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Abstract: In model-driven engineering, developing a textual domain-specific language (DSL) involves constructing a meta-model, which defines an underlying abstract syntax, and a grammar, which defines the concrete syntax for the DSL. Language workbenches such as Xtext allow the grammar to be automatically generated from the meta-model, yet the generated grammar usually needs to be manually optimized to impr...
Abstract: Driving automation systems, including autonomous driving and advanced driver assistance, are an important safety-critical domain. Such systems often incorporate perception systems that use machine learning to analyze the vehicle environment. We explore new or differing topics and challenges experienced by practitioners in this domain, which relate to requirements engineering (RE), quality, and sys...
Abstract: Building and operating software-intensive systems involves exploring decision spaces composed of large numbers of variables and their complex relations. We report on using dimensionality reduction techniques that enable decision makers in different domains to focus on crucial elements of the decision space....
Abstract: : This study explores the integration of Multimodal Learning Analytics (MMLA) within the dynamic learning ecosystem of the Glocal Classroom (GC). By employing the MMLA Model for Design and Analysis (MAMDA), our research proposes a conceptual model leveraging the GC's existing infrastructure into an MMLA system to enrich learning experiences and inform course design. Our methodology involves a case...
Abstract: The integration of technology in education offers transformative potential, especially with the advent of data-driven approaches that can personalize learning, support educators, and provide valuable insights into the learning process. Multimodal learning analytics (MMLA) holds remarkable promise within this context. By capturing and analyzing data from multiple sources—including video, audio, and...
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