Narrow Results

An Intelligent Exam Management System (IEMS) improves education information and handles an important role in education administration and management. Test optimization is the practice of successfully reducing test execution time and expenses. However, this can come at the expense of accuracy, efficiency, and the quality of testing that existed prior to optimization. In addition to being time-consuming and labor-intensive for teachers, grading essay responses may lead to inequity since it is impossible to apply consistent requirements to all of the responses. Creating assessments is a challenging task that involves optimizing parameters while adhering to several limitations. The Genetic Algorithm (GA) has been enhanced to address the issues of speed and low paper quality in intelligent test paper development. Educational management at universities has been the suggested approach for IEMS-GA to facilitate smarter education, test and course management, promote the improvement of ideas related to school administration, and provide a more equal system of evaluation. Students rely on exams as a means of gauging their own academic strengths and weaknesses. Students’ test scores provide valuable information about their academic strengths and areas for improvement, allowing them to better plan for the future and attain their academic goals. Results are an important part of the educational process as they reveal a great deal about the way students are doing in class. Knowledge and skills necessary to succeed in the real world are evaluated by such assessments.

An instructional approach identified as a personalized learning path aims to adjust regulations to the particular interests’ necessities, talents, and skills of each pupil. Research on personalized path recommendation is critical for the development of sophisticated learning systems. The challenge emerges from the difficulty in developing personalized learning paths that properly fit every pupil’s unique characteristics and learning needs. This paper examines personalized educational routes and how deep learning (DL) algorithms can be used to construct them, with a particular emphasis on English language learning. This research proposed a novel fruit fly optimizer-tuned adjustable recurrent neural network (FFO-ARNN) to employ the students learning performance and effectively personalized English learning. For this study, gathering data on student’s demographics and English proficiency levels through assessments and educational records, in that Data on the linguistic competency of English linguistic Learners (ELLs) in grades 8–12 was collected. The data were pre-processed using tokenization for the obtained data. Term Frequency-Inverse Document Frequency (TF-IDF) is a method using extracts the feature from pre-processed data. These personalized learning approaches encourage self-directed learning and independence while increasing student engagement and academic achievement. The proposed method is implemented using Python software. In comparative analysis, the proposed method evaluates various performance metrics. The results show that the proposed method achieved superior performance in personalized learning paths in English learning. This study demonstrates how DL has the potential to revolutionize language instruction by providing customized solutions for every student.

In order to optimize the evaluation of students’ English creative thinking ability under the multimedia assisted teaching mode, a training model of students’ English Creative Thinking Ability Based on big data analysis is proposed. Establish a big data block fusion structure analysis model, analyze the fuzzy parameters of constraint indicators, obtain a quantitative table for the state allocation of evaluation constraint indicators, formulate an evaluation quantitative analysis scoring table, and use hierarchical structural reconstruction to construct the clustering model parameters for the evaluation of students’ English creative thinking ability training. Through the quantitative index feature analysis, the optimization design of teaching activities and teaching methods for the evaluation of the cultivation process of students’ English creative thinking ability are realized. In the multi-dimensional hierarchical structure parameter model, the parameter configuration of students’ English creative thinking ability training indicators is realized, and the evaluation characteristic index fusion clustering processing is carried out on the parameter configuration results to form a pair of classification prediction and index analysis model. According to the hierarchical distribution density and grid clustering of indicators, the evaluation of students’ English creative thinking ability under the multimedia-assisted teaching mode is realized. The results of empirical analysis show that the fuzzy parameter analysis ability of the constraint index of process evaluation using this method is strong, the evaluation results are accurate and reliable, and the reliability and confidence level of evaluation are improved.

Introduction:A combination of internal characteristics, such as motivation, personality, beliefs, and dispositions that combine with external circumstances to affect student results is referred to as teaching efficacy. The English-speaking environment becomes a major problem as students learn by interaction. To communicate effectively, they do not acquire the speaking and listening abilities necessary in English.

Objective:This study examines the impact of computer programming courses and learning analytics on student’s computer programming skills.

Methods:In this study, we proposed a novel Dung Beetle Optimized Flexible Random Forest (DBO-FRF) to evaluate the teaching effectiveness. In this study, 175 students’ data were collected for teaching effectiveness. Using student data, prediction model was constructed based on the attributes of the students, their past academic records, their interactions with online resources, and their advancement in laboratory work related to programming. The proposed method is compared to other traditional algorithms.

Results and conclusion:The proposed method is implemented using Python software. The expected performance in the course and, in the instance that any submitted programs failed to meet the requirements, a programming recommendation from one of the class’s top students. The result shows the proposed method achieved better performance in terms of accuracy, precision, recall, and $F$1-score. This decreased the performance gap between students who performed lower and those who performed higher, enabling students who adjusted their programs to learn more.

