Andy Smith

Cluster-Based Analysis of Novice Coding Misconceptions in Block-Based Programming

Proceedings of the 51st ACM Technical Symposium on Computer Science Education.

Designing Block-Based Programming Language Features to Support Upper Elementary Students in Creating Interactive Science Narratives

Proceedings of the 51st ACM Technical Symposium on Computer Science Education.

Predictive Student Modeling in Block-Based Programming Environments with Bayesian Hierarchical Models

Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization.

The agency effect: The impact of student agency on learning, emotions, and problem-solving behaviors in a game-based learning environment

Computers and Education, 147.

A Multimodal Assessment Framework for Integrating Student Writing and Drawing in Elementary Science Learning

IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES, 12(1), 3–15.

Construction and Validation of an Anticipatory Thinking Assessment

FRONTIERS IN PSYCHOLOGY, 10.

DeepStealth: Game-Based Learning Stealth Assessment with Deep Neural Networks

IEEE Transactions on Learning Technologies.

Position: IntelliBlox: A Toolkit for Integrating Block-Based Programming into Game-Based Learning Environments

Proceedings - 2019 IEEE Blocks and Beyond Workshop, B and B 2019, 55–58.

Prime: Engaging STEM undergraduates in computer science with intelligent tutoring systems

ASEE Annual Conference and Exposition, Conference Proceedings.

Toward a Responsive Interface to Support Novices in Block-Based Programming

Proceedings - 2019 IEEE Blocks and Beyond Workshop, B and B 2019, 9–13.

Enhancing complex mathematics problem solving through learning by teaching with a teachable agent

IMSCI 2018 - 12th International Multi-Conference on Society, Cybernetics and Informatics, Proceedings, 2, 31–36.

Towards Adaptive Support for Anticipatory Thinking

PROCEEDINGS OF THE TECHNOLOGY, MIND, AND SOCIETY CONFERENCE (TECHMINDSOCIETY'18).

Enhancing student models in game-based learning with facial expression recognition

UMAP 2017 - Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, 192–201.

Is more agency better? The impact of student agency on game-based learning

In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 335–346).

Drawing and Writing in Digital Science Notebooks: Sources of Formative Assessment Data

Journal of Science Education and Technology, 25(3), 474–488.

Integrating real-time drawing and writing diagnostic models: An evidence-centered design framework for multimodal science assessment

Intelligent tutoring systems, its 2016, 0684, 165–175.

Integrating real-time drawing and writing diagnostic models: An evidence-centered design framework for multimodal science assessment

In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9684, pp. 165–175).

Diagrammatic student models: Modeling student drawing performance with deep learning

In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9146, pp. 216–277).

Two modes are better than one: A multimodal assessment framework integrating student writing and drawing

Artificial intelligence in education, aied 2015, 9112, 205–215.

A glove for tapping and discrete 1D/2D input

International Conference on Intelligent User Interfaces, Proceedings IUI, 101–104.

P-methylstyrene

Polymer Data Handbook, 688–695.

Statistical properties of fitted estimates of apparent in vivo metabolic constants obtained from gas uptake data. I. Lipophilic and slowly metabolized VOCs

Inhalation Toxicology, 10(5), 383–409.

Diagrammatic student models: Modeling student drawing performance with deep learning

Smith, A., Min, W., Mott, B. W., & Lester, J. C. User modeling, adaptation and personalization, 9146, 216–227.

Is more agency better? The impact of student agency on game-based learning

Sawyer, R., Smith, A., Rowe, J., Azevedo, R., & Lester, J. Artificial intelligence in education, aied 2017, 10331, 335–346.