Prof. Dr. Niels Pinkwart
Profil
Forschungsthemen29
Aaron Matchmaking Tool V1 (Transferbonus)
Quelle ↗Förderer: Wirtschaftsunternehmen / gewerbliche Wirtschaft Zeitraum: 12/2015 - 05/2016 Projektleitung: Prof. Dr. Niels Pinkwart
Analyse und Verbesserung der Usability des WebST-Autorensystems für die Online-Lehre / Transferbonus
Quelle ↗Förderer: Wirtschaftsunternehmen / gewerbliche Wirtschaft Zeitraum: 02/2015 - 07/2015 Projektleitung: Prof. Dr. Niels Pinkwart
Anforderungsprofil für Software
Quelle ↗409-02-A · SoftwaretechnikZeitraum: 02/2016 - 04/2016 Projektleitung: Prof. Dr. Niels Pinkwart
Anwendungsorientierte Infrastruktur für KI-Communities in Lehr-Lern-Settings
Quelle ↗Förderer: Bundesministerium für Forschung, Technologie und Raumfahrt Zeitraum: 12/2021 - 11/2025 Projektleitung: Malte Dreyer, Dr. Andrea Beyer, Prof. Dr. Niels Pinkwart, Uwe Pirr, Prof. Dr. Elisabeth Mayweg, Prof. Dr. Robert Jäschke, Wolfgang Deicke
Chinese-German Perspectives on AI-supported Educational Technologies
Quelle ↗Förderer: DFG sonstige Programme Zeitraum: 02/2019 - 03/2019 Projektleitung: Prof. Dr. Niels Pinkwart, Prof. Dr. Sannyuya Liu
Die Zukunft des MINT-Lernens
Quelle ↗Förderer: Andere inländische Stiftungen Zeitraum: 02/2019 - 12/2022 Projektleitung: Prof. Dr. Niels Pinkwart
Digitale Medien im Unterricht: MINT macht's vor!
Quelle ↗Förderer: Andere inländische Stiftungen Zeitraum: 02/2020 - 12/2021 Projektleitung: Prof. Dr. Niels Pinkwart, Mina Ghomi
Digitalisierung und Inklusion – Grundsatzfragen und Gelingensbedingungen einer inklusiven digitalen Schul- und Unterrichtsentwicklung
Quelle ↗Förderer: Bundesministerium für Forschung, Technologie und Raumfahrt Zeitraum: 01/2019 - 12/2021 Projektleitung: Dr. Heike Schaumburg
Digitalisierung und Inklusion - Grundsatzfragen und Gelingesbedingungen einer inklusiven digitalen Schul- und Unterrichtsentwicklung (Teilprojekt III)
Quelle ↗Förderer: Bundesministerium für Forschung, Technologie und Raumfahrt Zeitraum: 01/2019 - 06/2022 Projektleitung: Prof. Dr. Michael Wahl, Prof. Dr. Niels Pinkwart, Dr. Heike Schaumburg
Erstellung eines inklusiven e-Learning-Tools im Rahmen des Projekts „Gebärdengrips"
Quelle ↗Förderer: Wirtschaftsunternehmen / gewerbliche Wirtschaft Zeitraum: 07/2015 - 09/2016 Projektleitung: Prof. Dr. Niels Pinkwart
Erstellung und Umsetzung eines didaktischen Konzeptes für Übungsaufgabenzu Online-Kursen
Quelle ↗Förderer: Wirtschaftsunternehmen / gewerbliche Wirtschaft Zeitraum: 08/2015 - 12/2015 Projektleitung: Prof. Dr. Niels Pinkwart
EXC 2002/1: From Understanding Learners’ Adaptive Motivation and Emotion to Designing Social Learning Companions (TP 06)
Quelle ↗Förderer: DFG Exzellenzstrategie Cluster Zeitraum: 10/2019 - 09/2024 Projektleitung: Prof. Dr. sc. nat. Verena Hafner, Prof. Dr. Niels Pinkwart
Exist-Gründerstipendium: Supporthub - Technologie zur personalisierten und unmittelbaren Vermittlung von Wissen und Fähigkeiten
Quelle ↗Förderer: Bundesministerium für Wirtschaft und Energie Zeitraum: 06/2015 - 05/2016 Projektleitung: Prof. Dr. Niels Pinkwart
Fachdidaktische Qualifizierung Inklusion angehender Lehrkräfte an der Humboldt-Universität zu Berlin
