Hector Research Institute of Education Sciences and Psychology

INFER

Intelligent Feedback System for Observing Videotaped Classroom Situations

Symbolbild: Videoaufnahme einer Unterrichtsstunde

Our Goal

The INFER project develops and tests intelligent feedback systems for professional teaching perception for teacher training students and teachers. Using natural language processing (NLP) and machine learning (ML) methods, the systems provide immediate feedback on the perception of teaching quality, which is recorded in written reflection texts.

The Challenge

The ability to observe lessons and reflect on them in writing is a central component of high-quality teacher training. Analyzing lessons recorded on video enables the linking of conceptual professional knowledge with authentic practice.

However, the systematic evaluation of these written reflection texts is very resource-intensive and prevents timely feedback to students and teachers. INFER addresses this challenge.

Our Questions

The project pursues three objectives:

  1. Automated Assessment
    Development and validation of AI models for assessing the quality of student video interpretations in written reflection texts based on the dimensions of description, explanation, and prediction.
  2. Adaptive Feedback
    Implementation of a web-based interface that provides individual, direct feedback and promotes digital self-determination among students.
  3. Scaling and Transfer
    Integration of INFER into teacher training at several universities and preparation for broader use by students and teachers.

Intervention Design

The project comprises three central phases:

  1. Phase 1 (AI Development & Validation):
    Training and validating ML models on student and teacher reflections of both staged and authentic classroom videos, comparing automated classifications with expert ratings.
  2. Phase 2 (Experimental Testing):
    Embedding INFER into a classroom management seminar at the University of Tübingen. Conducting a randomized controlled trial (RCT) to compare outcomes with traditional video-based seminars.
  3. Phase 3 (Scaling & Implementation):
    Transferring INFER to five German-speaking universities (Tübingen, LMU/TUM, Kiel, Jena, Basel). Providing manuals, training materials, and open-access resources.

Timeline

2021-2025
Preliminary work, e.g., data collection
2025-2026
System development and validation
2026
Experimental testing (pilot study)
2027
Public beta version
2027-2028
Scaling & Implementation

Project participants

The project is being conducted at the Hector Research Institute of Education Sciences and Psychology at the University of Tübingen.

Project management (Germany): 

National partners:

International partners:

  • Dr. Ha Nguyen, School of Education, University of North Carolina at Chapel Hill

The development of the INFER app is based on seed funding from the Universities of Tübingen and UNC-Chapel Hill (2025–2026).

In addition, national funding applications (e.g., DFG, Stiftung Innovation in der Hochschullehre) are planned to conduct basic research and secure long-term funding for scaling and implementation.