E-Mentoring: Insights for Building Transformative Learning Online

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Zahed Babolan, A., Moeini Kia, M., Khalegh Khah, A., & Bagheri, Z. (2025). An integrated e-mentoring model: Bridging human skills and technology for transformative learning. Journal of Teacher’s Professional Development, 10(4), 51–71.

Key Takeaways

  • Babolan and colleagues (2025) propose an integrated conceptual model of e-mentoring that treats human relationships and technology as interdependent rather than separate domains.
  • Drawing on a qualitative meta-synthesis of prior research, the authors show that planning and management, relational processes, and technological infrastructure jointly shape mentoring outcomes in higher education.
  • The model positions skill development and continuous evaluation as central mechanisms linking mentoring practice to transformative learning.

Introduction

As higher education becomes increasingly digital, institutions face mounting pressure to provide meaningful academic and professional support in technology-mediated environments. The authors argue that existing e-mentoring research remains fragmented, often privileging tools or relationships in isolation, and lacks a unified framework capable of explaining how these elements interact to support transformative learning. Their study seeks to address this gap by constructing a paradigmatic model that integrates human skills and technological conditions within e-mentoring systems.

Methods

The authors used a qualitative meta-synthesis grounded in Strauss and Corbin’s coding framework, systematically reviewing peer-reviewed studies published between 2004 and 2024. Twelve high-quality qualitative or mixed-methods studies were selected for analysis. Data were coded through open, axial, and selective stages, with reliability supported by peer review and reanalysis procedures yielding an inter-coder agreement of 86.7 percent.

Results

Analysis produced sixty initial codes organized into seven core categories, including mentoring planning and management, technological infrastructure, communication tools, human relationships, skill development, and evaluation. These categories were arranged into a paradigmatic model linking causal conditions, contextual factors, intervening relational processes, strategic actions, and outcomes. Human relationships emerged as a critical mediating force shaping how technology supports learning.

Discussion

The findings suggest that e-mentoring functions as a dynamic system rather than a linear intervention. Effective programs depend on coordinated planning, reliable infrastructure, and sustained relational engagement. The authors interpret these dynamics as supporting transformative learning, arguing that reflective dialogue and ongoing feedback enable deeper shifts in learners’ perspectives.

Implications for Mentoring Programs

For practitioners, the model points to the need for intentional program design that balances relational support with technological capacity. Programs should invest in mentor training, align tools with pedagogical goals, and embed continuous evaluation to sustain mentoring quality over time.

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