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    What to Learn Next? Designing Personalized Learning Paths for Re-&Upskilling in Organizations
    The fast-paced acceleration of digitalization requires extensive re-&upskilling, impacting a significant proportion of jobs worldwide. Technology-mediated learning platforms have become instrumental in addressing these efforts, as they can analyze platform data to provide personalized learning journeys. Such personalization is expected to increase employees’ empowerment, job satisfaction, and learning outcomes. However, the challenge lies in efficiently deploying these opportunities using novel technologies, prompting questions about the design and analysis of generating personalized learning paths in organizational learning. We, therefore, analyze and classify recent research on personalized learning paths into four major concepts (learning context, data, interface, and adaptation) with ten dimensions and 34 characteristics. Six expert interviews validate the taxonomy’s use and outline three exemplary use cases, undermining its feasibility. Information Systems researchers can use our taxonomy to develop theoretical models to study the effectiveness of personalized learning paths in intra-organizational re-&upskilling.
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    Automized Assessment for Professional Skills –A Systematic Literature Review and Future Research Avenues
    (2023-09-21) ;
    Ulrich Bretschneider
    Globalization, technological progress, and demographic trends increasingly influence our labor markets. With changing labor markets and increasing digitalization, new competencies of workers are needed to meet demands. However, as a first step to developing these new skills, knowledge about the existing skills and their status quo is necessary. Here, automated skill assessment offers a crucial added value, as it can create a reliable and objective database. Based on a systematic investigation, our analysis shows, in four different areas, how skills and competencies in the automated assessment are (1) defined, (2) included as an element of analysis, (3) methodically recorded and processed, (4) which data source is used. In doing so, we offer insights into existing approaches to automated assessment of professional skills. We contribute to a better under-standing of the design of automated skill assessment methods and provide perspectives on future research directions.
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