Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
SpringerLink
Log in

CDM4MMLA: Contextualized Data Model for MultiModal Learning Analytics

  • Chapter
  • First Online:
The Multimodal Learning Analytics Handbook

  • 1242 Accesses

Abstract

When MultiModal Learning Analytics (MMLA) are applied in authentic educational scenarios, multiple stakeholders (such as teachers, researchers and developers) often communicate to specify the requirements of the envisioned MMLA solution. Later on, developers instantiate the software solution for the MMLA data processing needed, as per the stakeholders’ specification, to fit the concrete setting of implementation (e.g., a set of classrooms with a certain technological setup). Current MMLA development practice, however, is relatively young and there is still a dearth of standardized practices at different phases of the development lifecycle. Such standardization may lead to interoperability among solutions that the current ad-hoc and tailor-made solutions lack. This chapter presents the Contextualized Data Model for MultiModal Learning Analytics (CDM4MMLA), a data model to represent, organize, and structure contextualized MMLA process specifications, to be later used by MMLA solutions. To illustrate the model’s expressivity and flexibility, the CDM4MMLA has been applied to three authentic MMLA scenarios. While not a definitive and universal proposal yet, this kind of common computer-interpretable models can not only help in specification reusability (e.g., if the underlying processing technologies change in the future), but also serve as a sort of ‘lingua franca’ within the MMLA research and development community to more consistently specify its processes and accumulate knowledge.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    A Data Requirements Specification is a formal document used for specifying the requirements about what kind of data processing activities and methods, the data-intensive software solutions should involve (Valderas & Pelechano, 2011; Muslah & Ghoul, 1904).

  2. 2.

    We have coined the term ‘Contextualized MMLA Process Specifications’ (CMPS) to refer to the union of the Data Requirements Specification (DRS), the Contextual Information (CI)—coming from both the planning as well as the enactment of the learning situation, and the metadata of the heterogeneous datasets that the MMLA system needs to process.

  3. 3.

    The spreadsheet containing the template with the description of the tables and the data related to the three cases can be accessed at: https://tinyurl.com/8xjysjk.

  4. 4.

    See ‘Case-1-MUTLA’ in: https://tinyurl.com/8xjysjk.

  5. 5.

    See ‘Case-2-MultiSimo’ in: https://tinyurl.com/8xjysjk.

  6. 6.

    https://www.mentimeter.com.

  7. 7.

    See ‘Case-3-SecondaryEnglish’ in: https://tinyurl.com/8xjysjk.

References

  • Andrus, B., Bar-El, D., Msall, C., Uttal, D., & Worsley, M. (2020). Minecraft as a generative platform for analyzing and practicing spatial reasoning. In German Conference on Spatial Cognition (pp. 297–302). Springer.

    Google Scholar 

  • Anseeuw, J., Verstichel, S., Ongenae, F., Lagatie, R., Venant, S., & De Turck, F. (2016). An ontology-enabled context-aware learning record store compatible with the experience api. In 8th International joint conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (KEOD) (pp. 88–95). SCITEPRESS (Science and Technology Publications).

    Google Scholar 

  • Bakharia, A., Kitto, K., Pardo, A., Gašević, D., & Dawson, S. (2016). Recipe for success: lessons learnt from using xAPI within the connected learning analytics toolkit. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (pp. 378–382).

    Google Scholar 

  • Benedek, A. (2013). Learning design versus learning experience design: Is the experience api making the difference. In Edulearn13 Proceedings (pp. 2609–2621).

    Google Scholar 

  • Blikstein, P. (2013). Multimodal learning analytics. In Proceedings of the third International Conference on Learning Analytics and Knowledge (pp. 102–106).

    Google Scholar 

  • Borges, V. A., Nogueira, B. M., & Barbosa, E. F. (2016). A multidimensional data model for the analysis of learning management systems under different perspectives. In 2016 IEEE Frontiers in Education Conference (FIE) (pp. 1–8). IEEE.

    Google Scholar 

  • Bouihi, B., & Bahaj, M. (2017). An ontology-based architecture for context recommendation system in e-learning and mobile-learning applications. In 2017 International Conference on Electrical and Information Technologies (ICEIT) (pp. 1–6). IEEE.

