This work presents a predictive analysis of the academic performance of students enrolled in project management courses in two different engineering degree programs. Data were gathered from a virtual learning environment that was designed to support the specific needs of the proposed learning experience. The analyzed data included individual attributes related to communication, time, resources, information and documentation activity, as well as behavioral assessment. Also, students’ marks on two exams that took place during the first half of the course were considered as input variables of the predictive models. Results obtained using several regression and classification algorithms –support vector machines, random forests, and gradient boosted trees–confirm the usefulness of Educational Data Mining to predict students’ performance. These models can be used for early identification of weak students who will be at risk in order to take early actions to prevent these students from failure.

This work presents an online system to facilitate the assessment and feedback in project management education. Students are involved in real-world engineering projects in order to promote professional project management learning. Thus, students share an experience in executing and managing projects and are able to put into practice different skills and competences that a project member should possess in the development of a project. The proposed system considers competence assessment through different pieces of evidence that are pertinent to each assessed competence. Information from the three main actors in learning activities (teacher, peer, and learner) is collected by means of specifically developed online forms. All the gathered evidences are considered in a weighted integration to yield a numerical assessment score of each competence that is developed for each student. Furthermore, three different types of feedback are implemented and provided several times in order to promote and improve students' learning. Data analysis from a specific academic course suggest that the presented system has a positive impact on students' academic performance. © 2017 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.

In many application domains, such as education, sequences of events occurring over time need to be studied in order to understand the generative process behind these sequences, and hence classify new examples. In this paper, we propose a novel multi-granularity sequence classification framework that generates features based on frequent patterns at multiple levels of time granularity. Feature selection techniques are applied to identify the most informative features that are then used to construct the classification model. We show the applicability and suitability of the proposed framework to the area of educational data mining by experimenting on an educational dataset collected from an asynchronous communication tool in which students interact to accomplish an underlying group project. The experimental results showed that our model can achieve competitive performance in detecting the students' roles in their corresponding projects, compared to a baseline similarity-based approach.

This paper studies the application of Educational Data Mining to examine the online communication behaviour of students working together on the same project in order to identify the different roles played by the students. Analysis was carried out using real data from students’ participation in project communication tools. Several sets of features including individual attributes and information about the interactions between the project members were used to train different classification algorithms. The results show that considering the individual attributes of students provided regular classification performance. The inclusion of information about the reply relationships among the project members generally improved mapping students to their roles. However, “time-based” features were necessary to achieve the best classification results, which showed both precision and recall of over 95% for a number of algorithms. Most of these “time-based” features coincided with the first weeks of the experience, which indicates the importance of initial interactions between project members.

This paper presents a framework for project management competence assessment based on participant’s performance and contribution in a simulated environment. The proposed framework considers competence assessment through evidences and the participation of different roles. The system enforces the assessment of individual competences by means of a set of performance indicators. A specific case study is presented and the relationship between exhibited transversal competences and project quality is analysed.

This work introduces a web-based learning environment to facilitate learning in Project Management. The proposed web-based support system integrates methodological procedures and information systems, allowing to promote learning among geographically-dispersed students. Thus, students who are enrolled in different universities at different locations and attend their own project management courses, share a virtual experience in executing and managing projects. Specific support systems were used or developed to automatically collect information about student activities, making it possible to monitor the progress made on learning and assess learning performance as established in the defined rubric.

A work in defining and implementing a biometric system based on hand geometry identification is presented here. Hand features are extracted from a color photograph taken when the user has placed his hand on a platform designed for such a task. Different pattern recognition techniques have been tested to be used in classification and/or verification from Euclidean distance to neural networks. Experimental results, up to a 97 percent rate of success in classification, will show the possibility of using this system in medium/high security environments with full acceptance from all users.