Hasan Abu Rasheed | Publikationen
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Experimental Interface for Multimodal and Large Language Model Based Explanations of Educational Recommender Systems
14th International Learning Analytics and Knowledge conference (LAK24), 2024, Kyoto, Japan
[Link][BibTex]
@misc{aburasheed2024experimental, title={Experimental Interface for Multimodal and Large Language Model Based Explanations of Educational Recommender Systems}, author={Hasan Abu-Rasheed and Christian Weber and Madjid Fathi}, year={2024}, eprint={2402.07910}, archivePrefix={arXiv}, primaryClass={cs.HC} }
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Problem-Based Learning-Path Recommendations Through Integrating Knowledge Graphs and Large Language Models
14th International Learning Analytics and Knowledge conference (LAK24), 2024, Kyoto, Japan
[Link][BibTex]
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Supporting Student Decisions on Learning Recommendations: An LLM-Based Chatbot with Knowledge Graph Contextualization for Conversational Explainability and Mentoring
Joint proceedings of the 14th International Learning Analytics and Knowledge conference (LAK24) workshops, 2024, Kyoto, Japan
[Link][BibTex]
@misc{aburasheed2024supporting, title={Supporting Student Decisions on Learning Recommendations: An LLM-Based Chatbot with Knowledge Graph Contextualization for Conversational Explainability and Mentoring}, author={Hasan Abu-Rasheed and Mohamad Hussam Abdulsalam and Christian Weber and Madjid Fathi}, year={2024}, eprint={2401.08517}, archivePrefix={arXiv}, primaryClass={cs.AI} }
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Knowledge Graphs as Context Sources for LLM-Based Explanations of Learning Recommendations
2024 IEEE Global Engineering Education Conference, 2024, Kos, Greece
[Link][BibTex]
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Supporting Remote Students Through Utilizing Web-Based Exercise-Templates and a Mobile Learning Chatbot for Creating and Interacting with Learning Materials
In: Viberg, O., Jivet, I., Muñoz-Merino, P., Perifanou, M., Papathoma, T. (eds) Responsive and Sustainable Educational Futures. EC-TEL 2023. Lecture Notes in Computer Science, vol 14200. Springer, Cham.
[Link][BibTex]
@InProceedings{10.1007/978-3-031-42682-7_59, author="Abu-Rasheed, Hasan and Efthymiou, Yannis and Fathi, Madjid and Ghadamighalandari, Parvin and Medina, Juli{\'a}n L{\'o}pez and Garc{\'i}a, Covadonga Ordo{\~{n}}ez and Tsardanidis, Gregory and Zenkert, Johannes and Zgeras, Giannis", editor="Viberg, Olga and Jivet, Ioana and Mu{\~{n}}oz-Merino, Pedro J. and Perifanou, Maria and Papathoma, Tina", title="Supporting Remote Students Through Utilizing Web-Based Exercise-Templates and a Mobile Learning Chatbot for Creating and Interacting with Learning Materials", booktitle="Responsive and Sustainable Educational Futures", year="2023", publisher="Springer Nature Switzerland", address="Cham", pages="668--673", abstract="During COVID-19 pandemic, and in the post-pandemic era, the dependency on digital learning tools and integrating them into regular teaching practices revealed several challenges and requirements from those tools. Such tools are needed to accommodate existing learning materials that teachers use, and deliver them to students who are learning remotely. In this paper, we demonstrate a mobile learning approach for supporting remote students and teachers. Our proposed approach utilizes a pedagogically informed design of exercise templates to construct learning materials as stand-alone exercises. A web application is designed to enable teachers of creating those exercises. On the learner's side, a Chatbot-based mobile application is developed to ensure an easy and understandable interaction with the system for young students. Students could engage in a conversation with the Chatbot, solve exercises and get predefined teacher feedback on their solutions, all without a direct contact with the teacher who created those exercises. We implement our system and test it in three European contexts, in Spain, Greece and Germany. Our evaluation reveals high acceptance of the system's ability to integrate existing materials to offer them to remote students; high student engagement with the learning Chatbot; and several lessons-learned from the user interaction with the system.", isbn="978-3-031-42682-7" }
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Pedagogically-Informed Implementation of Reinforcement Learning on Knowledge Graphs for Context-Aware Learning Recommendations
In: Viberg, O., Jivet, I., Muñoz-Merino, P., Perifanou, M., Papathoma, T. (eds) Responsive and Sustainable Educational Futures. EC-TEL 2023. Lecture Notes in Computer Science, vol 14200. Springer, Cham.
