This title slide features the presentation titled "Simulation of Right-to-Disconnect Policy for Reducing Technostress in High School Students." It credits authors Dr. Nandana Khare and Nalin Khare, with their affiliations listed as [Institution Names].

Dr. Nandana Khare & Nalin Khare

Affiliations: [Institution Names]

Source: ICCCA 2025

This introduction slide highlights rising digital overload in schools and technostress impacting student well-being. Its objective is to simulate the effects of a Right-to-Disconnect (RTD) policy.

Introduction

• Rising digital overload in schools
• Technostress impacting student well-being
• Objective: Simulate Right-to-Disconnect (RTD) policy effects

Existing approaches include extensive workplace technostress research (e.g., Tarafdar et al., 2007) and Right-To-Disconnect (RTD) laws in France (2017) and Portugal (2021) that curb after-hours connectivity to boost well-being. The slide identifies a research gap in high school students amid edtech overload, with no tailored RTD policies or simulations addressing adolescent technostress, academic pressure, and sleep disruption.

Existing Approaches / Related Work

Existing approaches lack student-level Right-to-Disconnect (RTD) policies and overrely on empirical data and surveys. They also neglect theoretical simulation models and high school technostress dynamics.

Problems in Existing Approaches

• Absence of student-level Right-to-Disconnect (RTD) policies
• Overreliance on empirical data and surveys
• Lack of theoretical simulation models
• Neglect of high school technostress dynamics

Source: ICCCA-2025 Presentation

The Proposed Methodology slide depicts a three-layer workflow: Policy Layer defines RTD intervention signals (e.g., cutoff times), feeding into the Behavioural Layer's probabilistic models of student behaviors (e.g., reduced LMS checks). The Outcome Layer then computes metrics like technostress reduction and sleep improvements, comparing baseline (S₀) and intervention (S₁) scenarios.

Proposed Methodology

{ "headers": [ "Layer", "Key Equations & Parameters", "Description & Outputs" ], "rows": [ [ "Policy Layer", "RTD policy rules (e.g., notification cutoff times, disconnection hours)", "Defines intervention signals → Input to Behavioural Layer" ], [ "Behavioural Layer", "Probabilistic models: P(LMS check) = f(policy, habits); Academic pressure response functions", "Simulates student behaviors (e.g., reduced checking frequency) → Input to Outcome Layer" ], [ "Outcome Layer", "Technostress = β₁·overload + β₂·pressure + ε; Sleep regularity = g(behavioral changes)", "Computes metrics: technostress reduction, sleep improvement, etc. (S₀ vs. S₁ scenarios)" ] ] }

Source: RTD Simulation Model (RTD-SM): Key equations, parameters Three-layer architecture: Policy → Behavioural → Outcome layers

The "Results and Discussion" slide showcases key stats from RTD simulation, with technostress reduced by 36.6%, nightly LMS checks down 85.2%, and academic pressure lowered 28.5%. Sleep regularity also improved by 23.6% post-RTD.

Results and Discussion

• -36.6%: Technostress Reduction

Baseline to RTD simulation

• -85.2%: LMS Checking Decrease

Nightly checks dropped sharply

• -28.5%: Academic Pressure Relief

Perceived stress lowered

• +23.6%: Sleep Regularity Gain

Improved consistency post-RTD Source: RTD Simulation (S₀ vs S₁)

The conclusion slide highlights key implications for school policies, emphasizing a low-cost, effective intervention under the subtitle "Pioneering student well-being through RTD policies." It outlines future work on empirical validation and AI-driven predictive models, ending with thanks and an invitation for Q&A.

Conclusion and Future Work

• Key implications for school policies

• Low-cost, effective intervention
• Future work:
• Empirical validation
• AI-driven predictive models

Thank you for your attention!

Q&A?

Pioneering student well-being through RTD policies

Source: A Simulation-Based Evaluation of Right-to-Disconnect Policy for Reducing Technostress in High School Students | ICCCA 2025

This References slide lists five academic citations on technostress, workplace policies, and simulation methods. Sources range from 2015 studies on individual well-being to 2025 ICCCA guidelines on policy analysis frameworks.

References

• [1] M. Tarafdar et al., “Impact of technostress on individual well-being,” Inf. Syst. Res., vol. 25, no. 4, pp. 383–402, 2015.
• [2] R. A. Day, “Right-to-disconnect policies in workplaces,” Labor Law J., vol. 71, no. 2, pp. 112–130, 2020.
• [3] S. L. Clarke, “Technostress in educational settings,” J. Educ. Psychol., vol. 113, no. 5, pp. 987–1002, 2021.
• [4] J. K. Smith and L. M. Johnson, “Agent-based simulation for policy evaluation,” Simul. Model. Pract. Theory, vol. 120, Art. no. 102345, 2022.
• [5] ICCCA 2025 Guidelines, “Simulation-based policy analysis framework,” Proc. ICCCA, pp. 1–10, 2025.

Source: ICCCA-2025 template