مطالب

While the use of apps specifically designed for language learning has witnessed a surge over the past decade, research on the productivity of these apps for language learning purposes along with their essential design and content features from students’ lens remains scant. The present paper illustrates an attempt to explore the way using language learning apps throughout an academic semester develops university level students’ semi-technical English language knowledge and the features and qualities they find essential in these educational apps. Drawing on empirical data obtained from pre- and post-tests and App Feature Interview form, it was observed that students who used language learning apps had a significantly better performance in a semi-technical English language post-test compared to the students who did not use language learning apps of their choice. Following content analysis, participants’ responses to an interview question were explored to determine the qualities they found crucial for language learning apps. Students highlighted 18 qualities, which were categorized under three main themes: smartphone, pedagogical content, and design features, as required for effective language learning apps. The findings of this study provide insights for educational app designers and educators on what works better in these apps from users’ perspective.

Climate change is significantly affecting the livelihoods and health of human societies. During the Holocene, climatic events have occurred repeatedly. These events typically have affected large regions between 100 and 600 years. Paleoclimate studies of the Northern Hemisphere have identified climatic events of 9.2, 8.2, 5.2, 4.2, 3.2 ka BP, Roman warming, the early medieval cooling, medieval warming, and Little Ice Age. Also, by adapting the archaeological studies and the paleoclimate research of Iran, other periods of climate change such as 7.5, 7, 6.2 and 4.8 ka BP can be introduced. Due to the vastness and geographical diversity of Iran, the impact of each of these events in different parts of the country has been different, but, in general, during droughts related to climate change, semi-arid to arid regions of Iran have been more vulnerable, and therefore, people were using a variety of strategies to resilience and adapt, such as changing subsistence patterns, managing water resources, and migrating. However, in some periods of climate change, the effects of climatic hazards have been such that it has led to the cultural, socio-economic and political decline of societies.

The agricultural drought, severely affecting human life, occurs unpredictably at different times with different intensities. The conventional methods for assessing drought often relay on indices obtained using meteorological data, but due to the low spatial coverage, incompleteness and inaccuracy of these data, meteorological indices cannot be considered as a comprehensive method. Therefore, it is suggested that remote sensing constitute more versatile approach, as it allows to assess the drought using the adequate spatial and temporal coverage for the study area. In the study, performed for the Panjshir river basin in Afghanistan, the 2010-2019 period is used to evaluate vegetation rate using NDVI data from MODIS. To calculate agricultural drought indices (DSI, VCI and TCI), May and June were selected, as the peak vegetation time occurs for these months. On the base of the remote sensing indicators it was shown that during the study period the drought conditions were normal in the region, except for 2011, 2017, and 2018, which were the driest years, and for 2019, which was the wettest year. Agricultural drought indices were compared to SPI index calculated using winter and spring precipitation data recorded at the meteorological stations. It was observed that the remote sensing indices showed the highest correlation with data from Kabul meteorological station, which is located at the same altitude and climate as the dense vegetation zone. Furthermore, the comparison showed that the ground precipitation data is characterized by higher amplitudes than the remote sensing data. From the above it steams that the vegetation in the Panjshir basin is influenced by both seasonal rainfall and rivers that continuously flood the area.