Joseph M. Reilly

Orcid: 0000-0001-8128-5201

Affiliations:
  • Harvard University, Harvard Graduate School of Education, Cambridge, MA, USA


According to our database1, Joseph M. Reilly authored at least 11 papers between 2018 and 2022.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Bibliography

2022
Details Matter: How Contrasting Design Features in Two MUVEs Impact Learning Outcomes.
Technol. Knowl. Learn., 2022

2021
Assessing computational thinking through the lenses of functionality and computational fluency.
Comput. Sci. Educ., 2021

2019
Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual World.
Proceedings of the 9th International Conference on Learning Analytics & Knowledge, 2019

Predicting the Quality of Collaborative Problem Solving Through Linguistic Analysis of Discourse.
Proceedings of the 12th International Conference on Educational Data Mining, 2019

Exploring Stealth Assessment via Deep Learning in an Open-Ended Virtual Environment.
Proceedings of the 12th International Conference on Educational Data Mining, 2019

2018
Supports for deeper learning of inquiry-based ecosystem science in virtual environments - Comparing virtual and physical concept mapping.
Comput. Hum. Behav., 2018

EcoMOD: Integrating Computational Thinking into Ecosystems Science Education via Modeling in Immersive Virtual Worlds (Abstract Only).
Proceedings of the 49th ACM Technical Symposium on Computer Science Education, 2018

Augmenting Qualitative Analyses of Collaborative Learning Groups Through Multi-Modal Sensing.
Proceedings of the Rethinking learning in the digital age: Making the Learning Sciences count, 2018

Toward Using Multi-Modal Learning Analytics to Support and Measure Collaboration in Co-Located Dyads.
Proceedings of the Rethinking learning in the digital age: Making the Learning Sciences count, 2018

Exploring Collaboration Using Motion Sensors and Multi-Modal Learning Analytics.
Proceedings of the 11th International Conference on Educational Data Mining, 2018

Using Physiological Synchrony as an Indicator of Collaboration Quality, Task Performance and Learning.
Proceedings of the Artificial Intelligence in Education - 19th International Conference, 2018


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