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Redressing Disparities in English Self-Learning Modules: A Gender-Based Content Analysis



Florabel L. Rebamonte and Dan O. Gomez, 2023. Redressing Disparities in English Self-Learning Modules: A Gender-Based Content Analysis. United International Journal for Research & Technology (UIJRT), 4(10), pp79-93.


This qualitative research delved into gender prejudices embedded in texts and images of the English self-learning modules through a content analysis approach using the Eight-Factor Procedure of Gender Discrimination proposed by Amerian and Esmaili (2015). Although most features revealed an imbalance of gender representation as evident in recorded frequencies, two (2) features portrayed both genders with equal value and accorded equal treatment. Further, five (5) participants were interviewed for focus-group discussions, while another nine (9) shared their insights through in-depth interviews after assessing the gender-fair language compliance of the selected corpora. The thematic analysis procedure has drawn ten (10) significant themes revealing the perpetuating gender stereotypes, specifically in semantic roles, pictorial representations, and order of appearance. The study results will be a reference for sociolinguistics, curriculum developers, policymakers, and stakeholders for pursuing collective and transformative efforts toward a gender-inclusive society. Acknowledging the educational sector’s response to the Covid-19 outbreak by developing self-learning modules used for distance learning, hence, the findings underscore the need for constructive evaluation and provision of specialized training for teachers to intensify the integration of gender-fair language and ensure the full implementation of gender-responsive basic education policy as mandated by the Philippine government so that ‘No child is left behind.’

Keywords: content analysis, thematic analysis, gender-fair language, gender-responsive basic education policy, English Self-Learning Modules.

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