Mathematical and Physical Sciences

Professor Shashi Kant Shankar has a bachelor’s in information technology from Kuvempu University (2011), a master’s in computer applications from Sikkim Manipal University (2014), and another master’s in computer science and engineering from Lovely Professional University (2016). He has a PhD from Tallinn University's School of Digital Technologies (2023), focusing on context-aware, reusable multimodal learning analytics solutions in authentic learning scenarios. His research supported Estonia's goal of promoting lifelong learning in Estonian society, utilising design-based research within the pragmatic constructivism paradigm. During his PhD, he worked as a junior researcher for four years at the Centre for Educational Technology, Tallinn University. Before joining his second master's, he also spent a year as a software developer and nearly three years as a professional IT trainer. From November 2022 to August 2024, he was an Assistant Professor at Amrita Vishwa Vidyapeetham, Kollam campus, Kerala.

Professor Shashi Kant Shankar explores the intricate interplay of Technology in, of, for, and across Education (T4E), with a strong focus on Technology-Enhanced Learning and Teaching, the integration of (Generative) AI tools in authentic educational settings, the co-existence of human intelligence and artificial intelligence (Hybrid Intelligence) in educational ecosystem, and the Technological Pedagogical and Content Knowledge (TPACK) skills of teachers. His research considers the socio-economic backgrounds of students and the educational context influencing education, aiming to foster Evidence-Based Decision-Making (EBDM) through a (Multimodal) Learning Analytics approach.

A key theme in his work is promoting Lifelong Learning among diverse learner groups, and he often employs Design-Based Research (DBR), iteratively collaborating with educational practitioners, software developers, and stakeholders to design and implement Technology-Enhanced educational solutions. His research typically operates within the Pragmatic Constructivism paradigm. One of his major contributions is the development of a data infrastructure that supports context-aware and reusable Multimodal Learning Analytics solutions. This infrastructure consists of a Multimodal Data Value Chain, a contextualized data model, and an adaptable software architecture, all designed to improve authentic learning scenarios through the involvement of multiple cross-disciplinary stakeholders.

List of published articles [APA style]

[2024]

1. Shankar, S. K., Pothancheri, G., Sasi, D., & Mishra, S. (2024). Bringing Teachers in the Loop: Exploring Perspectives on Integrating Generative AI in Technology-Enhanced Learning. International Journal of Artificial Intelligence in Education, 1–26. https://doi.org/10.1007/s40593-024-00428-8 
2. Chejara, P., Prieto, L. P., Dimitriadis, Y., Rodríguez-Triana, M. J., Ruiz-Calleja, A., Kasepalu, R., & Shankar, S. K. (2024). The Impact of Attribute Noise on the Automated Estimation of Collaboration Quality Using Multimodal Learning Analytics in Authentic Classrooms. Journal of Learning Analytics, 11(2), 73-90. https://doi.org/10.18608/jla.2024.8253 
3. Muravevskaia, E., Kuriappan, B., Shankar, S. K., Krishnaveni, M., & AS, S. L. (2024, July). Observing the Gaps from Theory to Practice in Rural Indian Public School Teachers’ Pedagogical Competencies in Social-Emotional Learning. In 2024 IEEE International Conference on Advanced Learning Technologies (ICALT) (pp. 250-254). IEEE. https://doi.org/10.1109/ICALT61570.2024.00079

[2023]

1. Chejara, P., Prieto, L. P., Rodríguez-Triana, M. J., Ruiz-Calleja, A., Kasepalu, R., & Shankar, S.K. (2023, March). How to Build More Generalizable Models for Collaboration Quality? Lessons Learned from Exploring Multi-Context Audio-Log Datasets using Multimodal Learning Analytics. In LAK23: 13th International Learning Analytics and Knowledge Conference (LAK2023). ACM, USA, 111–121. https://doi.org/10.1145/3576050.3576144 
2. Shankar, S.K., Ruiz-Calleja, A., Prieto, L.P., Rodríguez-Triana, M.J., Chejara, P., & Tripathi, S. (2023), CIMLA: A Modular and Modifiable Data Preparation, Organization, and Fusion Infrastructure to Partially Support the Development of Context-aware MMLA Solutions. J.UCS - Journal of Universal Computer Science 29(3): 265-297. https://doi.org/10.3897/jucs.84558
3. Chejara, P., Kasepalu, R., Prieto, L. P., Rodríguez-Triana, M. J., Ruiz-Calleja, A., & Shankar, S.K. (2023, March), Multimodal Learning Analytics research in the wild: challenges and their potential solutions. In CEUR Workshop proceeding of CrossMMLA'23.
4. Shankar, S.K., & Sasi, D. (2023), A Set of Evidence-based Guidelines for Planning Authentic Multimodal Learning Analytics Situations by Involving Cross-Disciplinary Stakeholders. Dykinson, ISBN 978-84-1170-558-5.

