Amine Abbad-Andaloussi

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Abbad-Andaloussi
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Amine
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amine.abbad-andaloussi@unisg.ch
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+41 71 224 2635

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    Item type:Publication,
    Process Model Complexity Metrics, Cognitive Load and Visual Behavior: A Multi-granular Eye-Tracking Analysis
    (Springer Nature Switzerland, 2025-06-14)
    Thierry Sorg  
    ;
    Amine Abbad-Andaloussi  
    ;
    Kindler, Ekkart
    ;
    Barbara Weber  
    Complexity metrics are widely used to estimate the difficulty of understanding process models. However, the relationship between these metrics and the concept of cognitive load, which captures the difficulty experienced by users, is not fully understood in the process modeling literature. In neighboring fields like Software Engineering, researchers could only to a limited degree establish a relationship between complexity metrics and users' cognitive load. To investigate the extent to which such a relationship exists in the process modeling field, we conduct an eye-tracking experiment that assesses how a suite of metrics, capturing both the essential complexity inherent to the process specifications and the accidental complexity emerging from the model layout, aligns with users' cognitive load during model comprehension tasks. Our findings show that the used metrics suite aligns well with users' cognitive load. Moreover, our analysis of users' behavior suggests that different levels of model complexity yield distinct visual behaviors. The implications of our work extend to both practice and research, validating a comprehensive suite of complexity metrics and delivering a multi-granular approach that can be reproduced in other experiments to enable the analysis of users' cognitive load and behavior on simple but also complex models.
    Type:journal article
    Journal:Lecture Notes in Business Information Processing
    Volume:558
    URL:https://link.springer.com/chapter/10.1007/978-3-031-95397-2_6
    DOI:10.1007/978-3-031-95397-2_6
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/123387
    Scopus© Citations 2
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    On the cognitive and behavioral effects of abstraction and fragmentation in modularized process models
    (Elsevier BV, 2024-11)
    Clemens Schreiber
    ;
    Amine Abbad-Andaloussi  
    ;
    Barbara Weber  
    Process model comprehension is essential for a variety of technical and managerial tasks. To facilitate comprehension, process models are often divided into subprocesses when they reach a certain size. However, depending on the task type this can either support or impede comprehension. To investigate this hypothesis, we conduct a comprehensive eye-tracking study, where we test two different types of comprehension tasks. These are local tasks focusing on a single subprocess, thereby benefiting from abstraction (i.e., irrelevant information is hidden), and global tasks comprising multiple subprocesses, thereby also benefiting from abstraction but impeded by fragmentation (i.e., relevant information is distributed across multiple fragments). Our subsequent analysis at task (coarse-grained) and phase (fine-grained) levels confirms the opposing effects of abstraction and fragmentation. For global tasks, we observe lower task comprehension, higher cognitive load, as well as more complex search and inference behaviors, when compared to local ones. An additional qualitative analysis of search and inference phases, based on process maps and time series, provides additional insights into the evolution of information processing and confirms the differences between the two task types. The fine-grained analysis at the phase level is based on a novel research method, allowing to clearly separate information search from information inference. We provide an extensive validation of this research method. The outcome of this work provides a more thorough understanding of the effects of fragmentation, in the context of modularized process models, at a coarse-grained level as well as at a fine-grained level, allowing for the development of task- and user-centric support, and opening up future research opportunities to further investigate information processing during process comprehension.
    Type:journal article
    Journal:Information Systems
    Volume:125
    URL:https://www.sciencedirect.com/science/article/pii/S0306437924000826
    DOI:10.1016/j.is.2024.102424
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/122087
    Scopus© Citations 12
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    On the relationship between source-code metrics and cognitive load: A systematic tertiary review
    (2023)
    Amine Abbad-Andaloussi  
    The difficulty of software development tasks depends on several factors including the characteristics of the underlying source-code. These characteristics can be captured and measured using source-code metrics, which, in turn, can provide indications about the difficulty of the source-code. From a cognitive perspective, this difficulty is due to an increase in developers’ cognitive load, which can be estimated using psycho-physiological measures. Based on these measures, a handful of studies investigated the relationship between source-code metrics and cognitive load. For most of the metrics, such a relationship could not be established. While these studies used a small subset of metrics, the literature comprises hundreds of other metrics. Despite the existing reviews surveying these metrics, a consolidated overview is still needed to understand their properties and leverage their potential to align with cognitive load. This need is addressed in this paper through a Systematic Tertiary Review (STR) covering the full spectrum of source-code metrics, studying their properties and investigating their potential relationship to cognitive load. The outcome of this STR is intended to guide practitioners in choosing appropriate metrics, set the grounds for conceptualizing the relationship between source-code metrics and cognitive load and raise new research challenges for the future.
