IT Students Career Confidence and Career Identity During COVID-19
- URL: http://arxiv.org/abs/2503.09882v1
- Date: Wed, 12 Mar 2025 22:37:44 GMT
- Title: IT Students Career Confidence and Career Identity During COVID-19
- Authors: Sophie McKenzie,
- Abstract summary: This research paper explores the career confidence and identity of university students in Information Technology (IT) prior and during the COVID-19 period.<n>1349 IT students from an Australian University reported their career confidence.<n>The results indicate IT students' career confidence maintained during the period.<n>In 2021, the results indicate increased career commitment of IT students showing higher professional expectations.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: COVID-19 disrupted the professional preparation of university students, with less opportunity to engage in professional practice due to a reduced employment market. Little is known about how this period impacted upon the career confidence and career identity of university students. This research paper explores the career confidence and identity of university students in Information Technology (IT) prior and during the COVID-19 period. Using a survey method and quantitative analysis, ANOVA and Kruskal-Wallis tests with different sensitivity and variance standards were used during analysis to present mean and mean rank of data collected during 2018, 2019, 2020 and 2021. 1349 IT students from an Australian University reported their career confidence. The results indicate IT students' career confidence maintained during the period. In 2021, the results indicate increased career commitment of IT students showing higher professional expectations to work in IT along with greater self-awareness regarding their professional development needs. Even with increased career confidence as observed in this study, supporting university students to explore their career options and build upon their career identity, and more broadly their employability, remains an important activity for universities to curate in their graduates.
Related papers
- Factors Influencing Gender Representation in IT Faculty Programmes: Insights with a Focus on Software Engineering in a Nordic Context [0.0]
Software engineering remains male-dominated despite efforts to attract and retain women.
Family and personal interest are among the main factors motivating women to choose an IT programme.
Women perceive more challenges following their chosen career path than men.
arXiv Detail & Related papers (2025-04-11T20:25:52Z) - Exit the Code: A Model for Understanding Career Abandonment Intention Among Software Developers [0.6117371161379209]
Career abandonment involves frustration with the lost investment and emotional and financial costs.<n>Previous studies have identified work-related motivators for career abandonment, such as the threat of obsolescence.<n>This article investigates the relationship between these motivators and the intention to abandon among currently active developers.
arXiv Detail & Related papers (2025-03-06T14:13:10Z) - DASKT: A Dynamic Affect Simulation Method for Knowledge Tracing [51.665582274736785]
Knowledge Tracing (KT) predicts future performance by students' historical computation, and understanding students' affective states can enhance the effectiveness of KT.
We propose Affect Dynamic Knowledge Tracing (DASKT) to explore the impact of various student affective states on their knowledge states.
Our research highlights a promising avenue for future studies, focusing on achieving high interpretability and accuracy.
arXiv Detail & Related papers (2025-01-18T10:02:10Z) - Effect of Information Technology on Job Creation to Support Economic: Case Studies of Graduates in Universities (2023-2024) of the KRG of Iraq [0.0]
This study uses a descriptive research methodology and a quantitative approach to understand variables.<n>The sample size was established by the use of judgmental sampling procedure and consisted of 314 people.
arXiv Detail & Related papers (2025-01-08T11:39:28Z) - From student to working professional: A graduate survey [0.2999888908665658]
This paper reports on the results of a 2023 survey that explores the experiences of recent Computer Science (CS) graduates.
The survey asked about the graduates' perceptions within a continuum of Work Integrated Learning (WiL) experiences.
Results indicate that graduates value their capstone experiences and believe that they provide transferable skills.
arXiv Detail & Related papers (2024-10-10T02:58:34Z) - Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Forecasting and Benchmarking [59.87055275344965]
Job-SDF is a dataset designed to train and benchmark job-skill demand forecasting models.<n>Based on 10.35 million public job advertisements collected from major online recruitment platforms in China between 2021 and 2023.<n>Our dataset uniquely enables evaluating skill demand forecasting models at various granularities, including occupation, company, and regional levels.
arXiv Detail & Related papers (2024-06-17T07:22:51Z) - Investigating the Impact of Project Risks on Employee Turnover Intentions in the IT Industry of Pakistan [0.0]
This study investigates the influence of project risks in the IT industry on job satisfaction and turnover intentions.
It examines the role of both external and internal social links in shaping perceptions of job satisfaction.
arXiv Detail & Related papers (2024-03-09T11:06:49Z) - Intelligent System for Assessing University Student Personality
Development and Career Readiness [0.0]
This research paper explores the impact of various factors on university students' readiness for change and transition.
The collected data from a KBTU student survey was processed through machine learning models.
An intelligent system was built using these models and fuzzy sets.
arXiv Detail & Related papers (2023-08-29T20:32:58Z) - Exploring the Confounding Factors of Academic Career Success: An
Empirical Study with Deep Predictive Modeling [43.91066315776696]
We propose to explore the determinants of academic career success through an empirical and predictive modeling perspective.
We analyze the co-author network and find that potential scholars work closely with influential scholars early on and more closely as they grow.
We find that being a Fellow could not bring the improvements of citations and productivity growth.
arXiv Detail & Related papers (2022-11-19T08:16:21Z) - A Survey of Knowledge Tracing: Models, Variants, and Applications [70.69281873057619]
Knowledge Tracing is one of the fundamental tasks for student behavioral data analysis.
We present three types of fundamental KT models with distinct technical routes.
We discuss potential directions for future research in this rapidly growing field.
arXiv Detail & Related papers (2021-05-06T13:05:55Z) - Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper [50.25428141435537]
Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between machine learning, big data, streaming analytics, and the management of IT operations.
Main aim of the AIOPS workshop is to bring together researchers from both academia and industry to present their experiences, results, and work in progress in this field.
arXiv Detail & Related papers (2021-01-15T10:43:10Z) - Graduate Employment Prediction with Bias [44.38256197478875]
Failure of landing a job for college students could cause serious social consequences such as drunkenness and suicide.
We develop a framework, i.e., MAYA, to predict students' employment status while considering biases.
arXiv Detail & Related papers (2019-12-27T07:30:28Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.