Min Chi

2.1k citations
94 papers · 984 indexed · h-index 17

Impact in

Papers in

    • Online Learning and Analytics 24
    • Teaching and Learning Programming 13
    • Intelligent Tutoring Systems and Adaptive Learning 43
    • Machine Learning in Healthcare 17
    • Topic Modeling 11
    • Reinforcement Learning in Robotics 7

Min Chi

88 papers receiving 927 citations

Peers

Min Chi
Comparison fields: 5 of 109
  • Computer Science Applications 358
  • Artificial Intelligence 653
  • Developmental and Educational Psychology 226
  • Health Information Management 55
  • Signal Processing 102
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Citations per field
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Citations per year

Countries citing papers authored by Min Chi

Since Specialization
Citations

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

Fields of papers citing papers by Min Chi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Min Chi, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Min Chi Line = papers co-authored together Min Chi links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 94 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201180
2 201860
3 201957
4 200141
5 201939
6 201836
7
Meta-Cognitive Strategy Instruction in Intelligent Tutoring Systems: How, When, and Why
201035
8 201135
9 201629
10 201829
11 201928
12 201828
13 201624
14 202123
15 201721
16
Deep Learning + Student Modeling + Clustering: a Recipe for Effective Automatic Short Answer Grading.
201620
17
Reinforcement Learning-based Feature Seleciton For Developing Pedagogically Effective Tutorial Dialogue Tactics.
200817
18
One minute is enough: Early Prediction of Student Success and Event-level Difficulty during Novice Programming Tasks
201915
19 201815
20 202215

About Min Chi

Min Chi is a scholar working on Computer Science Applications, Artificial Intelligence, Computational Mathematics, Developmental and Educational Psychology and Signal Processing, having authored 94 papers that have together received 984 indexed citations. Recurring topics across this work include Intelligent Tutoring Systems and Adaptive Learning (43 papers), Online Learning and Analytics (24 papers), Machine Learning in Healthcare (17 papers), Innovative Teaching and Learning Methods (15 papers), Teaching and Learning Programming (13 papers), Topic Modeling (11 papers), Time Series Analysis and Forecasting (9 papers) and Reinforcement Learning in Robotics (7 papers). The work is most often cited by research in Computer Science Applications (358 citations), Artificial Intelligence (653 citations), Developmental and Educational Psychology (226 citations), Health Information Management (55 citations) and Signal Processing (102 citations). Min Chi has collaborated with scholars based in United States, China and Canada. Frequent co-authors include Kurt VanLehn, Tiffany Barnes, Pamela Jordan, Yuan Zhang, Julie S. Ivy, Shitian Shen, Diane Litman, Xi Yang, Thomas Price and Yeo Jin Kim. Their work appears in journals such as International Journal of Artificial Intelligence in Education, Cognitive Science, User Modeling and User-Adapted Interaction, Construction and Building Materials and Information Sciences.

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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