Chris Piech
- Computer Science Applications top 0.05%
- Online Learning and Analytics 21
- Teaching and Learning Programming 21
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- Innovative Teaching and Learning Methods 5
- Software top 5%
- Software Testing and Debugging Techniques 4
- Artificial Intelligence top 2%
- Intelligent Tutoring Systems and Adaptive Learning 7
- Machine Learning and Algorithms 5
- Machine Learning and Data Classification 4
- Health Informatics top 5%
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- Software Engineering Research 9
- Co-authors
- René F. KizilcecEmily SchneiderMehran SahamiJonathan HuangLeonidas GuibasDaphne KollerPaulo BliksteinJascha Sohl‐Dickstein
- Journals
- Ophthalmology (1 paper)Journal of Educational Measurement (1 paper)Journal of the Learning Sciences (1 paper)
- Partner nations
- United StatesUnited KingdomCzechia
In The Last Decade
Chris Piech
40 papers receiving 1.8k citations
Hit Papers
Peers
Comparison fields: 5 of 82
- Computer Science Applications 1.5k
- Developmental and Educational Psychology 378
- Software 104
- Artificial Intelligence 779
- Health Informatics 27
Countries citing papers authored by Chris Piech
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
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
The 25 scholars most cited alongside Chris Piech, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2024 | 18 | |
| 4 | 2024 | 0 | |
| 5 | 2024 | 1 | |
| 6 | 2023 | 12 | |
| 7 | 2023 | 1 | |
| 8 | 2023 | 3 | |
| 9 | 2022 | 10 | |
| 10 | 2022 | 10 | |
| 11 | 2021 | 9 | |
| 12 | 2021 | 25 | |
| 13 | Measuring Ability-to-Learn Using Parametric Learning-Gain Functions. | 2020 | 2 |
| 14 | Variational Item Response Theory: Fast, Accurate, and Expressive | 2020 | 1 |
| 15 | Grades Are Not Normal: Improving Exam Score Models Using the Logit-Normal Distribution. | 2019 | 8 |
| 16 | 2018 | 8 | |
| 17 | Learning to Represent Student Knowledge on Programming Exercises Using Deep Learning. | 2017 | 34 |
| 18 | 2017 | 68 | |
| 19 | Syntactic and Functional Variability of a Million Code Submissions in a Machine Learning MOOC. | 2013 | 43 |
| 20 | Tuned Models of Peer Assessment in MOOCs. | 2013 | 56 |
About Chris Piech
Chris Piech is a scholar working on Computer Science Applications, Software and Health Informatics, having authored 45 papers that have together received 2.0k indexed citations. Recurring topics across this work include Online Learning and Analytics (21 papers), Teaching and Learning Programming (21 papers), Software Engineering Research (9 papers), Intelligent Tutoring Systems and Adaptive Learning (7 papers), Machine Learning and Algorithms (5 papers), Innovative Teaching and Learning Methods (5 papers), Software Testing and Debugging Techniques (4 papers) and Machine Learning and Data Classification (4 papers). The work is most often cited by research in Computer Science Applications (1.5k citations), Developmental and Educational Psychology (378 citations) and Software (104 citations). Chris Piech has collaborated with scholars based in United States, United Kingdom and Czechia. Frequent 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. Their work appears in journals such as Ophthalmology, Journal of Educational Measurement, Journal of the Learning Sciences, Journal of Educational and Behavioral Statistics and Educational Evaluation and Policy Analysis.
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.