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Comparing the Quality of AI-generated and Instructor Feedback in a University Writing Program
Get PDFFeedback is an undeniably important aspect of the language learning process. It helps students recognize their strengths and weaknesses and identifies ways they can improve. Over the years, feedback has been provided by teachers, peers and Automated Writing Evaluation (AWE) tools. However, in recent years, artificial intelligence applications have proliferated significantly. With abilities to analyze and generate any kind of content, these models are being used to generate scores and feedback on written assignments to help lighten teachers’ load. ChatGPT has been called “the world’s most advanced chatbot” and “a potential chance to improve second language learning and instruction” (Shabara et al., 2024). The present study aims to investigate the quality of AI-generated scores and feedback on writing in comparison to teacher scores and feedback. Using a mixed methods design, the study compared ChatGPT-generated and its regenerated scores and qualitative comments to those assigned by experienced university instructors. A total of 89 argumentative essays were collected from the archives of a private university in Egypt. ChatGPT- 4o and two human raters scored them using a rubric that evaluates writing based on four criteria: content and development, organization and connection of ideas, linguistic range and control, and communicative effect. All scores were statistically analyzed to examine the consistency and accuracy of ChatGPT in scoring. Similarly, the written feedback was thematically analyzed and compared to teacher feedback. Themes identified from the data included tone of feedback, following the rubric, prioritizing certain writing features, and providing judgmental or improvement-oriented feedback. The quantitative data revealed a moderate correlation between AI-generated and teacher scores, with the only strong relationship being in the linguistic precision criterion. The results also showed a weak consistency in ChatGPT-generated and regenerated scores. In terms of qualitative feedback, it was found to be considerably close in quality to teacher feedback. Additionally, the study tapped into the effect of writing proficiency on the nature of the feedback, and the data showed that ChatGPT did not differentiate between students based on abilities whereas the teachers did, especially in terms of tone. This lack of differentiation, however, indicates that ChatGPT’s feedback may not be as personalized to students’ needs as the teacher feedback. Implications of the study include using ChatGPT for scoring language areas and generating feedback provided that teachers revise this evaluation. Study limitations such as evaluating the effectiveness of the feedback are also discussed
Period Poverty in Egypt
Get PDFPeriod poverty or Menstrual Hygiene Management (MHM) is a critical issue that affects women and girls globally. This study examines the socioeconomic and cultural barriers that contribute to period poverty in Egypt and proposes solutions to address these challenges. One survey was conducted among nursing schools across various regions in Egypt, yielding a sample size of 2,228 respondents. The study explores constructs including sanitary product accessibility, distress surrounding menstruation, affordability of sanitary products, and knowledge on menstrual hygiene. Results indicate significant challenges faced by females in accessing sanitary products, experiencing emotional distress due to insufficient access, and feeling embarrassed when purchasing from male pharmacists. Furthermore, respondents expressed a desire to lower the prices of sanitary products and advocated for institutions providing them at minimal fees. Based on these findings, grass-roots recommendations are proposed to mitigate the effects of period poverty in Egypt, emphasizing the need for improved accessibility, affordability, and education on menstrual hygiene
Stereotypes of Female Characters in Disney
Get PDFThe depiction of female characters in Disney films has undergone a significant transformation throughout the decades, reflecting societal shifts and changing perceptions of gender roles. As young girls tend to idolize fictional female characters, ensuring a healthy portrayal of women in animated films is crucial. This paper aims to discuss the portrayal of female characters in Disney movies within certain frames and their impact on the general public through a case analysis of literature reviews. It underscores the dynamic interplay between societal change and cinematic narratives, particularly in Disney. The research’s findings indicate that the negative portrayal of Disney female characters happens through three possible frames; the damsel in distress complex, the physical appearance of female characters, and the representation of female villains. Using the Content Analysis method, the study examines early female protagonists, emphasizing their obedient behavior and dependence on men. It also traces the evolution of female characters, from classic princesses to more diverse and empowered figures in contemporary Disney films. Additionally, the research explores how physical appearance and beauty standards are portrayed, particularly in regard to villains, linking unattractiveness with evil. This study contributes to a larger field of research that sheds light on ongoing discussions on the nuanced complexities of women’s identity in animated cinemas and underscores the need for a positive, more empowering portrayal of women in media
Passport to Public Affairs: A Guide to International Study Tours