    Google Scholar 

  • Campos, A. M., Alvarez-Gonzalez, L. A., & Livingstone, D. E. (2012). Analyzing effectiveness of pedagogical scenarios for learning programming a learning path data model. Editor: Ion Mierluş-Mazilu, 51, 51–59.

    Google Scholar 

  • Charlton, P., & Magoulas, G. D. (2010a). Autonomic computing and ontologies to enable context-aware learning design. In 2010 22nd IEEE International Conference on Tools with Artificial Intelligence (Vol. 2, pp. 286–291). IEEE.

    Google Scholar 

  • Charlton, P., & Magoulas, G. D. (2010b). Self-configurable framework for enabling context-aware learning design. In 2010 5th IEEE International Conference Intelligent Systems (pp. 1–6). IEEE.

    Google Scholar 

  • Chatti, M. A., Muslim, A., & Schroeder, U. (2017). Toward an open learning analytics ecosystem. In Big data and learning analytics in higher education (pp. 195–219). Springer.

    Google Scholar 

  • Chejara, P., Prieto, L. P., Ruiz-Calleja, A., Rodríguez-Triana, M. J., & Shankar, S. K. (2019). Exploring the triangulation of dimensionality reduction when interpreting multimodal learning data from authentic settings. In European Conference on Technology Enhanced Learning (pp. 664–667). Springer.

    Google Scholar 

  • Chejara, P., Prieto, L. P., Ruiz-Calleja, A., Rodríguez-Triana, M. J., Shankar, S. K., & Kasepalu, R. (2020). Quantifying collaboration quality in face-to-face classroom settings using mmla. In International Conference on Collaboration Technologies and Social Computing (pp. 159–166). Springer.

    Google Scholar 

  • Chikh, A. (2014). A general model of learning design objects. Journal of King Saud University-Computer and Information Sciences, 26(1), 29–40.

    Article  Google Scholar 

  • Deschaine, M. E., Francis, R., & Ann, S. (2018). Minimizing data errors through reflective process and knowledge management structures. Responsible analytics and data mining in education: Global perspectives on quality, support, and decision making (p. 131). Routledge.

    Google Scholar 

  • Di Mitri, D., Schneider, J., Klemke, R., Specht, M., & Drachsler, H. (2019). Read between the lines: An annotation tool for multimodal data for learning. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge (pp. 51–60).

    Google Scholar 

  • Di Mitri, D., Schneider, J., Specht, M., & Drachsler, H. (2018a). The big five: Addressing recurrent multimodal learning data challenges. In CrossMMLA@ LAK.

    Google Scholar 

  • Di Mitri, D., Schneider, J., Specht, M., & Drachsler, H. (2018b). From signals to knowledge: A conceptual model for multimodal learning analytics. Journal of Computer Assisted Learning, 34(4), 338–349.

    Article  Google Scholar 

  • Eradze, M., Rodriguez-Triana, M., Milikic, N., Laanpere, M., & Tammets, K. (2020). Contextualising learning analytics with classroom observations: A case study. In Journal of Interaction Design and Architecture(s), 44(2020), 71–95.

    Article  Google Scholar 

  • Eradze, M., Rodriguez Triana, M. J., & Laanpere, M. (2017). Semantically annotated lesson observation data in learning analytics datasets: a reference model. Interaction Design and Architecture (s) Journal-IxD&A, 33(ARTICLE), 75–91.

    Google Scholar 

  • Fischbach, M., Wiebusch, D., & Latoschik, M. E. (2017). Semantic entity-component state management techniques to enhance software quality for multimodal VR-systems. IEEE Transactions on Visualization and Computer Graphics, 23(4), 1342–1351.

    Article  Google Scholar 

  • Franconi, E., & Sattler, U. (1999). A data warehouse conceptual data model for multidimensional aggregation. In DMDW (Vol. 19, p. 13).

    Google Scholar 

  • Fuller, K. A., Karunaratne, N. S., Naidu, S., Exintaris, B., Short, J. L., Wolcott, M. D., Singleton, S., & White, P. J. (2018). Development of a self-report instrument for measuring in-class student engagement reveals that pretending to engage is a significant unrecognized problem. PloS One, 13(10), e0205828.

