Joseph E. Beck

3.3k total citations
57 papers, 1.1k citations indexed

About

Joseph E. Beck is a scholar working on Artificial Intelligence, Computer Science Applications and Developmental and Educational Psychology. According to data from OpenAlex, Joseph E. Beck has authored 57 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 49 papers in Artificial Intelligence, 27 papers in Computer Science Applications and 23 papers in Developmental and Educational Psychology. Recurrent topics in Joseph E. Beck's work include Intelligent Tutoring Systems and Adaptive Learning (42 papers), Online Learning and Analytics (24 papers) and Innovative Teaching and Learning Methods (15 papers). Joseph E. Beck is often cited by papers focused on Intelligent Tutoring Systems and Adaptive Learning (42 papers), Online Learning and Analytics (24 papers) and Innovative Teaching and Learning Methods (15 papers). Joseph E. Beck collaborates with scholars based in United States, Netherlands and Italy. Joseph E. Beck's co-authors include Jack Mostow, Mia Stern, Yue Gong, Neil T. Heffernan, Xiaolu Xiong, Beverly Park Woolf, Carole R. Beal, Dovan Rai, Siyuan Zhao and Mingyu Feng and has published in prestigious journals such as Journal of Pharmacology and Experimental Therapeutics, Journal of Educational Computing Research and Journal of Engineering for Gas Turbines and Power.

In The Last Decade

Joseph E. Beck

56 papers receiving 932 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Joseph E. Beck United States 18 769 583 322 183 120 57 1.1k
John Stamper United States 16 665 0.9× 778 1.3× 431 1.3× 187 1.0× 188 1.6× 66 1.2k
Randi Williams United States 14 336 0.4× 494 0.8× 228 0.7× 163 0.9× 183 1.5× 23 1.0k
Collin Lynch United States 14 519 0.7× 543 0.9× 443 1.4× 167 0.9× 220 1.8× 91 1.0k
Pamela Jordan United States 18 1.1k 1.4× 282 0.5× 418 1.3× 75 0.4× 96 0.8× 67 1.3k
Arto Hellas Finland 20 459 0.6× 1.3k 2.1× 350 1.1× 440 2.4× 198 1.6× 115 1.7k
Sergey Sosnovsky United States 16 477 0.6× 561 1.0× 338 1.0× 263 1.4× 195 1.6× 62 1.0k
Anna N. Rafferty United States 14 457 0.6× 211 0.4× 222 0.7× 142 0.8× 200 1.7× 49 924
Matthew Berland United States 12 428 0.6× 617 1.1× 432 1.3× 192 1.0× 171 1.4× 44 1.2k
Marcelo Worsley United States 17 299 0.4× 681 1.2× 528 1.6× 186 1.0× 265 2.2× 63 1.1k
Safinah Ali United States 13 188 0.2× 431 0.7× 168 0.5× 130 0.7× 97 0.8× 31 853

Countries citing papers authored by Joseph E. Beck

Since Specialization
Citations

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

Fields of papers citing papers by Joseph E. Beck

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Joseph E. Beck. 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 Joseph E. Beck. The network helps show where Joseph E. Beck may publish in the future.

Co-authorship network of co-authors of Joseph E. Beck

This figure shows the co-authorship network connecting the top 25 collaborators of Joseph E. Beck. A scholar is included among the top collaborators of Joseph E. Beck 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 Joseph E. Beck. Joseph E. Beck 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.
Botelho, Anthony F., et al.. (2019). Developing Early Detectors of Student Attrition and Wheel Spinning Using Deep Learning. IEEE Transactions on Learning Technologies. 12(2). 158–170. 27 indexed citations
2.
Heffernan, Neil T., et al.. (2017). Using a Single Model Trained across Multiple Experiments to Improve the Detection of Treatment Effects.. Educational Data Mining. 2 indexed citations
3.
Beck, Joseph E., Min Chi, & Ryan S. Baker. (2017). Workshop proposal: deep learning for educational data mining.. Educational Data Mining. 1 indexed citations
4.
Xiong, Xiaolu, et al.. (2016). Going Deeper with Deep Knowledge Tracing.. Educational Data Mining. 545–550. 72 indexed citations
5.
Beck, Joseph E. & Xiaolu Xiong. (2013). Limits to accuracy: how well can we do at student modeling?. Educational Data Mining. 4–11. 19 indexed citations
6.
Wang, Yutao & Joseph E. Beck. (2012). Incorporating Factors Influencing Knowledge Retention into a Student Model.. Educational Data Mining. 201–203. 6 indexed citations
7.
Suay, Halit Bener, Joseph E. Beck, & Sonia Chernova. (2012). Using Causal Models for Learning from Demonstration.. National Conference on Artificial Intelligence. 1 indexed citations
8.
Wang, Yutao & Joseph E. Beck. (2012). Using Student Modeling to Estimate Student Knowledge Retention.. Educational Data Mining. 13 indexed citations
9.
Gong, Yue & Joseph E. Beck. (2011). Looking beyond transfer models: finding other sources of power for student models. 135–146. 3 indexed citations
10.
Rai, Dovan & Joseph E. Beck. (2011). Exploring User Data From a Game-like Math Tutor: A Case Study in Causal Modeling.. Educational Data Mining. 307–313. 7 indexed citations
11.
Gong, Yue & Joseph E. Beck. (2011). Items, Skills, and Transfer Models: Which Really Matters for Student Modeling?. Educational Data Mining. 81–90. 9 indexed citations
12.
Rai, Dovan & Joseph E. Beck. (2010). Analysis of a causal modeling approach: a case study with an educational intervention.. Educational Data Mining. 313–314. 3 indexed citations
13.
Wang, Yutao, Neil T. Heffernan, & Joseph E. Beck. (2010). Representing Student Performance with Partial Credit.. Educational Data Mining. 335–336. 10 indexed citations
14.
Feng, Mingyu, Joseph E. Beck, & Neil T. Heffernan. (2009). Using Learning Decomposition and Bootstrapping with Randomization to Compare the Impact of Different Educational Interventions on Learning. Educational Data Mining. 51–60. 14 indexed citations
15.
Feng, Mingyu & Joseph E. Beck. (2009). Back to the future: a non-automated method of constructing transfer models. Educational Data Mining. 240–249. 7 indexed citations
16.
Mostow, Jack & Joseph E. Beck. (2009). Why, What, and How to Log? Lessons from LISTEN. Educational Data Mining. 269–278. 2 indexed citations
17.
Gong, Yue, Dovan Rai, Joseph E. Beck, & Neil T. Heffernan. (2009). Does Self-Discipline impact students' knowledge and learning?. Educational Data Mining. 61–70. 21 indexed citations
18.
Beck, Joseph E. & Beverly Park Woolf. (2000). Reasoning from Data Rather than Theory. The Florida AI Research Society. 34–39. 1 indexed citations
19.
Beal, Carole R., et al.. (2000). Gaining Confidence in Mathematics: Instructional Technology for Girls. 2000(1). 57–64. 3 indexed citations
20.
Arroyo, Ivon, Joseph E. Beck, Beverly Park Woolf, Carole R. Beal, & David Hart. (1999). WhaleWatch: An Intelligent Multimedia Math Tutor. 1999(1). 565–570. 4 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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