Publications

Biswas, G., Rajendran, R., Mohammed, N., Goldberg, B.S., Sottilare, R.A., Brawner, K., and Hoffman, M. (2019), “Multilevel Learner Modeling in Training Environments for Complex Decision Making,” IEEE Transactions on Learning Technologies (In Press), 2019. DOI:10.1109/TLT.2019.2923352 [Link]

Taub, M., Azevedo, R., Rajendran, R., Cloude, E. B., Biswas, G., & Price, M. J. (2019). How are students’ emotions related to the accuracy of cognitive and metacognitive processes during learning with an intelligent tutoring system? (In Press). Learning and Instruction. DOI: https://doi.org/10.1016/j.learninstruc.2019.04.001 [Link]

Rajendran, R., Iyer, S., & Murthy, S. (2019). Personalized affective feedback to address students frustration in ITS. IEEE Transactions on Learning Technologies, 12(1), 87-97. [Link]

Andrade, D., Bai B., Rajendran R., Watanabe Y. (2018), Leveraging Knowledge Bases for Future Prediction with Memory Comparison Networks. AI Communication, 31(6), 465-483. [Link]

Rajendran, R., Iyer, S., Murthy, S., Wilson, C., & Sheard, J. (2013). A theory-driven approach to predict frustration in an ITS. IEEE Transactions on Learning Technologies, 6(4), 378-388. [Link]

Pathan, R., Shaikh, U., & Rajendran, R. (2019, December). Capturing Learner Interaction in Computer-Based Learning Environment: Design and Application. In 2019 IEEE Tenth International Conference on Technology for Education (T4E)(pp. 146-153). IEEE.

Singh, A., Mohan, S., Singhal, V., Krishnan, R., & Rajendran, R. (2019, December). What Factors Affect a Primary Student's Performance?. In 2019 IEEE Tenth International Conference on Technology for Education (T4E) (pp. 286-287). IEEE.

Rajendran, R., Munshi, A., Emara, M., and Biswas, G., A Temporal Model of Learner Behaviors in OELEs using Process Mining., To be appeared in the Proceedings of International Conference on Computers in Education (ICCE) 2018, Manila. Recipient of Best Technical Design Paper Award. [Link]

Emara, M., Rajendran, R., and Biswas, G., Do Students Learning Behaviors Differ when they Collaborate in Open-Ended Learning Environments?, 21st ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW) 2018, US. [Link][Link2]

Rajendran, R., Kumar, A., Carter, K. E., Levin D. T., and Biswas, G., Predicting Learning by Analyzing Eye-Gaze Data of Reading Behavior. International conference on Educational Data Mining (EDM) 2018, Buffalo, USA. [Link]

Munshi, A., Rajendran, R., Moore, A., Ocumpaugh, J., and Biswas, G., Studying the Interactions between Components of Self Regulated Learning in Open Ended Learning Environments. International Conference of the Learning Sciences (ICLS), 2018, London. [Link]

Munshi, A., Rajendran, R., Ocumpaugh, J., Biswas, G., Baker, R. S., & Paquette, L. Modeling Learners' Cognitive and Affective States to Scaffold SRL in Open-Ended Learning Environments. In Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization (UMAP). ACM 2018, Singapore. [Link]

Taub, M., Mudrick, N. V., Rajendran, R., Dong, Y., Biswas, G., and Azevedo, R., How are students emotions associated with the accuracy of their note taking and summarizing during learning with ITSs?, Intelligent Tutoring Systems (ITS) 2018, Montreal, Canada. [Link]

Daniel, A., Bai, B., Rajendran, R., and Watanabe, Y., Analogy-based Reasoning With Memory Networks for Future Prediction. Cognitive Computation Workshop at Advances in Neural Information Processing Systems (NIPS), 2016. [Link]

Rajendran, R., and Biswas, G., Modeling Learners Metacognitive Skills in Open Ended Learning Environments. Workshop Proceedings of the 24th International Conference on Computers in Education (ICCE), 2016, India. [Link]

Tscholl, M., Rajendran, R., & BISWAS, G., Data Collection in Open Ended Learning Environment for Learning Analytics. Workshop Proceedings of the 24th International Conference on Computers in Education (ICCE), 2016, India. [Link]

Rajendran, R., Muralidharan. A., Impact of Mindspark's Adaptive Logic on Student Learning. In International Conference on Technology for Education (T4E), 2013, India. [Link]

Rajendran, R., Iyer, S., & Murthy, S. Literature driven method for modeling frustration in an ITS. In IEEE 12th International Conference on Advanced Learning Technologies (ICALT), 2012 (pp. 405-409). Rome, Italy. [Link]

Rajendran, R. Automatic identification of affective states using student log data in ITS. In International Conference on Artificial Intelligence in Education (AIED), 2011. Auckland, NZ. [Link]

Rajendran, R., Mohammed, N., Biswas, G., Goldberg, B. S., and Sottilare, R. A., Multi-level User Modeling in GIFT to Support Complex Learning Tasks, In Proceedings of the 5th Annual Generalized Intelligent Framework for Tutoring (GIFT) Users Symposium (GIFTSym5), May 2017, Orlando, USA.

Rajendran, R., Enriching the student model in Intelligent Tutoring System. Doctoral Symposium in Melbourne Computing Education Conventicle, Nov 2011, Swinburne Hawthorn Campus, Melbourne, Australia.

Rajendran, R., Poster Presentation at IITB-Monash Research Academy Symposium 2011, Mumbai, India

Henry Selvaraj, Thamarai Selvi, D Selvathi, and R Ramkumar. Support Vector Machine Based Automatic Classification of Human Brain using MR Image Feature. International Journal of Computational Intelligence and Applications, Vol 6(3), Pages 357-370, 2006. [Link]

ST Selvi, D Selvathi, H Selvaraj, and R Ramkumar. Least squares support vector machine based classification of abnormalities in brain MR images. Journal of Systems Science - Wroclaw, Vol 32(1), Pages 89-103, 2006.