    Article  Google Scholar 

  • Gavrilova, T. A., & Leshcheva, I. A. (2015). Ontology design and individual cognitive peculiarities: A pilot study. Expert Systems with Applications, 42(8), 3883–3892.

    Article  Google Scholar 

  • Healion, D., & Russell, S. (2016). The development of an evaluation methodology to assess the efficacy of a furniture design for stem education. ITERATIONS, (4), 24–31.

    Google Scholar 

  • Jørnø, R. L., & Gynther, K. (2018). What constitutes an” actionable insight” in learning analytics? Journal of Learning Analytics, 5(3), 198–221.

    Article  Google Scholar 

  • Kalou, A., Solomou, G., Pierrakeas, C., & Kameas, A. (2012). An ontology model for building, classifying and using learning outcomes. In 2012 IEEE 12th International Conference on Advanced Learning Technologies (pp. 61–65). IEEE.

    Google Scholar 

  • Karnitis, G., & Arnicans, G. (2015). Migration of relational database to document-oriented database: Structure denormalization and data transformation. In 2015 7th International Conference on Computational Intelligence, Communication Systems and Networks (pp. 113–118). IEEE.

    Google Scholar 

  • Kim, J., & Chung, K. Y. (2014). Ontology-based healthcare context information model to implement ubiquitous environment. Multimedia Tools and Applications, 1(2), 873–888.

    Article  Google Scholar 

  • Koutsombogera, M., & Vogel, C. (2018). Modeling collaborative multimodal behavior in group dialogues: The multisimo corpus. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018).

    Google Scholar 

  • Law, N., & Liang, L. (2020). A multilevel framework and method for learning analytics integrated learning design. Journal of Learning Analytics, 7(3), 98–117.

    Article  Google Scholar 

  • Leigh Star, S. (2010). This is not a boundary object: Reflections on the origin of a concept. Science, Technology, & Human Values, 35(5), 601–617.

    Article  Google Scholar 

  • Liu, K., Tatinati, S., & Khong, A. W. (2020). Context-based data model for effective real-time learning analytics. IEEE Transactions on Learning Technologies, 13(4), 790–803.

    Article  Google Scholar 

  • Lukarov, V., Chatti, M. A., Thüs, H., Kia, F. S., Muslim, A., Greven, C., & Schroeder, U. (2014). Data models in learning analytics. In DeLFI Workshops (Vol. 1014, pp. 88–95). Citeseer.

    Google Scholar 

  • Malik, K. R., Ahmad, T., Farhan, M., Aslam, M., Jabbar, S., Khalid, S., & Kim, M. (2016). Big-data: Transformation from heterogeneous data to semantically-enriched simplified data. Multimedia Tools and Applications, 75(20), 12727–12747.

    Article  Google Scholar 

  • Mangaroska, K., Sharma, K., Gasevic, D., & Giannakos, M. (2020). Multimodal learning analytics to inform learning design: Lessons learned from computing education. Journal of Learning Analytics, 7, 79–97.

    Article  Google Scholar 

  • Martinez-Maldonado, R., Echeverria, V., Fernandez Nieto, G., & Buckingham Shum, S. (2020). From data to insights: A layered storytelling approach for multimodal learning analytics. In Proceedings of the 2020 Chi Conference on Human Factors in Computing Systems (pp. 1–15).

    Google Scholar 

  • Martinez-Maldonado, R., Echeverria, V., Santos, O. C., Santos, A. D. P. D., & Yacef, K. (2018). Physical learning analytics: A multimodal perspective. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge (pp. 375–379).

    Google Scholar 

  • Mavroudi, A., Hadzilacos, T., & Kalles, D. (2010). Learning design for mobile and contextual learning. In Proceedings of the 9th World Conference on Mobile and Contextual Learning (pp. 362–365).

    Google Scholar 

  • Miller, H. G., & Mork, P. (2013). From data to decisions: A value chain for big data. It Professional, 15(1), 57–59.

    Article  Google Scholar 

  • Mu, S., Cui, M., & Huang, X. (2020). Multimodal data fusion in learning analytics: A systematic review. Sensors, 20(23), 6856.

    Article  Google Scholar 

  • Muslah, E., & Ghoul, S. (1904). Requirements variability specification for data intensive software. Preprint arXiv:1904.12314.