    Type:journal article
    Journal:Journal of Systems and Software
    Volume:198
    URL:https://www.sciencedirect.com/science/article/pii/S0164121223000146
    DOI:10.1016/j.jss.2023.111619
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/118745
    Scopus© Citations 17
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    Complexity in declarative process models: Metrics and multi-modal assessment of cognitive load
    (2023)
    Amine Abbad-Andaloussi  
    ;
    Andrea Burattin
    ;
    Tijs Slaats
    ;
    Ekkart Kindler
    ;
    Barbara Weber  
    Complex process models can hinder the comprehension of the underlying business processes. While several metrics have been suggested in the literature to evaluate the complexity of imperative process models, little is known about their declarative counterparts. In this paper, we address this gap through a suite of metrics that we propose to capture the complexity of declarative process models. Following this, we empirically investigate the impact of complexity, as measured by the suggested metrics, on users’ cognitive load when comprehending declarative process models. Therein, we use a multi-modal approach including eye-tracking and electrodermal activity. The findings of the empirical study provide evidence about the cognitive load emerging as a result of increased model complexity. Overall, the outcome of this paper presents empirically validated metrics to evaluate the complexity of declarative process models. Implementing these metrics and incorporating them in intelligent modeling tools would help assessing the complexity of declarative process models before being deployed. Furthermore, our empirical approach can be adopted by researchers in upcoming empirical studies to provide a multi-perspective assessment of users’ cognitive load when engaging with process models.
    Type:journal article
    Journal:Expert Systems with Applications
    Volume:233
    DOI:10.1016/j.eswa.2023.120924
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/118749
    Scopus© Citations 34
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    Conducting eye-tracking studies on large and interactive process models using EyeMind
    (2023)
    Amine Abbad-Andaloussi  
    ;
    Daniel Lübke
    ;
    Barbara Weber  
    The understandability of process models has been subject to extensive research in which eye-tracking has demonstrated great capability to deliver meaningful insights. However, the full potential of this technology is not fully exploited due to the complexity of using dynamic stimuli in experiments (i.e., large and interactive process models) and the common use of static stimuli (i.e., small non-interactive models) as a cheap alternative limiting the ecological validity of the used experimental setting and the generalizability of the results. This paper presents EyeMind, a solution to overcome this limitation by supporting the whole experimental workflow using dynamic stimuli and offering a comprehensive analysis toolkit of eye-tracking data. All these features facilitate experiments on large and interactive process models as well as the extraction of meaningful insights.
    Type:journal article
    Journal:SoftwareX
    Volume:24
    DOI:10.1016/j.softx.2023.101564
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/118748
    Scopus© Citations 16
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    On the declarative paradigm in hybrid business process representations: A conceptual framework and a systematic literature study
    (Elsevier BV, 2020-07)
    Amine Abbad-Andaloussi  
    ;
    Andrea Burattin
    ;
    Tijs Slaats
    ;
    Ekkart Kindler
    ;
    Barbara Weber  
    Process modeling plays a central role in the development of today’s process-aware information systems both on the management level (e.g., providing input for requirements elicitation and fostering communication) and on the enactment level (providing a blue-print for process execution and enabling simulation). The literature comprises a variety of process modeling approaches proposing different modeling languages (i.e., imperative and declarative languages) and different types of process artifact support (i.e., process models, textual process descriptions, and guided simulations). However, the use of an individual modeling language or a single type of process artifact is usually not enough to provide a clear and concise understanding of the process. To overcome this limitation, a set of so-called “hybrid” approaches combining languages and artifacts have been proposed, but no common grounds have been set to define and categorize them. This work aims at providing a fundamental understanding of these hybrid approaches by defining a unified terminology, providing a conceptual framework and proposing an overarching overview to identify and analyze them. Since no common terminology has been used in the literature, we combined existing concepts and ontologies to define a “Hybrid Business Process Representation” (HBPR). Afterwards, we conducted a Systematic Literature Review (SLR) to identify and investigate the characteristics of HBPRs combining imperative and declarative languages or artifacts. The SLR resulted in 30 articles which were analyzed. The results indicate the presence of two distinct research lines and show common motivations driving the emergence of HBPRs, a limited maturity of existing approaches, and diverse application domains. Moreover, the results are synthesized into a taxonomy classifying different types of representations. Finally, the outcome of the study is used to provide a research agenda delineating the directions for future work.
