Project INF

Project INF is responsible for the information infrastructure and research data management for all SHARP projects.

Summary
Participants
Goal
Research Questions
Methodology
Role Within the Collaborative Research Center
Publications

Summary

The Information Infrastructure Project INF is responsible for developing and maintaining the SHARP-specific technical and data infrastructure that supports the collaborative research activities of the CRC. The project provides expertise, tools, and technological solutions to enable the state-of-the-art implementation of personalised simulations supported by artificial intelligence for experimental studies conducted within the SHARP research projects. In addition, this project will offer expertise and technology to establish and maintain an advanced research data management (RDM) based on the FAIR principles. The project also promotes data science literacy within the CRC by supporting researchers with training, guidance, and best practices in data handling, analysis, and computational methods.

Participants

Principal Investigators

Prof. Dr. Enkelejda Kasneci

TUM

Human-Centered Technologies for Learning

enkelejda.kasneci@tum.de

C01 C03 INF
Prof. Dr. Matthias Stadler

LMU University Hospital

Medical Education and Training Research

matthias.stadler@med.uni-muenchen.de

A03 A06 B03 M INF
Dr. Stephan Hachinger

LRZ

Research Data Management

stephan.hachinger@lrz.de

INF

Research associates

Dr. Constanze Richters

LMU University Hospital

Medical Education

constanze.richters@med.uni-muenchen.de

A01 M INF
Smaran Chaudhary

LRZ

smaran.chaudhary@lrz.de

INF
Yifan Ding

TUM

Human-Centered Technologies for Learning

yifan.ding@tum.de

INF
Sezen Perçin

TUM

Human-Centered Technologies for Learning

sezen.percin@tum.de

INF

Goal

The Information Infrastructure Project INF is responsible for developing the SHARP-specific part of the technical and data infrastructure. It provides expertise and technology for state-of-the-art AI-supported technical implementations of personalised simulations in SHARP research projects and establishes and maintains advanced FAIR-based research data management (RDM). Finally, it will facilitate the data science literacy of researchers in the CRC.

Research Questions

How to create and interpret learner models utilizing AI models, eye tracking and log data while emphasising the importance of personalisation?
How can automated processes, evaluation, and adaptation of simulations be implemented based on real-time learner data to dynamically respond to learners’ needs?
How to create a unified, collaborative infrastructure complementing SHARP’s competitive research?
How to establish a comprehensive, semi-automated data lifecycle supporting FAIR RDM steps from the write up of data management plans over warm (meta-) data management to data publication and archival?

Methodology

The project aims to develop data analytics and management methods together with IT infrastructure for SHARP. To create learner models that are interpretable, the project methodologically uses eye-tracking tools utilizing gaze and log data and interpretation methods to analyse the interaction of the student with educational content and enhance the personalisation process. Moreover, the project will support other projects in the technical implementation of automated analyses enabling dynamic adaptations of simulations based on learner models during data processing. To handle incoming data and data products that will be published and archived, the project also provides specialized hardware and software infrastructure for an efficient and FAIR RDM in the setting of SHARP.

Role Within the Collaborative Research Center

As the central project for information infrastructure and research data management within the CRC SHARP, INF has established close cooperation and collaboration with all subprojects.

Publications

2024

Hou, R., Fütterer, T., Bühler, B., Bozkir, E., Gerjets, P., Trautwein, U., & Kasneci, E. (2024)

Automated assessment of encouragement and warmth in classrooms leveraging multimodal emotional features and ChatGPT

The 25th International Conference on Artificial Intelligence in Education (pp. 101–112)

https://link.springer.com/chapter/10.1007/978-3-031-64302-6_5

INF
Kasneci, E., Gao, H., Özdel, S., Maquiling, V., Thaqi, E., Lau, C., Rong, Y., Kasneci, G., & Bozkir, E. (2024)

Introduction to eye tracking: A hands-on tutorial for students and practitioners

arXiv

https://arxiv.org/abs/2404.15435

INF

2023

Stadler, M., Brandl, L., & Greiff, S. (2023)

20 years of interactive tasks in large‐scale assessments: Process data as a way towards sustainable change?

