceedings of the 2019 ACM Conference on International Computing Education Research, 61–70.

Defining Tinkering Behavior in Open-Ended Block-Based Programming Assignments

Proceedings of the 50th ACM Technical Symposium on Computer Science Education, 1204–1210.

Evaluating the Effectiveness of Parsons Problems for Block-Based Programming

Proceedings of the 2019 ACM Conference on International Computing Education Research, 51–59.

Exploring the Impact of Worked Examples in a Novice Programming Environment

Proceedings of the 50th ACM Technical Symposium on Computer Science Education, 98–104.

The Impact of Adding Textual Explanations to Next-Step Hints in a Novice Programming Environment

Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education, 520–526.

Toward Data-Driven Example Feedback for Novice Programming

Proceedings of the International Conference on Educational Data Mining, 218–227.

Exploring Instructional Support Design in an Educational Game for K-12 Computing Education

Proceedings of the 49th ACM Technical Symposium on Computer Science Education, 747–752.

The Continuous Hint Factory - Providing Hints in Vast and Sparsely Populated Edit Distance Spaces

Journal of Educational Data Mining, 10(1), 1–35.

The Impact of Data Quantity and Source on the Quality of Data-Driven Hints for Programming

In Lecture Notes in Computer Science (pp. 476–490).

iSnap: Automatic Hints and Feedback for Block-based Programming

SIGCSE'18: PROCEEDINGS OF THE 49TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, pp. 1113–1113.

Evaluation of a Data-driven Feedback Algorithm for Open-ended Programming

Proceedings of the International Conference on Educational Data Mining.

Factors influencing students' help-seeking behavior while programming with human and computer tutors

Proceedings of the 2017 ACM Conference on International Computing Education Research (ICER 17), 127–135.

Hint generation under uncertainty: The effect of hint quality on help-seeking behavior

Artificial intelligence in education, aied 2017, 10331, 311–322.

Position paper: Block-based programming should offer intelligent support for learners

2017 IEEE Blocks and Beyond Workshop (B&B), 65–68.

Sharing and Using Programming Log Data (Abstract Only)

Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education, 729.

iSnap: Towards Intelligent Tutoring in Novice Programming Environments

Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education - SIGCSE '17, 483–488.

Clashroom: A Game to Enhance the Classroom Experience (Abstract Only)

Proceedings of the 47th ACM Technical Symposium on Computing Science Education, 692.

Evaluation of a frame-based programming editor

Proceedings of the 2016 ACM Conference on International Computing Education Research (ICER'16), 33–42.

Generating data-driven hints for open-ended programming

Proceedings of the 9th International Conference on Educational Data Mining, International Educational Data Mining Society, 191–198.

Lessons Learned from “BJC” CS Principles Professional Development

Proceedings of the 47th ACM Technical Symposium on Computing Science Education, 467–472.

Question Answering in the Context of Stories Generated by Computers

Advances in Cognitive Systems, 4, 227–245.

An Exploration of Data-Driven Hint Generation in an Open-Ended Programming Problem

International Workshop on Graph-Based Educational Data Mining.

An Improved Data-Driven Hint Selection Algorithm for Probability Tutors

The 8th International Conference on Education Data Mining.

BJC in action: Comparison of student perceptions of a computer science principles course

Research in Equity and Sustained Participation in Engineering, Computing, and Technology (RESPECT), 2015, 1–4.

Comparing Textual and Block Interfaces in a Novice Programming Environment

Proceedings of the Eleventh Annual International Conference on International Computing Education Research, 91–99.

Creating data-driven feedback for novices in goal-driven programming projects

International Conference on Artificial Intelligence in Education, 9112, 856–859.

Integrating Intelligent Feedback into Block Programming Environments

Proceedings of the Eleventh Annual International Conference on International Computing Education Research, 275–276.

The Impact of Granularity on Worked Examples and Problem Solving

Annual Meeting of the Cognitive Science Society (CogSci).

Using the Hint Factory to Compare Model-based Tutoring Systems

International Workshop on Graph-Based Educational Data Mining.

Towards an Extended Declarative Representation for Camera Planning

Workshops at the Twenty-Eighth AAAI Conference on Artificial Intelligence.

The Impact of Granularity on the Effectiveness of Students’ Pedagogical Decision

Zhou, G., Lynch, C. F., Price, T. W., Barnes, T., & Chi, M.