Campus Placement Research Articles

CAMPUS CONNECT

The project named “Campus Connect” is aimed to create a centralized web-based platform that is intended to fill the gap between fresh graduates and target employers. The system will be an online web application that can be accessed by the college student , the company and the college itself with proper login provided. The system can be used as an application for student or the fresher’s to apply for the job. This application is built using modern web development technologies, ensuring the scalability and ease of use. It also aims to reduce efforts with campus placement. The entire recruitment process gets easy for both the students and the recruiters. This campus connect platform enhances the experience of seeking a job for fresher’s. It also postthe opportunities of job and also help for hiringprocess. This system is designed with three user roles: students, college as a mediators, recruiterseach are provided with special login access and functionalities.

Kurikuler Mulok Perikanan Untuk Mengembangkan Literasi Laut Siswa Melalui Program Mandiri Kampus Mengajar VII 2024

One the campus placement schools teaching class VII 2024 is UPT SD Negeri 182 Pinrang, located at jln.H. Kanebo No.1 Ujung Lero, Suppa sub-district, Pinrang district, South Sulawesi. Lero village is in the coastal area and is known as a maritime community whose source of income or livelihood is the majority of the population as fisherman and their income comes from the sea. As om of the school located at the tip of lero, the aim of this service is to increase students marine literacy who are expected to be able to understand and evaluate every from of activity or activity in the fisheries sektor by teaching basic concepts through curricular activities, namely local fisheries content. The learning media used are learning videos and the implementation of pre-test and post-tests to determine the level of students understanding of the learning outcomes. Based on the results of the pre-test and post- test, about fisheries mulok has increased their knowledge and understanding of marine literacy.

Dynamic Landscape of Higher Education, The Intersection of Student Skills and Industry Demand

In today's dynamic job market, higher education students are confronted with the challenge of sifting through numerous career options that align with their skills, interests, and educational background. To tackle this hurdle, "Dynamic Landscape Of Higher Education, The Intersection Of Students Skills And Industry Demand" introduces a personalized job recommendation system tailored explicitly for higher education students. This system utilizes advanced recommendation algorithms and data analysis techniques to provide students with targeted job suggestions based on their academic qualifications, skills, interests, and career preferences. With a user-friendly interface, students can effortlessly input their information and receive tailored recommendations that evolve over time through continuous learning from user feedback. By offering data-driven insights and facilitating informed career decisions, this system empowers students to explore diverse career pathways and seamlessly transition from education to employment. The user interface of this system is designed to be intuitive and user-friendly, enabling students to easily input their information and preferences to receive tailored job recommendations. The recommendation system is adaptive, continuously learning from user feedback and interaction, ensuring that the recommendations provided evolve over time to better meet the changing needs and preferences of students. Recognizing the widening gap between industry requirements and graduating students' skills, this project proposes a comprehensive job recommendation system to bridge this divide and enhance placement opportunities. Leveraging advanced algorithms and machine learning techniques, the system evaluates students' skill sets and matches them with relevant job roles, facilitating a smooth transition from academia to the workforce. This multifaceted approach includes data collection, algorithm development, user interface design, and integration with existing campus placement systems. Collaborative efforts with academic institutions and industry partners aim to provide personalized job recommendations tailored to individual students' abilities and aspirations, with a focus on fairness and equity. Key components of the project include the design and implementation of an intuitive user interface, data-driven skill assessment methodologies, and continuous refinement of the recommendation algorithm based on user feedback and industry insights. Ethical considerations regarding data privacy and security are carefully addressed, reflecting a commitment to responsible use of student data. Ultimately, the success of the job recommendation system is measured by its ability to match students with suitable employment opportunities and its broader impact on career readiness and long-term professional success. By empowering students with the tools and resources needed to navigate the complexities of the modern job market, this project aims to catalyze positive change and foster a more inclusive and equitable future for all stakeholders involved.