[Link][BibTex]
@InProceedings{10.1007/978-3-031-42682-7_35, author="Abu-Rasheed, Hasan and Weber, Christian and Dornh{\"o}fer, Mareike and Fathi, Madjid", editor="Viberg, Olga and Jivet, Ioana and Mu{\~{n}}oz-Merino, Pedro J. and Perifanou, Maria and Papathoma, Tina", title="Pedagogically-Informed Implementation of Reinforcement Learning on Knowledge Graphs for Context-Aware Learning Recommendations", booktitle="Responsive and Sustainable Educational Futures", year="2023", publisher="Springer Nature Switzerland", address="Cham", pages="518--523", abstract="Context-aware recommender systems are important tools to address the learning context in technology enhanced learning. However, the contextual factors of learning, as well as the mechanisms of integrating them into the recommendation, are mostly defined from a technical perspective, rather than a pedagogical one. In this paper, we introduce a new approach for generating pedagogically informed, context-aware, learning recommendations. We build on the context definition in situated and subject-oriented learning theories. Then, we utilize a knowledge graph structure to build the environment for a path exploration and ranking algorithm, which is influenced by agent exploration in reinforcement learning (RL), for creating sequential learning-path recommendations. Our design of the agent's reward function integrates learning-context factors in the recommender system. We evaluate the proposed solution qualitatively with domain experts, and quantitatively using semantic-similarity measures to compare our recommended paths to expert-curated learning content. Our evaluation shows an enriched recommendation based on the learners' context, as well as a better discovery of relevant educational content. ", isbn="978-3-031-42682-7" }
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Building Contextual Knowledge Graphs for Personalized Learning Recommendations Using Text Mining and Semantic Graph Completion
2023 IEEE International Conference on Advanced Learning Technologies (ICALT), 2023, pp. 36-40
[Link][BibTex]
@INPROCEEDINGS {10260850, author = {H. Abu-Rasheed and M. Dornhofer and C. Weber and G. Kismihok and U. Buchmann and M. Fathi}, booktitle = {2023 IEEE International Conference on Advanced Learning Technologies (ICALT)}, title = {Building Contextual Knowledge Graphs for Personalized Learning Recommendations Using Text Mining and Semantic Graph Completion}, year = {2023}, volume = {}, issn = {}, pages = {36-40}, abstract = {Modelling learning objects (LO) within their context enables the learner to advance from a basic, remembering-level, learning objective to a higher-order one, i.e., a level with an application- and analysis objective. While hierarchical data models are commonly used in digital learning platforms, using graph-based models enables representing the context of LOs in those platforms. This leads to a foundation for personalized recommendations of learning paths. In this paper, the transformation of hierarchical data models into knowledge graph (KG) models of LOs using text mining is introduced and evaluated. We utilize custom text mining pipelines to mine semantic relations between elements of an expert-curated hierarchical model. We evaluate the KG structure and relation extraction using graph quality-control metrics and the comparison of algorithmic semantic-similarities to expert-defined ones. The results show that the relations in the KG are semantically comparable to those defined by domain experts, and that the proposed KG improves representing and linking the contexts of LOs through increasing graph communities and betweenness centrality.}, keywords = {text mining;measurement;learning systems;semantics;pipelines;knowledge graphs;metadata}, doi = {10.1109/ICALT58122.2023.00016}, url = {https://doi.ieeecomputersociety.org/10.1109/ICALT58122.2023.00016}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, month = {jul} }
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Context based learning: a survey of contextual indicators for personalized and adaptive learning recommendations – a pedagogical and technical perspective
Frontiers in Education, Volume 8, 2023
[Link][BibTex]