No full textThis article aims to clarify the role of international study tours in enhancing public affairs programs. We outline the benefits of these tours and share key lessons from past experiences. Specifically, we explore the motivations for leading such tours—particularly those centered on student development—the strategies for selecting destinations, recruiting participants, and designing activities. We also address some of the challenges educators may face. Finally, we offer practical tips and reflections on international study tours from a university leadership perspective
Future of Work: Implication of Green Economy on Employment Dynamics in Egypt
Get PDFIs Egypt\u27s green economy a reality or just a policy-level aspiration? This thesis explores the tangible impact of the green economy on Egypt\u27s labor market by examining key sectors—automotive, agriculture, and renewable energy. Through qualitative interviews and focus group discussions with private sector representatives, the study assesses the true implications of the green transition from a business-level perspective, beyond the propaganda and hype surrounding climate policies. What is the current scale of the green economy in Egypt\u27s labor market? What existing policies and regulatory frameworks are in place to support the green economy transition? how urgent is the transition to a green economy across different sectors in Egypt? Are there significant changes in the workforce composition, including the emergence of new occupations or modifications to the tasks of existing roles, because of the green economy transition? The findings reveal that the green economy in Egypt is still in its early stages, with some sectors advancing more quickly than others. Agriculture stands out as the most dynamic sector, with the highest urgency to adopt sustainable practices and create green jobs. Across all sectors, changes in workforce composition are occurring, with new roles emerging, particularly in renewable energy, while other sectors are adapting by evolving existing tasks and skills to meet the demands of the green economy
Portfolio Strategies Evaluation in Developed Markets: A Sectoral Analysis of Minimum- and Mean-Variance Portfolios
No full textThis paper evaluates the performance of four fundamental economic sectors – energy, utilities, real estate, and the financial sector – in five advanced countries: the US, Canada, UK, Germany, and France throughout the period 2015 – 2024. It attempts to assess the best performing and most efficient sector-country combinations by applying time variant (dynamic) portfolio optimization frameworks, primarily the minimum-variance and mean-variance models. The study will present recommendations for investment policy that are based on risk-adjusted returns and empirical findings
Implications of the Mind’s Active Participation in the Formation of Perception
Get PDFThe mind-body problem, particularly the issue of qualia, remains a fundamental challenge in understanding the nature of the mind and its relation to the body. In this thesis I argue that the problem is epistemic, stemming from how we perceive and know ourselves as physical and mental beings. This argument will be explored through the respective of idealisms proposed by George Berkeley and Immanuel Kant. Berkeley\u27s idealism suggests that reality is rooted in perception, offering a major steppingstone by demonstrating the major role perception plays in understanding and acquiring knowledge. Kant contributes by asserting that the thing-in-itself is beyond our empirical grasp, and our perceptions do not fully represent the mind\u27s true nature. Our understanding is limited to phenomena, not noumena, leading to the persistence of the explanatory gap. In this thesis I propose that this limitation might be evolutionarily advantageous, as the mind evolved as a survival tool rather than a means for uncovering ultimate truths. This perspective suggests that our restricted perceptive access has evolved to prioritize survival through recognizing faces, dangers, and the like, rather than exploring the deepest truths of our nature and reality. Thus, the mind-body problem reflects the inherent limitations of human perception and knowledge in the pursuit of understanding our fundamental nature
Optimization of Pile Construction Sequence Using CFA Machines
No full textIn most construction projects with deep foundations, piling activities are critical to the project schedule and often fall on the critical path. The piling construction process requires a diverse range of resources, including equipment, materials, and skilful labour. Without carefully planning the construction process according to installation standards, there is a significant risk of time and cost overruns. These overruns are caused by inefficiencies such as equipment idle or lost time due to required lags between closely spaced piles. Therefore, it is essential to develop an optimized construction sequence to minimize these risks and enhance overall project efficiency. The primary objective of this research is to develop a tool that schedules & optimizes the construction piling sequence to minimize the duration to complete the piles. Therefore, the optimization shall lead to enhanced resource usage and reduce the associated resource costs, particularly those incurred from equipment rentals. The research methodology involves developing a genetic algorithm code that incorporates time-space constraints to model the temporal lags between piles in proximity. The outcome of using the tool is a schedule that establishes an effective construction plan by providing an optimized construction sequence and a resource utilization schedule which enhances project efficiency and cost-effectiveness.
