مطالب

Few studies have addressed the interaction among reader and text variables in reading comprehension. This research probed the role of aspects of lexical knowledge to the TOEFL reading comprehension of passages with low and high lexical cohesion. The extent of this contribution was checked for five types of TOEFL reading test items: Main Idea, Stated Detail, Inference, Reference, and Lexical Inferencing. For this purpose, 60 Iranian English as a Foreign Language (EFL) took the New Vocabulary Levels Test (NVLT), Word Associates Test (WAT), and TOEFL reading comprehension tests with low and high lexical cohesion determined through the computational tool Coh-Metrix. Results of Wilcoxon signed-rank tests revealed that the students outperformed on three types of TOEFL reading items (Main Idea, Inference, and Lexical Inferencing) for texts with high lexical cohesion. Multiple linear regression analyses further showed that vocabulary depth was a stronger predictor of the participants’ scores on Lexical Inferencing item of TOEFL reading texts with high lexical cohesion, while vocabulary size predicted the same item performance for the low-lexical-cohesion subtest. Moreover, vocabulary depth could significantly predict performance on Main Idea test item of TOEFL high-lexical-cohesion subtest over and above the low-lexical-cohesion subtest. The implications for teachers, test designers, and materials writers are discussed.

One of the most important aspects of learning is attention. This is even more pronounced in online education due to the instructor’s need for sufficient control over the learner’s environment. This study aims to identify the pattern of changes in the cognitive state of learners, from unconsciousness to consciousness. By observing the brain’s response while learners watch micro videos, we sought to understand the impact of these ego states on learners’ performance in E-Learning environments. Our findings suggest that learners' ego states significantly impact their learning performance. The first phase aimed to precisely detect the transition point from the unconscious to the conscious state. In the second phase, we tried to differentiate between these two states by comparing their learning performance. Finally, the obtained results led us to believe that learning outcomes are subject to a significant increase when the brain state changes. These findings emphasize the importance of early engagement strategies in online learning, as improving the initial phase of content delivery significantly increases overall learning outcomes. By understanding the transition between different ego states, educational content authors would create more effective learning materials that maintain continuous learner engagement.

Background: The rapid growth of computer technology in recent decades suggests that people today, in addition to print media, have many electronic information items that need to be managed appropriately and have increased the use of personal computers.Aims: This Researches descriptive and analytical based on the purpose of applied Research, and in terms of data collection and nature and comparison between the activities of personal information management and two measures of knowledge and skills has been presented.Methodology: The statistical population includes the students of Jundishapur University of Medical Sciences in Ahvaz in the academic year 2017-2018. To determine the statistical population, the sampling method was used by determining the total number of students 1300 and using the Karjesi-Morgan table, the approximate number of the sample was determined to be 297. The data collection was done using the questionnaire of 2012 and in order to analyze Pearson and Spearman correlation tests and AMOS and SPSS software were used.Results: The results of the survey show that knowledge and skill variables have a significant and positive effect on personal information management, and age, level of education, and school of study have a significant and positive effect on personal information management.Conclusions: The results of the research show that the status of personal information management of medical students in Jundishapur Ahvaz has a positive and meaningful effect based on the knowledge and skills of the students. According to the changes made in the management of personal information activities, it can be said that the difference in the amount and amount created in the variables of personal information management can be seen. The main reasons for this can include not having enough time, work pressures and lack of familiarity with the activities required for personal information management.

The purpose of the present study is to explore the two concepts of time and space in postmodern lyric poetry of the two poets of the 1950s through the lens of the Heideggerian existential theory of time and space, which regards time as a horizon for understanding Being and distinguishes three different types of space: (1) world-space, (2) regions , and (3) Dasein's spatiality. To fulfill this objective, some selected poems of the two poets, including Allen Ginsberg and Ted Hughes, were analyzed temporally and spatially. The findings suggested that the two poets tend to treat time and space existentially and reject eternality. It was revealed that they are existential poets whose existence is manifested in their quest for identity within the immediate world or the global world as well as their concerns for their homeland and ideals. In their poems, time and space are intermingled with Being and reflect each individual’s relationship with the world. The result of the analysis of poems showed that their poetry is not just the language of imagination and perception, but also the language of existence. The world is regarded as an existential space-time continuum and being-in-the-world is the fundamental ontological situation for Dasein. Accordingly, the world, like poetry, is a disclosure of things in nearness or distance, which matters to human beings

The Fourth Industrial Revolution (4IR) and its associated technologies are revolutionising socio-economic sectors, including agriculture. This article explores the impact of digitalisation on African food systems. The article uses secondary and primary sources in academic and grey formats to examine how digital tools can shape agriculture and create new opportunities for developing Africa's food system. It identifies digital tools usable across the food system chain and explores their potential impact on food production, processing, storage, marketing, transportation, and consumption. The article also examines the challenges to transforming the digital food system in Africa. The article shows that while digital technology has the potential to increase food production, reduce food waste, and improve food marketing and distribution, several economic, political, and social challenges exist that frustrate digital uptake. It argues that African governments should enhance the capacity of actors in the food systems chain to adopt digital tools. This can be achieved through adopting relevant policies and rolling out digital infrastructure to enable actors to access it in their areas of operation.

This study aims to analyze 45 definitions of digital transformation (DT) to identify key drivers and propose a conceptual framework to outline their impact on business performance. Through content analysis, 24 key drivers were identified, focusing on the frequency of occurrence across the definitions. The analysis highlights drivers such as IT technologies & innovation, business model, business performance, customer experience, and operational processes. The results show a significant emphasis placed on various drivers of DT, reflecting its multidimensional nature. Key drivers include technological innovation, organizational adaptation, customer-centric strategies, and change management practices. By conceptualizing the relationships between key drivers and performance outcomes, the proposed conceptual framework provides theoretical insights into the mechanisms underlying digital transformation and its impact on business performance. The proposed framework integrates technological, strategic, organizational, and cultural dimensions. The analysis underscores the complexity and multidimensional nature of DT as a strategic phenomenon and offers drivers on which the organizations should focus to face the challenges of digital disruption. This paper's original theoretical contribution lies in synthesizing various definitions of digital transformation from the past two decades to propose a comprehensive definition of Corporate Digital Transformation.

In today’s data-driven hospitality sector, customer interactions increasingly occur through digital platforms, generating extensive behavioral and transactional information. This study analyse the prediction of Customer Lifetime Value (CLV) using machine learning models—Linear Regression, Random Forest, and LightGBM—trained on features derived from hotel website interactions and booking records. After comprehensive data preprocessing, the models were evaluated using MAE, RMSE, and R² metrics. LightGBM achieved the highest predictive performance (R² = 0.504), followed by Random Forest (R² = 0.497), while Linear Regression underperformed (R² = 0.386), highlighting the advantages of non-linear models in modeling intricate customer patterns. Residual analyses confirmed LightGBM's stability and low bias across diverse customer profiles. Apart from prediction, the study applies Recency-Frequency-Monetary (RFM) analysis to segment customers into distinct value-based groups. These segments form the basis for tailored marketing strategies, allowing hotels to allocate resources more efficiently, enhance customer retention, and develop targeted campaigns aligned with customer potential. By integrating web-derived behavioral data with advanced modeling and segmentation, this research offers hotel managers practical tools for strategic planning in customer relationship management.