Teachers Emotional Responses to Critical Incidents in Digital Entrepreneurial Learning Environments

In the contemporary edupreneurial ecosystem, optimizing human capital and engineering adaptive Educational Technology (EdTech) architectures demand a mathematical, data-driven understanding of stakeholders' internal emotional and behavioral dynamics. This study examines how secondary school teachers depict their emotions toward critical incidents in classroom discourse by applying an advanced Systemic Functional Linguistics (SFL) appraisal framework, mapping these dynamics for structural integration into automated affective computing systems. Involving two public Islamic secondary school teachers within a boarding school environment as focal instructional assets, this investigation dissects teachers' emotions toward critical incidents, their cognitive emotion regulation strategies, and the resultant vectors of emotional understanding or misunderstanding across gendered and socio-educational strata. The empirical findings reveal that positive affect structures predominantly map the emotional architecture of the male teacher, whereas negative affect systems characterize the communicative patterns of the female teacher when navigating disruptive critical incidents. These behaviors are heavily conditioned by systemic gender roles and institutional socio-religious capital. Furthermore, while both educators leverage cognitive change (reappraisal) as an emotional regulation strategy, the male teacher operates through exclusive positive affect pathways, whereas the female teacher exhibits a dual-phase response (negative affect transitioning into positive affect), highly mediated by pedagogical tenure and experience. Crucially, the male teacher fosters sustained emotional understanding via positive inclination and happiness systems, whereas the female teacher's discourse reflects severe emotional misunderstanding, characterized by linguistic domination, discourse control, and subtle verbal oppression. This study synthesizes these socio-linguistic dimensions into an Edu-Affective Analytics Framework, presenting actionable technopreneurial recommendations for scaling empathetic AI tutors, deploying automated performance tracking matrices, and optimizing human resource workflows within modern educational enterprises.