Two ions in one trap: ultra-high precision mass spectrometry? - Simon Rainville; James K. Thompson; David E. Pritchard (IEEE Trans. Instrum. Meas., 2003)

Model-Based Collaborative Filtering Analysis of Student Response Data: Machine-Learning Item Response Theory. - Yoav Bergner; Stefan Dröschler; Gerd Kortemeyer; Saif Rayyan; Daniel T. Seaton; David E. Pritchard (EDM, 2012)

Analysis of Video Use in edX Courses. - Daniel T. Seaton; Albert J. Rodenius; Cody A. Coleman; David E. Pritchard; Isaac L. Chuang (AIED Workshops, 2013)

Bringing student backgrounds online: MOOC user demographics, site usage, and online learning. - Jennifer DeBoer; Glenda S. Stump; Daniel T. Seaton; Andrew D. Ho; David E. Pritchard; Lori Breslow (EDM, 2013)

Adapting Bayesian Knowledge Tracing to a Massive Open Online Course in edX. - Zachary A. Pardos; Yoav Bergner; Daniel T. Seaton; David E. Pritchard (EDM, 2013)

Exploring the relationship between course structure and etext usage in blended and open online courses. - Daniel T. Seaton; Yoav Bergner; David E. Pritchard (EDM, 2013)

Towards Real-Time Analytics in MOOCs. - Daniel T. Seaton; Yoav Bergner; Isaac L. Chuang; Piotr Mitros; David E. Pritchard (IWTA@LAK, 2013)

CS circles: an in-browser python course for beginners. - David Pritchard 002; Troy Vasiga (SIGCSE, 2013)

Comparing Learning in a MOOC and a Blended, On-Campus Course. - Kimberly F. Colvin; John Champaign; Alwina Liu; Colin Fredericks; David E. Pritchard (EDM, 2014)

Correlating skill and improvement in 2 MOOCs with a student's time on tasks. - John Champaign; Kimberly F. Colvin; Alwina Liu; Colin Fredericks; Daniel T. Seaton; David E. Pritchard (L@S, 2014)

Who does what in a massive open online course? - Daniel T. Seaton; Yoav Bergner; Isaac L. Chuang; Piotr Mitros; David E. Pritchard (Commun. ACM, 2014)

Harvesting latent and usage-based metadata in a course management system to enrich the underlying educational digital library - A case study. - Gerd Kortemeyer; Stefan Dröschler; David E. Pritchard (Int. J. Digit. Libr., 2014)

Discovering the Pedagogical Resources that Assist Students to Answer Questions Correctly - A Machine Learning Approach. - Giora Alexandron; Qian Zhou; David E. Pritchard (EDM, 2015)

Methodological Challenges in the Analysis of MOOC Data for Exploring the Relationship between Discussion Forum Views and Learning Outcomes. - Yoav Bergner; Deirdre Kerr; David E. Pritchard (EDM, 2015)

Estimation of ability from homework items when there are missing and/or multiple attempts. - Yoav Bergner; Kimberly F. Colvin; David E. Pritchard (LAK, 2015)

Learning Experiments Using AB Testing at Scale. - Christopher Chudzicki; David E. Pritchard; Zhongzhou Chen (L@S, 2015)

The State of CS Circles: Open Source and Outreach with an Introductory Python Website (Abstract Only). - David Pritchard 002; Sandy Graham; Troy Vasiga (SIGCSE, 2015)

Examining the necessity of problem diagrams using MOOC AB experiments. - Zhongzhou Chen; Neset Demirci; David E. Pritchard (EDM, 2016)

Using Multiple Accounts for Harvesting Solutions in MOOCs. - José A. Ruipérez-Valiente; Giora Alexandron; Zhongzhou Chen; David E. Pritchard (L@S, 2016)

Detecting Cheaters in MOOCs Using Item Response Theory and Learning Analytics. - Giora Alexandron; Sunbok Lee; Zhongzhou Chen; David E. Pritchard (UMAP (Extended Proceedings), 2016)

A MOOC based on blended pedagogy. - Saif Rayyan; Colin Fredericks; Kimberly F. Colvin; Alwina Liu; Raluca Teodorescu; Analia Barrantes; Andrew Pawl; Daniel T. Seaton; David E. Pritchard (J. Comput. Assist. Learn., 2016)