The social and ideology education, focus instruction, hands-on learning, and a variety of means, students are cultivated in their political quality, moral character, and sense of social responsibility while it is being guided in understanding the nation’s important policies and social current affairs. Ideological and political disciplines are implementing the problem of transformation within the framework of the major criteria of the new curriculum reform. In the conventional teaching process, students face several challenges in achieving the essential quality of ideological and political education. The artificial intelligence-assisted interactive modeling on the Internet of Things platform (AI-IM-IoT) is able to generate a smart system for ideological and political education, addressing various problems in the current situation. Performance in conventional ideological and political learning platforms, an investigation on the construction of intelligent media ideological and political learning platforms are based on AI technology. Hence AI-IM-IoT has been the Ideology of ideas and beliefs that influence social behavior and can possess an effect on educational achievements. Ideological viewpoints on education can influence educational institutions’ aims, values, and priorities, as well as how resources and opportunities are allocated. Students are encouraged to develop an understanding of social, economic, and political issues via ideological and political education programs.

Background: Knowledge management is greatly aided by the important educational components of emotional learning and student well-being. Better information acquisition, retention and application can result from incorporating emotional learning into the educational system, which can also have a favourable effect on students’ general well-being. Students’ emotional learning skills may have an impact on their mental and physical well-being both inside and external of the educational setting. Improving theoretical success, decreasing substance usage, increasing general well-being and reducing violent behaviour are all possible outcomes of strengthening these abilities. This investigation’s objective is to explore how emotional learning affects students’ overall well-being. Also, it examines how emotional learning and students’ well-being relate to students’ academic achievement. Study participants were recruited across higher learning institutions from selected universities in India.

Problem statement: The study explores the relationship between emotional intelligence (EI), social–emotional learning and psychological well-being. Despite previous research showing conflicting results, this study aims to fill knowledge gaps by examining the impact of emotional learning on students’ happiness, life satisfaction, self-esteem and EI. The research aims to fill in knowledge gaps by examining predictors of student behaviour and attitudes, thereby enhancing the understanding of EI and its correlation with well-being.

Methodology: A structured questionnaire is developed with 26 questions and data are collected from 365 students of higher education in India. The collected data were analysed using SEM. By performing hypothesis testing we conclude that self-regulation, relationship skills, learning attitude and prosocial behaviour have a positive and beneficial impact on students’ well-being. Also, found that emotional awareness does not have a favourable and beneficial influence on the student’s well-being.

Findings: The outcomes show that self-regulation, relationship skills, learning attitude and prosocial behaviour have a positive and beneficial impact on student’s well-being. Also, found that emotional awareness does not have a favourable and beneficial influence on the student’s well-being. Overall, these results suggest some potential changes in our interpretation of the role that emotional learning plays in fostering academic performance, as its main function seems to be in mitigating the negative consequences of mental health issues.

Discussion: This study reveals five direct correlations that positively impact student well-being. The first hypothesis suggests that self-regulation positively impacts student well-being, while the second hypothesis suggests that emotional awareness does not. The third hypothesis suggests that relationship skills positively impact student well-being, while the fourth hypothesis suggests that learning attitude positively influences well-being. The fifth hypothesis suggests that prosocial behaviour positively influences student well-being.

Entrepreneurs are a product of their social environment. The manner by which they perceive opportunities; access or process information; and make decisions is, influenced by both social interaction, and their social background. Using insights from Socially Situated Cognition (SSC) theory, that posits one’s social environment can have a normative or informative effect on decision-making process we consider proximal social factors influencing the decision-making processes of student entrepreneurs. We propose that entrepreneurial education, networking, and incubation spaces provide direct information to students to aid entrepreneurial decision-making, and indirect informational cues that are situational, synergistic and omnipresent. Noting the multi-faceted and dynamic nature of the entrepreneurial journey of the student, we explore the potential effect of each of these factors on the student decision-making process. We discuss the implications of this inquiry from a researcher and educator perspective, and note the current challenges faced by student entrepreneurs in a socially distanced educational and entrepreneurial context. It is envisaged that this paper will serve as the basis for further thought and empiricism.

The abundance of online educational resources has made it increasingly difficult for students to identify the correct learning materials in recent years. Overcoming the information overload that has emerged in the new education systems is possible via a tailored recommendation system. It encourages students to look for new ways to get around the subject matter and to use information from all across the world. Because of this, many academics are working to create learning systems that incorporate methods for creating a unique learning experience for each user. Therefore, our proposed approach was to create an appropriate learning route for each student, and they are using Educational Resource Information Based on an Adaptable Genetic Algorithm(ERI-AGA). Evidence from studies shows that the suggested technique can provide relevant course materials for students based on the specific needs of students to help them study better in a Web-based system. Personal recommendation engine, pre-processing and learning-based model development, and implementation of the recommendation system will be researched. Participatory budgeting PB-level data storage and processing as well as the ability to suggest in real time will be studied. The capacity to make real-time suggestions and the storing and processing of PB-level data will be investigated. It was critical to check the system’s availability by running associated tasks and performance tests. The comparison values demonstrated that ERI-AGA was a reliable and accurate assessment procedure.

An Exclusive Interview with Executive Vice Dean of Duke—NUS Graduate Medical School Singapore, Dr Ranga Krishnan.

