درخت حوزه‌های تخصصی

The present study attempted to investigate the role of digital testing on EFL learners’ vocabulary learning and retention. Sixty EFL students were divided into two groups: control (n=30) and experimental (n=30). A vocabulary test was administered to both groups in order to ensure that they were equivalent. Then, all participants attended 18 sessions. where participants of the experimental group used an English vocabulary that included unknown vocabulary and different online tests while participants of the control group practiced vocabulary in the class but without any application. Immediately after the treatment was over, the vocabulary test was administered to both groups in order to evaluate the differences between the two groups in vocabulary acquisition. Moreover, the same test was administered to both groups two weeks after the administration of the post-test with the purpose of evaluating the differences between the two groups in vocabulary retention. The statistical analysis revealed a significant difference between the two groups in both the immediate and the delayed administrations of the vocabulary test. Therefore, it was concluded that digital testing enhanced both vocabulary acquisition and retention among EFL students. The findings of this study have implications for teachers. It can be used by other researchers and EFL students.

Background: The 2024 Iranian presidential election intensified political polarization on Persian Twitter (X), where ideological factions engaged in networked contestation. This study employs social network analysis to examine polarization mechanics, mapping key actors, echo chambers, and discursive strategies. Findings illuminate digital factionalism, algorithmic amplification, and the role of influencers in political mobilization.Aims: This study explores the mechanisms driving political polarization on Persian Twitter (X) during the 2024 Iranian presidential election.Methodology: Utilizing computational social science methodologies, the research combines social network analysis (SNA) and thematic analysis to examine over 133,000 active users and numerous election-related hashtags. Data collection spanned June 1–16, 2024, leveraging Twitter's API to identify clusters, user interactions, and thematic trends. Key tools included the Louvain algorithm for community detection and centrality measures for network analysis, visualized through Gephi software.Findings: Findings reveal a fragmented political landscape characterized by ideological divides, echo chambers, and limited interaction between opposing factions. Analysis identified six major clusters, each aligned with distinct political affiliations, including reformists, conservatives, and opposition groups. Thematic analysis further highlighted the rhetoric surrounding leading candidates Masoud Pezeshkian and Saeed Jalili, revealing polarized sentiments and distinct narratives among their supporters and detractors. The study also underscores the role of platform algorithms, influencer strategies, and group identities in deepening polarization.Conclusions: This research contributes to the understanding of digital polarization within Iran's socio-political context. It emphasizes the dual role of social media as both a space for public discourse and a driver of ideological segregation. Recommendations include strategies for promoting digital literacy, fostering inclusive discussions, and enhancing algorithmic transparency to mitigate polarization in Iran.