The work presented in this study contributes to the literature of specialized scheduling techniques in the construction industry, as it allows practitioners to utilize a framework that identifies an optimum piling schedule that abides by time-space constraints. This can also benefit the construction industry as the framework provides useful resource usage data that could contribute into the creation of the budgeting and resource management plans for contractors, thus contributing to the successful management of a project
Mining the Microbial Treasure Trove: Multitasking Deep Learning Framework for Functional Discovery of Novel Prokaryotic Argonaute Proteins in Metagenomes
No full textProkaryotic Argonaute proteins represent a mechanistically versatile yet sequence‑divergent family of nucleic‑acid–guided effectors whose functional and evolutionary breadth remains incompletely charted, especially within the vast reservoir of metagenomic “dark” proteins. Here, we present a multitask deep‑learning framework that unifies transformer‑derived residue embeddings, predicted contact topologies, and domain‑aware sequence constraints in graph attention–conditional random‑field (GAT‑CRF) architecture. Trained on a curated cohort of 6,615 residue‑annotated protein graphs, the model simultaneously classifies global Argonaute identity and delineates PAZ, MID, and PIWI boundaries with residue‑level resolution.
In comparative benchmarks, the network outperforms Foldseek and HMMER, recalling up to 35 % more domains at matched precision, and surpasses PSI‑BLAST in family‑level retrieval while operating entirely without pairwise alignment. Deployment across 43.2 million non‑redundant MGnify proteins yields 1,459 high‑confidence Argonaute homologs, expanding the known repertoire by nearly an order of magnitude, and reveals a previously unrecognized monophyletic clade that branches from longA Argonautes but lacks both the catalytic DEDX tetrad and PAZ/APAZ guide‑anchoring modules. Detailed motif analysis confirms 168 catalytically competent longA enzymes, maps MID‑anchor diversification across clades and uncovers rare aromatic amplifications (YY, FWK) suggestive of enhanced guide affinity. Accessory‑domain scans identify Tudor, Topoisomerase‑I, and S13‑like H2TH fusions, highlighting modular accretion as a driver of functional innovation.
This work demonstrates that graph‑based representation learning can transcend the limitations of alignment‑centric pipelines, resolve residue‑level architectures, and illuminate hidden enzymatic diversity at the metagenomic scale. The resulting atlas of canonical and novel pAgos provides a rich source of candidates for mechanistic exploration and biotechnological development, while the methodological blueprint is broadly transferable to other fast‑evolving protein families residing in the microbial dark proteome
Multi-Parameter Optimization of Brine Desalination Using Machine Learning
No full textAir Gap Membrane Distillation (AGMD) is a promising desalination technology with significant potential for addressing global water scarcity. However, the interplay of operational parameters significantly impacts its performance, making optimization a challenging task. This research focuses on brine desalination as a means to mitigate the negative environmental impacts of brine disposal which will eventually help in provide a sustainable solution for handling brine while producing freshwater. The study seeks to develop a predictive model and optimize the AGMD process for efficient brine desalination. To achieve this, Response Surface Methodology (RSM) and Artificial Neural Networks (ANNs) were utilized to develop predictive models for AGMD desalination. Results have shown that both RSM and ANN achieved comparable performance, with ANN providing slightly better accuracy. The ANN model is trained and validated using experimental data while varying membrane pore size, feed salinity, feed flow rate and feed temperature to predict two critical performance metrics: permeate flux and specific thermal energy consumption (STEC). Different activation functions and different numbers of neurons were tested. The sigmoid activation function was found to be the most effective with 10 neurons resulting in a RMSE of 0.03. The model achieved an R² value of 98.42%, 97.91%, and 97.80% for the training, validation, and test datasets, respectively. For the combined dataset, the model attained an R² value of 98.26%. While flux predictions yielded a higher R² value of 99.28% compared to STEC which achieved an R² value of 97.04%, showing higher precision in the prediction of permeate flux. Although RSM provides more interpretable insights into the effect of different parameters, ANN models are more suitable for integration into real-time process control systems where adaptability and continuous learning from new data are essential. Differential evolution is then applied using the ANN model to predict optimal performance metrics by assigning different weights to flux and STEC. This approach allows for the identification of operating conditions that best meet specific application needs, ensuring a balance between water production and energy efficiency. Optimization results demonstrated a trade-off between maximizing flux and minimizing STEC for both membrane types, with the 0.22 µm membrane achieving a 140.5% increase in flux and the 0.45 µm membrane achieving an 83.2% increase across scenarios. By addressing the challenges of brine desalination through AGMD, this study provides an approach for reducing the environmental risks associated with brine disposal through enabling the efficient recovery of freshwater from brine