David H. Smith

665 total citations
37 papers, 371 citations indexed

About

David H. Smith is a scholar working on Computer Science Applications, Artificial Intelligence and Information Systems. According to data from OpenAlex, David H. Smith has authored 37 papers receiving a total of 371 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Computer Science Applications, 8 papers in Artificial Intelligence and 7 papers in Information Systems. Recurrent topics in David H. Smith's work include Teaching and Learning Programming (18 papers), Online Learning and Analytics (14 papers) and Software Engineering Research (5 papers). David H. Smith is often cited by papers focused on Teaching and Learning Programming (18 papers), Online Learning and Analytics (14 papers) and Software Engineering Research (5 papers). David H. Smith collaborates with scholars based in United States, Canada and New Zealand. David H. Smith's co-authors include Qiang Hao, Craig Zilles, Amy J. Ko, Michail Tsikerdekis, Howard Schuman, Alex Inkeles, Paul Denny, Leo Porter, Stephen MacNeil and David Tripe and has published in prestigious journals such as Finance research letters, Economic Development and Cultural Change and International Journal of Educational Technology in Higher Education.

In The Last Decade

David H. Smith

33 papers receiving 345 citations

Peers

David H. Smith

CSA

AI

IS

DEP

EDUCA

Luciana Benotti Argentina

Mikel Larrañaga Spain

Mustafa Yağcı Türkiye

Amali Weerasinghe Australia

Daniel Amo-Filvà Spain

Leandro Silva Galvão de Carvalho Brazil

Qiao Lin United States

Bruno Gadelha Brazil

Bonnie MacKellar United States

Alex Baker United States

Luciana Benotti Argentina

David H. Smith

242 ×1.3

CSA

179 ×2.0

AI

66 ×0.9

IS

87 ×1.3

DEP

43 ×1.3

EDUCA

Citations per year, relative to David H. Smith David H. Smith (= 1×) peers Luciana Benotti

Countries citing papers authored by David H. Smith

Since Specialization

Citations

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

Fields of papers citing papers by David H. Smith

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David H. Smith. 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 David H. Smith. The network helps show where David H. Smith may publish in the future.

Co-authorship network of co-authors of David H. Smith

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

All Works

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20 of 20 papers shown

1.

Smith, David H., et al.. (2025). Evaluating AI Models for Autograding Explain in Plain English Questions: Challenges and Considerations. ACM Transactions on Interactive Intelligent Systems. 15(4). 1–29.

2.

Denny, Paul, David H. Smith, Juho Leinonen, et al.. (2025). Exploring Student Reactions to LLM-Generated Feedback on Explain in Plain English Problems. Aaltodoc (Aalto University). 575–581. 1 indexed citations

3.

Prather, James, Juho Leinonen, Natalie Kiesler, et al.. (2025). Beyond the Hype: A Comprehensive Review of Current Trends in Generative AI Research, Teaching Practices, and Tools. VTechWorks (Virginia Tech). 300–338. 10 indexed citations

4.

Smith, David H., et al.. (2025). Counting the Trees in the Forest: Evaluating Prompt Segmentation for Classifying Code Comprehension Level. 37–43.

5.

Smith, David H., et al.. (2025). Mining Hierarchies with Conviction: Constructing the CS1 Skill Hierarchy with Pairwise Comparisons over Skill Distributions. 1583–1584. 1 indexed citations

6.

Smith, David H., et al.. (2025). Achievement Goals in CS1-LLM. 144–153. 2 indexed citations

7.

Smith, David H., et al.. (2025). Mining hierarchies with conviction: constructing the CS1 skill hierarchy with pairwise comparisons over skill distributions. Computer Science Education. 1–22.

8.

Prather, James, Juho Leinonen, Natalie Kiesler, et al.. (2024). How Instructors Incorporate Generative AI into Teaching Computing. OHMdok (Technische Hochschule Nürnberg). 771–772. 10 indexed citations

9.

Denny, Paul, et al.. (2024). Explaining Code with a Purpose: An Integrated Approach for Developing Code Comprehension and Prompting Skills. Aaltodoc (Aalto University). 283–289. 15 indexed citations

10.

Smith, David H., et al.. (2024). Prompting for Comprehension: Exploring the Intersection of Explain in Plain English Questions and Prompt Writing. 39–50. 10 indexed citations

11.

Smith, David H., et al.. (2024). Distractors Make You Pay Attention: Investigating the Learning Outcomes of Including Distractor Blocks in Parsons Problems. 177–191. 1 indexed citations

12.

Denny, Paul, David H. Smith, James Prather, et al.. (2024). Integrating Natural Language Prompting Tasks in Introductory Programming Courses. 88–94. 3 indexed citations

13.

Zingaro, Daniel, et al.. (2024). CS1-LLM: Integrating LLMs into CS1 Instruction. 297–303. 32 indexed citations

14.

Smith, David H., et al.. (2024). Quickly Producing "Isomorphic" Exercises: Quantifying the Impact of Programming Question Permutations. 178–184. 3 indexed citations

15.

Smith, David H., et al.. (2023). Comparing the Impacts of Visually Grouped and Jumbled Distractors on Parsons Problems in CS1 Assessments. 154–160. 3 indexed citations

16.

Smith, David H., et al.. (2023). "\"I Don't Gamble To Make My Livelihood\": Understanding the Incentives For. 430–443.

17.

Smith, David H., et al.. (2023). Investigating the Effects of Testing Frequency on Programming Performance and Students' Behavior. 757–763. 1 indexed citations

18.

Smith, David H., et al.. (2023). Investigating the Role and Impact of Distractors on Parsons Problems in CS1 Assessments. 417–423. 3 indexed citations

19.

Hao, Qiang, et al.. (2021). Towards understanding the effective design of automated formative feedback for programming assignments. Computer Science Education. 32(1). 105–127. 35 indexed citations

20.

Smith, David H., et al.. (1995). Crude oil, petroleum products, process flowsheets. 35 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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