Quelle ↗Förderer: Bundesministerium für Forschung, Technologie und Raumfahrt Zeitraum: 07/2019 - 12/2023 Projektleitung: Prof. Dr. Stephan Breidbach, Prof. Dr. Detlef Pech, Prof. Dr. Vera Moser
Fachdidaktische Qualifizierung Inklusion angehender Lehrkräfte an der Humboldt-Universität zu Berlin
Quelle ↗Förderer: Bundesministerium für Forschung, Technologie und Raumfahrt Zeitraum: 01/2016 - 06/2019 Projektleitung: Dr. Kristina Hackmann, Prof. Dr. Vera Moser, Prof. Dr. Detlef Pech
Fehlererkennung und Textniveauerkennung in Onlinesprachkursen
Quelle ↗Förderer: Wirtschaftsunternehmen / gewerbliche Wirtschaft Zeitraum: 07/2019 - 12/2019 Projektleitung: Prof. Dr. Niels Pinkwart
Fortbildungsreihe zur Entwicklung und Erprobung technologiegestützter Konzepte für den MINT-Unterricht
Quelle ↗Zeitraum: 01/2018 - 12/2019 Projektleitung: Prof. Dr. Niels Pinkwart
Humboldt-ProMINT-Kolleg: Teilprojekt AG Informatik
Quelle ↗Zeitraum: 08/2010 - 07/2016 Projektleitung: Prof. Dr. Niels Pinkwart
Konzeptstudie "Übungsaufgaben in der Online-Lehre mit WebST / Transferbonus
Quelle ↗Förderer: Wirtschaftsunternehmen / gewerbliche Wirtschaft Zeitraum: 05/2014 - 10/2014 Projektleitung: Prof. Dr. Niels Pinkwart
Konzeptstudie "Übungsaufgaben in der Online-Lehre mit WebST / Transferbonus
Quelle ↗Förderer: Wirtschaftsunternehmen / gewerbliche Wirtschaft Zeitraum: 05/2014 - 10/2014 Projektleitung: Prof. Dr. Niels Pinkwart
Learning Analytics für Diversity‐Inspired Adaptive Learning
Quelle ↗Förderer: Land Nordrhein-Westfalen Zeitraum: 12/2019 - 12/2024 Projektleitung: Prof. Dr. Niels Pinkwart
Learning Analytics für sensorbasiertes adaptives Lernen
Quelle ↗Förderer: Bundesministerium für Forschung, Technologie und Raumfahrt Zeitraum: 04/2016 - 03/2019 Projektleitung: Prof. Dr. Niels Pinkwart
Lernen auf der Plattform. Inklusives eLearning als Weiterbildungsangebot für eingeschränkte Beschäftigte
Quelle ↗Förderer: Hans-Böckler-Stiftung Zeitraum: 10/2016 - 09/2018 Projektleitung: Prof. Dr. Niels Pinkwart
Lernen von dynamisiertem Feedback in Intelligenten Tutorensystemen
Quelle ↗Förderer: DFG Sachbeihilfe Zeitraum: 03/2015 - 07/2018 Projektleitung: Prof. Dr. Niels Pinkwart
Machine Learning basierte Dialogführung
Quelle ↗Förderer: Wirtschaftsunternehmen / gewerbliche Wirtschaft Zeitraum: 02/2019 - 07/2019 Projektleitung: Prof. Dr. Niels Pinkwart
SOKRA
Quelle ↗Förderer: BMWE: EXIST Zeitraum: 03/2026 - 02/2027 Projektleitung: Prof. Dr. Niels Pinkwart
SPP1527: FIT
Quelle ↗Förderer: DFG Sachbeihilfe Zeitraum: 07/2013 - 02/2015 Projektleitung: Prof. Dr. Niels Pinkwart
Verbesserung der Gesundheitssituation für Schwangere im ländlichen Südwesten von Uganda durch Verwendung einer mobiltelefonbasierten Multimedia-Anwendung
Quelle ↗Förderer: Bundesministerium für Forschung, Technologie und Raumfahrt Zeitraum: 02/2018 - 12/2020 Projektleitung: Prof. Dr. Niels Pinkwart, Prof. Dr. Angella Musiimenta
WayIn – Der Inklusionswegweiser für Arbeitgeber: Technische Entwicklung und wissenschaftliche Begleitanalyse
Quelle ↗Förderer: Bundesministerium für Forschung, Technologie und Raumfahrt Zeitraum: 01/2018 - 12/2020 Projektleitung: Prof. Dr. Niels Pinkwart
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Publikationen25
Top 25 nach Zitationen — Quelle: OpenAlex (BAAI/bge-m3 embedded für Matching).
