TY - CHAP A1 - Valencia, Yeimy A1 - Normann, Marc A1 - Sapsai, Iryna A1 - Abke, Jörg A1 - Madsen, Anders L. A1 - Weidl, Galia T1 - Learning Style Classification by Using Bayesian Networks Based on the Index of Learning Style T2 - ECSEE '23: Proceedings of the 5th European Conference on Software Engineering Education, June 2023 KW - Lernstil KW - Fragebogen Y1 - 2023 U6 - https://doi.org/10.1145/3593663.3593685 SP - 73 EP - 82 ER - TY - JOUR A1 - Sapsai, Iryna A1 - Valencia, Yeimy A1 - Abke, Jörg T1 - Learning Analytics Dashboard for Educators: Proposed Project to Design with Pedagogical Background JF - ECSEE '23: Proceedings of the 5th European Conference on Software Engineering Education N2 - 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. KW - Lerntechnik KW - Computerunterstütztes Lernen Y1 - 2023 UR - https://dl.acm.org/doi/abs/10.1145/3593663.3593686 U6 - https://doi.org/https://doi.org/10.1145/3593663.3593686 VL - 2023 IS - ECSEE '23: Proceedings of the 5th European Conference on Software Engineering Education SP - 38 EP - 47 ER - TY - GEN A1 - Hochstetter, Marvin A1 - Abke, Jörg T1 - AdLer - Adaptive digitale Lernräume: Autorentool und 3D-Lernumgebung N2 - Die fortschreitende Digitalisierung im Bildungssektor ist gerade für die Berufliche Weiterbildung von zentraler Bedeutung. Bereits seit längerer Zeit sind in diesem Kontext zeit- und ortsunabhängige Lehr-Lern-Formate sehr wichtig. Allerdings bietet digitales Lernen auch großes Potential hinsichtlich dem Thema Motivationsförderung. 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 ermöglichen, der zeitgleich motivierend gestaltet ist. Das AdLer-Projekt hat zum Ziel ein frei verfügbares Instrument zu entwickeln, mit welchem Lehrende virtuelle 3D-Lernumgebungen erstellen und in ihrer Lehre in unterschiedlichen Formen einsetzen können. Mit Hilfe eines Autorentools werden Lehrende bei der Konzeption und Erstellung von 3D-Lernumgebungen unterstützt. Dadurch können 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 können Lernende nach den Prinzipien des GBL erkundungsorientiert und interaktiv lernen. Dazu können Lernende mit einem virtuellen Avatar die AdLer-3D-Lernumgebung betreten und verschiedene Handlungsmöglichkeiten wahrnehmen. Sie können sich frei in dieser Lernumgebung bewegen und unterschiedliche Lernangebote (z.B. die Bearbeitung von Lernelementen, wie H5P, Text und Video) durchführen. Im geplanten Poster-Pitch wird eine aktuelle Version der Software AdLer präsentiert und steht für die Konferenzteilnehmer zum Ausprobieren bereit. N2 - Poster KW - 3D-Lernumgebung Autorentool Game-Based-Learning KW - Lernspiel KW - Dreidimensionale Computergrafik Y1 - 2023 ER - TY - CHAP A1 - Haug, Jim A1 - Sapsai, Iryna A1 - Hock, Isabell A1 - Abke, Jörg A1 - Hagel, Georg ED - Haug, Jim T1 - EVALUATING AN AI-BASED ADAPTIVE LEARNING SYSTEM: GOALS, METHODS AND INITIAL RESULTS T2 - 16th International Conference on Education and New Learning Technologies, 1-3 July, 2024, Palma, Spain N2 - 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. KW - Adaptive learning environment, tertiary education, personalized e-learning, evaluation. KW - E-Learning KW - Evaluation KW - Künstliche Intelligenz Y1 - 2024 UR - https://library.iated.org/view/HAUG2024EVA U6 - https://doi.org/10.21125/edulearn.2024.0834 VL - 2024 IS - EDULEARN24 Proceedings SP - 3157 EP - 3166 ER - TY - JOUR A1 - Sapsai, Iryna A1 - Haug, Jim A1 - Abke, Jörg A1 - Hagel, Georg