Repair

To better understand the pattern of language use and classroom interactions, this sequential mixed-methods study investigated the teachers’ use of self-repair strategies based on their level of reflection. To this end, 33 Iranian EFL teachers were selected from various institutions in Tehran. Data for the quantitative phase were collected from the reflectivity questionnaire developed by Akbari, Behzadpour, and Dadvand (2010). Regarding the qualitative phase of the study, 70 hours of English instruction and classroom interactions of the 33 teachers were recorded and analyzed, using Fox and Jasperson’s (1995) classification of self-repair strategies. The quantitative analyses of the results, employing one-way Analysis of Variance (ANOVA), indicated that there was a significant difference between the reflective groups in terms of the total repair strategy use. Further, the results of Kruskal Wallis analysis revealed that there was a statistically significant difference between high and low reflective teachers in terms of the repair strategies types “H, J, K, and L”, which generally refer to the “Replacement, Repetition, and Addition of a lexical item”. The results of the qualitative analysis also showed that the most frequent self-repair strategy of high, mid, and low reflective teachers was strategy “A” or “repetition of a lexical item”.

Nowadays, information-making is growing rapidly due to the developments in technology; thus, it is critical to maintain information and establish integrated systems for storage and office automation in any organization; thereby, the establishment of datacenters has become a requirement for them. Despite offering models related to some parts of the data centers, there are still ambiguities in identifying the influential and key variables. This study aimed at offering a dynamic model for the parts related to the attractiveness of data center services by using Vensim software. Five reinforcing loops and seven balancing loops were extracted through studying the review of literature; then, the relevant stock and flow variables have been defined. Afterwards, the model is presented in the form of cause and effect model and the stock-flow model. Also, various scenarios have been studied, including increasing the training capacity and attractiveness, imposing an overload on the network and switch failure, upgrading the bandwidth carrying capacity, reducing the maximum maintenance. According to the results and sensitivity analysis done in this research, it can be said that the attractiveness of services, the acquired skills, and the maximum feasible maintenance along with the bandwidth carrying capacity, storage, and processing power of the data centers is so vital in determining the performance level of data centers and the attractiveness of services.