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DELTA, Digital Education for Learning and Teaching Advances

Marina Marchisio Conte - PI

Marina Marchisio Conte
Biographical sketch

Full Professor, Department of Molecular Biotechnology and Health Sciences, University of Turin, Italy

team
Main group members

  • Alberto Conte Emeritus Professor 

  • Sergio Rabellino Res. technician, ICT Services

  • Alice Barana Assistant Professor (RTDb)

  • Matteo Sacchet Research Fellow (RTDa)

  • Francesco Floris Adm. technician and Phd Student

  • Cecilia Fissore, Fabio Roman Post-doctoral Fellows

  • Daniela Salusso CEL (English Language Collaborator)

  • Giulia Boetti, Valeria Fradiante Phd Students

  • Sara Omegna Postgraduate Fellow

Research activity

Figure 1

The main research lines include: i) Integrated Digital Learning Environments and Learning Analytics in Open Educational Practices ii) Adaptive, inclusive and data driven digital methodologies for learning and teaching scientific disciplines, iii) Computational Mathematical Models for Biotechnologies, iv) Biostatistical Methods applied in Biotechnology and Medicine. In today’s fast-paced world, education is evolving at a rapid rate, driven by advancements in technology. Digital tools have become an integral part of modern education, transforming teaching methodologies and empowering educators to create engaging and interactive learning experiences for their students. A key element of Digital Education is the Digital Learning Environment (DLE), which can be defined as a learning ecosystem in which teaching, learning and the development of competence are fostered. It is composed of a human component, a technological component, and the interrelationships between the two. The DLEs we mainly focus on are composed of a Moodle platform (developed by the Department of Computer Science - ICT Services of the University of Turin) integrated with an Advanced Computing Environment (ACE), an Automatic Assessment System (AAS) and a web conference system. The research group studies the interactions occurring among the components of a DLE during learning activities in online, blended, hybrid, and classroom-based modalities (Fig. 1). 

Figure 2

In a human-centred approach, at the centre of the DLE, there is the learning community, composed of students, teachers, and peers: they can interact with the DLE through its functions receiving and sending information. “In Figure 1”: The blue dotted arrows in Figure 1 represent the interactions between the community and the digital systems that occur through human actions, such as reading, receiving, inserting, providing, digiting. The continuous double-ended orange arrows represent the interactions among students, teachers, and peers, which in classroom-based settings can be verbal while in online settings can be mediated by the technology. The research group develops innovative teaching and learning methodologies enhanced by technologies, such as: automatic formative assessment, problem posing and solving, adaptive teaching and learning, gamification, game-based learning, data driven learning, collaborative learning, learning by doing and so on. For example, the research group has developed and tested a model to design automatic formative assessment activities in a DLE, with immediate and interactive feedback. Students are guided step by step in solving tasks, individually or in groups, having multiple attempts to answer. 

We use an Advanced Computing Environment to model various phenomena in biology and medicine, such as population growth and the spread of viruses (Fig. 3). An ACE is a system that can perform numerical and symbolic computations, build graphical representations in 2 and 3 dimensions, and create mathematical simulations through interactive components. Mathematical modelling is the process of translation between the real world (including nature, society, everyday life and other scientific disciplines) and Mathematics in both directions. One of the aims of STEM education is to encourage students to develop models: representations that can describe and explain the phenomena of reality and predict the course of experiments, mechanisms and processes. Modelling is a valid approach to many different problems and therefore a common ground between the different STEM disciplines. Thanks to technology, Educational Data, which is continuously updated and growing, has now become Big Data and, to make the most of it, we use Learning Analytics (LA) to analyse it, interpret it, and derive enough information to make decisions. LA can be defined as: “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs”. LA can inform not only teachers and researchers, but also students about their achievements, thus letting them keep control of their learning path. 

Our future research intends to harness new technologies, such as AI, Deep Learning, Augmented Reality and Large Language Models to innovate STEM learning and teaching, fostering the teaching methodologies mentioned above. For instance, a DLE integrated with AI tools can give real-time information on student learning. This can enable teachers to adapt their teaching according to students’ needs, and students to have personalized learning. AI tools ofer new ways of teaching to better engage students, and Large Language Models have opened new horizons in digital education and educational research. Moreover, we plan to use these technologies also in order to enhance the development of computational models and biostatistical methods, thanks to the tools’ capabilities in assisting these processes.