Predictive Analysis of Campus Placement of Student Using Machine Learning Algorithms

In today's competitive job market, student campus placement process becomes very important role in the academic journey of students and the reputation of educational institutions alike. As the demand for skilled professionals continues to rise, the ability to accurately predict and optimize student placement outcomes becomes increasingly essential. This research paper proposes a machine learning algorithms analysis to forecast the campus placement of students with the help of various academic factors. The dataset contains historical placement records of students received from open-source repository named as kaggle. It includes information such as student academic achievements, internship, project, workshop, soft skill, extracurricular activities, and placement outcomes. Through data preprocessing and feature engineering, we transform raw data into a structured format suitable for predictive modeling. One hot encoding (Dummy encoding) applied on dataset to represent categorical variables as numerical values to provide more information to the model about the categorical variables. Predictive analysis work on various machine learning algorithms, including logistic regression, decision trees, random forests, support vector machines (SVM), XGBoost, AdaBoost, KNN and Naive Byes. The results of analysis demonstrate in predicting student placement outcomes with high accuracy and reliability. Accuracy is calculated in the form of precision, recall, f1-score, and support. Furthermore, we conduct feature importance analysis to identify the importance of different predictors in determining placement success. This research paper study can help to academic institutions that can identify at-risk students early in their academic journey and to improve their employability. In conclusion, research underscores the potential of predictive analysis and machine learning in optimizing student placement outcomes. By harnessing the power of data-driven insights, institutes can make use of placement prediction model to check the likelihood of campus placement of students.

Investigating mental health and well-being among MBA students during campus placement season in India

Purpose This paper aims to identify the factors that influence students' mental health, particularly in the context of MBA students passing through an emotional phase of the placement season. Design/methodology/approach A conceptual model through literature has been proposed. To test the proposed model of this study, a survey was conducted among the students of three MBA institutes of national reputation in India. The study's hypotheses were investigated using partial least squares-structural equations modeling and analysis of variance. To corroborate the findings of the survey data, a qualitative study in the form of open-ended interviews with five students was conducted. Findings The study revealed that female students, non-engineering graduates and students from non-family business backgrounds undergo stress, anxiety and depression higher than their classmates. Cumulative grade point average and bank loans do not significantly affect students' stress, anxiety and depression during the placement season. It was found that the increase in the levels of mindfulness scores led to a significant negative impact on stress, anxiety and depression among the students. Originality/value There is a gap in the literature that addresses the mental health of MBA students during campus job placements and the role of mindfulness in mitigating stress, anxiety and depression in these students. This research attempts to fill these research gaps.

College Campus Placement System

The project is to create an application for a college\'s placement management system, which is mostly web- based spring boot and react based secure project. It\'s a web-based application built on the Windows platform for the college\'s placement department to save information about its students in a database for firms to use in their recruitment process if they have the necessary login credentials. All of the necessary information about the pupils is stored in the system. The system saves all of the students\' personal information as well as their technical skills, which are needed for a CV to be sent to an employer. The system can be used by institutions to manage student information and placement details. Students should be able to access their accounts and upload their work. This project helps students to keep track of their personal and academic information. It saves time by decreasing manual labor and reducing the amount of paper used

ML-CPC: A Pathway for Machine Learning Based Campus Placement Classification

The intense competition among students for a limited number of job opportunities poses a significant challenge to campus placements. There are various strategies that organizations can employ to tackle this issue. Primarily, it is essential to provide high-quality educational programs and opportunities for professional development that align with current market needs. This involves regularly updating the curriculum, integrating sector-relevant projects, and facilitating hands-on training experiences. Campus placements play a crucial role in evaluating an institution's caliber and ensuring the employability of its students. Institutions can enhance their placement records by implementing innovative solutions to challenges encountered in placements, such as intense competition and economic fluctuations. This requires a proactive strategy, collaboration with businesses, a focus on skill enhancement, and support for students' soft skills and professional development. By implementing these corrective measures, institutions can contribute to students' future success by better preparing them for the workforce. The primary objective of this paper is to conduct an exploratory analysis of the recruitment dataset. The application of supervised machine learning is employed to predict whether a student was placed, utilizing classification models. The proposed approaches and methods surpass all other machine learning models, achieving a recall value of 1, accuracy of 0.9524, precision of 0.8667, and an F1 score value of 0.9286 to address the ensemble architectural.

