In this study, with the primary goal of capturing ongoing digital transformation and automation impacts on the mining industry and its workforce, we conduct several interviews with mining industry experts in the USA and analyze our survey reports qualitatively and quantitatively through exploratory analysis. After the interpretation of the insights of industry experts, we proceed to generate a personalized and customized data analysis through a novel metric based on skills, knowledge, competencies, and occupational requirements, which quantifies the job similarities for occupations in the mining industry based on the publicly available database of the United States Department of Labor. We utilize text analytics to tokenize and classify the interviews to capture a better understanding of major response categories. The temporal analysis shows that the critical competency needs in the data science and autonomy category increases from 28% in current demands to 43%. In defining our metric, we also calculate Kullback–Leibler (KL) divergence for each job profile that enables determining whether and to what extent that job is transitionary in our test set based on the mean, standard deviation, and kurtosis of each job of interest. Our analysis reveals that the in-group job transitions are significantly easier than the between-group transitions, proving our initial assumptions and common sense. The generated heat maps provide the opportunity to present the gap between the current job and desired job profiles that provide feasible career change options, among others, offering individualized career paths for job seekers and promoting potential job transitions. Through the collection of industry-specific individual employee data, the AI system is envisaged to continue to learn as end users engage with the system, thus creating a central data hub specifically for the future workforce in the mining industry. Although the study has limitations on generalizability for qualitative assessments, it presents itself as a valuable application of how qualitative and quantitative approaches could be of value for future worker training in the mining sector.
Multi-Output Career Prediction: Dataset, Method, and Benchmark Suite
In this paper, we investigate the career path prediction of an individual in the future. This benefits a variety of application in the industry including enhancing human resources, career guidance, and keeping track of future trends. To this end, we collected a dataset via LinkedIn network, with the job position and the job domain for each individual. There are many attributes related to historical background for each individual. For the career prediction, we investigate six different multi-class multi-output classification methods. Via the benchmark suite, the best classifier achieves an accuracy rate of 91.21% and 95.97% for the job domain and the job position, respectively.
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- Award ID(s):
- 2025234
- NSF-PAR ID:
- 10428230
- Date Published:
- Journal Name:
- 2023 57th Annual Conference on Information Sciences and Systems (CISS)
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract -
null (Ed.)Cloud computing today is dominated by multi-server jobs. These are jobs that request multiple servers simultaneously and hold onto all of these servers for the duration of the job. Multi-server jobs add a lot of complexity to the traditional one-server-per-job model: an arrival might not "fit'' into the available servers and might have to queue, blocking later arrivals and leaving servers idle. From a queueing perspective, almost nothing is understood about multi-server job queueing systems; even understanding the exact stability region is a very hard problem. In this paper, we investigate a multi-server job queueing model under scaling regimes where the number of servers in the system grows. Specifically, we consider a system with multiple classes of jobs, where jobs from different classes can request different numbers of servers and have different service time distributions, and jobs are served in first-come-first-served order. The multi-server job model opens up new scaling regimes where both the number of servers that a job needs and the system load scale with the total number of servers. Within these scaling regimes, we derive the first results on stability, queueing probability, and the transient analysis of the number of jobs in the system for each class. In particular we derive sufficient conditions for zero queueing. Our analysis introduces a novel way of extracting information from the Lyapunov drift, which can be applicable to a broader scope of problems in queueing systems.more » « less
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null (Ed.)Cloud computing today is dominated by multi-server jobs. These are jobs that request multiple servers simultaneously and hold onto all of these servers for the duration of the job. Multi-server jobs add a lot of complexity to the traditional one-server-per-job model: an arrival might not "fit" into the available servers and might have to queue, blocking later arrivals and leaving servers idle. From a queueing perspective, almost nothing is understood about multi-server job queueing systems; even understanding the exact stability region is a very hard problem. In this paper, we investigate a multi-server job queueing model under scaling regimes where the number of servers in the system grows. Specifically, we consider a system with multiple classes of jobs, where jobs from different classes can request different numbers of servers and have different service time distributions, and jobs are served in first-come-first-served order. The multi-server job model opens up new scaling regimes where both the number of servers that a job needs and the system load scale with the total number of servers. Within these scaling regimes, we derive the first results on stability, queueing probability, and the transient analysis of the number of jobs in the system for each class. In particular we derive sufficient conditions for zero queueing. Our analysis introduces a novel way of extracting information from the Lyapunov drift, which can be applicable to a broader scope of problems in queueing systems.more » « less