Researching for better instructional methods using AB experiments in MOOCs: results and challenges. - Zhongzhou Chen; Christopher Chudzicki; Daniel Palumbo; Giora Alexandron; Youn-Jeng Choi; Qian Zhou; David E. Pritchard (Res. Pract. Technol. Enhanc. Learn., 2016)

Dropout Prediction in MOOCs using Learners' Study Habits Features. - Han Wan; Jun Ding; Xiaopeng Gao; David Pritchard 002 (EDM, 2017)

Factor Analysis Reveals Student Thinking using the Mechanics Reasoning Inventory. - Sunbok Lee; Zhongzhou Chen; David E. Pritchard; Alex Kimn; Andrew Paul (L@S, 2017)

Copying@Scale: Using Harvesting Accounts for Collecting Correct Answers in a MOOC. - Giora Alexandron; José A. Ruipérez-Valiente; Zhongzhou Chen; Pedro J. Muñoz Merino; David E. Pritchard (Comput. Educ., 2017)

Evaluating the Robustness of Learning Analytics Results Against Fake Learners. - Giora Alexandron; José A. Ruipérez-Valiente; Sunbok Lee; David E. Pritchard (EC-TEL, 2018)

Mining Student Misconceptions from Pre- and Post-Testing Data. - Ángel Pérez-Lemonche; Byron Drury; David E. Pritchard (EDM, 2018)

Towards a General Purpose Anomaly Detection Method to Identify Cheaters in Massive Open Online Courses. - Giora Alexandron; José A. Ruipérez-Valiente; David E. Pritchard (EDM, 2019)

Mining Students Pre-instruction Beliefs for Improved Learning. - Ángel Pérez-Lemonche; John Stewart; Byron Drury; Rachel Henderson; Alex Shvonski; David E. Pritchard (L@S, 2019)

Are MOOC Learning Analytics Results Trustworthy? With Fake Learners, They Might Not Be! - Giora Alexandron; Lisa Y. Yoo; José A. Ruipérez-Valiente; Sunbok Lee; David E. Pritchard (Int. J. Artif. Intell. Educ., 2019)

Using Machine Learning to Detect 'Multiple-Account' Cheating and Analyze the Influence of Student and Problem Features. - José A. Ruipérez-Valiente; Pedro J. Muñoz Merino; Giora Alexandron; David E. Pritchard (IEEE Trans. Learn. Technol., 2019)

Deep-Cross-Attention Recommendation Model for Knowledge Sharing Micro Learning Service. - Jiayin Lin; Geng Sun 002; Jun Shen 001; David Pritchard 002; Tingru Cui; Dongming Xu; Li Li 006; Ghassan Beydoun; Shiping Chen 001 (AIED (2), 2020)

Deep Sequence Labelling Model for Information Extraction in Micro Learning Service. - Jiayin Lin; Zhexuan Zhou; Geng Sun 002; Jun Shen 001; David Pritchard 002; Tingru Cui; Dongming Xu; Li Li 006; Ghassan Beydoun (IJCNN, 2020)

Attention-Based High-Order Feature Interactions to Enhance the Recommender System for Web-Based Knowledge-Sharing Service. - Jiayin Lin; Geng Sun 002; Jun Shen 001; Tingru Cui; David Pritchard 002; Dongming Xu; Li Li 006; Wei Wei 006; Ghassan Beydoun; Shiping Chen 001 (WISE (1), 2020)

From ideal to reality: segmentation, annotation, and recommendation, the vital trajectory of intelligent micro learning. - Jiayin Lin; Geng Sun 002; Tingru Cui; Jun Shen 001; Dongming Xu; Ghassan Beydoun; Ping Yu 004; David Pritchard 002; Li Li 006; Shiping Chen 001 (World Wide Web, 2020)

MOOC Student Dropout Rate Prediction via Separating and Conquering Micro and Macro Information. - Jiayin Lin; Geng Sun 002; Jun Shen 001; David Pritchard 002; Ping Yu 004; Tingru Cui; Li Li 006; Ghassan Beydoun (ICONIP (6), 2021)

From computer vision to short text understanding: Applying similar approaches into different disciplines. - Jiayin Lin; Geng Sun 002; Jun Shen 001; David E. Pritchard; Ping Yu 004; Tingru Cui; Dongming Xu; Li Li 006; Ghassan Beydoun (Intell. Converged Networks, 2022)