    Google Scholar 

  • Muslim, A. (2018). OpenLAP: A user-centered open learning analytics platform. Ph.D. Thesis, RWTH Aachen University.

    Google Scholar 

  • Nguyen, A., Gardner, L. A., & Sheridan, D. (2018). Building an ontology of learning analytics. In PACIS (p. 158).

    Google Scholar 

  • Nguyen, Q., Rienties, B., & Toetenel, L. (2017). Mixing and matching learning design and learning analytics. In International Conference on Learning and Collaboration Technologies (pp. 302–316). Springer.

    Google Scholar 

  • Niemann, K., Wolpers, M., Stoitsis, G., Chinis, G., & Manouselis, N. (2013) Aggregating social and usage datasets for learning analytics: Data-oriented challenges. In Proceedings of the Third International Conference on Learning Analytics and Knowledge (pp. 245–249).

    Google Scholar 

  • Nouira, A., Cheniti-Belcadhi, L., & Braham, R. (2017). An ontological model for assessment analytics. In WEBIST (pp. 243–251).

    Google Scholar 

  • Nouira, A., Cheniti-Belcadhi, L., & Braham, R. (2018). An enhanced xapi data model supporting assessment analytics. Procedia Computer Science, 126, 566–575.

    Article  Google Scholar 

  • Ochoa, X., Lang, A. C., & Siemens, G. (2017). Multimodal learning analytics. The handbook of learning analytics (Vol. 1, pp. 129–141).

    Google Scholar 

  • Ochoa, X., & Worsley, M. (2016). Augmenting learning analytics with multimodal sensory data. Journal of Learning Analytics, 3(2), 213–219.

    Article  Google Scholar 

  • Oviatt, S., Cohen, A., & Weibel, N. (2013). Multimodal learning analytics: Description of math data corpus for ICMI grand challenge workshop. In Proceedings of the 15th ACM on International Conference on Multimodal Interaction (pp. 563–568).

    Google Scholar 

  • Oviatt, S., Grafsgaard, J., Chen, L., & Ochoa, X. (2018). Multimodal learning analytics: Assessing learners’ mental state during the process of learning. In The Handbook of Multimodal-Multisensor Interfaces: Signal Processing, Architectures, and Detection of Emotion and Cognition (Vol. 2, pp. 331–374). ACM.

    Google Scholar 

  • Papamitsiou, Z., Giannakos, M. N., & Ochoa, X. (2020) From childhood to maturity: Are we there yet? In The Tenth International Conference on Learning Analytics and Knowledge.

    Google Scholar 

  • Peña-Ayala, A. (2018). Learning analytics: A glance of evolution, status, and trends according to a proposed taxonomy. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(3), e1243.

    Google Scholar 

  • Popescu-Belis, A. (2009). Managing multimodal data, metadata and annotations: Challenges and solutions. In Multimodal signal processing: Theory and applications for human-computer interaction (p. 207).

    Google Scholar 

  • Prieto, L. P., Asensio-Pérez, J. I., Dimitriadis, Y., Gómez-Sánchez, E., & Munoz-Cristóbal, J. A. (2011). GLUE!-PS: a multi-language architecture and data model to deploy TEL designs to multiple learning environments. In European Conference on Technology Enhanced Learning (pp. 285–298). Springer.

    Google Scholar 

  • Prieto, L. P., Sharma, K., Kidzinski, Ł., Rodríguez-Triana, M. J., & Dillenbourg, P. (2018). Multimodal teaching analytics: Automated extraction of orchestration graphs from wearable sensor data. Journal of Computer Assisted Learning, 34(2), 193–203.

    Google Scholar 

  • Prieto, L. P., Rodríguez-Triana, M. J., Martínez-Maldonado, R., Dimitriadis, Y., & Gašević, D. (2019). Orchestrating learning analytics (OrLA): Supporting inter-stakeholder communication about adoption of learning analytics at the classroom level. Australasian Journal of Educational Technology, 35(4). https://doi.org/10.14742/ajet.4314

  • Rabelo, T., Lama, M., Amorim, R. R., & Vidal, J. C. (2015). Smartlak: A big data architecture for supporting learning analytics services. In: 2015 IEEE Frontiers in Education Conference (FIE) (pp. 1–5). IEEE.

