Matthew Mulholland

520 total citations
26 papers, 356 citations indexed

About

Matthew Mulholland is a scholar working on Artificial Intelligence, Developmental and Educational Psychology and Signal Processing. According to data from OpenAlex, Matthew Mulholland has authored 26 papers receiving a total of 356 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Artificial Intelligence, 7 papers in Developmental and Educational Psychology and 5 papers in Signal Processing. Recurrent topics in Matthew Mulholland's work include Topic Modeling (12 papers), Natural Language Processing Techniques (12 papers) and Speech and dialogue systems (8 papers). Matthew Mulholland is often cited by papers focused on Topic Modeling (12 papers), Natural Language Processing Techniques (12 papers) and Speech and dialogue systems (8 papers). Matthew Mulholland collaborates with scholars based in United States and Norway. Matthew Mulholland's co-authors include Hee‐Sun Lee, Ou Lydia Liu, Amy Pallant, Keelan Evanini, Xinhao Wang, Yao Qian, Sarah Pryputniewicz, Nitin Madnani, Chongmin Lee and Michael Heilman and has published in prestigious journals such as Science Education, International Journal of Artificial Intelligence in Education and Educational Assessment.

In The Last Decade

Matthew Mulholland

26 papers receiving 323 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Matthew Mulholland United States 11 219 90 83 61 40 26 356
Gregory Aist United States 11 301 1.4× 56 0.6× 130 1.6× 51 0.8× 22 0.6× 36 394
Michael Carbonaro Canada 10 73 0.3× 97 1.1× 79 1.0× 46 0.8× 46 1.1× 21 295
Peida Zhan China 14 193 0.9× 82 0.9× 83 1.0× 58 1.0× 37 0.9× 46 478
Sherry Ruan United States 7 221 1.0× 41 0.5× 46 0.6× 99 1.6× 47 1.2× 11 344
Fadhilah Rosdi Malaysia 11 86 0.4× 80 0.9× 89 1.1× 27 0.4× 103 2.6× 32 304
C. Oswald India 6 72 0.3× 120 1.3× 90 1.1× 153 2.5× 56 1.4× 18 336
Andrea Horbach Germany 12 342 1.6× 104 1.2× 56 0.7× 82 1.3× 105 2.6× 44 450
Michael Wixon United States 5 141 0.6× 62 0.7× 116 1.4× 159 2.6× 27 0.7× 7 247
Anastassia Loukina United States 11 288 1.3× 39 0.4× 65 0.8× 29 0.5× 56 1.4× 43 404
Akihiro Kashihara Japan 8 91 0.4× 35 0.4× 86 1.0× 95 1.6× 51 1.3× 83 247

Countries citing papers authored by Matthew Mulholland

Since Specialization
Citations

This map shows the geographic impact of Matthew Mulholland's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Matthew Mulholland with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew Mulholland more than expected).

Fields of papers citing papers by Matthew Mulholland

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Matthew Mulholland. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Matthew Mulholland. The network helps show where Matthew Mulholland may publish in the future.

