Suitable Courses Research Articles

AI-Enhanced Job Application Portal with Real-Time Evaluation and Skill Improvement

In the thriving job market, organizations needs an accurate,efficient and consistent candidate assessment to make sure that their applicant meet the necessary requirements. the traditional hiring processes rely on manual evaluation which is inconsistent and time-consuming which ultimately leads to the expansion of hiring cycles Along the side of candidates this hiring process lacks transparency and honesty and often fails to provide feedback leaving the applicant with uncertainty about their performance and unenlightened of the skills they need to enhance. In addition to that the unsucessful candidates will have confined access to the resources for their skill enhancement which can impact their future opportunities and personal development. To overcome these challenges, this proposed solution presents an AI chatbot integrated with the job application portal which will conduct time-constrained and domain-specific assessments providing an immediate feedback response and results for the eligibility for the application process. This portal is designed to evaluate a candidate with their skill proficiency precisely the AI-powered chatbot delivers a personalized experience by adapting the level of difficulty in real-time and suggesting suitable courses for the candidates who failed to pass the initial assessment .these candidates are offered with the choice to retake the test after 24 hours of time period ,this platform offers features of personalized dashboards where both the recruiters and the candidates can view the test results, progress, and insights. from these insights, the recruiters can gain detailed and analysis-based profiles allowing them to make their hiring decisions. the combination of AI-powered chatbot in integration with real-time feedback personalized dashboards, and course recommendations facilitates skill development and improves hiring efficiency which ensures only the efficient candidates are being recruited.

A Rule-Based System for Admission Recommendations in Nigerian Tertiary Institutions

This research presents a rule-based system to provide admission recommendations in Nigerian tertiary institutions. Unlike many existing studies that employ machine learning classifiers, this research aims to develop a transparent and deterministic system using predefined rules. The rule-based system recommends suitable courses based on candidates’ O’level results, Unified Tertiary Matriculation Examination (UTME) scores, computed post-UTME scores, and specific eligibility criteria. By focusing on a rule-based approach, the system mitigates the limitations and potential misclassifications associated with machine learning, thereby enhancing the efficiency and accuracy of the admissions process. The methodology follows predefined guidelines to ensure a transparent and understandable admissions procedure. This approach avoids the complexities and uncertainties of predictive models, providing an accurate and reliable method for course recommendations. The rule-based recommendation system ultimately aims to support educational institutions and admission offices in making informed decisions regarding candidate qualifications, eligibility, and course recommendations.

Comparison of digital and face-to-face coaching centers in the context of shadow education institutions: Student and teacher perceptions and academic achievements of students

This article compares digital and traditional face-to-face coaching within the scope of shadow education institutions. While analyzing the differences and similarities between the two educational models, their advantages and disadvantages are thoroughly discussed. In this context, interviews were conducted with students and teachers who receive education in both face-to-face and digital coaching, and the positive and negative aspects of both institutions, suitable and unsuitable courses, the future situation, and the effects on students’ academic achievements were revealed. According to the results obtained from the research, it is noteworthy that students who do not receive education in digital coaching have prejudices against digitalization. Additionally, no significant difference was found between the academic achievements of students receiving education in digital coaching and those receiving education in face-to-face coaching.

Students Exam Performance Prediction

Predicting student performance is crucial for understanding their progress and intervening effectively. This research aims to identify current student status and forecast future results to enable teachers to provide timely guidance and support. By analyzing dependencies for final examinations, we can recommend suitable courses for upcoming semesters, serving as advisor’s to students. Many students struggle due to a lack of proper guidance and monitoring, as teachers can’t monitor everyone simultaneously. An AI system can assist by identifying which students require specific types of support. Ultimately, the goal is to empower students to avoid predicted poor results through proactive intervention, potentially achieving accuracy rates as high as 94.88%.

