This quasi-experimental study investigates the effect of Collaborative Writing (CW) on the writing performance and their perceptions of 36 Grade 9 female students in Oman. The study, rooted in the shortcomings of traditional product-oriented writing instruction, employs pre-/post-writing tests and a questionnaire to measure the effectiveness of CW. The findings reveal a statistically significant improvement in the experimental group's writing scores, emphasizing the positive influence of CW on various writing components. Additionally, students express favorable attitudes towards CW, particularly in editing and revising texts collaboratively. Despite these positive outcomes, challenges such as unequal work distribution within groups are identified. The study concludes by acknowledging limitations and suggesting avenues for future research, emphasizing the potential of CW to enhance students' writing skills and foster constructive dialogue in language classrooms.

This research paper delves into the transformative potential of Adaptive Learning Systems (ALS) in revolutionizing education through the integration of Artificial Intelligence (AI). With traditional educational approaches often failing to accommodate individual learning needs, the answer to this problem is adaptive learning system which focuses on personalized content delivery, instructional methods, and assessments. Through case studies spanning various educational contexts, including various countries, higher education, and diverse cultures, we have evaluated the effectiveness of different ALS techniques in terms of different educational needs and requirements. By reviewing these techniques in terms of their features, capabilities and functionalities, we have tried to figure out, how does the use of AI in adaptive learning systems contribute to personalized learning experiences for students. The paper also highlights the key challenges and limitations associated with the integration of AI in ALS. It addresses issues like data protection, analyzes the ALS principles and investigates the ethical consideration which arises during implementation of AI in adaptive learning systems. Furthermore, it underscores the pivotal role educators’ play in collaborating with AI systems to create a balanced learning environment. By providing insights into future directions, such as advancements in personalization techniques and lifelong learning, this paper contributes to understanding the complex interplay between AI and personalized education. Ultimately, the research advocates for the widespread integration of ALS as a transformative approach that has the potential to redefine education and cater to the diverse needs of learners in the digital age. 

Purpose: Ischemic brain strokes have a high morbidity and death rate, thus it’s vital to obtain a quick diagnosis and imaging. Computer-aided diagnosis (CAD) has become popular in medical imaging and diagnostic radiology research. In the field of medical image analysis, deep learning (DL) approaches have recently shown greater performance over earlier, more advanced machine learning techniques. Acute Ischemic stroke (AIS) is one of the medical sectors where DL has conducted substantial research. The systematic review examines the performance of deep learning models across different imaging modalities, highlighting their strengths and limitations in identifying ischemic strokes. Key aspects such as sensitivity, specificity, and overall accuracy are assessed, providing insights into the comparative effectiveness of CT, MRI, and CTA in stroke detection. In contrast with other reviews in this domain, this paper offers a concise summary of the most notable DL methods applied in the classification, detection, and segmentation of acute ischemic brain stroke, focusing on popular imaging techniques like Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and CT Angiography (CTA). This survey also highlights datasets and data acquisition challenges and attempts to provide a comprehensive overview of data preprocessing, as well as insight into publicly available datasets.
Methods and Results: This study aims to give an idea of how training and testing datasets should be handled by evaluat- ing recent studies. This review discusses the challenges associated with each imaging modality, including image noise, artifacts, and variability in acquisition protocols. Strategies to mitigate these challenges through preprocessing techniques and model optimization are explored, aiming to improve the robustness and reliability of deep learning-based stroke de- tection systems. Moreover, this research contains a brief discussion of recent deep learning architectures and an analysis of performances.
Conclusions: Overall, this systematic review contributes to the understanding of current advancements in brain ischemic stroke detection through deep learning, offering valuable insights for researchers and clinicians seeking to leverage these technologies for improved patient outcomes. Future directions and potential research avenues are also discussed to guide further advancements in this critical area of medical imaging and diagnosis.

The modern world looks for employable graduates from the outcome based educational institutions. Outcome based education (OBE), creates competitive graduates who can reasonable in world wide. Due to this, now a day’s many educational institutions are starts to implement OBE instead of traditional educational system. OBE has gained much impetus in the education system. With no specific methods of teaching or performance assessment, the outcomes are built on scalability, accuracy, and real-time data. Studies say that OBE increases student centric learning instead of teacher centric. This helps the institutions in accreditations like national board of accreditation (NBA), national assessment and accreditation council (NAAC) etc. Recently some countries are accepted graduates for employment only from the NBA accredited programs. Course outcomes (CO’s), program outcomes (PO’s) and program specific outcomes (PSO’s) are the key terms in OBE. This article provides the course plan, attainment calculation of CO’s, Po’s and PSO’s. Here, author’s considered digital circuits and systems (DCS) as a sample course from undergraduate Electronics and Communication Engineering program. The DCS course considered here have 5 CO’s in apply level (K3). In order to calculate the attainment of CO’s, PO’s and PSO’s, first the mapping of CO’s with PO’s and PSO’s are presented. Then different direct assessment (Formative and Summative) tools like internal tests, assignment, quiz and end semester examinations and indirect assessment tools like course end survey are conducted to evaluate the attainment level of 5 course outcomes. Based on the performance of 128 students, attainment of CO’s are calculated  first. PO’s and PSO’s attainment are calculated from the CO’s attainment. From the calculated attainment values suggestions/proposals are made for the upcoming semester.

This research paper conducts a comparative analysis of Python and Scratch, exploring their strengths and weaknesses in introductory programming education. While Scratch serves as an excellent starting point, it has limitations, prompting discussions about its suitability for all learners. Some argue that starting with Scratch facilitates a smoother transition into Python, while others suggest its effectiveness in attracting beginners to computer science. The study, conducted over 12 weeks among beginners in a Nigerian higher institution, aims to assess factors such as ease of learning, versatility, community support, and real-world application on both platforms. The first 4 weeks, participants were introduced to Scratch, then were introduced to Python from week 5 to 8 and finally week 9 to 12 were to work on projects and compare both platforms. 
The research delves into the experiences of participants lacking prior programming experience, emphasizing the exploration of thematic analysis, System Usability Scale (SUS) scores and individual responses. A total of four evaluations were carried out.  Results from the thematic analysis of the 1st evaluation using thematic analysis reveals that Scratch has the ability to foster computational thinking. The 2nd evaluation reveals that Scratch is preferred for tasks such as game development which has the ability to further deepen their programming experience. In the third evaluation, 46.3% of the participants agreed that experience gained from Scratch was helpful in learning Python while 70% agreed to some or a great extent that knowledge and skills acquired from learning Scratch was transferable to learning Python. The fourth evaluation was to understand the ease of use of Scratch versus Python using SUS.  The results from SUS notably reveal that the limited number of female participants showed intriguing preferences, with a lone female participant indicating a higher preference for Scratch. However, examining individual responses revealed a consistent outlier, with all participants expressing a higher preference for using Python more frequently than Scratch, despite their initial exposure to both platforms. This research suggests that the choice between Python and Scratch goes beyond syntax preferences, involving pedagogical strategies and the learning experiences each platform offers. 
This research contributes insights into the effectiveness of Scratch and Python in an educational setting, offering a nuanced understanding of the preferences and experiences of beginners. The findings underscore the importance of considering not only platform features but also individual learning experiences and pedagogical strategies in shaping programming education for novices.