%0 Conference Paper %1 ls_leimeister %A Ritz, Eva %A Freise, Leonie %A Elshan, Edona %A Rietsche, Roman %A Bretschneider, Ulrich %B Hawaii International Conference on System Sciences (HICSS) %C Waikiki, Hawaii, USA %D 2024 %K itegpub itimpub large_language_models learning_paths personalized_learning pub_lfr pub_ubr re-&upskilling skill_profile %T What to Learn Next? Designing Personalized Learning Paths for Re-&Upskilling in Organizations %U https://pubs.wi-kassel.de/wp-content/uploads/2024/01/JML_959.pdf %X 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.

@inproceedings{ls_leimeister, abstract = {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.}, added-at = {2023-09-27T16:04:52.000+0200}, address = {Waikiki, Hawaii, USA}, author = {Ritz, Eva and Freise, Leonie and Elshan, Edona and Rietsche, Roman and Bretschneider, Ulrich}, biburl = {https://www.bibsonomy.org/bibtex/20ba057d0921aa4ffd479d417a327fa83/ls_leimeister}, booktitle = {Hawaii International Conference on System Sciences (HICSS) }, eventdate = {3-6 Jan 2023}, eventtitle = {Hawaii International Conference on System Sciences (HICSS)}, interhash = {b42821c5318f34e1d12da0d7bae29e5e}, intrahash = {0ba057d0921aa4ffd479d417a327fa83}, keywords = {itegpub itimpub large_language_models learning_paths personalized_learning pub_lfr pub_ubr re-&upskilling skill_profile}, language = {English}, timestamp = {2024-01-11T12:03:37.000+0100}, title = {What to Learn Next? Designing Personalized Learning Paths for Re-&Upskilling in Organizations}, url = {https://pubs.wi-kassel.de/wp-content/uploads/2024/01/JML_959.pdf}, venue = {Waikiki, Hawaii, USA}, year = 2024 }