International Journal of Computer-Supported Collaborative Learning · 404 Zitationen · DOI
313 Zitationen · DOI
With high dropout rates as observed in many current larger-scale online courses, mechanisms that are able to predict stu-dent dropout become increasingly impor-tant. While this problem is partially solved for students that are active in online fo-rums, this is not yet the case for the more general student population. In this pa-per, we present an approach that works on click-stream data. Among other features, the machine learning algorithm takes the weekly history of student data into ac-count and thus is able to notice changes in student behavior over time. In the later phases of a course (i.e., once such his-tory data is available), this approach is able to predict dropout significantly better than baseline methods.
International Journal of Artificial Intelligence in Education · 122 Zitationen · DOI
112 Zitationen
In order to make progress on Intelligent Tutoring in ill-defined domains it is helpful to start with a definition. In this paper we consider the existing definitions and select one for the basis of our discussion. We then summarize some of the more salient characteristics of ill-defined domains from a tutoring standpoint and some human and ITS strategies that have been employed to cope with them. We conclude with some challenges to the ITS community to spur further research.
Entertainment Computing · 84 Zitationen · DOI
International Journal of Artificial Intelligence in Education · 75 Zitationen · DOI
In this paper we consider prior definitions of the terms"ill-defined domain" and "ill-defined problem". We then present alternate definitions that better support research at the intersection of Artificial Intelligence and Education. In our view both problems and domains are ill-defined when essential concepts, relations, or criteria are un- or underspecified, open-textured, or intractable requiring a solver to recharacterize them. This definition focuses on the core structural and pedagogical features that make problems and domains ill-defined while providing a consistent and functional frame of reference for this special issue and for future work in this area. The concept of ill-definedness is an open-textured concept where no single static definition exists. We present the most suitable definition for the present goals of facilitating research in AI and Education, and addressing the pedagogical need to focus learners on addressing this ambiguity.
Extending a virtual chemistry laboratory with a collaboration script to promote conceptual learning
2010International Journal of Technology Enhanced Learning · 74 Zitationen · DOI
We developed collaborative extensions to 'Vlab', a web-based laboratory that supports students in conducting virtual chemistry experiments. While results from a recent study indicated that VLab promotes chemistry learning, they also revealed that there is room for improvement. We embedded VLab into a collaborative environment that implements a computer-supported collaboration script for guiding students through the stages of scientific experimentation. We describe our pedagogical approach, our collaboration script, and the collaborative learning environment which implements it. We present results from two small-scale studies and a contrasting-case analysis of how adaptive prompts, in addition to the fixed script, affected student behaviour.
Journal of Science Education and Technology · 68 Zitationen · DOI
HAL (Le Centre pour la Communication Scientifique Directe) · 62 Zitationen
Abstract. This paper presents ongoing work on a graphical modeling and discussion support system. One of the properties of the presented approach is that it tries to implement a “collaborative mind tool ” approach, bridging the gap between a communication means and a system with AI functionality. The paper also points out the XML based plug-in mechanism of the system and illustrates it by several categorized examples.
Advances in intelligent systems and computing · 57 Zitationen · DOI
Studies in computational intelligence · 52 Zitationen · DOI
Lecture notes in computer science · 49 Zitationen · DOI
Figshare · 43 Zitationen · DOI
Previous research has highlighted the advantages of graphical argument representations. A number of tutoring systems have been built that support students in rendering arguments graphically, as they learn argumentation skills.The relative tutoring benefits of graphical argument representations have not been reliably shown, however. In this paper we present an evaluation of the LARGO system which enables law students graphically to represent examples of legal interpretation with hypotheticals they observe while reading texts of U.S. Supreme Court oral arguments. We hypothesized that LARGO’s graphical representations and advice would help students to identify important elements of the arguments (i.e., proposed hypotheses, hypothetical challenges, and responses) and to reflect on their significance to the argument’s merits better than a purely text-based alternative. In an experiment, we found some empirical support for this hypothesis.
