Vol 6 - Special Issue INFORSID 2025

Open Journal in Information Systems Engineering

Guest editors

Lylia ABROUK, Université de Bourgogne

Elsa NEGRE, Université Paris Dauphine - PSL, LAMSADE

List of Articles

Ce numéro spécial de la Revue ouverte d’ingénierie des systèmes d’information est consacré à une sélection d’articles étendus issus de la conférence INFORSID 2025.

Understanding the learning process in programming poses a complex challenge due to the sequential and multidimensional nature of students’ interactions with digital environments. This study analyzes the activity logs of 70 beginner computer science students engaged in problem-solving programming tasks to identify encountered difficulties and propose tailored pedagogical interventions.We present a hybrid approach combining Markov Chains to model transitions between types of actions, Hidden Markov Models (HMM) to infer latent learning states (progress, hesitation, blockage), and Recurrent Neural Networks (RNN) enhanced with an attention mechanism to detect critical moments. This combination allows for the simultaneous exploitation of behavioral, cognitive, and sequential dimensions of learning.The methodology involves extracting temporal and structural features from the activity logs, modeling cycles of exploration, hesitation, and blockage, and integrating them into a unified framework to predict learning states. The data comes from several standardized practical work sessions, totaling over 80 MB of timestamped traces, collected with consent and stored in a NoSQL database. Each session is divided into sequences corresponding to coherent problem-solving phases, enabling a fine-grained analysis of learning trajectories.Experimental results show that the proposed hybrid model outperforms traditional approaches, achieving an accuracy of 93.5% and significantly reducing false positives in blockage detection.
The multidimensional analysis offers a richer understanding of learning trajectories, including their invisible dimensions such as unproductive engagement or phases of uncertainty.This research paves the way for intelligent educational platforms capable of providing personalized real-time feedback, sensitive to micro-indicators of activity, cognitive intensity, and individual context, thereby contributing to improved student success and enhanced engagement.

Data and information governance is an important activity for organizations seeking to leverage data as a strategic asset. Its goal is to maximize value while minimizing costs and risks. In this article, we present a conceptual framework for data and information governance that offers a holistic view, enriching existing academic and professional frameworks and models. Using bibliometric techniques, we analyze the existing literature to identify the key elements of data and information governance, including its intellectual structure, research themes, and the most influential articles that form its backbone. We compare the common and specific spheres of data governance and information governance. In a second step, we propose an enriched conceptual framework based on systems theory. This framework encompasses five key dimensions: purpose, structure, activities, environment, and outcome. It also allows for consideration of the interaction between these dimensions. To illustrate this conceptual framework, we describe how it was used to structure the questions in a barometer designed to assess the maturity of data and information governance in organizations.

This paper examines information system (IS) security as a foundational pillar of organisational continuity and resilience. In response to growing environmental responsibility, it becomes essential to adopt a vulnerability management approach that goes beyond purely technical considerations. The study proposes integrating business context and sectorspecific
priorities into the vulnerability prioritisation process, with the aim of optimising resource allocation and reducing the energy footprint of security remediation. We suggest extending the Common Vulnerability Scoring System (CVSS) by incorporating organisational criteria and analysing vulnerability chaining. This approach is illustrated through practical case
studies (banking, healthcare, and websites hosting), demonstrating that contextual factors significantly influence remediation priorities and promote more sustainable cybersecurity practices. The objective is to reconcile security, sustainability, and cost, positioning vulnerability management as a strategic lever for responsible IS governance.

In the field of intellectual property, patents are essential technical and legal documents whose drafting requires expertise that combines technical, legal, and linguistic skills. Patent drafting styles vary considerably depending on technological domains, jurisdictions, and protection strategies. This article proposes the design of SCASB (System for Stylometric Characterization and Automation of Patents), an approach that, for the first time, unifies the technical, legal, and stylistic dimensions within a coherent computational framework. We propose a two-dimensional taxonomy of patent analysis approaches (automatic computational methods for document analysis × level of analytical granularity) and highlight current shortcomings. Our system builds upon the rapid advances in artificial intelligence technologies, particularly natural language processing (NLP). This work opens the path toward intelligent automation of technical drafting that accounts for the stylistic nuances specific to each jurisdiction and protection strategy.

Explainable recommender systems aim to strengthen transparency and user trust by providing an explanation alongside each recommendation. However, these explanations are not interpreted uniformly : the same explanation may be understood by some users but not by others. Existing approaches based on user choice, the construction of explanatory profiles, or the highlighting of content justify a recommendation by relying on different information related to usage or content. Nevertheless, they do not take into account the way users actually interpret these explanations. To address this limitation, in this paper we propose two complementary directions : (i) identifying the internal human factors that most strongly influence the understanding of an explanation ; we hypothesize that the central factor is the interpretive schema, understood as a cognitive structure guiding the selection and understanding of information ; (ii) exploiting these factors to dynamically adapt the type, style, and level of detail of explanations. This positioning paves the way for a new generation of explainable recommender systems, capable of contextualizing explanations according to each user’s own mode of understanding, and thereby reinforcing their usefulness, readability, and the trust they inspire.