scholarly journals Career Success in University Graduates: Evidence from an Ecuadorian Study in Los Ríos Province

2021 ◽  
Vol 13 (16) ◽  
pp. 9337
Author(s):  
Roberto Pico-Saltos ◽  
Lady Bravo-Montero ◽  
Néstor Montalván-Burbano ◽  
Javier Garzás ◽  
Andrés Redchuk
Keyword(s):  
Career Success ◽  
Success Factors ◽  
Pearson Correlation ◽  
Learning Algorithms ◽  
Linear Functions ◽  
University Graduates ◽  
Classification Learning ◽  
Application Development ◽  
Mathematical Functions ◽  
Random Forest Tree

Career success and its evaluation in university graduates generate growing interest in the academy when evaluating the university according to its mission and social mandate. Therefore, monitoring university graduates is essential in measuring career success in the State Technical University of Quevedo (UTEQ, acronym in Spanish). In this sense, this article aims to identify the predictive career success factors through survey application, development of two mathematical functions, and Weka’s classification learning algorithms application for objective career success levels determination in UTEQ university graduates. Researchers established a methodology that considers: (i) sample and data analysis, (ii) career success variables, (iii) variables selection, (iv) mathematical functions construction, and (v) classification models. The methodology shows the integration of the objective and subjective factors by approximating linear functions, which experts validated. Therefore, career success can classify university graduates into three levels: (1) not successful, (2) moderately successful, and (3) successful. Results showed that from 548 university graduates sample, 307 are men and 241 women. In addition, Pearson correlation coefficient between Objective Career Success (OCS) and Subjective Career Success (SCS) was 0.297, reason why construction models were separately using Weka’s classification learning algorithms, which allow OCS and SCS levels classification. Between these algorithms are the following: Logistic Model Tree (LMT), J48 pruned tree, Random Forest Tree (RF), and Random Tree (RT). LMT algorithm is the best suited to the predictive objective career success factors, because it presented 76.09% of instances correctly classified, which means 417 of the 548 UTEQ university graduates correctly classified according to OCS levels. In SCS model, RF algorithm shows the best results, with 94.59% of instances correctly classified (518 university graduates). Finally, 67.1% of UTEQ university graduates are considered successful, showing compliance with the university’s mission.

scholarly journals Algorithmic and human prediction of success in human collaboration from visual features

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Martin Saveski ◽  
Edmond Awad ◽  
Iyad Rahwan ◽  
Manuel Cebrian
Keyword(s):  
Machine Learning ◽  
Visual Cues ◽  
Success Factors ◽  
Group Performance ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Adventure Game ◽  
Group Success ◽  
The Relationship ◽  
Better Than

AbstractAs groups are increasingly taking over individual experts in many tasks, it is ever more important to understand the determinants of group success. In this paper, we study the patterns of group success in Escape The Room, a physical adventure game in which a group is tasked with escaping a maze by collectively solving a series of puzzles. We investigate (1) the characteristics of successful groups, and (2) how accurately humans and machines can spot them from a group photo. The relationship between these two questions is based on the hypothesis that the characteristics of successful groups are encoded by features that can be spotted in their photo. We analyze >43K group photos (one photo per group) taken after groups have completed the game—from which all explicit performance-signaling information has been removed. First, we find that groups that are larger, older and more gender but less age diverse are significantly more likely to escape. Second, we compare humans and off-the-shelf machine learning algorithms at predicting whether a group escaped or not based on the completion photo. We find that individual guesses by humans achieve 58.3% accuracy, better than random, but worse than machines which display 71.6% accuracy. When humans are trained to guess by observing only four labeled photos, their accuracy increases to 64%. However, training humans on more labeled examples (eight or twelve) leads to a slight, but statistically insignificant improvement in accuracy (67.4%). Humans in the best training condition perform on par with two, but worse than three out of the five machine learning algorithms we evaluated. Our work illustrates the potentials and the limitations of machine learning systems in evaluating group performance and identifying success factors based on sparse visual cues.

scholarly journals A Comparison of Hybrid Machine Learning Algorithms for the Retrieval of Wheat Biophysical Variables from Sentinel-2

2019 ◽  
Vol 11 (5) ◽  
pp. 481 ◽  
Author(s):  
Deepak Upreti ◽  
Wenjiang Huang ◽  
Weiping Kong ◽  
Simone Pascucci ◽  
Stefano Pignatti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Least Squares ◽  
Chlorophyll Content ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Area Index ◽  
Random Forest Tree ◽  
Ground Validation ◽  
Study Sites ◽  
Sentinel 2