Journal of Computer Assisted Learning, 39(6), 1852-1859

https://onlinelibrary.wiley.com/doi/10.1111/jcal.12847

INF M
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., Stadler, M., ... & Kasneci, G (2023)

ChatGPT for good? On opportunities and challenges of large language models for education

Learning & Individual Differences, 103, 102274

https://www.sciencedirect.com/science/article/abs/pii/S1041608023000195?via%3Dihub

INF
Rong, Y., Leemann, T., Nguyen, T., Fiedler, L., & Qian, P. (2023)

Towards human-centered explainable AI: A survey of user studies for model explanations

IEEE Transactions on Pattern Analysis and Machine Intelligence, 46, 2104–2122

https://ieeexplore.ieee.org/document/10316181

INF
Richters, C., Stadler, M., Radkowitsch, A., Schmidmaier, R., Fischer, M. R., & Fischer, F. (2023)

Who is on the right track? Behavior-based prediction of diagnostic success in a collaborative diagnostic reasoning simulation

Large-Scale Assessments in Education, 11(1), Article 3

https://link.springer.com/article/10.1186/s40536-023-00151-1

B01 INF M

2022

Hachinger, S., Golasowski, M., Martinovič, J., Hayek, M., García-Hernández, R. J., Slaninová, K., Levrier, M., Scionti, A., Donnat, F., Vitali, G., Magarielli, D., Goubier, T., Parodi, A., Harsh, P., Dees, A., & Terzo, O. (2022)

Leveraging high-performance computing and cloud computing with unified big-data workflows: The LEXIS project

E. Curry, S. Auer, A. J. Berre, A. Metzger, M. S. Perez, & S. Zillner (Eds.), Technologies and Applications for Big Data Value (pp. 105–127). Springer, Cham

https://link.springer.com/chapter/10.1007/978-3-030-78307-5_8

INF

2021

Gao, H., Bozkir, E., Hasenbein, J., Göllner R., & Kasneci, E. (2021)

Digital transformations of classrooms in virtual reality

Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 1–10

https://dl.acm.org/doi/10.1145/3411764.3445596

INF
Sümer, Ö., Goldberg, P., D'Mello, S., Gerjets, P., Trautwein, U., & Kasneci, E. (2021)

Multimodal engagement analysis from facial videos in the classroom

IEEE Transactions on Affective Computing, 14(2)

https://ieeexplore.ieee.org/document/9613750

INF
Stadler, M., Iliescu, D., & Greiff, S. (2021)

Validly authentic: Some recommendations to researchers using simulations in psychological assessment [Editorial]

European Journal of Psychological Assessment, 37(6), 419–422

https://econtent.hogrefe.com/doi/10.1027/1015-5759/a000686

INF

Summary

Participants

Principal Investigators

Prof. Dr. Enkelejda Kasneci

Prof. Dr. Matthias Stadler

Dr. Stephan Hachinger

Research associates

Dr. Constanze Richters

Smaran Chaudhary

Yifan Ding

Sezen Perçin

Goal

Research Questions

Methodology

Role Within the Collaborative Research Center

Publications

2024

Automated assessment of encouragement and warmth in classrooms leveraging multimodal emotional features and ChatGPT

Introduction to eye tracking: A hands-on tutorial for students and practitioners

2023

20 years of interactive tasks in large‐scale assessments: Process data as a way towards sustainable change?

ChatGPT for good? On opportunities and challenges of large language models for education

Towards human-centered explainable AI: A survey of user studies for model explanations

Who is on the right track? Behavior-based prediction of diagnostic success in a collaborative diagnostic reasoning simulation

2022

Leveraging high-performance computing and cloud computing with unified big-data workflows: The LEXIS project

2021

Digital transformations of classrooms in virtual reality

Multimodal engagement analysis from facial videos in the classroom

Validly authentic: Some recommendations to researchers using simulations in psychological assessment [Editorial]