T1 - IDENTIFYING STUDENT EMOTIONS IN AN ADAPTIVE LEARNING SYSTEM WITH A BAYESIAN NETWORK MODEL JF - IATED Digital Library Home N2 - 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. KW - e-learning, Emotion Identification, Automatic Detection Techniques, Bayesian Networks. KW - Bayes-Netz KW - E-Learning Y1 - 2024 UR - https://library.iated.org/view/SAPSAI2024IDE U6 - https://doi.org/10.21125/iceri.2024.1192 VL - 2024 IS - ICERI2024 Proceedings SP - 4829 EP - 4837 ER - TY - CHAP A1 - Abke, Jörg A1 - Brune, Philipp A1 - Haupt, Wolfram A1 - Hagel, Georg A1 - Landes, Dieter A1 - Mottok, Jürgen T1 - EVELIN – ein Forschungsprojekt zur systematischen Verbesserung des Lernens von Software Engineering T2 - Tagungsband Embedded Software Engineering-Kongress 2012 N2 - 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 für qualitativ hochwertige Software, entscheidend sind auch Soft Skills wie Kommunikations- und Teamfähigkeit der beteiligten Ingenieure und Informatiker. Neben dem hohen Abstraktionsgrad stellen also auch weiche Faktoren die Ausbildung im Software Engineering vor große Herausforderungen. Didaktische Ansätze zur Vermittlung von Software Engineering müssen hinsichtlich beider Anforderungen systematisch weiterentwickelt werden. Die „klassische“ Ausbildung mit Elementen des Frontalunterrichts ist durch Methodenarrangements zu ergänzen, 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. KW - Software Engineering KW - Forschungsprojekt Y1 - 2012 SP - 653 EP - 658 ER - TY - CHAP A1 - Normann, Marc A1 - Haug, Jim A1 - Valencia, Yeimy A1 - Abke, Jörg A1 - Hagel, Georg ED - Mottock, Jürgen ED - Hagel, Georg T1 - Adaptive Learning Path Sequencing Based on Learning Styles within N-dimensional Spaces T2 - ECSEE '23: Proceedings of the 5th European Conference on Software Engineering Education N2 - 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. KW - Learning Path Sequencing KW - Adaptive Learning Path KW - Ant Colony KW - Genetic Algorithm KW - Adaptive Learning Environment KW - Lernstil KW - Lernerfolg Y1 - 2023 UR - https://dl.acm.org/doi/abs/10.1145/3593663.3593676 U6 - https://doi.org/https://doi.org/10.1145/3593663.3593676 VL - 2023 SP - 56 EP - 64 ER - TY - CHAP A1 - Klopp, Marco A1 - Dörringer, Antonia A1 - Eigler, Tobias A1 - Bartel, Paula A1 - Hochstetter, Marvin A1 - Weishaupt, Andreas A1 - Geirhos, Philipp A1 - Abke, Jörg A1 - Hagel, Georg A1 - Elsebach, Jens A1 - Rossmann, Raphael ED - Mottok, Jürgen T1 - Development of an Authoring Tool for the Creation of Individual 3D Game-Based Learning Environments T2 - ECSEE '23: Proceedings of the 5th European Conference on Software Engineering Education N2 - 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. KW - 3D Learning Environment KW - Authoring Tool KW - Game-based Learning KW - Serious Games KW - Lernspiel KW - Dreidimensionale Computergrafik Y1 - 2023 UR - https://dl.acm.org/doi/proceedings/10.1145/3593663 SN - 978-1-4503-9956-2 VL - 2023 IS - 5. SP - 204 EP - 209 PB - Association for Computing Machinery (ACM) CY - New York, NY, United States ER - TY - CHAP A1 - Dörringer, Antonia A1 - Klopp, Marco A1 - Schaab, Lukas A1 - Hochstetter, Marvin A1 - Glaab, Daniel A1 - Bartel, Paula A1 - Abke, Jörg A1 - Elsebach, Jens A1 - Rossmann, Raphael A1 - Hagel, Georg ED - Röpke, René ED - Schroeder, Ulrike T1 - AdLer: 3D-Lernumgebung für Studierende T2 - 21. Fachtagung Bildungstechnologien (DELFI) KW - Serious Games KW - 