Journal article
An extended community of inquiry framework for monitoring and predicting online peer learning participation
Journal of applied research in higher education, Vol.18(8), pp.113-141
14/12/2026
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Abstract
Purpose: Online learning communities on social media platforms can support peer learning, but educators often lack theoretically grounded and measurable approaches for monitoring how participation and discourse evolve across a semester. This study proposes an extended Community of Inquiry (CoI) evaluation framework that integrates Social, Teaching, and Cognitive Presence with a fourth behavioural dimension, Student Presence.
Design/methodology/approach: A sequential exploratory mixed-method design was adopted. Qualitative analysis of prior literature and semester-long observations of two large first-year engineering course Facebook groups (each enrolling 800–1000 students) informed an indicator-based coding scheme, applied quantitatively over Weeks 1–13. Predictive modelling used a persistence baseline, a multi-output Random Forest, and a multilayer perceptron under time-aware evaluation protocols.
Findings: Social Presence was enquiry-driven and peaked in Weeks 3–4; Teaching Presence was frontloaded and primarily reactive; Cognitive Presence was shallow, dominated by remembering and analysing. Student participation was consumption-oriented, with observers consistently outnumbering posters. Random Forest achieved consistent poster prediction (R2 ˜ 0.48–0.49), while observers and non-members remained difficult to forecast due to structural interdependence. Permutation importance identified remembering and evaluating as the most influential cognitive predictors.
Research limitations/implications: The dataset comprises 13 weekly observations from a single platform and institution, limiting generalisability. Future work should collect multi-cohort data, introduce lagged predictors, and explore individual-level modelling.
Practical implications: The framework provides instructors with an early-warning system for low poster activity, enabling timely, evidence-based interventions to support online peer learning communities.
Originality/value: This study makes three contributions: a multi-dimensional coding scheme grounded in the extended CoI framework; a data-driven analytics pipeline enabling descriptive monitoring and predictive modelling of participation roles; and an integrated evaluation framework that combines theory-grounded indicator coding with transparent machine learning to produce actionable insights from social media learning data.
Details
- Title
- An extended community of inquiry framework for monitoring and predicting online peer learning participation
- Creators
- Mohsen Dokhanchi - The University of QueenslandShahrzad Saremi - University of the Sunshine CoastRania Shibl - Southern Cross UniversityMaryam Heidari - Griffith UniversityHassan Ahmed - National University of Computer and Emerging SciencesDahlia Mansoor - Abu Dhabi UniversityYassine Himeur - Abu Dhabi UniversityMohammad Al-Zaffin - University of DubaiShadi Atalla - University of DubaiWathiq Mansoor - American University of Iraq Sulaimani
- Publication Details
- Journal of applied research in higher education, Vol.18(8), pp.113-141
- Publisher
- Emerald Group Publishing Limited
- Number of pages
- 29
- Identifiers
- 991013390731402368
- Copyright
- © Mohsen Dokhanchi, Shahrzad Saremi, Rania Shibl, Maryam Heidari, Hassan Ahmed, Dahlia Mansoor, Yassine Himeur, Mohammad Al-Zaffin, Shadi Atalla and Wathiq Mansoor.
- Academic Unit
- Faculty of Science and Engineering
- Language
- English
- Resource Type
- Journal article