[2022]

1. Shankar, S. K., Rodríguez-Triana, M. J., Prieto, L. P., Ruiz-Calleja, A., & Chejara, P. (2022). CDM4MMLA: Contextualized Data Model for MultiModal Learning Analytics. In the Multimodal Learning Analytics Handbook. Springer, Cham. https://doi.org/10.1007/978-3-031-08076-0_9 
2. Shankar, S. K., Tripathi, S., Nupur, N., & Chejara, P. (2022, July). Teachers' reflections on students’ learning approaches who resumed physical classrooms after almost two years due to COVID-19 pandemic-induced disruptions.  In the 14th International Conference on Education and New Learning Technologies (EDULEARN 2022). IATED (pp. 1656-1664). https://doi.org/10.21125/edulearn.2022.0437

[2021]

1. Chejara, P., Prieto, L. P., Ruiz-Calleja, A., Rodríguez-Triana, M. J., Shankar, S. K., & Kasepalu, R. (2021). EFAR-MMLA: An evaluation framework to assess and report generalizability of machine learning models in MMLA. Sensors, MDPI 21(8), 2863. https://doi.org/10.3390/s21082863 
2. Chejara, P., Prieto, L. P., Ruiz-Calleja, A., Rodríguez-Triana, M. J., Shankar, S. K., & Kasepalu, R. (2021). CoTrack2: A tool to track collaboration across physical and digital spaces with real time activity visualization. In Companion Proceedings of the 11th International Conference on Learning Analytics & Knowledge (LAK 2021). SoLAR (pp. 406-406). https://www.solaresearch.org/core/lak21-companion-proceedings/ 

[2020]

1. Shankar, S. K., Rodríguez-Triana, M. J., Ruiz-Calleja, A., Prieto, L. P., Chejara, P., & Martínez-Monés, A. (2020). Multimodal Data Value Chain (M-DVC): A conceptual tool to support the development of Multimodal Learning Analytics solutions. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, 15(2), (pp. 113-122). https://doi.org/10.1109/RITA.2020.2987887 
2. Huertas Celdrán, A., Ruipérez-Valiente, J. A., Garcia Clemente, F. J., Rodríguez-Triana, M. J., Shankar, S. K., & Martinez Perez, G. (2020). A scalable architecture for the dynamic deployment of Multimodal Learning Analytics applications in smart classrooms. Sensors, MDPI, 20 (10), 2923. https://doi.org/10.3390/s20102923 
3. De Silva, L. M. H., Rodríguez-Triana, M. J., Chounta, I., Tammets, K., Shankar, S. K. (2020, March). Curriculum analytics as a communication mediator among stakeholders to enable the discussion and inform decision-making. In Companion Proceedings of the 10th International Conference on Learning Analytics & Knowledge (LAK 2020). SoLAR (pp. 762-764). https://www.solaresearch.org/core/lak20-companion-proceedings/ 
4. Chejara, P., Kasepalu, R., Shankar, S. K., Prieto, L. P., Rodríguez-Triana, M. J., & Ruiz-Calleja, A. (2020, March). MMLA approach to track collaborative behavior in face-to-Face blended settings. In Companion Proceedings of the 10th International Conference on Learning Analytics & Knowledge (LAK 2020). SoLAR, (pp. 543-548). https://www.solaresearch.org/core/lak20-companion-proceedings/ 
5. Chejara, P., Prieto, L. P., Rodríguez-Triana, M., Ruiz-Calleja, A., & Shankar, S. K. (2020, March). Cotrack: A tool for tracking collaboration across physical and digital spaces in collocated blended settings. In Companion Proceedings of the 10th International Conference on Learning Analytics & Knowledge (LAK 2020). SoLAR, (pp. 186-186). https://www.solaresearch.org/core/lak20-companion-proceedings/
6. Shankar, S. K. (2020, March). Challenges in multichannel data discovery and integration for monitoring performance in self-regulated learning. In Companion Proceedings of the 10th International Conference on Learning Analytics & Knowledge (LAK 2020). SoLAR, (pp. 455- 458). https://www.solaresearch.org/core/lak20-companion-proceedings/ 
7. Shankar, S. K., Ruiz-Calleja, A., Prieto, L. P., & Rodríguez-Triana, M. J. (2020, July). A Multimodal Learning Analytics approach to support evidence-based teaching and learning Practices. In IEEE 20th International Conference on Advanced Learning Technologies (ICALT 2020). IEEE, (pp. 381-383). https://doi.org/10.1109/ICALT49669.2020.00120 
8. Chejara, P., Prieto, L. P., Ruiz-Calleja, A., Rodríguez-Triana, M. J., Shankar, S. K., & Kasepalu, R. (2020, September). Quantifying collaboration quality in face-to-face classroom settings using MMLA. In International Conference on Collaboration Technologies and Social Computing (CollabTech 2020). Lecture Notes in Computer Science, Springer, Cham, 12324 (pp. 159-166). https://doi.org/10.1007/978-3-030-58157-2_11

