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Start date: 2021 × DDC: 370 ×

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Now showing 1 - 10 of 23
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    Translating the Concept of Goal Setting into Practice: What ‘else’ Does It Require than a Goal Setting Tool?
    (Setúbal, Portugal : Science and Technology Publications, Lda, 2020) Kismihók, Gábor; Zhao, Catherine; Schippers, Michaéla; Mol, Stefan; Harrison, Scott; Shehata, Shady; Lane, H. Chad; Zvacek, Susan; Uhomoibhi, James
    This conceptual paper reviews the current status of goal setting in the area of technology enhanced learning and education. Besides a brief literature review, three current projects on goal setting are discussed. The paper shows that the main barriers for goal setting applications in education are not related to the technology, the available data or analytical methods, but rather the human factor. The most important bottlenecks are the lack of students’ goal setting skills and abilities, and the current curriculum design, which, especially in the observed higher education institutions, provides little support for goal setting interventions.
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    Open-Access-Finanzierung
    (Bonn : Bundesinstitut für Berufsbildung (BIBB), 2022) Kändler, Ulrike; Wohlgemuth, Michael; Ertl, Hubert; Rödel, Bodo
    [no abstract available]
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    The Concept of Identifiability in ML Models
    (Setúbal : SciTePress - Science and Technology Publications, Lda., 2022) von Maltzan, Stephanie; Bastieri, Denis; Wills, Gary; Kacsuk, Péter; Chang, Victor
    Recent research indicates that the machine learning process can be reversed by adversarial attacks. These attacks can be used to derive personal information from the training. The supposedly anonymising machine learning process represents a process of pseudonymisation and is, therefore, subject to technical and organisational measures. Consequently, the unexamined belief in anonymisation as a guarantor for privacy cannot be easily upheld. It is, therefore, crucial to measure privacy through the lens of adversarial attacks and precisely distinguish what is meant by personal data and non-personal data and above all determine whether ML models represent pseudonyms from the training data.
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    Digital Transformation of Education Credential Processes and Life Cycles – A Structured Overview on Main Challenges and Research Questions
    ([Wilmington, DE, USA] : IARIA, [2020], 2020) Keck, Ingo R.; Vidal, Maria-Esther; Heller, Lambert; Mikroyannidis, Alexander; Chang, Maiga; White, Stephen
    In this article, we look at the challenges that arise in the use and management of education credentials, and from the switch from analogue, paper-based education credentials to digital education credentials. We propose a general methodology to capture qualitative descriptions and measurable quantitative results that allow to estimate the effectiveness of a digital credential management system in solving these challenges. This methodology is applied to the EU H2020 project QualiChain use case, where five pilots have been selected to study a broad field of digital credential workflows and credential management. Copyright (c) IARIA, 2020
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    Crowdsourcing Scholarly Discourse Annotations
    (New York, NY : ACM, 2021) Oelen, Allard; Stocker, Markus; Auer, Sören
    The number of scholarly publications grows steadily every year and it becomes harder to find, assess and compare scholarly knowledge effectively. Scholarly knowledge graphs have the potential to address these challenges. However, creating such graphs remains a complex task. We propose a method to crowdsource structured scholarly knowledge from paper authors with a web-based user interface supported by artificial intelligence. The interface enables authors to select key sentences for annotation. It integrates multiple machine learning algorithms to assist authors during the annotation, including class recommendation and key sentence highlighting. We envision that the interface is integrated in paper submission processes for which we define three main task requirements: The task has to be . We evaluated the interface with a user study in which participants were assigned the task to annotate one of their own articles. With the resulting data, we determined whether the participants were successfully able to perform the task. Furthermore, we evaluated the interface’s usability and the participant’s attitude towards the interface with a survey. The results suggest that sentence annotation is a feasible task for researchers and that they do not object to annotate their articles during the submission process.
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    OER Recommendations to Support Career Development
    (Piscataway, NJ : IEEE, 2020) Tavakoli, Mohammadreza; Faraji, Ali; Mol, Stefan T.; Kismihók, Gábor
    This Work in Progress Research paper departs from the recent, turbulent changes in global societies, forcing many citizens to re-skill themselves to (re)gain employment. Learners therefore need to be equipped with skills to be autonomous and strategic about their own skill development. Subsequently, high-quality, on-line, personalized educational content and services are also essential to serve this high demand for learning content. Open Educational Resources (OERs) have high potential to contribute to the mitigation of these problems, as they are available in a wide range of learning and occupational contexts globally. However, their applicability has been limited, due to low metadata quality and complex quality control. These issues resulted in a lack of personalised OER functions, like recommendation and search. Therefore, we suggest a novel, personalised OER recommendation method to match skill development targets with open learning content. This is done by: 1) using an OER quality prediction model based on metadata, OER properties, and content; 2) supporting learners to set individual skill targets based on actual labour market information, and 3) building a personalized OER recommender to help learners to master their skill targets. Accordingly, we built a prototype focusing on Data Science related jobs, and evaluated this prototype with 23 data scientists in different expertise levels. Pilot participants used our prototype for at least 30 minutes and commented on each of the recommended OERs. As a result, more than 400 recommendations were generated and 80.9% of the recommendations were reported as useful.