International Journal of Human-Computer Studies · 43 Zitationen · DOI
International Journal of Artificial Intelligence in Education · 43 Zitationen · DOI
Argumentation is a process that occurs often in ill-defined domains and that helps deal with the illdefinedness. Typically a notion of"correctness" for an argument in an ill-defined domain is impossible to define or verify formally because the underlying concepts are open-textured and the quality of the argument may be subject to discussion or even expert disagreement. Previous research has highlighted the advantages of graphical representations for learning argumentation skills. A number of intelligent tutoring systems have been built that support students in rendering arguments graphically, as they learn argumentation skills. The relative instructional benefits of graphical argument representations have not been reliably shown, however. In this paper we present a formative evaluation of LARGO (Legal ARgument Graph Observer), a system that enables law students graphically to represent examples of legal interpretation with hypotheticals they observe while reading texts of U.S. Supreme Court oral arguments. We hypothesized that, compared to a text-based alternative, LARGO's diagramming language geared toward depicting hypothetical reasoning processes, coupled with non-directive feedback, helps students better extract the important information from argument transcripts and better learn argumentation skills. A first pilot study, conducted with volunteer first-semester law students, provided support for the hypothesis. The system especially helped lower-aptitude students learn argumentation skills, and LARGO improved the reading skills of students as they studied expert arguments. A second study with LARGO was conducted as a mandatory part of a first-semester University law course. Although there were no differences in the learning outcomes of the two conditions, the second study showed some evidence that those students who engaged more with the argument diagrams through the advice did better than the text condition. One lesson learned from these two studies is that graphical representations in intelligent tutoring systems for the ill-defined domain of argumentation may still be better than text, but that engagement is essential.
Educational Data Mining · 42 Zitationen
Perspectives on rethinking and reforming education · 36 Zitationen · DOI
36 Zitationen · DOI
Communication through artefacts, in the sense of objects (co-)constructed by learners, is a well known mechanism in synchronous shared workspace environments. In this article, we explore the potential of extending this principle to heterogeneous, anonymous and asynchronous learner communities by drawing on existing work, e.g. in the areas of "social navigation" and recommender systems. A new ingredient is the description and provision of "thematic objects" embedded in a task/activity context. Design principles and available technologies are discussed and an example implementation in a European project is presented from the perspective of technology design and development.
Lecture notes in computer science · 34 Zitationen · DOI
Digital Health · 33 Zitationen · DOI
The MatHealth app is an acceptable and feasible intervention among illiterate women, in a resource limited setting. Future efforts should focus on optimized application design, spouse orientation, and incorporating economic support to overcome the challenges we encountered.
33 Zitationen · DOI
Collaborative learning is an educational strategy which is popularly used in project-based courses in schools and colleges. The diversity of group members is frequently considered to be a crucial criterion that can promote intensive intra-group interaction and successful learning outcomes. Yet, when the number of students is up to several hundreds, it is challenging for instructors to look for an optimal group formation considering maximal diversity of students in every group. To address this problem, this paper presents a discrete particle swarm optimization approach to compose heterogeneous learning groups. We carried out simulations based on optimizing the heterogeneity of gender and personality type. The experimental results show that the proposed approach is an effective and stable method that can support instructors to compose heterogeneous collaborative learning groups.
Zenodo (CERN European Organization for Nuclear Research) · 31 Zitationen · DOI
Das Whitepaper "Künstliche Intelligenz in der Hochschulbildung" dient als Beschreibung der Möglichkeiten und Herausforderungen von Künstlicher Intelligenz in Studium und Lehre, fördert die Diskussion über Veränderungen der hochschulischen Lehr- und Lernkultur und von möglichen Lehr-/ Lerninhalten durch Künstliche Intelligenz. Zudem stellt es Visionen für das zukünftige Hochschulstudium aus Sicht von Studierenden und Lehrenden vor, um zu verdeutlichen, wie sich das Studium in den nächsten Jahren verändern kann.<br>
The Continuous Hint Factory - Providing Hints in Vast and Sparsely Populated Edit Distance Spaces
2018Zenodo (CERN European Organization for Nuclear Research) · 31 Zitationen · DOI
Intelligent tutoring systems can support students in solving multi-step tasks by providing hints regarding what to do next. However, engineering such next-step hints manually or via an expert model becomes infeasible if the space of possible states is too large. Therefore, several approaches have emerged to infer next-step hints automatically, relying on past students' data. In particular, the Hint Factory (Barnes and Stamper, 2008) recommends edits that are most likely to guide students from their current state towards a correct solution, based on what successful students in the past have done in the same situation. Still, the Hint Factory relies on student data being available for any state a student might visit while solving the task, which is not the case for some learning tasks, such as open-ended programming tasks. In this contribution we provide a mathematical framework for edit-based hint policies and, based on this theory, propose a novel hint policy to provide edit hints in vast and sparsely populated state spaces. In particular, we extend the Hint Factory by considering data of past students in all states which are similar to the student's current state and creating hints approximating the weighted average of all these reference states. Because the space of possible weighted averages is continuous, we call this approach the Continuous Hint Factory. In our experimental evaluation, we demonstrate that the Continuous Hint Factory can predict more accurately what capable students would do compared to existing prediction schemes on two learning tasks, especially in an open-ended programming task, and that the Continuous Hint Factory is comparable to existing hint policies at reproducing tutor hints on a simple UML diagram task.
