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All Journal International Journal Software Engineering and Computer Science (IJSECS)
Graf, Alex
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Computer Science & IT

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Exploring the Role of Personalization in Adaptive Learning Environments Graf, Alex
International Journal Software Engineering and Computer Science (IJSECS) Vol. 3 No. 2 (2023): AUGUST 2023
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v3i2.1200

Abstract

: In recent years, the integration of technology in education has sparked interest in enhancing learning experiences. Adaptive learning environments, which customize educational content and strategies to individual learners, offer a promising approach. Personalization within adaptive learning environments is crucial for addressing learner diversity and maximizing engagement and learning outcomes. This research project aims to explore the role of personalization in adaptive learning environments and its impact on the user experience and educational outcomes. By investigating the effectiveness of personalized interfaces and adaptive strategies, we seek to understand how these technologies can create more engaging and effective learning environments. The project objectives are threefold. Firstly, we aim to design and implement adaptive learning interfaces with personalized elements to customize the learning experience for each user. Secondly, we will evaluate the impact of personalization on user experience dimensions, including engagement, satisfaction, and motivation. Lastly, we will assess the effect of personalization on learning outcomes, such as knowledge acquisition and retention. This research contributes to the field of educational technology and user experience (UX) design. By examining the role of personalization in adaptive learning environments, we aim to inform the development of more effective and learner-centered educational tools and strategies.
Design Principles for Enhancing AI-Assisted Moderation in Hate Speech Detection on Social Media Platforms Graf, Alex; Coolsaet, Danny
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 2 (2024): AUGUST 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i2.2345

Abstract

Hate speech on social media poses a growing threat to individuals and society, necessitating technological support for moderators in detecting and addressing problematic content. This article explores the design principles essential for creating effective user interfaces (UIs) in decision support systems that employ artificial intelligence (AI) to aid human moderators. Through a comprehensive study involving 641 participants across three design cycles, we qualitatively and quantitatively evaluate various design options. Our assessment encompasses perceived ease of use, usefulness, and intention to use, while also delving into the impact of AI explainability on users' cognitive efforts, informativeness perception, mental models, and trustworthiness. Notably, software developers affirm the high reusability of the proposed design principles. The findings reveal that well-designed UIs can significantly enhance the effectiveness of AI-based moderation tools, providing clear and understandable explanations that improve user trust and engagement. By addressing both technical and user-centered aspects, this research contributes to the development of more robust and user-friendly AI systems for hate speech detection. Future work should focus on further refining these principles and exploring their applicability in diverse social media contexts to ensure comprehensive and adaptable solutions for content moderation.
Co-Authors Coolsaet, Danny