Understanding mechanisms of learning: a realist evaluation of the MRes research methods module
File(s)
Preprint version
Author(s)
Mangsat, Raleigh
Type
preprint
Abstract
Aims/Purpose: This study conducted a realist evaluation of an MRes Research Module to identify what mechanisms enable or hinder learning, for whom, and in what contexts.
Background: Research methods training is a cornerstone of postgraduate education. It is designed to support students in independent research. However, while research methods training is vital in postgraduate education, its effectiveness varies, and there is a need to understand the causal links between teaching strategies and student outcomes.
Methodology: A qualitative, realist evaluation approach was employed in the study, analysing postgraduate student experiences through the Context-Mechanism-Outcome (CMO) framework to explain how and why specific strategies succeeded or failed.
Results: Workshops, authentic assessments and field-specific supervision were effective mechanisms for learning. However, generic materials and inconsistent support hindered progress, particularly for postgraduate students in computational or dry-lab disciplines, leading to uneven outcomes.
Contribution: This study proposes the ‘Adaptive Nexus Model for Postgraduate Training’ to resolve the inequities of ‘one-size-fits-all’ module designs. This model provides context-sensitive recommendations for developing more equitable and effective postgraduate training, such as creating field-specific learning pathways to support diverse student cohorts better and ensuring that all master's students are equipped for success.
Background: Research methods training is a cornerstone of postgraduate education. It is designed to support students in independent research. However, while research methods training is vital in postgraduate education, its effectiveness varies, and there is a need to understand the causal links between teaching strategies and student outcomes.
Methodology: A qualitative, realist evaluation approach was employed in the study, analysing postgraduate student experiences through the Context-Mechanism-Outcome (CMO) framework to explain how and why specific strategies succeeded or failed.
Results: Workshops, authentic assessments and field-specific supervision were effective mechanisms for learning. However, generic materials and inconsistent support hindered progress, particularly for postgraduate students in computational or dry-lab disciplines, leading to uneven outcomes.
Contribution: This study proposes the ‘Adaptive Nexus Model for Postgraduate Training’ to resolve the inequities of ‘one-size-fits-all’ module designs. This model provides context-sensitive recommendations for developing more equitable and effective postgraduate training, such as creating field-specific learning pathways to support diverse student cohorts better and ensuring that all master's students are equipped for success.
Date Issued
2026-01-26
Citation
Research Square, 2026
Journal / Book Title
Research Square
Copyright Statement
Copyright © 2026 The Author. This work is licensed under a Creative Commons Attribution 4.0 International License.
License URL
Description
Preprint version
Identifier
10.21203/rs.3.rs-8694854/v1
Subjects
Teaching-Research Nexus
Realist Evaluation
Research Methods
Supervisory Support
Context-Mechanism-Outcome
Higher Education
Student Learning Teaching-Research Nexus
Realist Evaluation
Research Methods
Supervisory Support
Context-Mechanism-Outcome
Higher Education
Student Learning
Publication Status
Published online