@inproceedings{ValenciaNormannSapsaietal.2023, author = {Valencia, Yeimy and Normann, Marc and Sapsai, Iryna and Abke, J{\"o}rg and Madsen, Anders L. and Weidl, Galia}, title = {Learning Style Classification by Using Bayesian Networks Based on the Index of Learning Style}, series = {ECSEE '23: Proceedings of the 5th European Conference on Software Engineering Education, June 2023}, booktitle = {ECSEE '23: Proceedings of the 5th European Conference on Software Engineering Education, June 2023}, doi = {10.1145/3593663.3593685}, pages = {73 -- 82}, year = {2023}, subject = {Lernstil}, language = {en} } @article{SapsaiValenciaAbke2023, author = {Sapsai, Iryna and Valencia, Yeimy and Abke, J{\"o}rg}, title = {Learning Analytics Dashboard for Educators: Proposed Project to Design with Pedagogical Background}, series = {ECSEE '23: Proceedings of the 5th European Conference on Software Engineering Education}, volume = {2023}, journal = {ECSEE '23: Proceedings of the 5th European Conference on Software Engineering Education}, number = {ECSEE '23: Proceedings of the 5th European Conference on Software Engineering Education}, doi = {https://doi.org/10.1145/3593663.3593686}, pages = {38 -- 47}, year = {2023}, abstract = {In this article, the authors describe a prototype of a Learning Analytics Dashboard (LAD) for educators. It is based on the analysis of pedagogical actions and taking into the process and learning style of students in an online environment based on learning analytics (LA). A description of the Dashboard structure, divided into levels and categories based on available learning analytics, will allow the educator to dive deeper into the online course themselves and explore more. It will also allow them to determine the level of student performance, identify gaps in learning materials, and research student data. The authors have identified further directions for the development of a LAD for a professor, including modeling algorithms for researching student behavior and learning style using Artificial Intelligence and presenting LA in a visualized form. This paper shows the stages of creating a professor's LAD prototype as a functional part of the adaptive learning system in the HASKI-System to analyze visual information obtained from LA and the possibilities to monitor the learning process, learning progress, student activity, and make decisions on careful intervention in the students' learning process.}, subject = {Lerntechnik}, language = {en} } @misc{HochstetterAbke2023, author = {Hochstetter, Marvin and Abke, J{\"o}rg}, title = {AdLer - Adaptive digitale Lernr{\"a}ume: Autorentool und 3D-Lernumgebung}, year = {2023}, abstract = {Die fortschreitende Digitalisierung im Bildungssektor ist gerade f{\"u}r die Berufliche Weiterbildung von zentraler Bedeutung. Bereits seit l{\"a}ngerer Zeit sind in diesem Kontext zeit- und ortsunabh{\"a}ngige Lehr-Lern-Formate sehr wichtig. Allerdings bietet digitales Lernen auch großes Potential hinsichtlich dem Thema Motivationsf{\"o}rderung. Serious Games stellen ein vielversprechendes Beispiel von Game-based Learning (GBL) dar, um die Lernmotivation zu adressieren. Hierbei geht es darum, Mechaniken (digitaler) Spiele zu verwenden, um einen Wissenserwerb zu erm{\"o}glichen, der zeitgleich motivierend gestaltet ist. Das AdLer-Projekt hat zum Ziel ein frei verf{\"u}gbares Instrument zu entwickeln, mit welchem Lehrende virtuelle 3D-Lernumgebungen erstellen und in ihrer Lehre in unterschiedlichen Formen einsetzen k{\"o}nnen. Mit Hilfe eines Autorentools werden Lehrende bei der Konzeption und Erstellung von 3D-Lernumgebungen unterst{\"u}tzt. Dadurch k{\"o}nnen Lehrende auf schnellem und einfachem Weg eigene digitale 3D-Lernumgebungen erstellen sowie nach ihren individuellen Vorstellungen hinsichtlich der eigenen Lernszenarien konfigurieren. In der 3D-Lernumgebung k{\"o}nnen Lernende nach den Prinzipien des GBL erkundungsorientiert und interaktiv lernen. Dazu k{\"o}nnen Lernende mit einem virtuellen Avatar die AdLer-3D-Lernumgebung betreten und verschiedene Handlungsm{\"o}glichkeiten wahrnehmen. Sie k{\"o}nnen sich frei in dieser Lernumgebung bewegen und unterschiedliche Lernangebote (z.B. die Bearbeitung von Lernelementen, wie H5P, Text und Video) durchf{\"u}hren. Im geplanten Poster-Pitch wird eine aktuelle Version der Software AdLer pr{\"a}sentiert und steht f{\"u}r die Konferenzteilnehmer zum Ausprobieren bereit.