    Type:journal article
    Journal:Information Systems
    Volume:91
    URL:https://www.sciencedirect.com/science/article/pii/S0306437920300168
    DOI:10.1016/j.is.2020.101505
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/122089
    Scopus© Citations 40
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    Exploring how users engage with hybrid process artifacts based on declarative process models: a behavioral analysis based on eye-tracking and think-aloud
    (Springer Science and Business Media LLC, 2020-07-05)
    Amine Abbad-Andaloussi  
    ;
    Francesca Zerbato  
    ;
    Burattin, Andrea
    ;
    Slaats, Tijs
    ;
    Thomas T. Hildebrandt
    Type:journal article
    Journal:Software and Systems Modeling
    Volume:20
    Issue:5
    DOI:10.1007/s10270-020-00811-8
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/122084
    Scopus© Citations 24
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    Supporting the Process of Learning and Teaching Process Models
    (Institute of Electrical and Electronics Engineers (IEEE), 2020-07-01)
    Josep Sanchez-Ferreres
    ;
    Luis Delicado
    ;
    Amine Abbad-Andaloussi  
    ;
    Andrea Burattin
    ;
    Guillermo Calderon-Ruiz
    The creation of a process model is primarily a formalization task that faces the challenge of constructing a syntactically correct entity, which accurately reflects the semantics of reality, and is understandable to the model reader. This article proposes a framework called Model Judge, focused toward the two main actors in the process of learning process model creation: novice modelers and instructors. For modelers, the platform enables the automatic validation of the process models created from a textual description, providing explanations about quality issues in the model. Model Judge can provide diagnostics regarding model structure, writing style, and semantics by aligning annotated textual descriptions to models. For instructors, the platform facilitates the creation of modeling exercises by providing an editor to annotate the main parts of a textual description, which is empowered with natural language processing capabilities so that the annotation effort is minimized. So far around 300 students in process modeling courses of five different universities around the world have used the platform. The feedback gathered from some of these courses shows good potential in helping students to improve their learning experience, which might, in turn, impact process model quality and understandability. Moreover, our results show that instructors can benefit from getting insights into the evolution of modeling processes, including arising quality issues of single students, but also discovering tendencies in groups of students. Although the framework has been applied to process model creation, it could be extrapolated to other contexts where the creation of models based on a textual description plays an important role.
    Type:journal article
    Journal:IEEE Transactions on Learning Technologies
    Volume:13
    Issue:3
    URL:https://ieeexplore.ieee.org/document/9050851
    DOI:10.1109/TLT.2020.2983916
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/122086
    Scopus© Citations 15
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    Studying Webcam-based Gaze Estimation and Mouse Coordination for Cognitive Inferences
    (A, 2026-06-01)
    Doubabi, Anas
    ;
    Kenan Bektas  
    ;
    Aamouche, Ahmed
    ;
    El Kabtane Hamada
    ;
    Amine Abbad-Andaloussi  
    Adaptive e-learning systems require scalable signals for inferring learners’ cognitive states (e.g., attention, engagement, and cognitive load), yet webcam-based gaze estimation remains sensitive to calibration demands and real-world performance degradation. Mouse input is ubiquitous and calibration-free; however, cursor trajectories may only weakly reflect moment-to-moment visual attention. This work presents a preliminary research design that pairs a mouse-contingent blur interaction with a co-observation modeling view of cognitive state to make gaze–mouse data more useful under realistic constraints. First, we propose a mouse-contingent blur paradigm (i.e., delayed blur after mouse inactivity) and compare it with no blur and mouse-contingent blur to study how they affect gaze–mouse coordination and usability. Second, we frame webcam-based eye tracking and mouse input as cross-modal observations and motivate their fusion as a practical strategy to assess the changes in cognitive state of learners.
    Type:conference paper
    DOI:https://doi.org/10.1145/3797246.3805849
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/125471
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    On the Relationship Between Semantic Transparency and Cognitive Load: Does Context Matter In Process Models?
    (2025)
    Amine Abbad-Andaloussi  
    ;
    Dung My Thi Trinh
    ;
    Barbara Weber  
    ;
    López Hugo-andrés
    Declarative process models offer flexibility in representing complex processes, but can be difficult to understand due to their implicit control flow. Improving their representation, particularly by investigating and enhancing their semantic transparency, can help address this challenge. This paper proposes an empirical research model to examine how varying levels of semantic transparency, encoded in different visual notations of declarative models, affect users’ cognitive load and visual behavior in the presence and absence of contextual information. The findings will lead to a better understanding of the effects of semantic transparency and will support the design of understandable declarative models.
    Type:conference paper
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/122823