@ARTICLE{10.3389/feduc.2023.1210968, AUTHOR={Abu-Rasheed, Hasan and Weber, Christian and Fathi, Madjid}, TITLE={Context based learning: a survey of contextual indicators for personalized and adaptive learning recommendations – a pedagogical and technical perspective}, JOURNAL={Frontiers in Education}, VOLUME={8}, YEAR={2023}, URL={https://www.frontiersin.org/articles/10.3389/feduc.2023.1210968}, DOI={10.3389/feduc.2023.1210968}, ISSN={2504-284X}, ABSTRACT={Learning personalization has proven its effectiveness in enhancing learner performance. Therefore, modern digital learning platforms have been increasingly depending on recommendation systems to offer learners personalized suggestions of learning materials. Learners can utilize those recommendations to acquire certain skills for the labor market or for their formal education. Personalization can be based on several factors, such as personal preference, social connections or learning context. In an educational environment, the learning context plays an important role in generating sound recommendations, which not only fulfill the preferences of the learner, but also correspond to the pedagogical goals of the learning process. This is because a learning context describes the actual situation of the learner at the moment of requesting a learning recommendation. It provides information about the learner’s current state of knowledge, goal orientation, motivation, needs, available time, and other factors that reflect their status and may influence how learning recommendations are perceived and utilized. Context-aware recommender systems have the potential to reflect the logic that a learning expert may follow in recommending materials to students with respect to their status and needs. During the last decade, several approaches have emerged in the literature to define the learning context and the factors that may capture it. Those approaches led to different definitions of contextualized learner-profiles. In this paper, we review the state-of-the-art approaches for defining a user’s learning-context. We provide an overview of the definitions available, as well as the different factors that are considered when defining a context. Moreover, we further investigate the links between those factors and their pedagogical foundations in learning theories. We aim to provide a comprehensive understanding of contextualized learning from both pedagogical and technical points of view. By combining those two viewpoints, we aim to bridge a gap between both domains, in terms of contextualizing learning recommendations.} }
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Adding Context to Industry 4.0 Analytics: A New Document Driven Knowledge Graph Construction and Contextualization Approach
2022 IEEE International Conference on Electro Information Technology (eIT), 19-21 May 2022, pp. 550-555
[Link][BibTex]
@INPROCEEDINGS{9813992, author={Weber, Christian and Abu-Rasheed, Hasan and Fathi, Madjid}, booktitle={2022 IEEE International Conference on Electro Information Technology (eIT)}, title={Adding Context to Industry 4.0 Analytics: A New Document Driven Knowledge Graph Construction and Contextualization Approach}, year={2022}, volume={}, number={}, pages={550-555}, doi={10.1109/eIT53891.2022.9813992}}
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Recommendersysteme in der beruflichen Weiterbildung. Grundlagen, Herausforderungen und Handlungsempfehlungen. Ein Dossier im Rahmen des INVITE-Wettbewerbs.
Berlin 2022, 26 S.
[Link][BibTex]
@misc{reichow2022recommendersysteme, title={Recommendersysteme in der beruflichen Weiterbildung. Grundlagen, Herausforderungen und Handlungsempfehlungen. Ein Dossier im Rahmen des INVITE-Wettbewerbs}, author={Reichow, Insa and Buntins, Katja and Paa{\ss}en, Benjamin and Abu-Rasheed, Hasan and Weber, Christian and Dornh{\"o}fer, Mareike}, year={2022}, publisher={Berlin:} }
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Transferrable Framework Based on Knowledge Graphs for Generating Explainable Results in Domain-Specific, Intelligent Information Retrieval
Informatics 2022, 9(1), 6, MDPI
[Link][BibTex]
@Article{informatics9010006, AUTHOR = {Abu-Rasheed, Hasan and Weber, Christian and Zenkert, Johannes and Dornhöfer, Mareike and Fathi, Madjid}, TITLE = {Transferrable Framework Based on Knowledge Graphs for Generating Explainable Results in Domain-Specific, Intelligent Information Retrieval}, JOURNAL = {Informatics}, VOLUME = {9}, YEAR = {2022}, NUMBER = {1}, ARTICLE-NUMBER = {6}, URL = {https://www.mdpi.com/2227-9709/9/1/6}, ISSN = {2227-9709}, DOI = {10.3390/informatics9010006} }
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Explainable Job-Posting Recommendations Using Knowledge Graphs and Named Entity Recognition
2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Page(s): 3291-3296
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Explainable Job-Posting Recommendations Using Knowledge Graphs and Named Entity Recognition
In the IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2021, Melbourne, Australia, pp. 3291-3296, doi: 10.1109/SMC52423.2021.9658757.
