Current and future multimodal learning analytics data challenges

Multimodal Learning Analytics (MMLA) captures, integrates and analyzes learning traces from different sources in order to obtain a more holistic understanding of the learning process, wherever it happens. MMLA leverages the increasingly widespread availability of diverse sensors, highfrequency data collection technologies and sophisticated machine learning and artificial intelligence techniques. The aim of this workshop is twofold: first, to expose participants to, and develop, different multimodal datasets that reflect how MMLA can bring new insights and opportunities to investigate complex learning processes and environments; second, to collaboratively identify a set of grand challenges for further MMLA research, built upon the foundations of previous workshops on the topic

Insights from Learning Analytics for Hands-On Cloud Computing Labs in AWS

[EN] Cloud computing instruction requires hands-on experience with a myriad of distributed computing services from a public cloud provider. Tracking the progress of the students, especially for online courses, requires one to automatically gather evidence and produce learning analytics in order to further determine the behavior and performance of students. With this aim, this paper describes the experience from an online course in cloud computing with Amazon Web Services on the creation of an open-source data processing tool to systematically obtain learning analytics related to the hands-on activities carried out throughout the course. These data, combined with the data obtained from the learning management system, have allowed the better characterization of the behavior of students in the course. Insights from a population of more than 420 online students through three academic years have been assessed, the dataset has been released for increased reproducibility. The results corroborate that course length has an impact on online students dropout. In addition, a gender analysis pointed out that there are no statistically significant differences in the final marks between genders, but women show an increased degree of commitment with the activities planned in the course.This research was funded by the Spanish "Ministerio de Economia, Industria y Competitividad through grant number TIN2016-79951-R (BigCLOE)", the "Vicerrectorado de Estudios, Calidad y Acreditacion" of the Universitat Politecnica de Valencia (UPV) to develop the PIME B29 and PIME/19-20/166, and by the Conselleria d'Innovacio, Universitat, Ciencia i Societat Digital for the project "CloudSTEM" with reference number AICO/2019/313.Moltó, G.; Naranjo-Delgado, DM.; Segrelles Quilis, JD. (2020). Insights from Learning Analytics for Hands-On Cloud Computing Labs in AWS. Applied Sciences. 10(24):1-13. https://doi.org/10.3390/app10249148S1131024Motiwalla, L., Deokar, A. V., Sarnikar, S., & Dimoka, A. (2019). Leveraging Data Analytics for Behavioral Research. Information Systems Frontiers, 21(4), 735-742. doi:10.1007/s10796-019-09928-8Siemens, G., & Baker, R. S. J. d. (2012). Learning analytics and educational data mining. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge - LAK ’12. doi:10.1145/2330601.2330661Blikstein, P. (2013). Multimodal learning analytics. Proceedings of the Third International Conference on Learning Analytics and Knowledge - LAK ’13. doi:10.1145/2460296.2460316Hewson, E. R. F. (2018). Students’ Emotional Engagement, Motivation and Behaviour Over the Life of an Online Course: Reflections on Two Market Research Case Studies. Journal of Interactive Media in Education, 2018(1). doi:10.5334/jime.472Kahan, T., Soffer, T., & Nachmias, R. (2017). Types of Participant Behavior in a Massive Open Online Course. The International Review of Research in Open and Distributed Learning, 18(6). doi:10.19173/irrodl.v18i6.3087Cross, S., & Whitelock, D. (2016). Similarity and difference in fee-paying and no-fee learner expectations, interaction and reaction to learning in a massive open online course. Interactive Learning Environments, 25(4), 439-451. doi:10.1080/10494820.2016.1138312Charleer, S., Klerkx, J., & Duval, E. (2014). Learning Dashboards. Journal of Learning Analytics, 1(3), 199-202. doi:10.18608/jla.2014.13.22Worsley, M. (2012). Multimodal learning analytics. Proceedings of the 14th ACM international conference on Multimodal interaction - ICMI ’12. doi:10.1145/2388676.2388755Spikol, D., Prieto, L. P., Rodríguez-Triana, M. J., Worsley, M., Ochoa, X., Cukurova, M., … Ringtved, U. L. (2017). Current and future multimodal learning analytics data challenges. Proceedings of the Seventh International Learning Analytics & Knowledge Conference. doi:10.1145/3027385.3029437Ochoa, X., Worsley, M., Weibel, N., & Oviatt, S. (2016). Multimodal