Eva Ritz
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Ritz
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Eva
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eva.ritz@unisg.ch

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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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A Corpus for Suggestion Mining of German Peer Feedback

2022 , Rietsche, Roman , Ritz, Eva , Janda, Julius , Pfütze, Dominik

Peer feedback in online education becomes increasingly important to meet the demand for feedback in large scale classes, such as e.g. Massive Open Online Courses (MOOCs). However, students are often not experts in how to write helpful feedback to their fellow students. In this paper, we introduce a corpus compiled from university students’ peer feedback to be able to detect suggestions on how to improve the students’ work and therefore being able to capture peer feedback helpfulness. To the best of our knowledge, this corpus is the first student peer feedback corpus in German which additionally was labelled with a new annotation scheme. The corpus consists of more than 600 written feedback (about 7,500 sentences). The utilisation of the corpus is broadly ranged from Dependency Parsing to Sentiment Analysis to Suggestion Mining, etc. We applied the latter to empirically validate the utility of the new corpus. Suggestion Mining is the extraction of sentences that contain suggestions from unstructured text. In this paper, we present a new annotation scheme to label sentences for Suggestion Mining. Two independent annotators labelled the corpus and achieved an inter-annotator agreement of 0.71. With the help of an expert arbitrator a gold standard was created. An automatic classification using BERT achieved an accuracy of 75.3%.

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GETTING INTO FLOW!? ENHANCE FLOW-LIKE EXPERIENCES AND LEARNING PERFORMANCE THROUGH PERSONALIZED LEARNING ACTIVITIES

2023-06 , Ritz, Eva

Although intelligent learning systems provide new opportunities for personalizing learning activities, important design questions remain. To unleash the full impact of such systems, it is vital to examine how the use of Bayesian knowledge tracing can provide learners personalized learning activities and shape their flow experience, performance, and continuity intention. Further, this study explores the moderating role of a growth mindset on the relation between learning task adaptation and flow experience. I rely on electroencephalography to increase the internal validity of flow measurements. The study builds on Flow Theory and aims to empirically unveil the influence of an intelligent personalization of learning processes. The next step will be an experiment with 80 participants following the developed experimental design to evaluate flow experiences in intelligent learning systems. The results of this experiment aim to guide educational designers with prescriptive knowledge on how to design flow-like learning experiences in intelligent learning systems.

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Unleashing Process Mining for Education: Designing an IT-Tool for Students to Self-Monitor their Personal Learning Paths

2022-02-23 , Ritz, Eva , Wambsganss, Thiemo , Rietsche, Roman , Schmitt, Anuschka , Oeste-Reiß, Sarah , Leimeister, Jan Marco

The ability of students to self-monitor their learning paths is in demand as never before due to the recent rise of online education formats, which entails less interaction with lecturers. Recent advantages in educational process mining (EPM) offer new opportunities to monitor students’ learning paths by processing log data captured by technology-mediated learning environments. However, current literature falls short on providing user-centered design principles for IT-tools which can monitor learning paths using EPM. Hence, in this paper, we examine how to design a self-monitoring tool that supports students to evaluate their learning paths. Based on theoretical insights of 66 papers and nine user interviews, we propose seven design principles for an IT-tool which facilitates self-monitoring for students based on EPM. Further, we evaluate the design principles with seven potential users. Our results demonstrate a promising approach to help students improve their self-efficacy in their individual learning process using EPM.

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Artificial Socialization? How Artificial Intelligence Applications Can Shape A New Era of Employee Onboarding Practices

2023-01-06 , Ritz, Eva , Fabio, Donisi , Elshan, Edona , Rietsche, Roman

Onboarding has always emphasized personal contact with new employees. Excellent onboarding can extend employee retention and improve loyalty. Even in a physical setting, the onboarding process is demanding for both the newcomer and the onboarding organization. Remote work, in contrast, has made this process even more challenging by forcing a rapid shift from offline to online onboarding practices. Organizations are adopting new technologies like artificial intelligence (AI) to support work processes, such as hiring processes or innovation facilitation, which could shape a new era of work practices. However, it has not been studied how AI applications can or should support onboarding. Therefore, our research conducts a literature review on current onboarding practices and uses expert interviews to evaluate AI's potential and pitfalls for each action. We contribute to the literature by presenting a holistic picture of onboarding practices and assessing potential application areas of AI in the onboarding process.

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Learn Smarter, Not Harder – Exploring the Development of Learning Analytics Use Cases to Create Tailor-Made Online Learning Experiences

2021 , Ritz, Eva , Grüneke, Timo

Our world is significantly shaped by digitalization, fostering new opportunities for technology-mediated learning. Therefore, massive amounts of knowledge become available online. However, concurrently these formats entail less interaction and guidance from lecturers. Thus, learners need to be supported by intelligent learning tools that provide suitable knowledge in a tailored way. In this context, the use of learning analytics in its multifaceted forms is essential. Existing literature shows a proliferation of learning analytics use cases without a systematic structure. Based on a structured literature review of 42 papers we organized existing literature contributions systematically and derived four use cases: learning dashboards, individualized content, tutoring systems, and adaptable learning process based on personality. Our use cases will serve as a basis for a targeted scientific discourse and are valuable orientation for the development of future learning analytics use cases to give rise to the new form of Learning Experience Platforms.

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