  • European Research Project “Military Gender Studies” (MGS) Erasmus+ KA2. Grant 2020-1-PT01- KA203-078544. 

  • European Research Project “DIGItal COmpetencies for improving security and Defence Education Programme” (DIGICODE). Erasmus+ KA2. 2020-1-PL01- KA226-096192. 

  • European Research Project “Interdisciplinary Education and Training on Hybrid Warfare” (HYBRID). Erasmus+ KA2. KA220-HED-B9458C8A. 

  • European Research Project “Developing Competences and Innovative Designs for International Virtual and Blended Modalities” (INVITE). Erasmus+ KA2. 2021-1-DK01-KA220-HED-000031145. 

  • European Research Project “Time-Spatial-Linguistic Teaching and Learning Travel Machine platform for Connecting UNITA” (CONNECT- UNITA). Erasmus+ KA2. KA220-HED-E60423AD. 

  • European Research Project “DIgital Mathematics Applied in defence and Security education” (DIMAS), Erasmus+ KA2. 2023-1-BG01-KA220-HED-000156664.

  • Barana, A., Boetti, G., Marchisio, M., Perrotta, A., & Sacchet, M. (2023). Investigating the Knowledge Co-Construction Process in Homogeneous Ability Groups during Computational Lab Activities in Financial Mathematics. Sustainability, 15(18), 13466. doi: 10.3390/su151813466 

  • Fissore, C., Floris, F., Marchisio, M., & Rabellino, S. (2023). Learning analytics to monitor and predict student learning processes in problem solving activities during an online training. IEEE 47th Annual Computers, Sofware, and Applications Conference (COMPSAC), Torino, Italy, 2023, pp. 481-489 , doi: 10.1109/ COMPSAC57700.2023.00070 

  • Barana, A., Boetti, G., & Marchisio, M. (2022). Self-Assessment in the Development of Mathematical Problem-Solving Skills. Education Sciences, 12(2), 81. doi: 10.3390/educsci12020081 

  • Marchisio, M., Remogna, S., Roman, F., & Sacchet, M. (2022). Teaching mathematics to non-mathematics majors through problem solving and new technologies. Education Sciences, 12(1), 34. doi: 10.3390/ educsci12010034 

  • Corino, E., Fissore, C., & Marchisio, M. (2022). Data Driven Learning activities within a Digital Learning Environment to study the specialized language of Mathematics. In 2022 IEEE 46th Annual Computers, Sofware, and Applications Conference (COMPSAC) pp. 167-176. doi: 10.1109/COMPSAC54236.2022.00032 

  • Barana, A., & Marchisio, M. (2021). A Model for the Analysis of the Interactions in a Digital Learning Environment During Mathematical Activities. In International Conference on Computer Supported Education pp. 429-448. Cham: Springer International Publishing. doi: 10.1007/978-3-031-14756-2_21 

  • Barana, A., Marchisio, M., & Sacchet, M. (2021). Interactive feedback for learning mathematics in a digital learning environment. Education Sciences, 11(6), 279. doi: 10.3390/educsci11060279 

  • Barana, A., Fissore, C., & Marchisio, M. (2020). Automatic Formative Assessment Strategies for the Adaptive Teaching of Mathematics. In International Conference on Computer Supported Education pp. 341-365. Cham: Springer International Publishing. doi: 10.1007/978-3-030-86439-2_18 

  • Barana, A., Conte, A., Fissore, C., Marchisio, M., & Rabellino, S. (2019). Learning analytics to improve formative assessment strategies. JE-LKS. Journal of e-learning and knowledge society, 15(3), pp. 75-88. doi: 10.20368/1971-8829/1135057 

  • Marchisio, M., Barana, A., Fioravera, M., Rabellino, S., & Conte, A. (2018). A model of formative automatic assessment and interactive feedback for STEM. In 2018 IEEE 42nd Annual Computer Sofware and Applications Conference (COMPSAC) Vol. 1, pp. 1016-1025. IEEE. doi: 10.1109/COMPSAC.2018.00178 

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