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Determining student motivation within the context of Learning Analytics is fundamental for academic students to realize their educational goals. We aim to perceive the student’s motivation state at a high level of abstraction and act accordingly to deal with motivation issues. We investigate how Model-Driven Engineering paradigms capture the essence of a motivation domain and provide deep automation in stimulating students’ tasks. In this paper, first, we propose a Conceptual Modeling Approach that provides a unified environment in which all dimensions of students’ motivation are explicitly defined. Secondly, a guideline, allows educational stakeholders to perceive the states of change in students’ motivation. Third, the issue of student motivation is addressed by making a mechanism that stimulates students. Finally, to stress our approach and to prove how it is useful, we present a global usage scenario for our system called Hafezni . Sixteen Master’s students of the computer science department of the Ibn Khaldoun University of Algeria participated in the experiment. Results showed that our approach allows educational actors to perceive the motivational state of the student. The Hafezni mobile app is useful according to learners and educational stakeholders. Finally, the student motivation makes sense on the causality of failure/success with an acceptable percentage of correctly classified instances increased from 69.23% to 96.13%.

Designing a database cost model is one of the main research topics related to the physical design phase. It follows the evolution of database technology in order to evaluate and quantify the performance metrics (e.g., response time, energy consumption, etc.). Therefore, it makes the community researchers sensitive to the generated results.However, reusing and comparing database cost models require extracting related information manually from the research publications. This process is error-prone and time-consuming. Unfortunately, many researchers claim the difficulty of surveying and reproducing cost models already published in several/journal articles and/or reports. This difficulty is due to the absence of a process describing the cost model itself formally as well as the context of its utilization. This article presents an approach enabling the extraction of cost models information (context, parameters, features, etc.) as a set of orchestrated services. These services are implemented using natural language processing and machine-learning techniques via a work-flow pipeline inspired by DevOps practices. We illustrate our approach on a case study to stress the feasibility and benefits of our proposal by emphasizing the reproduction and automatization facilities.

A major challenge for many database management tasks including admission control, query scheduling, progress monitoring and self-driving data storage systems is to enhance queries performances which are based on computational models known as database cost models. One of the most challenging aspects of developing accurate database cost models is identifying their parameters and capturing their relationships, consequently we can derive the query execution cost on the basis of a specific database hosted on a given platform. Furthermore, the highly dynamic workload (i.e., a set of queries) and the query execution variation lead to performance degradation risk, therefore cost models need to be improved by considering newer software configuration and future workload characteristics. In this article, we propose a framework called DeepCM that is based on a min-max optimization for building robust database cost model against uncertainty parameters. Furthermore, our framework is based on Robust Deep Neural Networks to build database cost models that guarantee a high accuracy regardless of variations from software configuration and workload characteristics.Several experiments have been done to evaluate the robustness of produced cost models and findings show that DeepCM provides a high cost model prediction accuracy and stable performance.