Brainstorming and Fishbone diagram Tools for Enhancing the Student Placement in Technical Education System

Increased engineering colleges and unemployment have raised critical issues of quality for sustaining invisible competition all around the globe. In the olden days, higher education success was truly measured based on the performance (ability to apply knowledge and skills in resolving problems) of graduates in industries, which in turn ranked the colleges. In recent years, campus placement for engineering students has been treated as an institution’s responsibility, and the institute, upon successful job placement and average salary offered for the academic year. Since there are many engineering colleges, the reputed companies select to conduct placement drives for those institutes whose courses/programs have been accredited by quality assessment bodies. The accreditation to engineering institutions and their performance are tested for different attributes such as admission, teaching-learning process, infrastructure, curriculum and examination, placement, and career. Placement and carrier could directly or indirectly affect the other qualities and hence their improvement being the major concern to many engineering colleges. Brainstorming technique is first applied to know the possible methods for enhancing placement of students. The fishbone diagram is a standard way of representing in a more structured and precise manner that helps to identify the root causes that affect the graduate outcome, institution’s quality, which in turn is the placement of students. The generic framework module is defined for enhancing the placement of students with the help of brainstorming and the fishbone diagram tool.

Multi-objective Adaptive Fuzzy Campus Placement based Optimization Algorithm for optimal integration of DERs and DSTATCOMs

The novel Adaptive Fuzzy Campus Placement based Optimization Algorithm (AFCPOA) presented in this paper is expected to solve the issue of single objective and multi objective optimal optimization problems. AFCPOA’s performance was evaluated on 25 benchmark optimization test functions and compared to other existing methods. Optimal location and sizing of Distributed Generation (DG), Distribution STATtic COMpensator (DSTATCOM), DGs and DSTATCOMs, Battery Energy Storage Systems (BESS) in the presence of PVDGs, and BESS in the presence of PVDGs and DSTATCOMs were integrated into Radial Distribution System (RDS) to fulfil the objectives. Technical advantages of DG include active power losses reduction and control of voltage dips. DSTATCOM functions as a capacitor, injecting reactive power to improve the consistency and quality of power flow and sustain voltage against any imbalance and distortion on load side or supply side. The appropriate location and capacity for BESS present a significant barrier for the integration of BESS and renewable DGs with distribution networks. The objective functions in this problem are to minimize total real power losses, reactive power losses, and voltage deviation. Additionally, the improvement of voltage profile was taken into consideration as a constraint while choosing the best location. Finally, the suggested algorithm is evaluated on IEEE 33 bus RDS and PG & E 69 bus RDS. Simulation results show the proposed method’s excellent performance and applicability.

Online Advanced Skill Acquisition Program

Online ASAP is an innovative initiative implemented jointly by the Higher Education, General Education, Labour, and Local Self Government Departments of Kerala. Started in 2011, the program aims to empower the young community of Kerala by providing them with skill training and employment opportunities. This initiative aligns with the Prime Minister's nationwide campaign to skill 50 crore young people by 2023. To effectively execute the skill training program, a comprehensive state skills plan is being developed. This plan encompasses the identification of skills required within Kerala, as well as across India and globally. Additionally, it focuses on positioning Kerala as a destination for skills training, attracting students and learners from various regions. The Online ASAP project comprises several modules to facilitate its objectives. The Admin module enables administrators to manage course details, approve students and teachers, coordinate resources, conduct exams and practical sessions, and issue certificates. The Students module allows for student registration, verification, and participation in online classes with fixed schedules. Students can also post doubts and access practical videos. The Tutors module facilitates tutor registration and verification, ensuring quality instruction through online classes and addressing student queries. Resource Teachers play a crucial role in coordinating the program, including course and tutor coordination, setting timetables for students, and arranging exams and practical sessions. They also respond to student queries and concerns, ensuring a smooth learning experience. Recognizing the importance of employment, the Corporate Job Portal module allows employers to register, post job vacancies, view candidates' resumes, and conduct campus placement events. The Job Administrator module empowers administrators to approve corporate accounts and employee profiles, manage qualifications and notifications, and monitor candidate applications and progress throughout the recruitment process. Through Online ASAP, Kerala is committed to nurturing its young population, equipping them with essential skills, and providing them with employment opportunities. By leveraging technology and collaboration between various departments, the initiative strives to make a significant contribution to the national skill development agenda, fostering economic growth and prosperity for the state and the country as a whole.