learning analytics data challenges. Proceedings of the Sixth International Conference on Learning Analytics & Knowledge - LAK ’16. doi:10.1145/2883851.2883913Aguilar, J., Sánchez, M., Cordero, J., Valdiviezo-Díaz, P., Barba-Guamán, L., & Chamba-Eras, L. (2017). Learning analytics tasks as services in smart classrooms. Universal Access in the Information Society, 17(4), 693-709. doi:10.1007/s10209-017-0525-0Lu, O. H. T., Huang, J. C. H., Huang, A. Y. Q., & Yang, S. J. H. (2017). Applying learning analytics for improving students engagement and learning outcomes in an MOOCs enabled collaborative programming course. Interactive Learning Environments, 25(2), 220-234. doi:10.1080/10494820.2016.1278391Drachsler, H., & Kalz, M. (2016). The MOOC and learning analytics innovation cycle (MOLAC): a reflective summary of ongoing research and its challenges. Journal of Computer Assisted Learning, 32(3), 281-290. doi:10.1111/jcal.12135Ruiperez-Valiente, J. A., Munoz-Merino, P. J., Gascon-Pinedo, J. A., & Kloos, C. D. (2017). Scaling to Massiveness With ANALYSE: A Learning Analytics Tool for Open edX. IEEE Transactions on Human-Machine Systems, 47(6), 909-914. doi:10.1109/thms.2016.2630420Er, E., Gómez-Sánchez, E., Dimitriadis, Y., Bote-Lorenzo, M. L., Asensio-Pérez, J. I., & Álvarez-Álvarez, S. (2019). Aligning learning design and learning analytics through instructor involvement: a MOOC case study. Interactive Learning Environments, 27(5-6), 685-698. doi:10.1080/10494820.2019.1610455Tabaa, Y., & Medouri, A. (2013). LASyM: A Learning Analytics System for MOOCs. International Journal of Advanced Computer Science and Applications, 4(5). doi:10.14569/ijacsa.2013.040516Shorfuzzaman, M., Hossain, M. S., Nazir, A., Muhammad, G., & Alamri, A. (2019). Harnessing the power of big data analytics in the cloud to support learning analytics in mobile learning environment. Computers in Human Behavior, 92, 578-588. doi:10.1016/j.chb.2018.07.002Klašnja-Milićević, A., Ivanović, M., & Budimac, Z. (2017). Data science in education: Big data and learning analytics. Computer Applications in Engineering Education, 25(6), 1066-1078. doi:10.1002/cae.21844Logglyhttps://www.loggly.com/Molto, G., & Caballer, M. (2014). On using the cloud to support online courses. 2014 IEEE Frontiers in Education Conference (FIE) Proceedings. doi:10.1109/fie.2014.7044041Caballer, M., Blanquer, I., Moltó, G., & de Alfonso, C. (2014). Dynamic Management of Virtual Infrastructures. Journal of Grid Computing, 13(1), 53-70. doi:10.1007/s10723-014-9296-5AWS CloudTrailhttps://aws.amazon.com/cloudtrail/Amazon Simple Storage Service (Amazon S3)http://aws.amazon.com/s3/Naranjo, D. M., Prieto, J. R., Moltó, G., & Calatrava, A. (2019). A Visual Dashboard to Track Learning Analytics for Educational Cloud Computing. Sensors, 19(13), 2952. doi:10.3390/s19132952Baldini, I., Castro, P., Chang, K., Cheng, P., Fink, S., Ishakian, V., … Suter, P. (2017). Serverless Computing: Current Trends and Open Problems. Research Advances in Cloud Computing, 1-20. doi:10.1007/978-981-10-5026-8_1Zimmerman, D. W. (1987). Comparative Power of StudentTTest and Mann-WhitneyUTest for Unequal Sample Sizes and Variances. The Journal of Experimental Education, 55(3), 171-174. doi:10.1080/00220973.1987.10806451Kruskal, W. H., & Wallis, W. A. (1952). Use of Ranks in One-Criterion Variance Analysis. Journal of the American Statistical Association, 47(260), 583-621. doi:10.1080/01621459.1952.10483441Voyer, D., & Voyer, S. D. (2014). Gender differences in scholastic achievement: A meta-analysis. Psychological Bulletin, 140(4), 1174-1204. doi:10.1037/a0036620Ellemers, N., Heuvel, H., Gilder, D., Maass, A., & Bonvini, A. (2004). 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2nd Crossmmla: Multimodal learning analytics across physical and digital spaces

© 2018 CEUR-WS. All Rights Reserved. Students’ learning is ubiquitous. It happens wherever the learner is rather than being constrained to a specific physical or digital learning space (e.g. the classroom or the institutional LMS respectively). A critical question is: how to integrate and coordinate learning analytics to provide continued support to learning across physical and digital spaces? CrossMMLA is the successor to the Learning Analytics Across Spaces (CrossLAK) and MultiModal Learning Analytics (MMLA) series of workshops that were merged in 2017 after successful cross-pollination between the two communities. Although it may be said that CrossLAK and MMLA perspectives follow different philosophical and practical approaches, they both share a common aim. This aim is: deploying learning analytics innovations that can be used across diverse authentic learning environments whilst learners feature various modalities of interaction or behaviour

