Biography
Dr. Bita Akram is an assistant research professor with the Department of Computer Science. She has received her Ph.D. from NC State in 2019. Her research lies at the intersection of artifial intelligence and advanced learning technologies with its application on improving access and quality of CS Education. Dr. Akram has obtained her M.Sc. in computer science from University of Calgary where her research was focused on devising algorithms for conducting accurate and efficient scientific data visualization. She has earned her B.Sc. in Computer Engineering from Sharif University of Technology.
CV
Research Areas
- Advanced Learning Technologies
- Artificial Intelligence and Intelligent Agents
- Data Sciences and Analytics
- Graphics, Human Computer Interaction, & User Experience
Education
Ph.D., Computer Science, North Carolina State University, 2019
M.S., Computer Science, University of Calgary, 2015
B.S., Computer Engineering, Sharif University of Technology, 2013
Publications
(2023) Analysis of an Explainable Student Performance Prediction Model in an Introductory Programming Course
Hoq, M., Brusilovsky, P., and Akram, B.
doi: 10.5281/zenodo.8115693
(2023) SANN: Programming Code Representation Using Attention Neural Network with Optimized Subtree Extraction
Hoq, M., Chilla, S.R., Ranjbar, M., Brusilovsky, P., and Akram, B.
doi: 10.1145/3583780.3615047
(2022) Agents, Models, and Ethics: Importance of Interdisciplinary Explorations in AI Education
Jiang, S,. et. al.
(2022) Automating Personalized Feedback to Improve Students’ Persistence in Computing
Lina Battestilli, Susan Fisk, Cynthia Hunt, Akram, B., Spencer Yoder, Thomas Price and Tiffany Barnes
doi: 10.1145/3478432.3499144
(2022) Towards an AI-infused Interdisciplinary Curriculum for Middle-grade Classrooms
Akram, B., Yoder, S., Tatar, C., Boorugu, S., Aderemi, I., and Jiang, S.
doi: 10.1609/aaai.v36i11.21544
(2022) SANN: A Subtree-based Attention Neural Network Model for Student Success Prediction Through Source Code Analysis
Hoq, M., Brusilovski, P., Akram, B.
doi: 10.5281/zenodo.6983496
(2022) Predicting Student Performance with Control Flow Graph Embeddings
Marsden, J., Yoder, S., Akram, B.
doi: 10.5281/zenodo.6983401
(2022) Exploring Sequential Code Embeddings for Predicting Student Success in an Introductory Programming Course.
Yoder, S., Hoq M., Brusilovski, P., Akram, B.
doi: 10.5281/zenodo.6983194
(2022) Increasing Students’ Persistence in Computer Science through a Lightweight Scalable Intervention.
Akram B., Fisk S., Yoder S., Hunt C., Price T., Battestilli L., and Barnes, T.
doi: 10.1145/3502718.3524815
(2022) Gender, Self-Assessment, and Persistence in Computing: How gender differences in self-assessed ability reduce women’s persistence in computer science
Hunt C., Yoder S., Comment T., Price T., Akram B., Battestilli, L., Barnes, T., and Fisk S.
doi: 10.1145/3501385.3543963
(2022) Exploring Design Choices to Support Novices’ Example Use During Creative Open-Ended Programming
Wang, W., Bobbadi, B., Meur, A., Akram, B., Barnes, T., Martens C., and, Price, T.
doi: 10.1145/3478431.3499374
(2022) Adaptive Immediate Feedback for Block-Based Programming: Design and Evaluation
Marwan, S., Akram, B., Barnes, T., and Price, W.
doi: 10.1109/TLT.2022.3180984
(2021) Gaining Insight into Effective Teaching of AI Problem-Solving Through CSEDM: A Case Study
Yoder, S., Tatar, C., Aderemi, I., Boorugu, S., Jiang, S., and Akram, B.
CEUR-WS: Vol 3051 Paper 11
(2020) A Data-Driven Approach to Automatically Assessing Concept-Level CS Competencies Based on Student Programs
Akram, B., Azizolsoltani, H., Min, W., Wiebe, E., Navied, A., Mott, B., Boyer, K., & Lester, J.
CEUR-WS: Volume 2734 Paper 10
(2020) A conceptual assessment framework for K-12 computer science rubric design
Akram, B., Min, W., Wiebe, E., Navied, A., Mott, B., Boyer, K. E., & Lester, J.
doi: 10.1145/3328778.3372643
(2020) Automated Assessment of Computer Science Competencies from Student Programs with Gaussian Process Regression
Akram, B., Azizolsoltani, H., Min, W., Navied, A., Wiebe, E., Mott, B., Boyer, K., and Lester. J.
EDM 2020 Paper 113
(2020) Promoting computer science learning with block-based programming and narrative-centered gameplay
Min, W., Mott, B., Park, K., Taylor, S., Akram, B., Wiebe, E., & Lester, J.
doi: 10.1109/CoG47356.2020.9231881
(2020) Development and Validation of the Middle Grades Computer Science Concept Inventory (MG-CSCI) assessment
Rachmatullah, A., Akram, B., Boulden, D., Mott, B., Boyer, K., Lester, J., & Wiebe, E.
doi: 10.29333/ejmste/116600
(2019) Assessing Middle School Students’ Computational Thinking Through Programming Trajectory Analysis
Akram, B., Min, W., Wiebe, E., Mott, B., Boyer, K.E. and Lester, J.
doi: 10.1145/3287324.3293798
(2019) CEO: A Triangulated Evaluation of a Modeling-Based CT-Infused CS Activity for Non-CS Middle Grade Students
Lytle, N., Cateté, V., Dong, Y., Boulden, D., Akram, B., Houchins, J., Barnes, T. and Wiebe, E.
doi: 10.1145/3300115.3309527
(2018) Infusing Computational Thinking into Middle Grade Science Classrooms: Lessons Learned
Catete, V., Lytle, N., Dong, Y., Boulden, D., Akram, B., Houchins, J., Barnes, T., Wiebe, E., Lester, J., Mott, B., Boyer, K.
doi: 10.1145/3265757.3265778
(2018) Improving Stealth Assessment in Game-based Learning with LSTM-based Analytics
Akram, B., Min, W., Wiebe, E., Mott, B., Boyer, K., and Lester. J.
NSF PAR: 10100664
(2018) Computational Thinking Integration into Middle Grades Science Classrooms: Strategies for Meeting the Challenges
Boulden, D., Wiebe, E., Akram, B., Buffum, P., Aksit, O., Mott, B., Boyer, K., and Lester. J.
ERIC: EJ1201235
(2015) CINAPACT-splines: A family of infinitely smooth, accurate and compactly supported splines
Akram, B., Alim, U., and Samavati, F.
doi: 10.1007/978-3-319-27857-5_73