Do personality traits affect entrepreneurial intention? The mediating role of the theory of planned behavior

PurposeThe purpose of this study is to determine the factors affecting entrepreneurial intention by combining the big five personality traits with the theory of planned behavior (TPB).Design/methodology/approachThe study was conducted with the use of an online questionnaire. The sample was comprised of 257 students from the business department of a public university in Athens.FindingsThe results of this study indicated a statistically significant and positive relationship of openness, extraversion, attitude, and perceived behavioral control to entrepreneurial intention. Furthermore, there is a positive relationship of openness, conscientiousness and extraversion towards entrepreneurial intention through perceived behavioral control. Finally, the findings indicated a positive relationship of extraversion with entrepreneurial intention through attitude.Research limitations/implicationsThe findings are limited to a specific student environment and should be replicated across various university settings to reach generalizable conclusions. The sample consists of only Greek students, which is another limitation of the study. The effects of entrepreneurship education vary with different ethnic backgrounds as seen in other studies. Finally, the research did not address the issue of gender or work experience among other variables discussed in other studies.Practical implicationsSeveral practical aspects of entrepreneurial intention can be considered, including entrepreneurship education, strategic investment, government and business organizations, mentoring issues and policymaking. This research can assist in various ways, enabling educational institutions to customize entrepreneurship courses, helping students choose suitable courses, aiding policymakers in developing supportive policies, and allowing organizations to identify employees with an entrepreneurial mindset.Originality/valueAlthough the big five personality traits and the Theory of Planned Behavior have been combined in numerous fields such as behavioral economics, psychology, and health behaviors, their integration in studying entrepreneurial intention remains scant. With this approach, researchers can gain a deeper understanding of the factors that affect entrepreneurial intention.

An Effective Optimization in Education System using Decision Support Systems

For academics, the process of retrieving information from large datasets, known as data mining, has become a captivating area of research. The concept of utilizing data mining techniques to extract information has been in existence for several decades. The dataset was initially designed to be divided into sections and analyzed using classification and clustering methods to explore its intrinsic characteristics. They make their forecasts based on these features. These predictions have been generated in the field of educational data mining for several purposes, such as forecasting student achievement using individual traits and assisting students in identifying suitable professors and courses. These targets have been derived from the analysis of student attrition and retention. Our study is centered around the aims of student attrition and retention. In addition, we have discovered intriguing indicators that contribute to the prediction of students' success, indicating the most competent instructors, and helping them with their choice of courses.

Building a hybrid and networked university through long-term lifelong learning agreements with the surrounding society

The current study focused on lifelong learning as a tool for higher education institutions to build relationships with surrounding society. As a theoretical point of departure, the study built on empowerment and human capital discourses of lifelong learning and the ivory tower, the factory, and the network as modes of higher education institutions' collaboration with the surrounding society. The study focused on a lifelong learning initiative to move a higher education institution towards a hybrid and networked mode. Particularly, the initiative focused on building long-term relationships with organisations in the surrounding society. Therefore, this paper aims to report results, in terms of a preliminary conceptual framework, from a higher education lifelong learning initiative. It answers the following research question: How can higher education institutions build hybrid and networked long-term relationships with the surrounding society through lifelong learning initiatives? The study was based on a design-based research method and described and analysed the conceptual development of a Swedish collaborative lifelong learning initiative between a higher education institution and an organisation. Data included in the analysis is taken from documents and field notes, including a long-term agreement, conceptual documents, and plans. The studied initiative included three phases. The first phase, preparatory work, embraced a course for the top management team of the organisation to strengthen their ability to plan, lead and organise professional and organisational development, followed by identifying the needs of the organisation. The second phase, implementation, included dialogue and analysis of the identified needs and development and implementation of suitable courses matching prioritised needs. The third phase, follow-up, included an evaluation of the different parts of the initiative, possible impacts, and a discussion of further needs of collaboration for both professional and organisational development. The conceptual development work included the design of a hybrid and networked framework based on the idea of collaboration partners subscribing to higher education courses under a long-term agreement called POD (collaboration for Professional and Organisational Development). Among others, these PODs include regular dialogues on the needs of the organisations and courses built on networked learning principles. Bring-your-own-data is a core principle to ensure a strong link between theory and practice. The early stage of the initiative included promising results, going in the direction of becoming a hybrid and networked higher education institution by signing its first POD.

