Placement Package Research Articles

Finding the combined effect of academic and non-academic performance on management students’ placement: A fuzzy logic approach

The academic performance (scores/credentials) of a student during an educational program is generally linked to his/her employability (including the placement package he/she receives) (Cheong et al., 2018). However, in a program like MBA non-academic performance like extracurricular activities, interpersonal skills, etc., of a student is equally or more important as it could fetch him/her a better job with a higher package. Moreover, MBA graduates are expected to have not only academic knowledge but also practical skills such as leadership, attitude, efforts, and communication. This study relates both academic and non-academic performances of students to the respective packages they get and proposes a fuzzy expert system (FES) for the evaluation of students’ performances and its combined effect on placement (outcome). A suitable rule-based fuzzy inference mechanism (FIM) has been formed and discussed. The principles behind fuzzy logic in evaluating student academic as well as non-academic performances have been discussed and how these principles could be applied in measuring its impact on the placement packages (historical) is illustrated. Results (achieved placement performance/package of students) were also compared with the existing (traditional) statistical method. The fuzzy logic system was assessed as a more practical method of evaluation than the traditional method. This research uses a sample of 56 students in the MBA program who graduated in 2021 from the management department (b-school) of a premier private university in India. The research found that an increase in non-academic performance had a greater impact on placement outcomes than an increase in academic performance alone.

STUDENT PLACEMENT PREDICTION

In an educational institution, the most important objective is the placement of students. For each and every student, the placement part is a very important one in college life because for some sets of students it is the future. The prediction of students will not be 100% accurate but it depends how the students perform in every part of the placement. So, to predict the placement package chance of current students, we can analyze the previous year’s students data. The data has been collected from the institution and certain pre-processing techniques are applied to the models. Different algorithms have different accuracy. Depending on the type of issue and dataset to be solved, different algorithms have varied levels of accuracy. As a result, we decided to assess the accuracy levels of three methods, namely Logistic Regression, Decision Tree Classifier, and Random Forest Classifier, with respect to our challenge and dataset. The efficiency/accuracy of each model is visualized and tested and based on the performance analysis, the best model results are declared.

Analyzing and Forecasting the Students Placement Package

Nowadays, Educational Institutions are growing at high rates. The goal of all higher education institutions is to provide students with a job through their training and placement office. One of the biggest challenges facing these days is to analyze and increase student performance. The main purpose our developed system is to analyze student’s past historical data includes academic as well as extracurricular activities and forecast the approximate placement package. This system helps both institute and student to plan and improve their performance before actual placement drives by companies. The last 2-year real time dataset having 600+ records are collected from Ramrao Adik Institute of Technology (RAIT) training and placement office from Navi Mumbai, India. Analysis of this dataset is carried out using most popular Random Forest Regression, Multilinear Regression and Decision Tree Regression algorithms. 3 independent models are built, and best performing algorithm is selected, then dumped the model using pickle and deployed the system on cloud. Thus the model with accuracy achieved more than 95% for 10+ trails and experiments has made us clear that it really helps placement cell from time to time to identify potential students and to focus and develop the skills needed for students.

