Chris Piech

6.0k total citations · 2 hit papers
45 papers, 2.0k citations indexed

About

Chris Piech is a scholar working on Computer Science Applications, Artificial Intelligence and Information Systems. According to data from OpenAlex, Chris Piech has authored 45 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Computer Science Applications, 14 papers in Artificial Intelligence and 12 papers in Information Systems. Recurrent topics in Chris Piech's work include Online Learning and Analytics (21 papers), Teaching and Learning Programming (21 papers) and Software Engineering Research (9 papers). Chris Piech is often cited by papers focused on Online Learning and Analytics (21 papers), Teaching and Learning Programming (21 papers) and Software Engineering Research (9 papers). Chris Piech collaborates with scholars based in United States, United Kingdom and Belgium. Chris Piech's co-authors include René F. Kizilcec, Emily Schneider, Mehran Sahami, Jonathan Huang, Leonidas Guibas, Daphne Koller, Paulo Blikstein, Jascha Sohl‐Dickstein, Surya Ganguli and Steve Cooper and has published in prestigious journals such as Ophthalmology, Journal of the Learning Sciences and Educational Evaluation and Policy Analysis.

In The Last Decade

Chris Piech

40 papers receiving 1.8k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Deconstructing disengagement

2013 686 citations Chris Piech et al. profile →
Deep Knowledge Tracing

2015 365 citations Chris Piech, Jonathan Huang et al. arXiv (Cornell University) profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Chris Piech United States	16	1.5k	779	418	378	378	45	2.0k
Juho Leinonen Finland	21	1.4k 1.0×	660 0.8×	170 0.4×	549 1.5×	331 0.9×	118	2.1k
Arto Hellas Finland	20	1.3k 0.8×	459 0.6×	198 0.5×	440 1.2×	350 0.9×	115	1.7k
Benedict du Boulay United Kingdom	20	1.1k 0.7×	640 0.8×	233 0.6×	450 1.2×	678 1.8×	60	1.8k
Zoran Budimac Serbia	16	609 0.4×	520 0.7×	152 0.4×	612 1.6×	291 0.8×	103	1.4k
‪Marcos Román-González‬ Spain	23	2.1k 1.4×	340 0.4×	369 0.9×	411 1.1×	1.0k 2.7×	55	2.5k
John Stamper United States	16	778 0.5×	665 0.9×	188 0.4×	187 0.5×	431 1.1×	66	1.2k
Sergey Sosnovsky United States	16	561 0.4×	477 0.6×	195 0.5×	263 0.7×	338 0.9×	62	1.0k
Beverly Park Woolf United States	22	799 0.5×	1.1k 1.4×	297 0.7×	220 0.6×	715 1.9×	115	1.9k
Valentina Dagienė Lithuania	21	1.1k 0.7×	230 0.3×	363 0.9×	383 1.0×	461 1.2×	109	1.5k
Juan I. Asensio‐Pérez Spain	23	967 0.6×	254 0.3×	543 1.3×	485 1.3×	735 1.9×	125	1.7k

Countries citing papers authored by Chris Piech

Since Specialization

Citations

This map shows the geographic impact of Chris Piech'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 Chris Piech with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chris Piech more than expected).

Fields of papers citing papers by Chris Piech

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chris Piech. 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 Chris Piech. The network helps show where Chris Piech may publish in the future.

Co-authorship network of co-authors of Chris Piech

This figure shows the co-authorship network connecting the top 25 collaborators of Chris Piech. A scholar is included among the top collaborators of Chris Piech 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 Chris Piech. Chris Piech is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Piech, Chris, et al.. (2025). Infinite Story. 1750–1750.

2.

Nie, Allen, et al.. (2025). The GPT Surprise: Offering Large Language Model Chat in a Massive Coding Class Reduced Engagement But May Increase Adopters' Exam Performances. 376–380.

3.

Mitchell, John C., et al.. (2024). A Large Scale RCT on Effective Error Messages in CS1. 1395–1401. 18 indexed citations

4.

Burns, Kaylee, et al.. (2024). A Fast and Accurate Machine Learning Autograder for the Breakout Assignment. 736–742.

5.

Doumbouya, Moussa, et al.. (2024). Handwritten Code Recognition for Pen-and-Paper CS Education. 200–210. 1 indexed citations

6.

Piech, Chris, et al.. (2023). High-Resolution Course Feedback: Timely Feedback Mechanism for Instructors. 81–91. 1 indexed citations

7.

Piech, Chris, et al.. (2023). Detecting the Reasons for Program Decomposition in CS1 and Evaluating Their Impact. 1014–1020. 3 indexed citations

8.

Kochmar, Ekaterina, et al.. (2023). The BEA 2023 Shared Task on Generating AI Teacher Responses in Educational Dialogues. Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)). 785–795. 12 indexed citations

9.

Domingue, Benjamin W., Michael J. Sulik, Matthieu Brinkhuis, et al.. (2022). Speed–Accuracy Trade-Off? Not So Fast: Marginal Changes in Speed Have Inconsistent Relationships With Accuracy in Real-World Settings. Journal of Educational and Behavioral Statistics. 47(5). 576–602. 10 indexed citations

10.

Piech, Chris, et al.. (2022). Using NLP to Quantify Program Decomposition in CS1. 113–120. 10 indexed citations

11.

Domingue, Benjamin W., et al.. (2021). Variation in Respondent Speed and its Implications: Evidence from an Adaptive Testing Scenario. Journal of Educational Measurement. 58(3). 335–363. 9 indexed citations

12.

Wu, Mike, Richard L. Davis, Benjamin W. Domingue, Chris Piech, & Noah D. Goodman. (2020). Variational Item Response Theory: Fast, Accurate, and Expressive. arXiv (Cornell University). 1 indexed citations

13.

Piech, Chris, Engin Bumbacher, & Richard L. Davis. (2020). Measuring Ability-to-Learn Using Parametric Learning-Gain Functions.. Educational Data Mining. 2 indexed citations

14.

Tiwari, Mohit, et al.. (2020). Using Google Search Trends to Estimate Global Patterns in Learning. 185–195. 1 indexed citations

15.

Karayev, Sergey, et al.. (2019). Grades Are Not Normal: Improving Exam Score Models Using the Logit-Normal Distribution.. Educational Data Mining. 8 indexed citations

16.

Yan, Lisa, Nick McKeown, Mehran Sahami, & Chris Piech. (2018). TMOSS. 110–115. 20 indexed citations

17.

Wang, Lisa, et al.. (2017). Learning to Represent Student Knowledge on Programming Exercises Using Deep Learning.. Educational Data Mining. 34 indexed citations

18.

Piech, Chris, Jonathan Huang, Zhenghao Chen, et al.. (2013). Tuned Models of Peer Assessment in MOOCs.. Educational Data Mining. 153–160. 56 indexed citations

19.

Huang, Jonathan, Chris Piech, Andy Nguyễn, & Leonidas Guibas. (2013). Syntactic and Functional Variability of a Million Code Submissions in a Machine Learning MOOC.. 43 indexed citations

20.

Piech, Chris, Mehran Sahami, Daphne Koller, Steve Cooper, & Paulo Blikstein. (2012). Modeling how students learn to program. 153–160. 141 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.

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