Co-authorship network of co-authors of Matthew Mulholland

This figure shows the co-authorship network connecting the top 25 collaborators of Matthew Mulholland. A scholar is included among the top collaborators of Matthew Mulholland based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Matthew Mulholland. Matthew Mulholland is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Lee, Hee‐Sun, et al.. (2022). Comparing the Effect of Contextualized Versus Generic Automated Feedback on Students' Scientific Argumentation. ETS Research Report Series. 2022(1). 1–14. 1 indexed citations
2.
Wang, Xinhao, Keelan Evanini, Yao Qian, & Matthew Mulholland. (2021). Automated Scoring of Spontaneous Speech from Young Learners of English Using Transformers. 705–712. 8 indexed citations
3.
Loukina, Anastassia, et al.. (2020). Do Face Masks Introduce Bias in Speech Technologies? The Case of Automated Scoring of Speaking Proficiency. arXiv (Cornell University). 1942–1946. 3 indexed citations
4.
Qian, Yao, Patrick Lange, Keelan Evanini, et al.. (2019). Neural Approaches to Automated Speech Scoring of Monologue and Dialogue Responses. 8112–8116. 15 indexed citations
5.
Yoon, Su‐Youn, et al.. (2019). Toward Automated Content Feedback Generation for Non-native Spontaneous Speech. 306–315. 3 indexed citations
6.
Yoon, Su‐Youn, et al.. (2018). Word-Embedding based Content Features for Automated Oral Proficiency Scoring. International Conference on Computational Linguistics. 12–22. 5 indexed citations
7.
Liu, Ou Lydia, et al.. (2018). Validation of Automated Scoring for a Formative Assessment that Employs Scientific Argumentation. Educational Assessment. 23(2). 121–138. 58 indexed citations
8.
Madnani, Nitin, Aoife Cahill, Daniel Blanchard, et al.. (2018). A Robust Microservice Architecture for Scaling Automated Scoring Applications. ETS Research Report Series. 2018(1). 1–8. 5 indexed citations
9.
Qian, Yao, et al.. (2018). A Prompt-Aware Neural Network Approach to Content-Based Scoring of Non-Native Spontaneous Speech. 16. 979–986. 13 indexed citations
10.
Klebanov, Beata Beigman, Jill Burstein, Judith M. Harackiewicz, Stacy J. Priniski, & Matthew Mulholland. (2017). Reflective Writing About the Utility Value of Science as a Tool for Increasing STEM Motivation and Retention – Can AI Help Scale Up?. International Journal of Artificial Intelligence in Education. 27(4). 791–818. 30 indexed citations
11.
Wang, Xinhao, Keelan Evanini, Klaus Zechner, & Matthew Mulholland. (2017). Modeling Discourse Coherence for the Automated Scoring of Spontaneous Spoken Responses. 132–137. 2 indexed citations
12.
Shermis, Mark D., et al.. (2017). Use of Automated Scoring Features to Generate Hypotheses Regarding Language-Based DIF. International Journal of Testing. 17(4). 351–371. 5 indexed citations
13.
Lee, Chongmin, et al.. (2017). Off-Topic Spoken Response Detection Using Siamese Convolutional Neural Networks. 1427–1431. 13 indexed citations
14.
Qian, Yao, Keelan Evanini, Xinhao Wang, Chongmin Lee, & Matthew Mulholland. (2017). Bidirectional LSTM-RNN for Improving Automated Assessment of Non-Native Children’s Speech. 16 indexed citations
15.
16.
Klebanov, Beata Beigman, Jill Burstein, Judith M. Harackiewicz, Stacy J. Priniski, & Matthew Mulholland. (2016). Enhancing STEM Motivation through Personal and Communal Values: NLP for Assessment of Utility Value in Student Writing. 199–205. 3 indexed citations
17.
Leong, Chee Wee, Lei Chen, Gang Feng, Chongmin Lee, & Matthew Mulholland. (2015). Utilizing Depth Sensors for Analyzing Multimodal Presentations. 547–556. 11 indexed citations
18.
Flor, Michael, et al.. (2015). Patterns of misspellings in L2 and L1 English: a view from the ETS Spelling Corpus. 6(0). 13 indexed citations
19.
Heilman, Michael, et al.. (2014). Predicting Grammaticality on an Ordinal Scale. 174–180. 52 indexed citations
20.
Mulholland, Matthew & Joanne Quinn. (2013). Suicidal Tendencies: The Automatic Classification of Suicidal and Non-Suicidal Lyricists Using NLP. International Joint Conference on Natural Language Processing. 680–684. 12 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Gregory Aist Breakdown of academic impact, for papers by Michael Carbonaro Breakdown of academic impact, for papers by Peida Zhan Breakdown of academic impact, for papers by Sherry Ruan Breakdown of academic impact, for papers by Fadhilah Rosdi Breakdown of academic impact, for papers by C. Oswald Breakdown of academic impact, for papers by Andrea Horbach Breakdown of academic impact, for papers by Michael Wixon Breakdown of academic impact, for papers by Anastassia Loukina Breakdown of academic impact, for papers by Akihiro Kashihara
Rankless by CCL
2026