Learning Sciences Beyond Cognition: Exploring Student Interactions in Collaborative Problem Solving

Composed of insightful essays from top figures in their respective fields, the book also shows how a thorough understanding of this critical discipline all but ensures better decision making when it comes to education

Social Learning Analytics in Computer-Supported Collaborative Learning Environments: A Systematic Review of Empirical Studies

Social learning analytics (SLA) is a promising approach for identifying students’ social learning processes in computer-supported collaborative learning (CSCL) environments. To identify the main characteristics of SLA, gaps and future opportunities for this emerging approach, we systematically identified and analyzed 36 SLA-related studies conducted between 2011 and 2020. We focus on SLA implementation and methodological characteristics, educational focus, and the studies’ theoretical perspectives. The results show the predominance of SLA in formal and fully online settings with social network analysis (SNA) a dominant analytical technique. Most SLA studies aimed to understand students’ learning processes and applied the social constructivist perspective as a lens to interpret students’ learning behaviors. However, (i) few studies involve teachers in developing SLA tools, and rarely share SLA visualizations with teachers to support teaching decisions; (ii) some SLA studies are atheoretical; and (iii) the number of SLA studies integrating more than one analytical approach remains limited. Moreover, (iv) few studies leveraged innovative network approaches (e.g., epistemic network analysis, multimodal network analysis), and (v) studies rarely focused on temporal patterns of students’ interactions to assess how students’ social and knowledge networks evolve over time. Based on the findings and the gaps identified, we present methodological, theoretical and practical recommendations for conducting research and creating tools that can advance the field of SLA

The Big Five:Addressing Recurrent Multimodal Learning Data Challenges

The analysis of multimodal data in learning is a growing field of research, which has led to the development of different analytics solutions. However, there is no standardised approach to handle multimodal data. In this paper, we describe and outline a solution for five recurrent challenges in the analysis of multimodal data: the data collection, storing, annotation, processing and exploitation. For each of these challenges, we envision possible solutions. The prototypes for some of the proposed solutions will be discussed during the Multimodal Challenge of the fourth Learning Analytics & Knowledge Hackathon, a two-day hands-on workshop in which the authors will open up the prototypes for trials, validation and feedback

Multimodal Challenge: Analytics Beyond User-computer Interaction Data

This contribution describes one the challenges explored in the Fourth LAK Hackathon. This challenge aims at shifting the focus from learning situations which can be easily traced through user-computer interactions data and concentrate more on user-world interactions events, typical of co-located and practice-based learning experiences. This mission, pursued by the multimodal learning analytics (MMLA) community, seeks to bridge the gap between digital and physical learning spaces. The “multimodal” approach consists in combining learners’ motoric actions with physiological responses and data about the learning contexts. These data can be collected through multiple wearable sensors and Internet of Things (IoT) devices. This Hackathon table will confront with three main challenges arising from the analysis and valorisation of multimodal datasets: 1) the data collection and storing, 2) the data annotation, 3) the data processing and exploitation. Some research questions which will be considered in this Hackathon challenge are the following: how to process the raw sensor data streams and extract relevant features? which data mining and machine learning techniques can be applied? how can we compare two action recordings? How to combine sensor data with Experience API (xAPI)? what are meaningful visualisations for these data

Proyecto Docente e Investigador, Trabajo Original de Investigación y Presentación de la Defensa, preparado por Germán Moltó para concursar a la plaza de Catedrático de Universidad, concurso 082/22, plaza 6708, área de Ciencia de la Computación e Inteligencia Artificial

Este documento contiene el proyecto docente e investigador del candidato Germán Moltó Martínez presentado como requisito para el concurso de acceso a plazas de Cuerpos Docentes Universitarios. Concretamente, el documento se centra en el concurso para la plaza 6708 de Catedrático de Universidad en el área de Ciencia de la Computación en el Departamento de Sistemas Informáticos y Computación de la Universitat Politécnica de València. La plaza está adscrita a la Escola Técnica Superior d'Enginyeria Informàtica y tiene como perfil las asignaturas "Infraestructuras de Cloud Público" y "Estructuras de Datos y Algoritmos".También se incluye el Historial Académico, Docente e Investigador, así como la presentación usada durante la defensa.Germán Moltó Martínez (2022). Proyecto Docente e Investigador, Trabajo Original de Investigación y Presentación de la Defensa, preparado por Germán Moltó para concursar a la plaza de Catedrático de Universidad, concurso 082/22, plaza 6708, área de Ciencia de la Computación e Inteligencia Artificial. http://hdl.handle.net/10251/18903