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.
Context-Aware Attentive Knowledge Tracing
2020219 citationsNeil T. Heffernan, Andrew Lan et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Andrew Lan'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 Andrew Lan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andrew Lan more than expected).
This network shows the impact of papers produced by Andrew Lan. 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 Andrew Lan. The network helps show where Andrew Lan may publish in the future.
Co-authorship network of co-authors of Andrew Lan
This figure shows the co-authorship network connecting the top 25 collaborators of Andrew Lan.
A scholar is included among the top collaborators of Andrew Lan 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 Andrew Lan. Andrew Lan 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.
Sosnovsky, Sergey, Peter Brusilovsky, & Andrew Lan. (2025). Intelligent Textbooks. International Journal of Artificial Intelligence in Education. 35(3). 967–986.1 indexed citations
2.
Xing, Wanli, Scott A. Crossley, Paul Denny, et al.. (2025). The Use of Large Language Models in Education. International Journal of Artificial Intelligence in Education. 35(2). 439–443.4 indexed citations
Lan, Andrew, Anthony F. Botelho, Shamya Karumbaiah, Ryan S. Baker, & Neil T. Heffernan. (2020). Accurate and Interpretable Sensor-free Affect Detectors via Monotonic Neural Networks.1 indexed citations
11.
Ren, Zhiyun, Xia Ning, Andrew Lan, & Huzefa Rangwala. (2019). Grade Prediction Based on Cumulative Knowledge and Co-taken Courses.. Educational Data Mining.13 indexed citations
Chen, Wei-Yu, Andrew Lan, Da Cao, Christopher G. Brinton, & Mung Chiang. (2018). Behavioral Analysis at Scale: Learning Course Prerequisite Structures from Learner Clickstreams.. Educational Data Mining.10 indexed citations
14.
Mozer, Michael C., et al.. (2018). Textbook annotations as an early predictor of student learning.. Educational Data Mining.1 indexed citations
15.
Mozer, Michael C., et al.. (2018). Can Textbook Annotations Serve as an Early Predictor of Student Learning. Educational Data Mining.5 indexed citations
16.
Lan, Andrew, et al.. (2017). Behavior-based latent variable model for learner engagement. Educational Data Mining.20 indexed citations
17.
Waters, Andrew E., Phillip J. Grimaldi, Andrew Lan, & Richard G. Baraniuk. (2017). Short-Answer Responses to STEM Exercises: Measuring Response Validity and Its Impact on Learning.. Educational Data Mining.1 indexed citations
18.
Lan, Andrew & Richard G. Baraniuk. (2016). A Contextual Bandits Framework for Personalized Learning Action Selection.. Educational Data Mining. 424–429.39 indexed citations
Lan, Andrew, Christoph Studer, Andrew E. Waters, & Richard G. Baraniuk. (2013). Joint Topic Modeling and Factor Analysis of Textual Information and Graded Response Data. arXiv (Cornell University). 324–325.3 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.