This study focuses on the comparison of hybrid methods of estimation of biophysical variables such as leaf area index (LAI), leaf chlorophyll content (LCC), fraction of absorbed photosynthetically active radiation (FAPAR), fraction of vegetation cover (FVC), and canopy chlorophyll content (CCC) from Sentinel-2 satellite data. Different machine learning algorithms were trained with simulated spectra generated by the physically-based radiative transfer model PROSAIL and subsequently applied to Sentinel-2 reflectance spectra. The algorithms were assessed against a standard operational approach, i.e., the European Space Agency (ESA) Sentinel Application Platform (SNAP) toolbox, based on neural networks. Since kernel-based algorithms have a heavy computational cost when trained with large datasets, an active learning (AL) strategy was explored to try to alleviate this issue. Validation was carried out using ground data from two study sites: one in Shunyi (China) and the other in Maccarese (Italy). In general, the performance of the algorithms was consistent for the two study sites, though a different level of accuracy was found between the two sites, possibly due to slightly different ground sampling protocols and the range and variability of the values of the biophysical variables in the two ground datasets. For LAI estimation, the best ground validation results were obtained for both sites using least squares linear regression (LSLR) and partial least squares regression, with the best performances values of R2 of 0.78, rott mean squared error (RMSE) of 0.68 m2 m−2 and a relative RMSE (RRMSE) of 19.48% obtained in the Maccarese site with LSLR. The best results for LCC were obtained using Random Forest Tree Bagger (RFTB) and Bagging Trees (BagT) with the best performances obtained in Maccarese using RFTB (R2 = 0.26, RMSE = 8.88 μg cm−2, RRMSE = 17.43%). Gaussian Process Regression (GPR) was the best algorithm for all variables only in the cross-validation phase, but not in the ground validation, where it ranked as the best only for FVC in Maccarese (R2 = 0.90, RMSE = 0.08, RRMSE = 9.86%). It was found that the AL strategy was more efficient than the random selection of samples for training the GPR algorithm.

The career advancement of the professional women in the UK construction industry

2014 ◽  
Vol 12 (1) ◽  
pp. 53-70 ◽  
Author(s):  
Nirodha Gayani Fernando ◽  
Dilanthi Amaratunga ◽  
Richard Haigh
Keyword(s):  
Literature Review ◽  
Construction Industry ◽  
Career Success ◽  
Qualitative Data ◽  
Success Factors ◽  
Soft Skills ◽  
Training And Development ◽  
Professional Women ◽  
Content Type ◽  
The Uk

Purpose – This paper aims to explore and investigate the career success of professional women in the UK construction industry. Design/methodology/approach – The aim of the research was set following the literature review and synthesis, after which a multiple case study approach is adopted to conduct exploratory case studies among professional women in the UK construction industry. A mixed method design was used for data collection, whereby qualitative data were collected in the first study and quantitative data were collected in the second study. The researcher adopted this sequence in order to gather qualitative data and analysis of a relatively unexplored area of career success factors of professional women in the UK construction industry. The results from the qualitative method were used, along with a relevant literature review, to develop the focus and questions in the quantitative phase of the study. The individuals in the first stage of data collection were not the same participants as those in the second stage, because the purpose of the quantitative study was to generalise the results to a population. Findings – The results indicated that soft skills are very important for career success, while hard skills are essential thereafter for professional women in the UK construction industry. Accordingly, it is necessary to develop soft skills in order to advance the women's professional careers. Further, the results indicated that age and gender are the least important career success factors for women in construction. The ability to work with people, taking opportunities, confidence, adaptability, communication skills, dedication, competence, focus, supportive line management, integrity, leadership skills, ability to bring teams together, good mix of skills, honesty, networking, intelligence and logically approaching business problems identified as the critical career success factors. Research limitations/implications – The construction industry is limited to organisations that construct buildings and infrastructure, and those involved in property development. These organisations comprise client, contractor and consultancy organisations. Practical implications – The findings of the paper are useful to human resource development managers to understand and improve organisational training and development plans, which help to advance the career of professional women. By doing so, organisations could recruit and retain more professional women in the construction industry. Therefore, recruiting and retaining more professional women in the organisation helps to enhance productivity in the industry and to enhance their health and well being in society at large. Originality/value – The value of this paper is twofold. First, this study contributes to fill the knowledge gap in career success factors of professional women in the UK construction industry. Second, this empirical research will have implications in the identification of different training and development activities to advance the careers of women in the UK construction industry.

scholarly journals The design of information technology strategic plans in educational institutions

2018 ◽  
Vol 1 (1) ◽  
pp. 26
Author(s):  
Dwi Astuti ◽  
Wing Wahyu Winarno ◽  
Amir Fatah Sofyan
Keyword(s):  
Information System ◽  
Value Chain ◽  
Success Factors ◽  
Strategic Plan ◽  
Private University ◽  
Educational Institutions ◽  
Application Development ◽  
Critical Success ◽  
Strategic Plans ◽  
E Learning

