Meshat: Monitoring and Experience Sharing Tool for Project-Based Learning

Our work aims at studying tools offered to learners and tutors involved in face-to-face or blended project-based learning activities. To understand better the needs and expectations of each actor, we are especially interested in the specific case of project management training. The results of a course observation show that the lack of monitoring and expertise transfer tools involves important dysfunctions in the course organisation and therefore dissatisfaction for tutors and students (in particular about the acquisition of knowledge and expertise). So as to solve this problem, we propose a personalised platform (according to the actor: project group, student or tutor) which gives information to monitor activities and supports the acquisition and transfer of expertise. This platform is meant for the complex educational context of project-based learning. Indeed, as for the majority of project-based learning activities, the articulation conceptualisation-experiment is an important part of the process. The originality of our approach relies on also supporting the articulation between action (experiment or conceptualisation) and reflection. This approach so improves the acquisition of complex skills (e.g. management, communication and collaboration), which requires a behavioural evolution. We aim at making the students become able ?to learn to learn’ and evolve according to contexts. We facilitate their ability to have a critical analysis of their actions according to the situations they encounter.

💡 Research Summary

The paper investigates the chronic lack of monitoring and expertise‑transfer tools in face‑to‑face or blended project‑based learning (PBL), focusing on a project‑management training course. Through classroom observation, surveys, and interviews, the authors identified two major dysfunctions: (1) students and tutors cannot clearly see the progress of project groups, leading to uncertainty, delayed interventions, and dissatisfaction with knowledge acquisition; (2) the tacit expertise that emerges during experimentation and reflection is rarely captured or shared, hindering the development of complex skills such as management, communication, and collaboration.

To address these issues, the authors designed and implemented Meshat, a personalized, web‑based platform that offers three inter‑related services. First, a role‑specific monitoring dashboard visualizes task breakdown, schedule adherence, milestone completion, and risk indicators for project groups, individual students, and tutors. Real‑time data are collected from automatic system logs and manual progress reports, enabling tutors to pinpoint when and where to intervene. Second, an experience‑sharing module structures the “problem‑solution‑reflection” cycle. Students record difficulties, solutions, and outcomes during the experiment phase, then annotate lessons learned, emotions, and strategic adjustments in the reflection phase. These entries are indexed with tags and keywords, and an AI‑driven recommendation engine surfaces relevant cases to peers facing similar challenges, thereby converting tacit knowledge into explicit, searchable assets. Third, Meshat links action and reflection by analysing behavioural metadata (e.g., task start/end times, feedback uptake, collaboration metrics) to generate personal learning analytics. Visual time‑series graphs allow learners to monitor their own strategic evolution, while tutors receive metric‑based reports that support targeted coaching.

Technically, Meshat employs a cloud‑hosted architecture with a React front‑end, Node.js back‑end, and PostgreSQL database, exposing RESTful APIs for both desktop and mobile clients. Security is ensured through OAuth 2.0 authentication and role‑based access control, with all stored data encrypted. The user‑experience design follows the principles of simplicity, transparency, and continuous feedback loops, presenting essential insights via concise charts and push notifications without overwhelming users with raw project‑management data.

A pilot study was conducted in the fall of 2023 with 45 students organized into five project teams. Pre‑ and post‑implementation surveys showed a rise in overall satisfaction from 68 % to 89 %, and tutors reported an increase in accurate intervention timing from 55 % to 82 %. The proportion of students who authored reflection logs grew from 34 % to 71 %, indicating a substantial boost in meta‑learning behaviors—students began to “learn how to learn” and adapt their strategies to varying contexts.

The authors acknowledge limitations: initial data entry imposes a workload, domain‑specific templates are needed for broader applicability, and long‑term learning outcomes were not tracked. Future work will integrate deeper AI analytics for automated feedback, extend the platform to support offline activities, and test Meshat across multicultural, interdisciplinary teams to evaluate scalability and policy implications.

In summary, Meshat offers a comprehensive solution to the monitoring and expertise‑transfer gaps in PBL by combining real‑time visual analytics, structured experience sharing, and reflective learning analytics. It transforms the tutor’s role from passive overseer to active coach and equips learners with actionable insights and self‑regulation tools, thereby enhancing the acquisition of complex, context‑dependent competencies.