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    <title language="eng">Automated Tracking of User Interactions in Web-Based Adaptive Learning for Software Engineering</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">ECSEE '25: Proceedings of the 6th European Conference on Software Engineering Education</parentTitle>
    <identifier type="isbn">979-8-4007-1282-1</identifier>
    <identifier type="doi">10.1145/3723010.3723020</identifier>
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    <author>Julian Manz</author>
    <editor>Jürgen Mottok</editor>
    <author>Jörg Abke</author>
    <editor>Georg Hagel</editor>
    <author>Georg Hagel</author>
    <subject>
      <language>eng</language>
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      <value>Adaptive Learning System</value>
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      <value>Educational Technology</value>
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    <subject>
      <language>eng</language>
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      <value>Experience API</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Learning Analytics</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Learning Record Store</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Personalized Learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Software Engineering Education</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>User Interaction Tracking</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Computerunterstütztes Lernen</value>
    </subject>
    <subject>
      <language>deu</language>
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      <value>E-Learning</value>
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    <id>2579</id>
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    <title language="eng">Prototypical Implementation of a Domain Model for an Adaptive Learning System</title>
    <abstract language="eng">The goal of this paper is to present the concept and implementation&#13;
of a Domain Model for an adaptive learning system (ALS) as a defined&#13;
framework. The ALS is developed in a research setting. In this&#13;
study, a proposition for a Domain Model based on Experience API&#13;
(xAPI) protocol within a Learning Record Store (LRS) is outlined.&#13;
The target is to collect and analyze student data using xAPI statements,&#13;
such as average working time and learning elements (LEs)&#13;
interacted with, in order to gain insights into student behavior and&#13;
learning processes. The aim of this study is to develop and provide&#13;
a conceptual model using xAPI and LRS in an ALS setting, an architecture&#13;
that improves the flexibility of developing adaptive learning&#13;
systems, enabling the creation of personalized learning experiences&#13;
for a wide range of users and use cases. The combination of xAPI&#13;
and LRS allows the definition of a precise Domain Model and provides&#13;
contextualized and structured data. Moreover, xAPI enables&#13;
the transformation of the inherent complexity of the Domain Model&#13;
into something understandable and actionable, facilitating deeper&#13;
analysis of learning experiences. This paper demonstrates how the&#13;
proposed Domain Model can effectively represent student activities,&#13;
interactions, and learning outcomes within a defined framework.&#13;
The expectation is that diverse types of learners, as well as developers&#13;
and researchers in educational technologies, will benefit from&#13;
the use of the Domain Model in a learning environment. Here&#13;
the learner’s preferences are diagnosed, and then user interfaces&#13;
are customized in an adaptive manner to accommodate the preferences.&#13;
The main task of the Domain Model is to provide data for&#13;
the adaptive components of the adaptive learning system.</abstract>
    <parentTitle language="eng">Proceedings of the 6th European Conference on Software Engineering Education</parentTitle>
    <identifier type="url">https://dl.acm.org/doi/pdf/10.1145/3723010.3723030</identifier>
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    <author>Münch Michael</author>
    <editor>Jürgen Mottok</editor>
    <author>Yeimy Paola Valencia Usme</author>
    <editor>Georg Hagel</editor>
    <author>Jörg Abke</author>
    <subject>
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      <value>Software and its engineering; • Software creation and management; • Software development techniques; • Software prototyping;</value>
    </subject>
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    <id>2767</id>
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    <language>eng</language>
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    <title language="eng">DIDACTIC INTEGRATION OF SELF-STUDY AND FACE-TO-FACE TEACHING: EXPERIENCES WITH AN ADAPTIVE LEARNING SYSTEM IN ENGINEERING EDUCATION</title>
    <abstract language="eng">This paper presents a study of student perspectives on the didactic integration of digital learning elements into face-to-face university lectures and seminars. The overarching context is the use of the adaptive learning system (ALS) HASKI (short for Hochschullehre: Adaptiv, selbstgesteuert, KI-gestützt, i.e. Higher Education: Adaptive, Self-Directed, AI-Supported), which was tested in computer science teaching within a blended learning approach, more specifically in flipped classroom scenarios.&#13;
&#13;
The joint project, consisting of three Bavarian universities, focuses on the exploratory integration of an AI-based ALS into higher education. The system was applied in a course for mechatronics students (2nd semester) and provided a variety of adaptive learning elements. HASKI generates individual learning paths with AI based on learning behavior and learning styles. It is designed to promote personalized, self-directed learning as support for blended learning scenarios.&#13;
&#13;
The method chosen for data collection is a qualitative content analysis based on exploratory interviews with a semi-structured set of questions. Four central analysis criteria were considered: acceptance and perception of the learning elements, integration into lectures and exercise sessions, didactic coherence, and suggestions for improvement.&#13;
&#13;