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EduCOR: An Educational and Career-Oriented Recommendation Ontology
In The 20th International Semantic Web Conference (ISWC 2021), Virtual 24 - 28 October 2021
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Knowledge Integration in Smart Factories
Encyclopedia, 1(3), 792-811, 2021
[Link][BibTex]
@article{zenkert2021knowledge, title={Knowledge Integration in Smart Factories}, author={Zenkert, Johannes and Weber, Christian and Dornh{\"o}fer, Mareike and Abu-Rasheed, Hasan and Fathi, Madjid}, journal={Encyclopedia}, volume={1}, number={3}, pages={792--811}, year={2021}, publisher={Multidisciplinary Digital Publishing Institute} }
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Explainable Graph-based Search for Lessons-Learned Documents in the Semiconductor Industry
In Computing Conference 2021, 15-16 July 2021 | London, UK
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To Which Extent Can We Use Augmented Reality in Math Learning? A Survey on the Potentials and Limitations of Utilizing AR in High-School Math Lectures in Iran
In 12th International Conference on Education and New Learning Technologies (EDULEARN20), 6-7 July, 2020. DOI: https://doi.org/10.21125/edulearn.2020.1366
[Link][BibTex]
@InProceedings{FATHI2020TOW, author = {Fathi, H. and Abu Rasheed, H. and Fathi, M. and Lohrey, M.}, title = {TO WHICH EXTENT CAN WE USE AUGMENTED REALITY IN MATH LEARNING? A SURVEY ON THE POTENTIALS AND LIMITATIONS OF UTILIZING AR IN HIGH-SCHOOL MATH LECTURES IN IRAN}, series = {12th International Conference on Education and New Learning Technologies}, booktitle = {EDULEARN20 Proceedings}, isbn = {978-84-09-17979-4}, issn = {2340-1117}, doi = {10.21125/edulearn.2020.1366}, url = {http://dx.doi.org/10.21125/edulearn.2020.1366}, publisher = {IATED}, location = {Online Conference}, month = {6-7 July, 2020}, year = {2020}, pages = {5213-5222}}
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Educational Multimodal Data Mining and Fusion through Knowledge Graphs for Topic-Relation Extraction in Study Recommendations
In 12th International Conference on Education and New Learning Technologies (EDULEARN20), 6-7 July, 2020. DOI: https://doi.org/10.21125/edulearn.2020.1020
[Link][BibTex]
@InProceedings{TOMAR2020EDU, author = {Tomar, S. and Abu Rasheed, H. and Fathi, M.}, title = {EDUCATIONAL MULTIMODAL DATA MINING AND FUSION THROUGH KNOWLEDGE GRAPHS FOR TOPIC-RELATION EXTRACTION IN STUDY RECOMMENDATIONS}, series = {12th International Conference on Education and New Learning Technologies}, booktitle = {EDULEARN20 Proceedings}, isbn = {978-84-09-17979-4}, issn = {2340-1117}, doi = {10.21125/edulearn.2020.1020}, url = {http://dx.doi.org/10.21125/edulearn.2020.1020}, publisher = {IATED}, location = {Online Conference}, month = {6-7 July, 2020}, year = {2020}, pages = {3696-3705}}
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A Text Extraction-Based Smart Knowledge Graph Composition for Integrating Lessons Learned during the Microchip Design
In Intelligent Systems Conference (IntelliSys 2020), Amsterdam, The Netherlands. 3-4 September 2020.
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Enhancing Design-relevant Document Search and Visibility through Fusion of Multi-sourced Data in Knowledge Graphs
In 20th European Advanced Process Control and Manufacturing (APC|M) Conference, Toulon, France, March 30 – April 1, 2020 (submitted, conference shifted to 2021).
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Work in Progress – Establishing a Master Program in Cyber Physical Systems: Basic Findings and Future Perspectives
In 2019 International Conference on Promising Electronic Technologies (ICPET), Gaza City, Palestine, pp. 4-9, 2019.
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Conversational Chatbot System for Student Support in Administrative Exam Information
In 12th annual International Conference of Education, Research and Innovation (iCERi2019), Seville (Spain). 11th - 13th of November, 2019.
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Integration of Augmented Reality in Language Learning through the Concept of Imitating Mental Ability of Word Association (CIMAWA)
In proceedings of the 3rd Annual Learning and Student Analytics Conference, Loria, France, 2019.
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Language Learning Tool Based On Augmented Reality And The Concept For Imitating Mental Ability Of Word Association (CIMAWA)
Edulearn, Spain, July, 2019.
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Teacher, Student and Domain Based Educational Recommender System for Assessing Student's Preferences on Multiple Recommendation Sources
In proceedings of the 2nd Annual Learning & Student Analytics Conference, Amsterdam, 2018
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What to learn next: Incorporating student, teacher and domain preferences for a comparative educational recommender system
EDULEARN18 proceedings, 10.21125/edulearn.2018.1610, Palmas, Spain, 2-4 July, 2018
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