Enablers for good placements of graduates: fitting industry’s needs

PurposeCompanies are looking for certain employability attributes and personality traits while recruiting and selecting suitable candidates for their organizations and there is a mismatch in what the higher educational institutes are grooming the graduates. There is therefore a need for proactive management of career development of students. The paper aims to discuss these issues.Design/methodology/approachThis research involved an exploratory study on a database of 445 students enrolled and passed out from the five batches of two years business management course from 2012 to 2016 in a business school in India, to identify the parameters which led to generating good placement package for them. The impact of independent variables of live industry projects, communication skills, academic performance, classroom attendance and co-curricular activities on the placement package was studied using stepwise regression analysis.FindingsThe study revealed that industry projects, co-curricular activities, communication skills and academic performance were the key enablers which helped the students become industry ready and employable.Research limitations/implicationsThis research involved the study of effect of only four independent variables- academic performance, communication skills, participation in live industry projects and co-curricular activities on the placement package received by the students. There is a scope of extending this study by considering the effect of other variables such as educational background (graduation stream, performance in that stream, scores attained in competitive exams, etc.), family background (family income, occupation of parents and their qualification, family size, etc.), geographical background (rural, urban or semi-urban) and work experience on the final placement package received by the student.Practical implicationsEmployability depends on a multitude of factors which can be broadly put under three categories of knowledge, skills and attitude (Khare, 2014). Universities need to work right from the first year toward developing a wider range of employability skills rather than focusing only on developing generic competencies in the students. The results of regression analysis indicate that the impact of different predictors for a good placement package vary in strength and a student needs to focus on balancing all of them in order to get a good placement. Educational institutes can replicate this study to identify the overall employability of their students.Originality/valueWith the increase in demand from industry for work ready graduates, there is a huge pressure on educational institutes to prepare their students for the corporate world. Such studies would help the institutes in focusing on various parameters which would ultimately assist students pursuing courses in post graduate level like business management or other master courses in getting good placements.

Multilevel circuit partitioning

Many previous works in partitioning have used some underlying clustering algorithm to improve performance. As problem sizes reach new levels of complexity, a single application of a clustering algorithm is insufficient to produce excellent solutions. Recent work has illustrated the promise of multilevel approaches. A multilevel partitioning algorithm recursively clusters the instance until its size is smaller than a given threshold, then unclusters the instance, while applying a partitioning refinement algorithm. In this paper, we propose a new multilevel partitioning algorithm that exploits some of the latest innovations of classical iterative partitioning approaches. Our method also uses a new technique to control the number of levels in our matching-based clustering algorithm. Experimental results show that our heuristic outperforms numerous existing bipartitioning heuristics with improvements ranging from 6.9 to 27.9% for 100 runs and 3.0 to 20.6% for just ten runs (while also using less CPU time). Further, our algorithm generates solutions better than the best known mincut bipartitionings for seven of the ACM/SIGDA benchmark circuits, including golem3 (which has over 100000 cells). We also present quadrisection results which compare favorably to the partitionings obtained by the GORDIAN cell placement tool. Our work in multilevel quadrisection has been used as the basis for an effective cell placement package.

A NEW LAYOUT DESIGN SYSTEM FOR MULTICHIP MODULES

A new layout design system for multichip modules (MCMs) consisting of three components is described. It includes a k-way partitioning approach, an algorithm for pin assignment, and a placement package. For partitioning, we propose an analytical technique combined with a problem-specific multi-way ratio cut method. This method considers fixed module-level pad positions and assigns the cells to regularly arranged chips on the MCM substrate. In the subsequent pin assignment step the chip-level pads resulting from cut nets are positioned on the chip borders. Pin assignment is performed by an efficient algorithm, which profits from the cell coordinates generated by the analytical technique. Global and final placement for each chip is computed by the state-of-the-art placement tools GORDIANL and DOMINO. For the first time, results for MCM layout designs of benchmark circuits with up to 100,000 cells are presented. They show a small number of required chip-level pads, which is the most restricted resource in MCM design, and short total wire lengths.

I/O pad assignment for force-directed placement algorithms

Abstract A new method for I/O pad position assignment for force-directed placement algorithms is presented. The pre-placed I/O pads are used as boundary conditions and pulling-out forces in several placement algorithms which would otherwise have collapsed all the modules to the centre of the layout. The objective is to assign the I/O pads of the design to the available pad positions on the chip, based on connectivity information, such that the resulting module arrangement will minimize the total wire length. The results of the I/O pad assignment algorithm, used as a pre-processor to the placement package PROUD, have revealed improvement in minimizing the total wire length.

An algorithm for quadrisection and its application to standard cell placement

An efficient heuristic for hypergraph quadrisection is presented. A placement technique for standard cells based on quadrisection is also discussed in detail. Results show this method to be much superior to min-cut bisection, yielding improvements of up to 20% in area. It also compares favorably with simulated annealing, yielding improvements in area for most of the circuits under test. The placer runs about 100 times faster than a simulated-annealing based placement package.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>