CR-LCRP: Course recommendation based on Learner–Course Relation Prediction with data augmentation in a heterogeneous view

Course recommendation aims to offer suitable courses for learners and alleviate dropout issue in Massive Open Online Course (MOOC) learning scenarios. The present studies focus on learning semantic representations by modeling the learner’s personal information and the courses’ teaching attributes. However, with massive courses offered and various learners involved, the data sparsity and cold start are still two dominant challenges hindering the booming of online learning, which roughly results from the scarce exploitation on various relationships between learners and courses. To alleviate the above issues, we leverage the strong ability of Heterogeneous Information Network (HIN) in modeling heterogeneous data types and complex structural characteristics, and propose a Course Recommendation method based on Learner–Course Relation Prediction (CR-LCRP) with multi-granularity data augmentation strategy. Specifically, the multi-source interactive data on courses, the learner–learner similarity and the course–course similarity are integrated in building a heterogeneous information network. With both explicit and implicit features extracted and structural information from meta-paths enhanced, the CR-LCRP method learns high-quality representations of learners and courses, and eventually performs learner–course relation prediction in MOOC learning scenarios. Finally, extensive experiments are conducted on real-world MOOCs datasets and the experimental results demonstrate that the proposed CR-LCRP method outperforms the state-of-the-art baseline models with superior performance on several widely used evaluation metrics.

Improving Pilot Competence Through Flying Practice Learning Using Flight Simulator at the Indonesia Civil Pilot Academy of Banyuwangi

Objective: Indonesia Pilot Academy of Banyuwangi uses simulators to improve pilots' capabilities and maintain their skills. The pilot training process includes theoretical study, flight simulator and practical training. A total of eight simulators are available, including two ALSIMs and three each of the Redbird TD2 and FMX 1000. Using a simulator reduces the risk of flying in realistic conditions. Therefore, the purpose of this study is to measure the degree of effectiveness of practical flight learning, assess students' perceptions of practical flight learning, and analyze the impact of practical flight learning using a flight simulator on students' decision-making and processing abilities. Improving school pilot skills during aviation emergencies. The career of a pilot is Indonesia Pilot Academy of Banyuwangi. Method: As part of our research, we chose a quantitative research approach to objectively analyze the improvement of pilot capabilities through practical flight courses on a flight simulator at the Indonesia Pilot Academy of Banyuwangi. The main data sources are 23 flight instructors and 34 students at the Indonesian Pilot Academy in Banyuwangi. Secondary data sources, on the other hand, consist of sources that go beyond words and actions, such as written notes. These written sources can be divided into book and scholarly journal sources, archival data sources, personal documents, and official documents. In this study, the authors used a variety of documents, including documents that provide information on the number of flight instructors and student pilots currently using simulators. Results: Pilot skill development is progressing well through direct practice on flight simulators at the Banyuwangi Pilot Academy in Indonesia. This can be seen through metrics related to key improvement areas that increase flight simulation proficiency. Analyzing the previous discussion, if one considers the impact of various other factors in the questionnaire results on improving pilot abilities through practical flying courses, it can be concluded that helping to improve abilities through flight simulators is a commendable simulator. Conclusions: Improving pilot skills through direct hands-on flight simulator training at Indonesia Pilot Academy of Banyuwangi has proven successful. This success is measured by several criteria such as: good institutional management, pleasant training experience, qualified flight instructors, suitable flight simulators, suitable courses, discipline and achievement of flight objectives. Additionally, implementing a flight simulator training device (FSTD) helps maintain flight authorization and skills. The Indonesian Banyuwangi Aviation Academy uses FSTD for educational, research and community purposes. While simulator training has its benefits, it's worth noting that certain pilot skills require compliance with applicable regulations and actual flight experience.