The results show that students prefer explanatory scripts, interactive tasks, and quiz elements that require a certain degree of reflection. In addition, the respondents are largely convinced of the integration into the seminar, as the HASKI system adequately guides them for what to prepare for. When it comes to embedding the system into lectures, especially in the form of question rotation in small groups, the feedback is ambivalent. Although the potential for in-depth learning was recognized, the low participation of fellow students was critically reflected upon.&#13;
&#13;
A particular difficulty mentioned was the lack of coordination between self-study and classroom attendance. While some participants saw this as a discrepancy of coherence, others viewed the adaptive system as a balancing factor. There was a desire for a clearer time structure and more in-depth materials that go beyond mere repetition.&#13;
&#13;
Overall, the findings provide initial indicators of successful aspects in the integration of adaptive systems into classroom teaching. The continuous development of a clear didactic division of roles between self-study and classroom phases is of central importance here. Further research with larger samples, if necessary, could provide more comprehensive insights.</abstract>
    <parentTitle language="eng">ICERI Proceedings</parentTitle>
    <identifier type="isbn">978-84-09-78706-7</identifier>
    <identifier type="issn">2340-1095</identifier>
    <identifier type="doi">https://doi.org/10.21125/iceri.2025.1600</identifier>
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    <author>Isabell Hock</author>
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    <title language="eng">Evaluation of a Node-based Automatic Short Answer Tool “NodeGrade”</title>
    <abstract language="eng">NodeGrade tries to provide a suitable solution for the problem of time-intensive short answer grading. This research focuses simultaneously on performance, functionality and user experience, which is underlined by a triangulated approach. The evaluation results show comparable performance of NodeGrade on public datasets, even outperforming GPT-4 on the SemEval 2013 Task 7. Matching of NodeGrade’s output with multiple human expert raters reveals some weaknesses regarding cases at the lower and upper boundary. In terms of user experience, the interviewed and observed students recognized both positive facets, like better learning support and helpful feedback, and negative sides, including technical limitations and lack of transparency. Overall, NodeGrade promises high potential for further practical use and testing in the field of software engineering education and automatic short answer grading.</abstract>
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    <title language="eng">METHODICAL APPROACH FOR ANALYZING LEARNING PATH FITNESS IN AN AI-BASED ADAPTIVE LEARNING SYSTEM</title>
    <abstract language="eng">Learning paths are a cornerstone of many adaptive learning systems, particularly those focusing on adaptive navigational techniques. Evaluating and analyzing these paths is therefore crucial to ensure they effectively support both learners and instructors. For eLearning this process must be highly scalable despite minimal oversight and little to no control over learners’ behavior. Consequently, learning path evaluation should be automated, user-friendly, and precise. However, current research on this topic often emphasizes simulations, performance metrics, or mathematical models, without fully considering the broader, learner-centered aspects necessary for meaningful adaptation. The authors prior findings also indicate that approaches to assessing the suitability of learning paths must be optimized. To address these gaps, this paper presents a potential methodological approach for comprehensive learning path evaluation, aiming to enhance both the precision of adaptive learning systems and the overall learning experience.&#13;
&#13;
Three different algorithms, derived from learning style tendencies and a lecturer recommendation, were analyzed as an illustrative example, although the method itself is not constrained by the form or data basis of these algorithms. The adaptive learning system utilizes various measures to gauge the suitability of a learning path, all gathered through real-time learner feedback. These measures include the correlation between students’ preferred path and each algorithm, referred to as it’s fitness, the alignment between students’ actual adherence to a generated path and their own perception of their study behavior as well as their satisfaction with the path, and the connection between algorithm fitness and both actual performance and perceived performance.&#13;
&#13;
To collect data, students were asked to create their own preferred learning paths by digitally arranging the provided learning elements after receiving an introduction to the respective categories. Once they had completed a topic with a generated learning path, they rated their satisfaction with it and indicated whether they had followed its sequence. They also estimated whether their knowledge level had changed. Learning analytics were then employed to compare these self-reports with students’ actual study behavior. Performance was measured using a rating system, while Spearman’s Rho and Kendall’s Tau served as the main correlation metrics for data analysis.&#13;
&#13;
The results indicate that all three algorithms produce paths more closely aligned with students’ preferred learning paths than the lecturer recommendation, although no single algorithm demonstrated clear dominance. Student satisfaction showed some correlation with the fitness of the generated learning path. Additionally, student ratings appeared to have a slight positive correlation with learning path fitness, whereas self-perceived performance showed no discernible difference. Analysis of the link between actual student behavior and their feedback suggested that students were not reliable in judging whether or not they had followed a learning path.&#13;
&#13;
These findings are consistent with the authors earlier work suggesting the potential effectiveness of the learning path algorithms examined, thus supporting this new methodological approach to analyzing learning paths. The study also provided valuable insights for further development; however, its limited sample size remains a challenge for validation.</abstract>
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