Artificial intelligence · 30 Zitationen · DOI
Generative artificial intelligence (AI) (GenAI) has emerged as a transformative force in various fields, and its potential impact on education is particularly profound. This chapter presents the development trends of “GenAI in Education” by exploring the technical background, diverse applications, and multifaceted challenges associated with its adoption in education. The chapter briefly introduces the technical background of GenAI, particularly the development of large language models (LLMs) such as ChatGPT & Co. It provides key concepts, models, and recent technological advances. The chapter then navigates through the various applications of GenAI or LLMs in education, examining their impact on different levels of education, including school, university, and vocational training. The chapter will highlight how GenAI is reshaping the educational landscape through real-world examples and case studies, from personalized learning experiences to content creation and assessment. It also discusses various technical, ethical, and organizational/educational challenges to using technology in education.
International Journal of Learning Technology · 30 Zitationen · DOI
Intelligent tutoring systems (ITSs) typically rely on a formalised model of the underlying domain knowledge in order to provide feedback to learners adaptively to their needs. This approach implies two general drawbacks: the formalisation of a domain-specific model usually requires a huge effort, and in some domains it is not possible at all. In this paper, we propose feedback provision strategies in absence of a formalised domain model, motivated by example-based learning approaches. We demonstrate the feasibility and effectiveness of these strategies in several studies with experts and students. We discuss how, in a set of solutions, appropriate examples can be automatically identified and assigned to given student solutions via machine learning techniques in conjunction with an underlying dissimilarity metric. The plausibility of such an automatic selection is evaluated in an expert survey, while possible choices for domain-agnostic dissimilarity measures are tested in the context of real solution sets of Java programs. The quantitative evidence suggests that the proposed feedback strategies and automatic example assignment are viable in principle, further user studies in large-scale learning environments being the subject of future research.
Kooperationen7
Bestätigte Forscher↔Partner-Paare aus HU-FIS — Gold-Standard-Positive für das Matching.
WayIn – Der Inklusionswegweiser für Arbeitgeber: Technische Entwicklung und wissenschaftliche Begleitanalyse
other
Chinese-German Perspectives on AI-supported Educational Technologies
university
Digitalisierung und Inklusion – Grundsatzfragen und Gelingensbedingungen einer inklusiven digitalen Schul- und Unterrichtsentwicklung
university
Learning Analytics für Diversity‐Inspired Adaptive Learning
university
WayIn – Der Inklusionswegweiser für Arbeitgeber: Technische Entwicklung und wissenschaftliche Begleitanalyse
other
Verbesserung der Gesundheitssituation für Schwangere im ländlichen Südwesten von Uganda durch Verwendung einer mobiltelefonbasierten Multimedia-Anwendung
university
EXC 2002/1: From Understanding Learners’ Adaptive Motivation and Emotion to Designing Social Learning Companions (TP 06)
university
Stammdaten
Identität, Organisation und Kontakt aus HU-FIS.
- Name
- Prof. Dr. Niels Pinkwart
- Titel
- Prof. Dr.
- Fakultät
- Mathematisch-Naturwissenschaftliche Fakultät
- Institut
- Institut für Informatik
- Telefon
- +49 30 2093-41105
- HU-FIS-Profil
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- Zuletzt gescrapt
- 26.4.2026, 01:10:26