Assessing Decision Support Tools for Mitigating Tail Biting in Pork Production: Current Progress and Future Directions.

Tail biting (TB) in pigs is a complex issue that can be caused by multiple factors, making it difficult to determine the exact etiology on a case-by-case basis. As such, it is often difficult to pinpoint the reason, or set of reasons, for TB events, Decision Support Tools (DSTs) can be used to identify possible risk factors of TB on farms and provide suitable courses of action. The aim of this review was to identify DSTs that could be used to predict the risk of TB behavior. Additionally, technologies that can be used to support DSTs, with monitoring and tracking the prevalence of TB behaviors, are reviewed. Using the PRISMA methodology to identify sources, the applied selection process found nine DSTs related to TB in pigs. All support tools relied on secondary information, either by way of the scientific literature or expert opinions, to determine risk factors for TB predictions. Only one DST was validated by external sources, seven were self-assessed by original developers, and one presented no evidence of validation. This analysis better understands the limitations of DSTs and highlights an opportunity for the development of DSTs that rely on objective data derived from the environment, animals, and humans simultaneously to predict TB risks. Moreover, an opportunity exists for the incorporation of monitoring technologies for TB detection into a DST.

The Old and New Course Selection Methods in Chinese High School: Analysis from Gender Perspectives

After the Ministry of Education and the State Council in China enacted the regulations about the educational revolution in 2014, the new way of course selection in Chinese high schools gradually spread. At the same time, due to the obvious shortcomings of the old course selection method, it is progressively replaced by the new one. Based on the educational revolution, problems such as gender stereotypes still existed and they can influence students choice on course selection. This article compares two ways of course selection and analyses factors that can influence students course choices. In the two different ways of course selection, except for students interests, gender stereotypes from teachers and parents are the main factor affecting students. Meanwhile, these gender stereotypes will always exist in the choice of college majors and future jobs. In the new way of course selection, there is a certain degree of improvement. Therefore, based on the new and better way of course selection, schools and parents should avoid gender stereotypes, guide students to choose suitable courses and meet the personalized development of students.

Integrating platforms through content-based graph representation learning

The advancements in education technology have encouraged businesses to offer a wide range of educational resources aimed at teaching labor market-related skills. However, the ever-changing abundance of information in both course and job platforms makes it difficult for job seekers to identify the right learning resources for a particular job opportunity. As a result, it is important to integrate and analyze information from both platforms to recommend suitable courses and jobs to them. Meanwhile, integrating information from these distinct platforms for analysis purposes poses a challenge due to the presentation of data in free text. Previous studies have mainly focused on downstream tasks in one area and few have integrated both of them. Additionally, linking skills from these platforms through methods such as crowdsourcing or annotation mechanisms may not be reliable due to potential biases or inconsistencies. This study proposes a novel approach to integrate data from course and job advertising platforms using graph representation learning and embedding algorithms. The proposed approach predicts the relationship between the two platforms and has been tested in the form of link prediction tasks using several graph learning methods with RMSE scores below 0.1 and AUC scores between 70-75%. These results imply that our approach can be further refined and has the potential to be used in building recommendation systems that help address the problem of information overload among students and job seekers.

Course Recommendation Application for Admission to University

Studying in Higher Education institutions has been the dream of many students who completed their post-secondary schools. This study aims to assist the students in their decision for admission to the program courses offered at Universiti Kuala Lumpur (UniKL). A wide variety of technical program courses are offered on UniKL’s twelve campuses. Every campus institute has specific specialisation program courses. Students who wished to study at UniKL usually would have to search for courses offered on various UniKL campuses. Usually, students could not decide which courses suited their personality and future career path. This study proposes a prototype application that assists and recommends the students with their course selection according to their personality and final examination results. The prototype would let the students take a personality test that allows them to understand their personality and proposes a list of UniKL courses that match their personality and examination results. Therefore, the students would have a clear view of the prospective opportunities the program courses have to offer and the direction of their future careers. The career prediction recommendation would let the students target suitable courses that match their examination results and personality.