    Google Scholar 

  • Riquelme, F., Munoz, R., Mac Lean, R., Villarroel, R., Barcelos, T. S., & de Albuquerque, V. H. C. (2019). Using multimodal learning analytics to study collaboration on discussion groups. Universal Access in the Information Society, 18(3), 633–643.

    Article  Google Scholar 

  • Rodríguez-Triana, M. J., Martínez-Monés, A., & Villagrá-Sobrino, S. (2016). Learning analytics in small-scale teacher-led innovations: Ethical and data privacy issues. Journal of Learning Analytics, 3(1), 43–65.

    Article  Google Scholar 

  • Rodríguez-Triana, M. J., Prieto, L. P., Martínez-Monés, A., Asensio-Pérez, J. I., & Dimitriadis, Y. (2018). The teacher in the loop: Customizing multimodal learning analytics for blended learning. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge (pp. 417–426).

    Google Scholar 

  • Ruipérez-Valiente, J. A., Muñoz-Merino, P. J., & Kloos, C. D. (2013). An architecture for extending the learning analytics support in the khan academy framework. In Proceedings of the First International Conference on Technological Ecosystem for Enhancing Multiculturality (pp. 277–284).

    Google Scholar 

  • Sandberg, M. K., Rehm, J., Mnoucek, M., Reshodko, I., & Gundersen, O. E. (2020). Explaining traffic situations–architecture of a virtual driving instructor. In International Conference on Intelligent Tutoring Systems (pp. 115–124). Springer.

    Google Scholar 

  • Schmitz, M., Van Limbeek, E., Greller, W., Sloep, P., & Drachsler, H. (2017). Opportunities and challenges in using learning analytics in learning design. In European Conference on Technology Enhanced Learning (pp. 209–223). Springer.

    Google Scholar 

  • Sergis, S., & Sampson, D. (2019). An analysis of open learner models for supporting learning analytics. In Learning Technologies for Transforming Large-Scale Teaching, Learning, and Assessment (pp. 155–190). Springer.

    Google Scholar 

  • Shankar, S., Prieto, L., Rodríguez-Triana, M., & Ruiz-Calleja, A. (2018). A review of multimodal learning analytics architectures. In Proceedings of the 18th International Conference on Advanced Learning Technologies (ICALT) (pp. 212–214). Bombay, India.

    Google Scholar 

  • 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), 113–122.

    Article  Google Scholar 

  • Shankar, S. K., Ruiz-Calleja, A., Prieto, L. P., Rodríguez-Triana, M. J., & Chejara, P. (2019). An architecture and data model to process multimodal evidence of learning. In International Conference on Web-Based Learning (pp. 72–83). Springer.

    Google Scholar 

  • Shankar, S. K., Ruiz Calleja, A., Serrano Iglesias, S., Ortega Arranz, A., Topali, P., & Martínez Monés, A. (2019). A data value chain to model the processing of multimodal evidence in authentic learning scenarios. In CEUR proceedings (pp. 71–83).

    Google Scholar 

  • Sharma, K., Olsen, J. K., Aleven, V., & Rummel, N. (2021). Measuring causality between collaborative and individual gaze metrics for collaborative problem-solving with intelligent tutoring systems. Journal of Computer Assisted Learning, 37(1), 51–68.

    Article  Google Scholar 

  • Shum, S. B., Echeverria, V., & Martinez-Maldonado, R. (2019) The multimodal matrix as a quantitative ethnography methodology. In International Conference on Quantitative Ethnography (pp. 26–40). Springer.

    Google Scholar 

  • Sullivan, F. R., & Keith, P. K. (2019). Exploring the potential of natural language processing to support microgenetic analysis of collaborative learning discussions. British Journal of Educational Technology, 50(6), 3047–3063.

    Article  Google Scholar 

  • Tadjine, Z., Oubahssi, L., Piau-Toffolon, C., & Iksal, S. (2015). A process using ontology to automate the operationalization of pattern-based learning scenarios. In International Conference on Computer Supported Education (pp. 444–461). Springer.

    Google Scholar 

  • Thüs, H., Chatti, M. A., Brandt, R., & Schroeder, U. (2015). Evolution of interests in the learning context data model. In Design for Teaching and Learning in a Networked World (pp. 479–484). Springer.

