Strivr’s Immersive Learning is a groundbreaking training methodology that combines Virtual Reality with advanced learning theory, data science and spatial design.

Years of academic research have led to practical application, as Strivr has trained over 1M employees. Thanks to strong partnerships with our customers, Strivr has access to unique insights and expertise to connect immersive data to real-world performance We can now offer solutions that combine the best of four unique science pillars.

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Learning Theory

  • Learning by doing

    Experiences help the brain build the right connections for learning and knowledge retention.

  • Critical frequency

    Repetition, interval and variations of training help go beyond short-term memorization for real learning.

  • Decision-making

    Dopamine, which is released less than a second after a correct response is made, makes learning stick. It’s not just about memorizing but making decisions that integrate the learning.

  • Desirable difficulties

    Challenges and errors enhance long-term retention and knowledge transfer.

  • Arousal and affect

    Emotional responses like stress, anxiety and surprise make training more memorable and impactful.

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Virtual Reality

  • Embodiment

    A full-body experience, increasing engagement and retention.

  • Perceptual fidelity

    Virtual interactions mimic the physical world’s, activating the same neural pathways in the brain.

  • On-demand repetition

    Training can occur as many times as needed for sufficient learning.

  • Real-time feedback

    Make decisions like in the real world, with direct impact on the experience.

  • Emotional fidelity

    Sense of presence, with a person or in a scene that creates emotional responses.

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Spatial Design

  • Familiarity

    Brings in cues from 2D to ease the transition from the real world to the virtual environment.

  • Forgiveness

    Enables users to explore without repercussions.

  • Visual hierarchy

    Purposeful use of depth, perspective, audio and interactions to capture user attention.

Data Science

  • Usage

    Training frequency, duration and completion.

  • Sentiment

    Qualitative feedback from participants.

  • Performance

    Completion of tasks and correct answers to evaluate proficiency.

  • Attention and engagement

    Where and how trainees pay attention, head movement, eye tracking, interactions and clicks.

  • Predictive analytics

    A combination of performance and engagement data mapped to real-world data to create a machine learning-based predictive model.

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