Prathamik Shikshan Padwika Abhayaskramatil (D.El.Ed) Aakarik Mulyamapan Pranali.

"The future of the nation is taking shape in the classroom. This statement of the Kothari Commission report is familiar to all. The important burden of shaping the future rests on the shoulders of teachers. Therefore, inculcating values and developing skills in the student teacher who aspires to be a teacher is of utmost importance. -The State Teacher Training Board has been responsible for the development of suitable pre-service courses since 1968. Diploma in Primary Education course is a milestone in this development.

Technology-enhanced or Technology-exhausted Learning in Adult Migrant Literacy Education in Finland: Exploring Teachers’ Experiences and Views in Pre-pandemic and Pandemic Times

This exploratory mixed data study investigated the role of technology in adult migrant language and late literacy education in Finland. In addition to an online pre-pandemic survey targeted at Finnish language and literacy teachers, four in-service teachers were interviewed during the COVID-19 pandemic in late 2020. With means of qualitative content analysis an account of teachers’ individual experiences and views of technology-equipped language and literacy learning was generated. Problematic issues were the use of devices for educational purposes, lacking user experience, inadequate provision of suitable devices, IT courses, and insufficient support for learners and teachers, causing inequality. Teachers reported of various ways to ensure and enable device access and learning opportunities, resulting particularly during pandemic times in high workloads. The emergency remote teaching circumstances during this global health crisis posed an enormous additional challenge for teachers and students. Findings raise questions about the pedagogical cost of the pandemic for adult migrant literacy education in Finland and call for discussions on post-pandemic changes on a national and international level.

The Teaching Strategy of Socio-Political Education by Deep Learning Under Educational Psychology.

This study aims to optimize the teaching content of ideological and political courses and guide students to establish correct values. Inspired by Artificial Intelligence, the K-means clustering algorithm was applied to the neural collaborative filtering algorithm through temporal data. Besides, a deep learning algorithm was designed for the improved matrix factorization. The evaluation indicators were selected through experiments. The relevant data sets were used for simulation and testing. The test results indicated that the Root Mean Square Error of this scheme was 1.251, and the Mean Absolute Error was 0.625. These index measurement values were better than those of similar algorithms, indicating this model has better performance after optimization and can recommend suitable courses. The innovative algorithm designed to construct the classroom teaching model of social and political education can accurately recommend proper courses according to the students' learning situation reflecting their psychological states. The research provides adaptive teaching for students, enables interaction between teachers and students, and helps students form correct values. It also has an important role in improving the teaching strategies.

CIRCULAR ECONOMY TACTICS AND SUSTAINABLE DEVELOPMENT: A COMPARATIVE STUDY OF INDIAN INDUSTRIES

A circular economy maximises the amount of time that materials, goods, and services can be used for. In a circular economy, resources are used less frequently, materials, goods, and services are redesigned to use fewer resources, and "waste" is recovered and used as a resource to create new goods and materials. Any circular economy adaption is crucial to reducing global warming. As a result, any study about circular economy technologies, their attendant drawbacks, and policy suggestions for improvement assume primary importance in contemporary times. Towards that end, this paper examines the growing trends in three key industries in India – namely, agriculture, manufacturing, and technology – in an attempt to analyse the relative success of these adaptations and their wider impact. Qualitative and quantitative insights are drawn through case studies of companies working in India. The analysis is further bolstered by comparing the scenario in India with global trends that highlight the work that needs to be done. Accordingly, the paper concludes by underlining the most important advantages and disadvantages as well as suggesting suitable policy courses for the future.

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Fountain Unveristy, Oshogbo JUPEB /PRE-DEGREE Admission form 2022/2023 academic session is OUT Call @☎️ 09166948950 Candidates are hereby enjoined to purchase the form and apply for suitable Undergraduate Courses Candidates must have at least 6 (six) "Cs (Credits)" grades in their O'level result (s)Candidates must score at least 120 in JAMB UTME examination.This must include English Language, Mathematics and three

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