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Interview with Director of MDIS school of Life Sciences: Dr Andrew P. Lucy.

Certificate Course in Outcomes Research From Data to Publication.

Two Japanese Scientists Recognized for Mentoring in Science at 2009 Nature Awards.

The driving concept of students’ sports training involves a unique activity that is often tightly correlated to students’ efficiency and varies with the momentum of sports training. Supervised learning is one of the smart methods with positive results in the fields of classification techniques. Due to the excessive currency unit associated with sports, sports forecasting is a growing area that must be well predicted. Therefore, in this paper, sports training based on the supervised learning (STSLM) model has been proposed to evaluate and predict student sports efficiency. STSLM models are based on various variables, such as traditional student ratings, performance, and efficiency. The emphasis is on the efficiency of students predicting sports outcomes. STSLM defines evaluation methods, information sources, effective models for testing students’ sports training, and unique challenges to forecast sports outcomes. The experimental results have been performed. The suggested STSLM model enhances the efficiency ratio of 96.3%, injury prevention level of 98.2%, fitness level of 95.5%, evaluation ratio of 98.8%, and training optimization ratio of 97.2% compared to other existing approaches.

Group work can inspire students, encourage constructive learning, and improve essential critical thinking, communication, and decision-making in the present competitive world. The risk factors in group learning include students who prefer working alone and strongly despise dealing with things created by gathering in teams. Online learners often have problems locating lasting peace times for group therapy sessions are considered an essential factor. A predictive Group Learning Behavior Approach (PGLBA) has been proposed. Students who commute to college agree to group conferences and workgroup learning and Inquiry learning for higher education. The grounded Inquiry Learning Approach is invented to strengthen students’ enjoyment of active group learning, and the students find times for group meetings that are often mutually advantageous. The simulation analysis is performed based on performance, accuracy, and efficiency proves the proposed framework’s reliability. The experimental results show that the proposed PGLBA-IL model enhances the accuracy ratio of 81.2%, an efficiency ratio of the number of students 86.4%, and the overall performance analysis ratio of 85.1% compared to others existing approaches.

The concept of Personal Knowledge and Information Management (PKIM) is based, among others, on two theories: Personal Information Management (PTM) and Personal Knowledge Management (PKM), which hitherto were both subjects of separate studies. Moreover, the concept of PKIM is related to IL, which is a concept of information skills and competences of individuals — a person who manages knowledge has to be information literate. Some of the empirical studies results in the field of PKIM, started in Poland and recently continued in Germany, are presented. As the research method an unstructured questionnaire with open questions was used.

Given the results of the survey as well as taking into account the subject literature, the concepts of PIM, PKM, and Information Literacy (IL) seem to be compatible and connected with each other. Our respondents perceive Knowledge and Information as well as knowledge management (KM) and information management (IM) in the context of learning and studying as integrated areas of interests. Although they do see differences between them, interconnections and relations seem more important. Furthermore, KM and IM are recognized as tools of coping with information overload. All aspects that have repercussions on KM and IM are related to three categories: personal characteristics, environment (macro and micro environment), and knowledge and information sources.

Education is a dynamic system by which students perceive the factors necessary to fit them into the society. Education is mainly intentional learning that grooms individuals to achieve success in their adult lives. Evaluation of teaching techniques, course management (CM), communication, and student monitoring are the main characteristics of today’s education system. The aim to plan the curriculum of education management in both schools and colleges leads to the implementation of an MS-BDA. The development process for evaluation of teaching techniques and CM includes the use of the sentiment analysis method, which assesses the emotional feelings of students studying the course by managing curriculum quality. The big data analysis with MNN is developed by considering the communication and student monitoring system. This system evaluates the monitoring model provided in MS-BDA for assessing student communication on merging the voice-over with the communication language processing system. The simulation analysis is performed based on accessibility, adaptability, and efficiency, proving the proposed framework’s reliability. Therefore, the system outputs an accuracy of 99.1% when compared to the existing methods.

To deliver on the 2030 Agenda and the seventeen development goals, while facing complex health challenges, we need research and education that extend across multiple scientific fields. This will enable researchers from a variety of disciplines to meet, identify research issues, apply for funding, and conduct interdisciplinary research. In addition, student involvement is key in achieving the 2030 Agenda’s global goals – and beyond. Challenges include, climate change and child health, non-peaceful societies, gender inequalities and health.

The Swedish Institute for Global Health Transformation (SIGHT) was founded in 2017 at the Royal Swedish Academy of Sciences with the support of the Bill & Melinda Gates Foundation. SIGHT’s mission is to promote an interdisciplinary approach in research and education in the field of global health. In order to deliver on the commitment to global health among researchers and students in various scientific fields and at universities and colleges across Sweden, SIGHT has established SIGHT Fellows, a mentoring programme for academic researchers. In collaboration with universities, established research institutions, and other stakeholders, SIGHT Student Network holds dynamic meetings for students from a variety of disciplines and universities to contribute to delivering the UN’s sustainability goals.