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nterest in science, technology, engineering, and mathematics (STEM) begins as early as elementary and middle school. As youth enter adolescence, they begin to shape their personal identities and start making decisions about who they are and could be in the future. Students form their career aspirations and interests related to STEM in elementary school, long before they choose STEM coursework in high school or college. Much of the literature examines either science or STEM identity and career aspirations without separating out individual sub-disciplines. Therefore, the purpose of this paper is to describe the development of a survey instrument to specifically measure engineering identity and career aspirations in adolescents and preadolescents. When possible, we utilized existing measures of STEM identity and career aspirations, adapting them when necessary to the elementary school level and to fit the engineering context. The instrument was developed within the context of a multi-year, NSF-funded research project examining the dynamics between undergraduate outreach providers and elementary students to understand the impact of the program on students’ engineering identity and career aspirations. Three phases of survey development were conducted that involved 492 elementary students from diverse communities in the United States. Three sets of items were developed and/or adapted throughout the four phases. The first set of items assessed Engineering Identity. Recent research suggests that identity consists of three components: recognition, interest, and performance/competence. Items assessing each of these constructs were included in the survey. The second and third sets of items reflected Career Interests and Aspirations. Because elementary and middle school students often have a limited or nascent awareness of what engineers do or misconceptions about what a job in science or engineering entails, it is problematic to measure their engineering identity or career aspirations by directly asking them whether they want to be a scientist/engineer or by using a checklist of broad career categories. Therefore, similar to other researchers, the second set of items assessed the types of activities that students are interested in doing as part of a future career, including both non-STEM and STEM (general and engineering-specific) activities. These items were created by the research team or adapted from activity lists used in existing research. The third set of items drew from career counseling measures relying on Holland’s Career Codes. We adapted the format of these instruments by asking students to choose the activity they liked the most from a list of six activities that reflected each of the codes rather than responding to their interest about each activity. Preliminary findings for each set of items will be discussed. Results from the survey contribute to our understanding of engineering identities and career aspirations in preadolescent and adolescent youth. However, our instrument has the potential for broader application in non-engineering STEM environments (e.g., computer science) with minor wording changes to reflect the relevant science subject area. More research is needed in determining its usefulness in this capacity.more » « less
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Preparing graduates to enter the workforce is a common goal of undergraduate geoscience degree programs. Determining what skills are necessary for new graduates to succeed in the workforce requires knowledge of the skills sought by employers of bachelors-level geoscientists. To investigate skills desired by employers, we systematically analyzed job advertisements retrieved from 4 search engines between May and November 2020. We used 15 search words derived from the 2018 Status of the Geoscience Workforce (AGI) report to select job advertisements that required or preferred a geoscience-based bachelor's degree. Additionally, we categorized each advertisement by industry sector based on definitions in the 2018 AGI report. Each job advertisement (n=1214) was coded to identify skills sought by the employer. An initial set of codes was based on skills identified by the Future of Undergraduate Geoscience Education project and additional emergent codes were identified during the coding process. We generated a final set of 34 codes, with definitions and examples, through an iterative coding process, checking for inter-rater reliability. Advertisements were not coded for geoscience content knowledge. The most common skills sought by employers were the ability to conduct field work, teamwork, work with computers, collect, process and interpret data, and communicate effectively, however, the desirability of skills varied across industry sectors. For example, teamwork skills were sought in 60% of mining sector advertisements but only 22% of oil and gas sector advertisements. Our results provide insight into the expectations of potential employers for recent graduates seeking a career in geoscience. Additionally, our results provide geoscience degree programs with critical information required to prepare undergraduates with the necessary skills to be successful in the current job market.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/CISS56502.2023.10089642