3D-Lernumgebung KW - Game-based Learning KW - Lernspiel KW - Dreidimensionale Computergrafik Y1 - 2023 U6 - https://doi.org/10.18420/delfi2023-41 VL - 2023 SP - 251 EP - 252 ER - TY - CHAP A1 - Manz, Julian A1 - Abke, Jörg A1 - Hagel, Georg ED - Mottok, Jürgen ED - Hagel, Georg T1 - Automated Tracking of User Interactions in Web-Based Adaptive Learning for Software Engineering T2 - ECSEE '25: Proceedings of the 6th European Conference on Software Engineering Education N2 - 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. KW - Adaptive Learning System KW - Educational Technology KW - Experience API KW - Learning Analytics KW - Learning Record Store KW - Personalized Learning KW - Software Engineering Education KW - User Interaction Tracking KW - Computerunterstütztes Lernen KW - E-Learning Y1 - 2025 SN - 979-8-4007-1282-1 U6 - https://doi.org/10.1145/3723010.3723020 SP - 180 EP - 184 PB - Association for Computing Machinery CY - New York, NY, USA ER - TY - CHAP A1 - Michael, Münch A1 - Valencia Usme, Yeimy Paola A1 - Abke, Jörg ED - Mottok, Jürgen ED - Hagel, Georg T1 - Prototypical Implementation of a Domain Model for an Adaptive Learning System T2 - Proceedings of the 6th European Conference on Software Engineering Education N2 - The goal of this paper is to present the concept and implementation of a Domain Model for an adaptive learning system (ALS) as a defined framework. The ALS is developed in a research setting. In this study, a proposition for a Domain Model based on Experience API (xAPI) protocol within a Learning Record Store (LRS) is outlined. The target is to collect and analyze student data using xAPI statements, such as average working time and learning elements (LEs) interacted with, in order to gain insights into student behavior and learning processes. The aim of this study is to develop and provide a conceptual model using xAPI and LRS in an ALS setting, an architecture that improves the flexibility of developing adaptive learning systems, enabling the creation of personalized learning experiences for a wide range of users and use cases. The combination of xAPI and LRS allows the definition of a precise Domain Model and provides contextualized and structured data. Moreover, xAPI enables the transformation of the inherent complexity of the Domain Model into something understandable and actionable, facilitating deeper analysis of learning experiences. This paper demonstrates how the proposed Domain Model can effectively represent student activities, interactions, and learning outcomes within a defined framework. The expectation is that diverse types of learners, as well as developers and researchers in educational technologies, will benefit from the use of the Domain Model in a learning environment. Here the learner’s preferences are diagnosed, and then user interfaces are customized in an adaptive manner to accommodate the preferences. The main task of the Domain Model is to provide data for the adaptive components of the adaptive learning system. KW - Software and its engineering; • Software creation and management; • Software development techniques; • Software prototyping; KW - Computerunterstütztes Lernen Y1 - 2025 UR - https://dl.acm.org/doi/pdf/10.1145/3723010.3723030 U6 - https://doi.org/https://dl.acm.org/doi/pdf/10.1145/3723010.3723030 VL - 2025 IS - ECSEE '25: Proceedings of the 6th European Conference on Software Engineering Education SP - 190 EP - 194 ER -