A model of factors influencing in-service teachers’ social network prestige in online peer assessment
DOI:
https://doi.org/10.14742/ajet.7622Keywords:
online peer assessment, participation gap, social network prestige, participation behaviour, attitude towards participation, motivation to participateAbstract
The rise of teacher training in online interactive learning environments has contributed to teachers’ professional development and brought new vitality to the informatisation of education. Many researchers have reported that there is a participation gap in online interactive learning environments. Research on the factors influencing this is very important. Social network prestige, which measures the degree to which learners gain peer attention in directed social networks, is one of the important metrics to characterise the participation gap. In this study, we offered an online teacher training course, and 1438 in-service teachers from primary and secondary schools attended. Among them, we selected 457 in-service teachers who participated in the three peer assessment activities as the final participants. To analyse the factors influencing learners’ social network prestige in online peer assessment, we first conducted a partial least squares structural equation modelling analysis to construct a model of factors influencing social network prestige. Then, we adopted several semi-structured interviews to investigate learners' perspectives to provide an in-depth analysis of the factors influencing social network prestige. The purpose of this study was to gain insight into the participation gap in online interactions and make effective suggestions on how to improve learning performance in online peer assessment.
Implications for practice or policy:
- Course designers could improve the design of the introduction to peer assessment to motivate learners and enhance their acceptance of the activities.
- Course designers could reduce participation gap by assigning work from low-prestige learners to high-prestige learners in a non-mandatory way later in the course.
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Copyright (c) 2022 Ning Ma, Lei Du, Yao Lu
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