Diversity as an Advantage: An Analysis of Career Competencies for It Students

Brockmann, Patricia; Schuhbauer, Heidi; Hinze, Annika

doi:10.33965/celda2019_201911l026

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…In addition, several researchers have employed ML based approaches to predicting the employability of college graduates in order to develop study plans to guide that match the demands of the labor market (Brockmann et al, 2019;ElSharkawy et al, 2022;Januzaj et al, 2022;Mewburn et al, 2020;Saidani et al, 2022;Sobnath et al, 2020). These kinds of studies are useful for HEI´s to develop a study curriculum that satisfies the demands of the job market.…”

Section: Prediction Of Student Performance and Employabilitymentioning

confidence: 99%

“…Finally, (Brockmann et al, 2019) was the only study that focused on students from underrepresented groups: first generation students from non-academic families, minorities, single parents and women. The study in question explored how computer science graduates from underrepresented groups can add value to software corporations.…”

Section: Prediction Of Student Performance and Employabilitymentioning

confidence: 99%

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How Machine Learning (ML) is Transforming Higher Education: A Systematic Literature Review

Pinto

Abreu

Costa³

et al. 2023

J INFORM SYSTEMS ENG

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In the last decade, artificial intelligence (AI), machine learning (ML) and learning data analytics have been introduced with great effect in the field of higher education. However, despite the potential benefits for higher education institutions (HIE´s) of these emerging technologies, most of them are still in the early stages of adoption of these technologies. Thus, a systematic literature review (SLR) on the literature published over the last 5 years on potential applications of machine learning in higher education is necessary. Following the PRISMA guidelines, out of the 1887 initially identified SCOPUS-indexed publications on the topic, 171 articles were selected for review. To screen the abstracts and titles of each citation, Rayyan QCRI was used. VOSViewer, a software tool for constructing and visualizing bibliometric networks, and Microsoft Excel were used to generate charts and figures. The findings show that the most widely researched application of ML in higher education is related to the prediction of academic performance and employability of students. The implications will be invaluable for researchers and practitioners to explore how ML and AI technologies ,in the era of ChatGPT, can be used in universities without jeopardizing academic integrity.

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Section: Prediction Of Student Performance and Employabilitymentioning

confidence: 99%

Section: Prediction Of Student Performance and Employabilitymentioning

confidence: 99%

How Machine Learning (ML) is Transforming Higher Education: A Systematic Literature Review

Pinto

Abreu

Costa³

et al. 2023

J INFORM SYSTEMS ENG

View full text Add to dashboard Cite

show abstract

“…As machines transition from being useful instruments for production or usage to playing a crucial part in various aspects of organizational and economic life, the ramifications of technological advancements and innovations for humans are getting more complex (Brockmann P, Schuhbauer H, Hinze A (2019). Work in modern culture is characterized by constant connectedness, immediacy, and a wide range of issues in the work-life balance because to more pervasive digitalization and communication technology (Derks et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

A Conceptual Analysis of the Difficulties and Potential HRM Techniques for the Interaction of Manpower With Artificial Intelligence

Dr. Aajaz Ahmad Hajam,

Alphonsa S John

2022

MORFAI

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Objective - This essay intends to particularly address the difficulties that modern organizations' human resource management (HRM) departments and leaders confront as a result of the close interaction between human workers and artificial intelligence (AI)—primarily robots—especially at the team level. On the basis of a conceptual evaluation of the available research, it also offers some prospective solutions that may be helpful to overcome these difficulties. Design, methodology, and approach — To give a very comprehensive yet critical overview of the interaction between AI (especially robots) and HRM in modern organizations, the current study engages in conceptual work that integrates several streams of literature. Results - We emphasize that interactions and teamwork between human workers and robots are evident across a variety of organizational tasks and industries, where both are contributing team members. The HRM function in modern organizations is faced with some particularly difficult challenges as a result of the need to address workers' anxiety about working with AI, particularly in relation to potential job loss and the challenging dynamics of establishing trust between human employees and AI-enabled robots as team members. Along with these, HRM staff members need to carefully handle employee performance evaluations in the future as well as human workers' expectations for task completion with their AI-enabled robot co-workers. When placing human workers in teams with robots, the authors discovered that organizational support mechanisms such as a welcoming climate, training opportunities, and assuring a suitable technological competence level are crucial. Finally, we discovered that one of HRM's most difficult difficulties is performance evaluation in teams where humans and AI (including robots) coexist. We emphasized the possibility of drawing insights from the computer gaming literature, where performance evaluation models have been developed to analyze human and AI interactions while keeping the context and limitations of both in mind. We discussed the lack of frameworks to guide HRM managers in this concern. Originality/value - Our study is one of the few to go beyond a broad or functional analysis of AI in the context of human resource management. It focuses primarily on the teamwork aspect, where human employees and AI-powered robots (robots) collaborate and provide insights and suggestions for such teams' efficient operation.

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Understanding Career Trajectories of IT Professionals - A Machine Learning Approach

Ponnada,

Sabarish,

Srehari

et al. 2023

Communications in Computer and Information Science

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Diversity as an Advantage: An Analysis of Career Competencies for It Students

Cited by 4 publications

References 18 publications

How Machine Learning (ML) is Transforming Higher Education: A Systematic Literature Review

How Machine Learning (ML) is Transforming Higher Education: A Systematic Literature Review

A Conceptual Analysis of the Difficulties and Potential HRM Techniques for the Interaction of Manpower With Artificial Intelligence

Understanding Career Trajectories of IT Professionals - A Machine Learning Approach

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