    Google Scholar 

  • Valderas, P., & Pelechano, V. (2011). A survey of requirements specification in model-driven development of web applications. ACM Transactions on the Web (TWEB), 5(2), 1–51.

    Article  Google Scholar 

  • Van Houwelingen, J., & Le Cessie, S. (1990). Predictive value of statistical models. Statistics in Medicine, 9(11), 1303–1325.

    Article  Google Scholar 

  • Williamson, B. (2019). Intimate data infrastructure: Emerging comparative methods of predictive analytics and psycho-informatics. Comparative Methodology in the Era of Big Data and Global Networks (pp. 59–75).

    Google Scholar 

  • Winslow, L., Benson, B., Chiu, K., Hanson, P., & Kratz, T. (2008). Vega: A flexible data model for environmental time series data. In Proceedings of the Environmental Information Management Conference (pp. 10–11).

    Google Scholar 

  • Worsley, M., Abrahamson, D., Blikstein, P., Grover, S., Schneider, B., & Tissenbaum, M. (2016). Situating multimodal learning analytics. In Proceedings of International Conference of the Learning Sciences, ICLS (Vol. 2, pp. 1346–1349).

    Google Scholar 

  • Xu, F., Wu, L., Thai, K., Hsu, C., Wang, W., & Tong, R. (2019). Mutla: A large-scale dataset for multimodal teaching and learning analytics. Preprint arXiv:1910.06078.

    Google Scholar 

  • Yassine, S., Kadry, S., & Sicilia, M. (2016). Learning analytics and learning objects repositories: Overview and future directions. In Learning, design, and technology: An international compendium of theory, research, practice, and policy (pp. 1–29).

    Google Scholar 

  • Zervas, P., Ardila, S. E. G., Fabregat, R., & Sampson, D. G. (2011). Tools for context-aware learning design and mobile delivery. In 2011 IEEE 11th International Conference on Advanced Learning Technologies (pp. 534–535). IEEE.

    Google Scholar 

Download references

Acknowledgements

This research has been partially funded by the European Union via the European Regional Development Fund and in the context of CEITER (Horizon 2020 Research and Innovation Programme, grant agreement no. 669074). Moreover, this research is partially funded by the European Regional Development Fund and the National Research Agency of the Spanish Ministry of Science, Innovations and Universities under project grants TIN2017-85179-C3-2-R and TIN2014-53199-C3-2-R, by the European Regional Development Fund and the Regional Ministry of Education of Castile and Leon under project grant VA257P18, and by the European Commission under project grant 588438-EPP-1-2017-1-EL-EPPKA2-KA.

Author information

Authors and Affiliations

  1. School of Digital Technologies, Tallinn University, Tallinn, Estonia

    Shashi Kant Shankar, María Jesús Rodríguez-Triana & Pankaj Chejara

  2. School of Educational Sciences, Tallinn University, Tallinn, Estonia

    Luis P. Prieto

  3. GSIC-EMIC Research Group, Universidad de Valladolid, Valladolid, Spain

    Adolfo Ruiz-Calleja

Authors
  1. Shashi Kant Shankar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. María Jesús Rodríguez-Triana
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Luis P. Prieto
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Adolfo Ruiz-Calleja
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Pankaj Chejara
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shashi Kant Shankar .

Editor information

Editors and Affiliations

  1. Norwegian University of Science and Technology, Trondheim, Norway

    Michail Giannakos

  2. University of Copenhagen, Copenhagen, Denmark

    Daniel Spikol

  3. DIPF, Frankfurt am Main, Germany

    Daniele Di Mitri

  4. Norwegian University of Science and Technology, Trondheim, Norway

    Kshitij Sharma

  5. New York University, New York, NY, USA

    Xavier Ochoa

  6. University of East London, London, UK

    Rawad Hammad

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

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: Giannakos, M., Spikol, D., Di Mitri, D., Sharma, K., Ochoa, X., Hammad, R. (eds) The Multimodal Learning Analytics Handbook. Springer, Cham. https://doi.org/10.1007/978-3-031-08076-0_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-08076-0_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-08075-3

  • Online ISBN: 978-3-031-08076-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation