Linlin Hu

659 total citations · 1 hit paper
18 papers, 430 citations indexed

About

Linlin Hu is a scholar working on Computer Science Applications, Developmental and Educational Psychology and Education. According to data from OpenAlex, Linlin Hu has authored 18 papers receiving a total of 430 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Computer Science Applications, 7 papers in Developmental and Educational Psychology and 3 papers in Education. Recurrent topics in Linlin Hu's work include Teaching and Learning Programming (13 papers), Online Learning and Analytics (10 papers) and Educational Games and Gamification (7 papers). Linlin Hu is often cited by papers focused on Teaching and Learning Programming (13 papers), Online Learning and Analytics (10 papers) and Educational Games and Gamification (7 papers). Linlin Hu collaborates with scholars based in China, Singapore and South Korea. Linlin Hu's co-authors include Lihui Sun, Danhua Zhou, Weipeng Yang, Zhen Guo, Xiaoqian Wang, Hao Wang, Zhen Tian, Jinli Zhao, Hongjie Jia and Hao Wang and has published in prestigious journals such as Computers & Education, Journal of Computer Assisted Learning and Education and Information Technologies.

In The Last Decade

Linlin Hu

17 papers receiving 418 citations

Hit Papers

Factors influencing Chinese pre-service teachers’ adoptio... 2025 2026 2025 5 10 15
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.

  1. Factors influencing Chinese pre-service teachers’ adoption of generative AI in teaching: an empirical study based on UTAUT2 and PLS-SEM
    2025 15 citations Linlin Hu, Hao Wang et al. Education and Information Technologies profile →

Peers

Linlin Hu
Danhua Zhou China
Lihui Sun China
İbrahim Çetin Türkiye
Jason Zagami Australia
Roxana Hadad United States
Gabrielė Stupurienė Lithuania
Kathryn M. Rich United States
Raina Mason Australia
Maria Kallia United Kingdom
Lechen Zhang Sweden
Danhua Zhou China
Linlin Hu
Citations per year, relative to Linlin Hu Linlin Hu (= 1×) peers Danhua Zhou

Countries citing papers authored by Linlin Hu

Since Specialization
Citations

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

Fields of papers citing papers by Linlin Hu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Linlin Hu

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

All Works

18 of 18 papers shown
1.
2.
Hu, Linlin, et al.. (2025). Factors influencing Chinese pre-service teachers’ adoption of generative AI in teaching: an empirical study based on UTAUT2 and PLS-SEM. Education and Information Technologies. 30(9). 12609–12631. 15 indexed citations breakdown →
3.
Hu, Linlin, et al.. (2025). Impact of Integrating Computational Thinking in STEM Education on Students' Cognitive and Non‐Cognitive Skills: A Meta‐Analysis. Journal of Computer Assisted Learning. 41(4). 1 indexed citations
4.
Hu, Linlin & Hao Wang. (2024). Unplugged activities in the elementary school mathematics classroom: The effects on students’ computational thinking and mathematical creativity. Thinking Skills and Creativity. 54. 101653–101653. 2 indexed citations
5.
Hu, Linlin. (2024). Utilization of differentiated instruction in K-12 classrooms: a systematic literature review (2000–2022). Asia Pacific Education Review. 25(2). 507–525. 2 indexed citations
6.
Hu, Linlin, et al.. (2024). Unplugged programming practice in Chinese rural primary schools: a method to foster students’ computational thinking and resilience. Interactive Learning Environments. 33(1). 387–407. 1 indexed citations
7.
Hu, Linlin. (2024). Exploring Gender Differences in Computational Thinking Among K-12 Students: A Meta-Analysis Investigating Influential Factors. Journal of Educational Computing Research. 62(5). 1138–1164. 8 indexed citations
8.
Hu, Linlin. (2023). Programming and 21st century skill development in K‐12 schools: A multidimensional meta‐analysis. Journal of Computer Assisted Learning. 40(2). 610–636. 17 indexed citations
9.
Sun, Lihui, Linlin Hu, Danhua Zhou, & Weipeng Yang. (2022). Evaluation and developmental suggestions on undergraduates’ computational thinking: a theoretical framework guided by Marzano’s new taxonomy. Interactive Learning Environments. 31(10). 6588–6610. 13 indexed citations
10.
Sun, Lihui, Linlin Hu, & Danhua Zhou. (2022). Programming attitudes predict computational thinking: Analysis of differences in gender and programming experience. Computers & Education. 181. 104457–104457. 82 indexed citations
11.
Sun, Lihui, Linlin Hu, & Danhua Zhou. (2022). The bidirectional predictions between primary school students’ STEM and language academic achievements and computational thinking: The moderating role of gender. Thinking Skills and Creativity. 44. 101043–101043. 20 indexed citations
12.
Sun, Lihui, Linlin Hu, & Danhua Zhou. (2021). Which way of design programming activities is more effective to promote K‐12 students' computational thinking skills? A meta‐analysis. Journal of Computer Assisted Learning. 37(4). 1048–1062. 56 indexed citations
13.
Sun, Lihui, Linlin Hu, & Danhua Zhou. (2021). Single or Combined? A Study on Programming to Promote Junior High School Students’ Computational Thinking Skills. Journal of Educational Computing Research. 60(2). 283–321. 28 indexed citations
14.
Sun, Lihui, Linlin Hu, & Danhua Zhou. (2021). Improving 7th-graders’ computational thinking skills through unplugged programming activities: A study on the influence of multiple factors. Thinking Skills and Creativity. 42. 100926–100926. 54 indexed citations
15.
Sun, Lihui, Zhen Guo, & Linlin Hu. (2021). Educational games promote the development of students’ computational thinking: a meta-analytic review. Interactive Learning Environments. 31(6). 3476–3490. 53 indexed citations
16.
Sun, Lihui, Linlin Hu, Weipeng Yang, Danhua Zhou, & Xiaoqian Wang. (2020). STEM learning attitude predicts computational thinking skills among primary school students. Journal of Computer Assisted Learning. 37(2). 346–358. 73 indexed citations
17.
Hu, Linlin, Jinli Zhao, Hongjie Jia, Xiaolong Guo, & Qiang Zeng. (2012). Computing the boundary of simple power system feasible region based on the optimization method. 28. 1–6. 3 indexed citations
18.
Jia, Hongjie, et al.. (2012). Short-term wind speed forecasting based on non-parametric kernel density estimation. 2. 1–5. 2 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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