Bita Akram

28 papers receiving 236 citations

Peers

Bita Akram
Comparison fields: 5 of 40
  • Computer Science Applications 172
  • Health Informatics 10
  • Developmental and Educational Psychology 80
  • Software 12
  • Artificial Intelligence 78
Replace Vangel V. Ajanovski with:
Vangel V. Ajanovski North Macedonia
Susumu Kanemune Japan
Kathryn Irene Cunningham United States
Stephen E. Fancsali United States
Javier del Olmo Muñoz Spain
Nanxi Meng United States
Sven Strickroth Germany
Xinying Hou United States
Daria Bogdanova Belgium
David Braga Fernandes de Oliveira Brazil
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Citations per field
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Citations per year

Countries citing papers authored by Bita Akram

Since Specialization
Citations

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

Fields of papers citing papers by Bita Akram

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Bita Akram, 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 Bita Akram Line = papers co-authored together Bita Akram links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 34 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202434
2 202228
3 201823
4
Improving Stealth Assessment in Game-Based Learning with LSTM-Based Analytics
201820
5 202219
6 202019
7 202017
8 20239
9 20189
10 20229
11 20249
12 20228
13 20238
14 20244
15 20224
16 20233
17 20203
18 20193
19
A Data-Driven Approach to Automatically Assessing Concept-Level CS Competencies Based on Student Programs.
20203
20
Automated Assessment of Computer Science Competencies from Student Programs with Gaussian Process Regression.
20202

About Bita Akram

Bita Akram is a scholar working on Computer Science Applications, Developmental and Educational Psychology, Information Systems, Artificial Intelligence and Safety Research, having authored 34 papers that have together received 245 indexed citations. Recurring topics across this work include Teaching and Learning Programming (19 papers), Online Learning and Analytics (11 papers), Educational Games and Gamification (10 papers), Software Engineering Research (5 papers), Intelligent Tutoring Systems and Adaptive Learning (5 papers), Experimental Learning in Engineering (3 papers), Genetics, Bioinformatics, and Biomedical Research (2 papers) and Educational Technology and Assessment (2 papers). The work is most often cited by research in Computer Science Applications (172 citations), Health Informatics (10 citations), Developmental and Educational Psychology (80 citations), Software (12 citations) and Artificial Intelligence (78 citations). Bita Akram has collaborated with scholars based in United States, Canada and Finland. Frequent co-authors include Thomas Price, Tiffany Barnes, Eric Wiebe, James C. Lester, Bradford Mott, Kristy Elizabeth Boyer, Samiha Marwan, Wookhee Min, Juho Leinonen and Collin F. Lynch. Their work appears in journals such as IEEE Transactions on Learning Technologies, Frontiers in Psychiatry, Frontiers in Artificial Intelligence, Eurasia Journal of Mathematics Science and Technology Education and Lecture notes in computer science.

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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