Background: The modern world demands an effective educational approach to meet its requirements. In this study, the modern taxonomy of significant learning was applied to investigate the impact of personalized learning on achieving learning objectives. Methods: The study utilized an experimental pretest-posttest control group design. Thirty undergraduate educational sciences students from Allameh Tabataba’, i University participated in our study. They enrolled in the media education course in the spring semester of 2019-2020, and were randomly assigned to experimental and control groups. The learning topic was “, media message analysis, ”,and lesson objectives were defined based on the taxonomy of significant learning required for modern world. Personalized learning was implemented in an online environment for the experimental group. By choosing authentic assignments, we provided the students with learning paths based on their cognitive styles and gave them a sense of control over their own learning. Students in the control group received an online “, one-size-fits-all”,education. The engagement questionnaire was used to evaluate integration, human dimension, and categories of significant learning taxonomy,to measure students’,ability to control their learning, an online self-regulated learning questionnaire was employed. A researcher-made exam was designed to measure content mastery in fundamental knowledge and application categories. All three measurement tools were applied at baseline and two weeks after the intervention. The independent t-test was used to compare the two groups in each related category. Results: The results revealed that a personalized learning approach could lead to significant improvement in content mastery, cognitive, agentic, and emotional engagement, as well as self-regulated learning in the experimental group (P=0. 007, 0. 02, 0. 048, 0. 048, <0. 001, respectively). Conclusion: Teachers can help students achieve different categories of significant learning taxonomy through applying personalized learning to their courses. Therefore, implementing a personalized learning environment is recommended for higher education.

Purpose: The aim of this study was to compare higher-order aberrations between personalized treatment (PT) versus personalized treatment advanced (PTA) photorefractive keratectomy (PRK) for myopia and myopic astigmatism.Methods: In this clinical trial, 80 eyes of 40 patients who met inclusion criteria were enrolled. They underwent PRK using PT or PTA algorithm randomly. Higher-order aberrations were measured and compared between the study groups pre-operatively and at months 3 and 6 postoperatively.Results: The two study groups were comparable in terms of preoperative higher-order aberrations.Compared to the preoperative values, higher-order aberrations were significantly increased in the PT group (p<0.001), but decreased in the PTA group (p<0.001). Spherical aberration exhibited a significant increase postoperatively in the PT group (p<0.001) but remained unchanged in the PTA group (P=0.57).Conclusion: PRK using PT algorithm resulted in an increase higher-order aberrations. PTA algorithm provided better results in terms of postoperative higher-order aberrations and could treat pre-existing higher-order aberrations.

Education is the foundation of future human generations; Therefore, it is necessary to use the most intelligent and advanced technologies in this field. One of the latest technologies is artificial intelligence. Artificial intelligence is the ability of a computer or machine to think or learn. The expansion of artificial intelligence applications in education has created tremendous changes in this field. The global development of this technology in education has changed the way of teaching and learning. In personalized learning, the teacher does not teach the same lessons to all students, but guides each student in a unique path. The present research has been carried out using a descriptive-analytical method with the aim of investigating the applications of artificial intelligence in chemistry education. The findings of the research show that the use of artificial intelligence can help in providing educational materials to students and their learning process and provide a platform to support teachers. Educational methods have been improved with the help of this technology. Moreover, it helps enhance motivation, cooperation, interaction, and academic achievements.

This research was conducted to investigate personalized WebQuest-based learning in EFL students of higher education by applying the method of documentary data mining research, therefore organized in the framework of analytical descriptive studies. Based on the investigation of the available data in the field of personalized WebQuest-based learning, This article provided clear guidance on the initial studies of the integration of adaptive educational hypermedia methods as well as personalization approaches of web-based teaching-learning platforms in EFL students and preliminaries of the work to identify the personalization field of WebQuest-based learning. The results indicated that while many researches have been conducted in the field of WebQuest-based learning in English as a foreign language and the development of WebQuest technologies in the education of EFL students of higher education is undeniable, Specific studies have not been conducted in the personalization of WebQuest learning approaches and more studies are needed to identify the area of integration of adaptive personalization procedures and WebQuest learning methods. Field studies in the territory of this type of web-based learning dimensions can provide the basis for the realization of valuable achievements to guide researchers and English language students in designing and compiling personalized WebQuest learning-teaching packages.

Lifelong learning process is recommended for all health care providers, while emphasis is always on the routine procedures or the most frequently used parts of knowledge (1). As anesthesiologists encounter drug administration more than any other specialty in medicine, this field seems to be partly neglected in continuing medical education...

The evolving educational landscape requires innovative teaching methods to enhance learning and accommodate diverse styles. Technologies like artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) are transforming education by improving student performance and engagement. This research introduces an Artificial Intelligent Personal Recommender System Algorithm (AIPRS) that focuses on individual learning styles and interests to recommend customized learning programs. Unlike traditional methods, AIPRS personalizes education by analyzing learners' backgrounds and preferences, utilizing AI, ML, and IoT. This tailored approach marks a significant shift from conventional systems, emphasizing the importance of individual learning styles to improve the overall educational experience. The study highlights the potential of personalized education, offering a solution to efficiently deliver knowledge and enhance student satisfaction.