# Modelling the Balance Axiom in Flow Theory: A Physiological and Computational Approach in STEAM Education

## TL;DR

This study explores how physiological data can help teachers guide students into a state of flow during STEAM education, improving learning efficiency and validating a key theory.

## Contribution

The paper introduces a physiological flow advisory system and provides new mathematical support for the balance axiom in Flow Theory.

## Key findings

- Physiological guidance helps reach the Zone of Proximal Development more quickly.
- Nonlinear analysis supports the balance axiom in Flow Theory.
- Group-based nonlinear analysis resolves contradictions in Flow Theory.

## Abstract

This paper addresses the axiom of balance in Flow Theory from a physiological-and-group-based approach by a quasi-experimental study using mixed methods across two action–research cycles, each divided into pre-test, intervention, and post-test phases. The study involved 56 students in two control and two experimental groups attending robotics and design STEAM courses in natural settings, wearing Polar H10 bands. Each group participated in nine one-hour sessions, with the same instructor. While flow in control groups was measured with intuition-based teacher actions, in experimental groups the teacher received support from a synchronous physiological flow advisory system. Data from these groups were analysed using nonlinear techniques, finding preliminary evidence that suggests (1) more quickly reaching of the Zone of Proximal Development when the teacher has physiological guidance, (2) mathematical physiologically-based support for the axiom of balance of Flow Theory, and (3) nonlinear analysis in group contexts offer quantification to the previously found contradictions in Flow Theory. Moreover, these findings propose new hypotheses and potential redefinitions in Flow Theory.