Paving the Way: Exploring Influential Factors that Shape Career Preferences among Students in Higher Education Institutions - Insights from Selected Arts and Science Colleges in Kerala

The aim of the research was to examine the elements that influence the career choices of university students. It is a selected study conducted across ten Kerala arts and science colleges ranked in the 2022 ranking of the National Institutional Ranking Framework (NIRF). A sample of 375 students was selected from the selected higher education institutions. The results show that gender is significantly related to career choice. In addition, except for family factors like parents ‘education and family support, all other factors examined show weak, negative correlation with career choice. Family members’ profession and parental pressure prove to have significant negative association with the career path choice of students. Among the financial factors examined, future job opportunities, knowledge of high-paying jobs, job security, salary increases and perks, and opportunities for promotion and growth show a significant association with students' career choices. All of the personality factors examined correlate significantly with the career choice of the students. Among social factors, with the exception of teacher advice, college career guidance services, and alumni/friend success stories, all other variables studied show a significant association with career choice. Investigating on the factors that impact the career decisions of students, the results show that parental pressure, support from family members, future career opportunities, personal interest, social recognition related to a career, campus placements, influence of social media, and graduate/friend success stories have a significant impact on the students' choice of career. Among these predictor variables, the variables that have the greatest influence on career choices are parental pressure, future job opportunities, personal interest and campus placement. The study recommends universities to have an active careers advice center with a team consisting of a full-time placement officer and a student advisor. Recommendations for conducting psychometric tests to assess skills and conducting parent awareness programs to avoid parental pressure on students' career choices are recommended. Key Words: Career choice, career guidance, family factors, financial factors, personal factors, social factors.

Smart Campus 4.0: Digitalization of University Campus with Assimilation of Industry 4.0 for Innovation and Sustainability

According to the United Nations, global sustainability in terms of social, economic, and environmental issues must be achieved by 2030. SDGs 4 and 9 are related to education and strengthen the attainment of quality education and infrastructure innovation. Resilient infrastructure plays a significant role in strengthening the campus in terms of education, management, placement and environment. These all aspects come under the smart campus. Smart campus 4.0 is the amalgamation of multitude industry 4.0 enabling technologies for delivering smart and innovative facilities with the aspect of sustainability. The previous studies have proved that the sustainable development goals (SDGs) can be achieved with the amalgamation of industry 4.0 enabling technologies in the campus such as cloud computing, artificial intelligence (AI), Internet of things (IoT), edge/fog computing, blockchain, robot process automation (RPA), drones, augmented reality (AR), virtual reality (VR), big data, digital twin, and metaverse. The main objective of this study to provide the detailed discussion of all industry 4.0 enabling technologies in single research related to smart campus. The findings observed are IoT-Based Drone system is intended to ground patrolling, and a cloud server to develop a smart campus energy monitoring system. AI for campus placement prediction model; cloud and Edge computing architecture to build an intelligent air-quality monitoring system. The novelty of the study, it has discussed all industry 4.0 enabling technologies for a smart campus with challenges, recommendations, and future directions.

Inter-Linked Platform for Campus Placement in Higher educational Institutions of India

Abstract: Campus placement, also known as campus recruitment, is a crucial process for connecting students with potential employers and facilitating their entry into the job market. However, the traditional campus placement process can be timeconsuming, cumbersome, and inefficient, both for students and employers. In today's competitive job market, it is imperative to enhance the efficiency of the campus placement process and improve student employability. To address these challenges, we propose an interlinked platform for campus placement that leverages modern technology to streamline the recruitment process and enhance student employability. The proposed platform brings together various stakeholders, including students, employers, placement officers, and alumni, on a unified digital platform to create a seamless and efficient recruitment ecosystem. The interlinked platform incorporates a range of features to optimize the campus placement process. These features include an online portal for students to create and manage their profiles, upload their resumes, and apply for job opportunities. The platform also provides tools for employers to post job openings, search and shortlist candidates, and schedule interviews. Additionally, the platform facilitates communication between students and employers, allowing for real-time feedback and updates. One of the unique aspects of the interlinked platform is the integration of alumni networks. Alumni can serve as mentors, providing guidance and insights to students on the platform. They can also participate in recruitment activities, such as conducting mock interviews, providing industry-specific advice, and networking with potential employers. This integration of alumni networks adds value to the campus placement process by leveraging their expertise and connections to enhance student employability.

Career Success: A Campus Placement Recruitment System

Career Success: A Campus Placement Recruitment System

Big Data Analytics & Visualization From Google Form Data For Placement

A data analytics model for Educational institutions can help to predict the outcome of student’s results in the Campus Placement drives. It shows the progress of the students and can point out the students who need improvement and the institution can take further measures to improve the results of the students.