Strategic Plan, which is usually taken from Vision, Mission, Objectives, Policies, Programs and activities that are oriented towards the goal for a certain period relating to the main tasks and functions of the Agency / Institution, prepared by consider the developments of strategic environment, the sustainability of organization without strategic plan will not be directed and guaranteed because there are no management guidelines and system improvements in order to increase the competition with other business actors. STMIK Bina Patria is a private university (PTS), but it does not have an information system (SI) strategic plan. The information system contributes to improve the quality of students’ services, operational efficiency, and students’ satisfaction. With SI / IT, the monitoring, coordination, and decision can be performed effectively. The goal can be achieved if the organization has a clear plan. And, researchers make an IT strategic plan for STMIK Bina Patria according to the TOGAF Framework with data analysis methods of Value Chain, Critical Success Factors, and SWOT. The analysis results showed that by the availability of 4 applications, 3 applications do not require any improvement, namely SI-KEU, E-LEARNING and E-JOURNAL. In contrast, an application which is SI-AKAD requires additional features. There were 5 proposed applications to be built, namely SI-PMB, SI-ALUMNI, SI-MUTU, SI-PERPUST and SI-DASHBOARD. All of application proposals are mapped into the application development roadmap within the next 5 years.

scholarly journals A fast learn++.NSE classification algorithm based on weighted moving average

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1737-1745
Author(s):  
Yan Shen ◽  
Yuquan Zhu ◽  
Jianguo Du ◽  
Yong Chen
Keyword(s):  
Big Data ◽  
Incremental Learning ◽  
Moving Average ◽  
Learning Algorithms ◽  
Sliding Window ◽  
Error Rates ◽  
Classification Learning ◽  
Incremental Classification ◽  
Weighted Moving Average ◽  
Vote Weight

Current researches of incremental classification learning algorithms mainly focus on learning from data in a stationary environment. The incremental learning in a non-stationary environment (NSE), where the underlying data probability distribution changes over time, however, has received much less attentions despite the abundant real applications have generated the long-term and cumulative big data in NSE. Thus, the incremental learning in NSE has gradually received extensive attentions. Nevertheless, the popular incremental classification learning algorithms currently for NSE such as SEA and DWM generally place strict restrictions on the changes. These algorithms can only deal with gradual drift and noncyclical and no new category situations. Therefore, it is highly necessary to develop a novel efficient incremental classification learning algorithm for the gradually cumulative big data in complex NSE. The recently proposed Learn++.NSE algorithm is an important research achievement in this field. However, the vote weight of each base-classifier of the Learn++.NSE depends on its whole error rates in the environments experienced. Therefore, the classification learning efficiency of the Learn++.NSE should be further improved. A novel fast Learn++.NSE algorithm based on weighted moving average (WMA-Learn++.NSE) is presented in this paper, which computes the weighted average of error rates using the sliding window technology to optimize the weight calculation. By only using the recent classification error rates of each base-classifier inside the sliding window to calculate the vote weight, the WMA-Learn++.NSE accelerates the compute of vote weight and improves the efficiency of classification learning. The verification experiments and performance analyses on both synthetic and real data set are presented in this paper. The experimental results show that the WMA-Learn++.NSE can achieve a higher execution efficiency compared to the Learn++.NSE in getting the equivalent classification correct rate.

Model-Driven Prototyping Support for Pervasive Healthcare Applications

2010 ◽  
pp. 251-281
Author(s):  
Werner Kurschl ◽  
Stefan Mitsch ◽  
Johannes Schoenboeck
Keyword(s):  
Machine Learning ◽  
Programming Languages ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Application Development ◽  
Healthcare Applications ◽  
Development Environment ◽  
Pervasive Healthcare ◽  
Model Driven

Pervasive healthcare applications aim at improving habitability by assisting individuals in living autonomously. To achieve this goal, data on an individual’s behavior and his or her environment (often collected with wireless sensors) is interpreted by machine learning algorithms; their decision finally leads to the initiation of appropriate actions, e.g., turning on the light. Developers of pervasive healthcare applications therefore face complexity stemming, amongst others, from different types of environmental and vital parameters, heterogeneous sensor platforms, unreliable network connections, as well as from different programming languages. Moreover, developing such applications often includes extensive prototyping work to collect large amounts of training data to optimize the machine learning algorithms. In this chapter the authors present a model-driven prototyping approach for the development of pervasive healthcare applications to leverage the complexity incurred in developing prototypes and applications. They support the approach with a development environment that simplifies application development with graphical editors, code generators, and pre-defined components.

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