}, subject = {Lernspiel}, language = {de} } @inproceedings{HaugSapsaiHocketal.2024, author = {Haug, Jim and Sapsai, Iryna and Hock, Isabell and Abke, J{\"o}rg and Hagel, Georg}, title = {EVALUATING AN AI-BASED ADAPTIVE LEARNING SYSTEM: GOALS, METHODS AND INITIAL RESULTS}, series = {16th International Conference on Education and New Learning Technologies, 1-3 July, 2024, Palma, Spain}, volume = {2024}, booktitle = {16th International Conference on Education and New Learning Technologies, 1-3 July, 2024, Palma, Spain}, number = {EDULEARN24 Proceedings}, editor = {Haug, Jim}, doi = {10.21125/edulearn.2024.0834}, pages = {3157 -- 3166}, year = {2024}, abstract = {The aim of this paper is to describe the evaluation process and findings of an AI-based Adaptive Learning System for the Computer Science discipline at two different German universities and discuss an array of methods in regard to assessing such a system. The primary objectives have been twofold: firstly, to examine the reception of selected learning elements, which were conceptually outlined based on relevant literature, among the student body; and secondly, to investigate the efficacy of individualized adaptive learning paths. These paths were generated by employing a variety of algorithms to analyze students learning style tendencies, with a particular emphasis on adaptive navigational techniques. The used algorithms encompassed a modified version of a literature based adaptive mechanism, an Ant-Colony-Algorithm and a Genetic Algorithm, alongside a lecturer-recommended learning path for a non-adaptive comparison. While the system suggested suitable learning paths based on student data, it never forced the individuals to give up their self-directed learning. The evaluation criteria revolved around the evolution of student motivation, interest levels, and knowledge acquisition during the time they spent working in the system. The evaluation sought to facilitate comparative analyses and assess algorithmic fitness for proficient learning path generation. The methods included both quantitative and qualitative approaches to gather data, seeking to strike a balance between being student-friendly and scientifically informative. They ranged from Likert Scale self-assessments to screen and video observations with retrospective interviews. Since the purpose of adaptive learning systems is intertwined with personalized learning it seems imperative to already take the preferences and opinions of students into account while the system is still in development. This complexity underscores the challenge of evaluating such systems, as significant constraints on student choice - though simplifying evaluation - directly oppose the ethos of individualized, self-directed learning. Initial findings suggest that the underlying theoretical considerations on sequencing and structuring of learning elements are confirmed, coupled with providing adequate flexibility to meet diverse learning needs. Cross-site evaluation of the literature-based learning elements indicated a high comprehensibility and positive student ratings. While significant positive trends were observed regarding knowledge acquisition, they cannot be definitively attributed to a specific method of learning path generation. Motivation and interest analyses show no significant differences among learning path types, albeit heavily limited by sample size. Similarly, emotion measurements, though limited, hint at positive impacts from HASKI system use. Despite limitations, early indications suggest student acceptance and potential effectiveness of learning paths, highlighting the need for larger sample sizes for validation and expansion. Ensuring alignment with student needs and user-friendly design are crucial considerations.