[2019]

1. Shankar, S. K., Calleja, A. R., Iglesias, S. S., Arranz, A. O., Topali, P., & Monés, A. M. (2019, June). A data value chain to model the processing of multimodal evidence in authentic learning scenarios. In CEUR Workshop proceeding of Learning Analytics Summer Institute Spain (LASI 2019). CEUR Proc., 2415 (pp. 71-83). 
2. Chejara, P., Prieto, L. P., Ruiz-Calleja, A., Rodríguez-Triana, M. J., & Shankar, S. K. (2019, September). Exploring the triangulation of dimensionality reduction when interpreting multimodal learning data from authentic settings. In European Conference on Technology Enhanced Learning (EC-TEL 2019). Lecture Notes in Computer Science, Springer, Cham, 11722 (pp. 664-667). https://doi.org/10.1007/978-3-030-29736-7_62 
3. Shankar, S. K., Ruiz-Calleja, A., Prieto, L. P., Rodríguez-Triana, M. J., & Chejara, P. (2019, September). An architecture and data model to process multimodal evidence of learning. In International Conference on Web-Based Learning (ICWL 2019). Lecture Notes in Computer Science, Springer, Cham, 11841 (pp. 72-83). https://doi.org/10.1007/978-3-030-35758-0_7

[2018]

1. Shankar, S. K., Prieto, L. P., Rodríguez-Triana, M. J., & Ruiz-Calleja, A. (2018, July). A review of multimodal learning analytics architectures. In IEEE 18th International Conference on Advanced Learning Technologies (ICALT 2018). IEEE, (pp. 212-214). https://doi.org/10.1109/ICALT.2018.00057

[2016]

1. Kaur, N., Grewal, D. K., & Shankar, S. K. (2016, April). Typical and atypical hierarchical routing protocols for WSNs: A review. In IEEE International Conference on Computing, Communication and Automation (ICCCA 2016). IEEE, (pp. 465-470). https://doi.org/10.1109/CCAA.2016.7813764
2. Shankar, S. K., & Tomar, A. S. (2016, May). A survey on wireless body area network and electronic-healthcare. In IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT 2016). IEEE, (pp. 598-603). https://doi.org/10.1109/RTEICT.2016.7807892
3. Shankar, S. K., & Kaur, A. (2016, May). Constraint data mining using apriori algorithm with AND operation. In IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT 2016). IEEE, (pp. 1025-1029). https://doi.org/10.1109/RTEICT.2016.7807985
4. Kaur, A., Aggarwal, V., & Shankar, S. K. (2016, May). An efficient algorithm for generating association rules by using constrained itemsets mining. In IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT 2016). IEEE, (pp. 99-102). https://doi.org/10.1109/RTEICT.2016.7807791
5. Tomar, A. S., Shankar, S. K., Sharma, M., & Bakshi, A. (2016, September). Enhanced image based authentication with secure key exchange mechanism using ECC in cloud. In Security in Computing and Communications (SSCC 2016). Communications in Computer and Information Science, 625 (pp. 63-73). Springer, Singapore. https://doi.org/10.1007/978-981-10-2738-3_6

[2015]

1. Shankar, S. K., Tomar, A. S., & Tak, G. K. (2015, December). Secure medical data transmission by using ECC with mutual authentication in WSNs. In Fourth International Conference on Eco-friendly Computing and Communication Systems (ICECCS 2015). Procedia Computer Science, Elsevier, 70, (pp. 455-461). https://doi.org/10.1016/j.procs.2015.10.078

• Object-oriented Programming Languages (Theoretical and Practical using C# & Java)
• Software Engineering
• Data Processing Pipelines
• AI and Big Data in Education
• Mixed-methods Research
• Design-based Research
• Searching, Reading, and Writing Scientific Articles