SMI Digital Classroom for Educational Research

Our multi-station eye tracking and behavioral data collection framework for researchers in educational and learning sciences.

Product and Benefits

Expand the spectrum of your single-station laboratory setups to the classroom and collect data in everyday educational scenarios from up to 40 students simultaneously.

The SMI Digital Classroom is the ideal framework for researchers in Educational Science, Psychology, Cognitive Science and Neuroscience with a strong focus on the experimental evaluation of key indicators of learning progress and success.

  • Collect eye, gaze and behavioral data from several students simultaneously
  • Identify and track key metrics of visual attention, learning and memory in the classroom
  • Test interactive scenarios and monitor students’ responses to educational interventions
  • Manage educational stimuli, student data and results in one centralized framework


The SMI Digital Classroom is an innovative solution for behavioral researchers aiming at collecting eye tracking and behavioral data from several students or entire classes:

  • Design dynamic experiments comprising multi-sensory educational stimuli including texts, images, videos, PDFs, dynamic websites, surveys and exams.
  • Assign experiments automatically to all students in the classroom.
  • Collect research-grade eye tracking and behavioral data.
  • Monitor students’ screen, visual attention and behavioral actions as well as data quality live during data collection and trigger automatic or manual educational interventions.
  • Analyze gaze and reading behavior using rich visualizations and comprehensive statistics.

Case Studies

Cognitive processes of successful multimedia learning

The Leibniz Knowledge Media Research Center Tübingen (Germany) is investigating cognitive processes of learners when confronted with educational material comprising several modalities. Eye tracking with the SMI RED250mobile is used to detect cognitive strategies of students during filtering, selecting, organizing and integrating information from text and images. Further, gaze replays of successful learners can be identified and shown to learners who still lack adequate strategies. Ultimately, the team aims to use gaze-based cognitive metrics for adaptive learning environments that flexibly adjust to the individual learning strategy of each student.

Visual expertise of teachers in the classroom

Expert teachers use classroom cues to interpret student engagement and motivation, and adjust educational strategies based on students’ attention states. Researchers at the Welten Institute, Open Universiteit (Netherlands) are examining how novice and expert educators differ in the perception and interpretation of problematic classroom scenes, where detecting relevant cues is crucial for preventing conflicts from escalating. Teachers are exposed to video replays of potentially escalating classroom scenarios. Eye tracking (SMI RED250mobile) and think aloud protocols are employed to determine which cues teachers direct their attention to. Data is analyzed using Areas of Interest (AOIs) and related Key Performance Indicators (KPIs) such as fixation dwell times, revisits, and skipped areas.


Identify key metrics of learning progress

The SMI Digital Classroom allows you to identify key metrics of students’ attention, learning and memory.

  • Understand learners’ individual mastery and comprehension levels.
  • Gain insights into students’ behavior, problem-solving and cognitive strategies when interacting with educational material.
  • Identify manifestations and predictors of challenging or disruptive behaviors.
  • Optimize learning environments for improved information uptake, memory consolidation and retrieval.

Test interactive scenarios

Investigate factors underlying successful learning in the classroom and how learning and behavior are modulated by policies and incentives, instructions and interventions.

  • Send messages to students and observe the effects of automatic behavioral triggers.
  • Monitor the efficacy of educational approaches and reinforcement strategies across the classroom or for specific student groups.
  • See how reward characteristics such as transparency, predictability and frequency take impact on students’ motivation.

Centralized study management

The SMI Digital Classroom framework automatizes the entire study workflow from experiment design to data analysis, allowing you to collect high-quality data faster and with less monitoring resources compared to traditional single-station laboratory setups:

  • Assignment of experiments to the classroom PCs does not require manual intervention.
  • Screen, gaze and behavioral data of all students as well as sensor status and data quality are monitored on a single PC, requiring only one operator for an entire classroom.
  • Results are merged automatically, ready at hand for your analysis.

Collect data in the classroom

Expand the spectrum of your single-station laboratory setups to the classroom and collect data in everyday educational scenarios from up to 40 students simultaneously.

  • Test interactive learning scenarios where students are provided with automatic or manual feedback based on their gaze patterns or behavior.
  • Draw from a rich repertoire of multimodal educational stimuli including texts, images, PDFs, videos, websites, questionnaires and quizzes.
  • Record eye tracking and behavior while students are actively interacting with objects and other social agents in educational games or VR environments.
  • Track students’ behavior while they organize their own course material.

Visualizations, metrics and analysis ready at hand

The SMI Digital Classroom provides a comprehensive spectrum of smart functions for data analysis, metrics extraction and visualization.

  • Extract saccades, fixations and blink-based metrics.
  • Evaluate advanced reading parameters such as direction of regressions, backtracks, look-aheads and leading saccades.
  • Visualize results using scan paths or heat maps, which can be exported as images or videos.
  • Generate aggregated statistics and visualizations for specific stimuli based on Areas of Interest (AOIs).
  • Compare different AOIs based on Key Performance Indicators (KPIs) such as fixation sequence and duration, entry and dwell times as well as return rate.
  • Export metrics with just a single click for consecutive analysis in third-party software.