}, subject = {E-Learning}, language = {en} } @article{SapsaiHaugAbkeetal.2024, author = {Sapsai, Iryna and Haug, Jim and Abke, J{\"o}rg and Hagel, Georg}, title = {IDENTIFYING STUDENT EMOTIONS IN AN ADAPTIVE LEARNING SYSTEM WITH A BAYESIAN NETWORK MODEL}, series = {IATED Digital Library Home}, volume = {2024}, journal = {IATED Digital Library Home}, number = {ICERI2024 Proceedings}, doi = {10.21125/iceri.2024.1192}, pages = {4829 -- 4837}, year = {2024}, abstract = {Analyzing scientific literature on the impact of emotions in e-learning highlights the importance of assessing students' emotional states and their role during online learning processes. This assessment often relies on physiological sensors, which may not be accessible to all institutions or acceptable to all students. Therefore, detecting students' emotional states in an online learning environment remains a complex challenge requiring a fundamental understanding of the role of emotions in such environments. This paper explores the potential benefits of using self-reported surveys to identify students' affective states and improve their learning experience by responding to specific emotional states during their learning progress. Our objectives include identifying emotional states through surveys, pinpointing frequently selected emotion-descriptive words, analyzing survey results, and developing a Bayesian Network (BN) model for automatic emotional state analysis. This BN model aims to facilitate real-time interactions within an adaptive learning system by autonomously assessing students' emotions without direct educator intervention. We present findings from an emotion identification survey conducted among engineering students in an e-learning course provided in an adaptive learning environment. Practically, we plan to implement the BN model within the aforementioned environment to interact with students through real-time pop-up messages, identifying and responding to their emotional states. Its generic character structure allows for changes in the set of analyzed words, ensuring its broad applicability. The challenges of identifying emotions during different online learning activities and the question of the optimal word selection remain for future exploration. Effective identification of students' emotional states is expected to enhance online learning, improving emotional well-being, motivation, and individual success.}, subject = {Bayes-Netz}, language = {en} } @inproceedings{AbkeBruneHauptetal.2012, author = {Abke, J{\"o}rg and Brune, Philipp and Haupt, Wolfram and Hagel, Georg and Landes, Dieter and Mottok, J{\"u}rgen}, title = {EVELIN - ein Forschungsprojekt zur systematischen Verbesserung des Lernens von Software Engineering}, series = {Tagungsband Embedded Software Engineering-Kongress 2012}, booktitle = {Tagungsband Embedded Software Engineering-Kongress 2012}, pages = {653 -- 658}, year = {2012}, abstract = {Software bestimmt unseren Alltag, ist aber abstrakt und physisch nicht greifbar. Wegen ihres hohen Stellenwerts ist eine gute Software-Engineering-Ausbildung von zentraler Bedeutung. Eine hohe Technologiekompetenz der Softwareentwickler ist allein kein Garant f{\"u}r qualitativ hochwertige Software, entscheidend sind auch Soft Skills wie Kommunikations- und Teamf{\"a}higkeit der beteiligten Ingenieure und Informatiker. Neben dem hohen Abstraktionsgrad stellen also auch weiche Faktoren die Ausbildung im Software Engineering vor große Herausforderungen. Didaktische Ans{\"a}tze zur Vermittlung von Software Engineering m{\"u}ssen hinsichtlich beider Anforderungen systematisch weiterentwickelt werden. Die „klassische" Ausbildung mit Elementen des Frontalunterrichts ist durch Methodenarrangements zu erg{\"a}nzen, die die Prozesse des Lernens und Lehrens fokussieren. Im Forschungsprojekt EVELIN („Experimentelle Verbesserung des Lernens von Software Engineering") arbeiten seit Anfang 2012 sechs Hochschulen an der systematischen kompetenzorientierten Weiterentwicklung der Software-Engineering- Ausbildung. Der Vortrag skizziert Ziele und den aktuellen Stand des Forschungsprojektes EVELIN.}, subject = {Software Engineering}, language = {de} } @inproceedings{NormannHaugValenciaetal.2023, author = {Normann, Marc and Haug, Jim and Valencia, Yeimy and Abke, J{\"o}rg and Hagel, Georg}, title = {Adaptive Learning Path Sequencing Based on Learning Styles within N-dimensional Spaces}, series = {ECSEE '23: Proceedings of the 5th European Conference on Software Engineering Education}, volume = {2023}, booktitle = {ECSEE '23: Proceedings of the 5th European Conference on Software Engineering Education}, editor = {Mottock, J{\"u}rgen and Hagel, Georg}, doi = {https://doi.org/10.1145/3593663.3593676}, pages = {56 -- 64}, year = {2023}, abstract = {Planning adaptive learning paths for students' progress throughout a