Enhancing student experience: A vision for the DMI university student app

The DMI University Student App will be a mobile-based software application designed to run on Android mobile phones. It will be used by potential students and students of DMI-St. John the Baptist University, as it will provide complete details of colleges, such as tuition and accommodation fees, eligibility criteria, campus placement opportunities, accommodation facilities, scholarship schemes, campus support services, rules, and regulations, etc. It will help students select various courses in engineering, social work, computer science, and business administration at DMI-St. John the Baptist University based on factors like qualifying exam details, technical expertise, and other details. The application will prepare the course list, which will be filtered based on parameters like institution ranking, department or field, fee limits, and chosen location. This application will help students find the most appropriate course in the field of their interest that corresponds to their location.

Predicting the eligibility of placement for students using data mining

Every educational institution uses campus placement to help students achieve their goals. Data mining techniques are frequently used in the educational system, which employs a variety of learning methods and approaches. A predictive model is built based on academic and extracurricular achievements to determine which category of placements (dream businesses, super dream companies, and mass recruiter companies) students are suitable for. Admission and the name of the institution are heavily influenced by placements. The main goal of this paper is to analyze previous year's applicant data in order to predict current students' placement chances and help institutions increase their placement percentage . Our project's main goal is to determine location using student data and to implement KNN models, logistic regression models, random forest models, and SVM models. Our project represents the candidate placement classification provided in the available data set. These algorithms predict the results on their own, and then we compare the algorithms' efficiency based on thedataset. This model assists a company's position cell in screening potential candidates and focusing and improving their technical and soft skills at regular intervals.

Undergraduate engineering students employment prediction using hybrid approach in machine learning

The knowledge discovery from student’s data can be very useful in predicting the employment under different categories. The machine learning is helping in this regard up to the great extent. In this paper, a hybrid model of machine learning has proposed to predict the jobs categories, students may get in their campus placement. The considered groups of students are from undergraduate courses from engineering stream having the semester’s scheme in their academic. The mapping of jobs has predicted based on their previous seven semesters marks as well as their personality index. The proposed hybrid model consists of three different model based on multilayer feed forward architecture, radial basis function neural network and K-means based clustering method. The proposed model provided the relative chances of available each job category with high accuracy and consistency.

Novel outlook to analyzing a gap between industrial and institutional skills at students in the faculty of engineering and natural sciences

In today’s scenario, Information Technology industries are compromising their expectations to fulfil the requirement of fresher candidates due to non-availability of job ready students. Industry invests huge cost to train the recruited manpower and make them ready for the on-job work. In this research paper, the researcher identified engineering graduate students’ employability skill sets and validated with the support of data provided by the students’ institute. All skills were grouped into four categories on the similarity and characteristics for the research work i.e., Aptitude, Communication, Technical and Personality skills set. Primary data was collected by using pen & paper test and secondary data was collected by using questionnaires method. The analysis of collected samples was carried out by using Software Package of Social Sciences (SPSS10). The researcher compared the expected performance by stakeholders and actual students’ performance in the designed test. Recruiters were unsatisfied on communication and technical skill sets from recruited engineering students in campus placement recruitment process. All technical institutes need to focus on employment skill sets from the first year to raise their employment possibilities.

Impact of Covid 19 on Career Opportunities of Fresher’s–A Study with Reference to Mayiladuthurai District

In the present employment market, campus placement holds a great experience and gift for students as well as educational institutes. While, it helps to the student community in building a strong foundation for the professional career ahead without facing the real-world job struggle, peer-competition or family pressure, a good placement record gives a competitive edge to an institution / University in their educational market. Campus placements provide the students with a foot-in- the-door opportunity, enabling them to start off their career right after they have completed their course curriculum. Furthermore, they get to interact and engage with the industry professionals during the placement drives, which further help lay a foundation for their prospective career in the future as they familiarize with potential contacts from their chosen career field. Placements have gradually become an integral part of an institute’s offerings, which was not the scenario earlier. Nowadays, students pay special attention to placement records while selecting a college or university for the admission. And it is rightly so, if a student is paying huge amount of parents’ hard-earned money as fees to the institutes/Universities, he/she has the right to calculate the ROI (return on investment), which in this case is a lucrative job. This research paper clearly identifies various influencing factors of Covid19 on employment market, employment opportunities of young graduates during pandemic period and impact of Covid19 on employment market for fresher during first wave period of Covid19 in the study area.