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Related Experiment Video

Updated: Jul 10, 2026

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

Published on: June 20, 2020

Interactive Dashboards for Modeling-Based Education: Methods and Best Practices with Python and Plotly Dash.

Renato Lombardo¹

¹Dipartimento di Scienze e Tecnologie Biologiche, Chimiche e Farmaceutiche, Università degli Studi di Palermo, viale delle Scienze, Palermo, Italy. renato.lombardo@unipa.it.

Methods in Molecular Biology (Clifton, N.J.)

|July 9, 2026

Summary

Interactive digital dashboards built with Python and Plotly Dash enhance science education by visualizing complex concepts like ligand binding equilibria. This method promotes active learning and deeper understanding of molecular interactions for students and researchers.

Keywords:

Active learning Binding models Chemistry education Data visualization Interactive dashboards Molecular modeling Plotly Dash Python programming Web applications

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Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

Published on: June 20, 2020

Area of Science:

Educational Technology
Computational Chemistry
Biophysics

Background:

Traditional science education struggles with abstract and multiparameter concepts.
Static figures often fail to convey complex scientific models effectively.
Interactive digital tools can bridge the gap between symbolic representation and intuitive understanding.

Purpose of the Study:

To present a reproducible method for creating interactive dashboards using Python and Plotly Dash.
To illustrate the application of these dashboards in teaching ligand binding equilibria.
To demonstrate the pedagogical value of interactive visualizations in science education.

Main Methods:

Developed a browser-based interface integrating classical binding models (single site, multiple independent sites, heterogeneous sites, cooperative Hill binding).
Enabled users to manipulate parameters (constants, number of sites, cooperativity factors) and visualize immediate curve changes.
Utilized Python and the Plotly Dash framework for a lightweight, modular, and open-source implementation.

Main Results:

Dashboards effectively bridge symbolic formalism and intuitive understanding in science education.
Learners can actively explore parameter effects on saturation curves and compare models.
The method fosters active learning, curiosity, and engagement, reducing the cognitive gap.

Conclusions:

Interactive dashboards are powerful pedagogical tools for quantitative models in chemistry, biology, and physics.
The described method is adaptable to various scientific disciplines and can be internationalized.
These dashboards serve as flexible tools for both educational purposes and interactive data analysis for researchers and students.