course can be a challenging task, although it can be helpful for their learning progress. Within the HASKI-System, students should be able to get their own, personalized learning paths. In this paper, we present an approach towards the learning path sequencing problem. This idea is based on a novel proposal for arranging learning objects in a multi-dimensional space, bringing the relationship and similarities of these objects into a new relationship. We show, that we can use both, the Ant Colony Optimization Algorithm and the Genetic Algorithm with the idea of the Traveling-Salesman-Problem and get results, that are comparable with a proposed literature-based adaption mechanism. Nevertheless, the learning paths are all personalized based on the Felder \& Silverman Learning Style Model and the hyperspace model will allow us later on to include more dimensions for other influencing factors.}, subject = {Lernstil}, language = {en} } @inproceedings{KloppDoerringerEigleretal.2023, author = {Klopp, Marco and D{\"o}rringer, Antonia and Eigler, Tobias and Bartel, Paula and Hochstetter, Marvin and Weishaupt, Andreas and Geirhos, Philipp and Abke, J{\"o}rg and Hagel, Georg and Elsebach, Jens and Rossmann, Raphael}, title = {Development of an Authoring Tool for the Creation of Individual 3D Game-Based Learning Environments}, series = {ECSEE '23: Proceedings of the 5th European Conference on Software Engineering Education}, volume = {2023}, booktitle = {ECSEE '23: Proceedings of the 5th European Conference on Software Engineering Education}, number = {5.}, editor = {Mottok, J{\"u}rgen}, publisher = {Association for Computing Machinery (ACM)}, address = {New York, NY, United States}, isbn = {978-1-4503-9956-2}, pages = {204 -- 209}, year = {2023}, abstract = {Game-based learning in general and serious games in particular have a promising potential in higher education. In this article we going to show the capability of serious games with regard to current challenges in higher education. The focus of this article is the presentation of the AdLer authoring tool, which offers lecturers the possibility to design and generate virtual 3D learning environments in which students can interact with learning content according to the principles of game-based learning.}, subject = {Lernspiel}, language = {en} } @inproceedings{DoerringerKloppSchaabetal.2023, author = {D{\"o}rringer, Antonia and Klopp, Marco and Schaab, Lukas and Hochstetter, Marvin and Glaab, Daniel and Bartel, Paula and Abke, J{\"o}rg and Elsebach, Jens and Rossmann, Raphael and Hagel, Georg}, title = {AdLer: 3D-Lernumgebung f{\"u}r Studierende}, series = {21. Fachtagung Bildungstechnologien (DELFI)}, volume = {2023}, booktitle = {21. Fachtagung Bildungstechnologien (DELFI)}, editor = {R{\"o}pke, Ren{\´e} and Schroeder, Ulrike}, doi = {10.18420/delfi2023-41}, pages = {251 -- 252}, year = {2023}, subject = {Lernspiel}, language = {de} } @inproceedings{ManzAbkeHagel2025, author = {Manz, Julian and Abke, J{\"o}rg and Hagel, Georg}, title = {Automated Tracking of User Interactions in Web-Based Adaptive Learning for Software Engineering}, series = {ECSEE '25: Proceedings of the 6th European Conference on Software Engineering Education}, booktitle = {ECSEE '25: Proceedings of the 6th European Conference on Software Engineering Education}, editor = {Mottok, J{\"u}rgen and Hagel, Georg}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, isbn = {979-8-4007-1282-1}, doi = {10.1145/3723010.3723020}, pages = {180 -- 184}, year = {2025}, abstract = {This paper explores the automation of generating and dispatching Experience API (xAPI) statements for comprehensive tracking of user interactions in e-learning environments. It introduces the react-xapi-wrapper library, an extension of the xAPI JavaScript library designed for use in web applications. Key aspects discussed include the library's features, its integration into a web-based adaptive learning system (ALS) for software engineering, and the custom verbs used. The goal is to reduce implementation effort for tutors and developers while taking advantage of xAPI's interoperability, scalability, and ability to track student learning activities and behaviors, laying the foundation for more responsive and personalized learning experiences.}, subject = {Computerunterst{\"u}tztes Lernen}, language = {en} }