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    Audio Ontologies for Intangible Cultural Heritage
    (Bramhall, Stockport ; EasyChair Ltd., 2022-04-12) Tan, Mary Ann; Posthumus, Etienne; Sack, Harald
    Cultural heritage portals often contain intangible objects digitized as audio files. This paper presents and discusses the adaptation of existing audio ontologies intended for non-cultural heritage applications. The resulting alignment of the German Digital Library-Europeana Data Model (DDB-EDM) with Music Ontology (MO) and Audio Commons Ontology (ACO) is presented.
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    Quantenphysik in Klasse 9: Ergebnisse einer Akzeptanzbefragung für ein Spin-First-Unterrichtskonzept
    (Berlin : FU Berlin, 2023) Albert, Carsten; Pospiech, Gesche
    Quantenphysik gilt als eines der herausforderndsten Themen bei der Vermittlung von Physik und ist gleichzeitig von herausragender Bedeutung bei künftigen technologischen Entwicklungen. Mit Blick auf den wachsenden Stellenwert der Quantenphysik innerhalb der schulischen Bildung wurde im Rahmen eines Design-Based-Research-Projektes ein Lehrkonzept entwickelt, das einen qualitativen und anknüpfungsfähigen Zugang zur Quantenphysik bereits in Klassenstufe 9 ermöglichen soll. Die Lerninhalte bauen dabei auf dem Elektronenspin als exemplarisches Zweizustandssystem auf. Mit dem Ziel einer ersten formativen Evaluierung wurden Akzeptanzbefragungen durchgeführt, um die Vermittlungsstrategie zu überprüfen und vor einer ersten Erprobung mit Schulklassen anzupassen. Die Interviews konnten zeigen, dass mit dem Ansatz die intendierten Inhalte überwiegend erfolgreich vermittelt werden können und dass ein weiterer Einsatz des Konzeptes im Rahmen von Feldstudien legitim erscheint. Außerdem gaben sie Hinweise und Impulse für erste Anpassungen am Konzept.
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    Combining statistical and machine learning methods to explore German students’ attitudes towards ICT in PISA
    (London : Taylor & Francis, 2021) Lezhnina, Olga; Kismihók, Gábor
    In our age of big data and growing computational power, versatility in data analysis is important. This study presents a flexible way to combine statistics and machine learning for data analysis of a large-scale educational survey. The authors used statistical and machine learning methods to explore German students’ attitudes towards information and communication technology (ICT) in relation to mathematical and scientific literacy measured by the Programme for International Student Assessment (PISA) in 2015 and 2018. Implementations of the random forest (RF) algorithm were applied to impute missing data and to predict students’ proficiency levels in mathematics and science. Hierarchical linear models (HLM) were built to explore relationships between attitudes towards ICT and mathematical and scientific literacy with the focus on the nested structure of the data. ICT autonomy was an important variable in RF models, and associations between this attitude and literacy scores in HLM were significant and positive, while for other ICT attitudes the associations were negative (ICT in social interaction) or non-significant (ICT competence and ICT interest). The need for further research on ICT autonomy is discussed, and benefits of combining statistical and machine learning approaches are outlined.
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    Effects of Open Access. Literature study on empirical research 2010–2021
    (Hannover : Technische Informationsbibliothek (TIB), 2024) Hopf, David; Dellmann, Sarah; Hauschke, Christian; Tullney, Marco
    Open access — the free availability of scholarly publications — intuitively offers many benefits. At the same time, some academics, university administrators, publishers, and political decision-makers express reservations. Many empirical studies on the effects of open access have been published in the last decade. This report provides an overview of the state of research from 2010 to 2021. The empirical results on the effects of open access help to determine the advantages and disadvantages of open access and serve as a knowledge base for academics, publishers, research funding and research performing institutions, and policy makers. This overview of current findings can inform decisions about open access and publishing strategies. In addition, this report identifies aspects of the impact of open access that are potentially highly relevant but have not yet been sufficiently studied.