Eva Ritz
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Ritz
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Eva
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eva.ritz@unisg.ch

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How to Support Students’ Self-Regulated Learning in Times of Crisis: An Embedded Technology-Based Intervention in Blended Learning Pedagogies

2023-09-22 , Eva Ritz , Roman Rietsche , Jan Marco Leimeister

With the increasing prevalence of technology-enhanced learning environments, self-regulated learning (SRL) has become a crucial skill for management students and graduates in the 21st century. Self-regulated learners can take control of their own learning process by setting learning objectives and selecting appropriate learning strategies. As a result of the recent COVID-19 crisis, universities were compelled to shift to online course delivery, which greatly reduced social interaction between educators and learners and challenged educators’ feedback practices. To address this issue, we developed and embedded a technology-based intervention with temporal-proximate and regular formative feedback assessments in a large-scale management course to promote graduate students’ SRL practices. We evaluated the intervention in a quasi-experiment, which found that students with the embedded SRL intervention had higher self-assessment and learning outcome scores and lower absolute self-assessment deviation. Our study makes at least three contributions. First, we shed light on students’ SRL strategies in times of emergency remote learning, highlighting their extensive need for social support and comparison. Second, we extend the literature on SRL and social-cognitive theory by unveiling a hidden effect when embedding temporal-proximate and regular interventions. Third, we contribute an empirically evaluated intervention to foster students’ SRL in blended learning and online pedagogies.

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What to Learn Next? Designing Personalized Learning Paths for Re-&Upskilling in Organizations

2023-01-06 , Eva Ritz , Leonie Freise , Edona Elshan , Roman Rietsche , Ulrich Bretschneider

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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