Evaluation of the Project Management Competences Based on the Semantic Networks

The paper presents the testing and evaluation facilities of the SinPers system. The SinPers is a web based learning environment in project management, capable of building and conducting a complete and personalized training cycle, from the definition of the learning objectives to the assessment of the learning results for each learner. The testing and evaluation facilities of SinPers system are based on the ontological approach. The educational ontology is mapped on a semantic network. Further, the semantic network is projected into a concept space graph. The semantic computability of the concept space graph is used to design the tests. The paper focuses on the applicability of the system in the certification, for the knowledge assessment, related to each element of competence. The semantic computability is used for differentiating between different certification levels.testing, assessment, ontology, semantic networks, certification.

Enhancing Learning Object Analysis through Fuzzy C-Means Clustering and Web Mining Methods

The development of learning objects (LO) and e-pedagogical practices has significantly influenced and changed the performance of e-learning systems. This development promotes a genuine sharing of resources and creates new opportunities for learners to explore them easily. Therefore, the need for a system of categorization for these objects becomes mandatory. In this vein, classification theories combined with web mining techniques can highlight the performance of these LOs and make them very useful for learners. This study consists of two main phases. First, we extract metadata from learning objects, using the algorithm of Web exploration techniques such as feature selection techniques, which are mainly implemented to find the best set of features that allow us to build useful models. The key role of feature selection in learning object classification is to identify pertinent features and eliminate redundant features from an excessively dimensional dataset. Second, we identify learning objects according to a particular form of similarity using Multi-Label Classification (MLC) based on Fuzzy C-Means (FCM) algorithms. As a clustering algorithm, Fuzzy C-Means is used to perform classification accuracy according to Euclidean distance metrics as similarity measurement. Finally, to assess the effectiveness of LOs with FCM, a series of experimental studies using a real-world dataset were conducted. The findings of this study indicate that the proposed approach exceeds the traditional approach and leads to viable results. Doi: 10.28991/ESJ-2023-07-03-010 Full Text: PD

LEARNING OBJECT. DEFINITION AND CLASSIFICATION

[EN] The current trend in higher education includes competencies in the curricula. This integration can be done through the competency-based learning. The competence is acquired through various learning objects to be achieved. In this paper different dimensions to define a learning object (LO) and different classifications associated to them have been proposed. An analysis and synthesis of the results obtained have been presented.Alarcón Valero, F.; Alemany Díaz, MDM.; Boza, A.; Cuenca, L.; Gordo Monzó, ML.; Fernández-Diego, M.; Ruiz Font, L. (2015). LEARNING OBJECT. DEFINITION AND CLASSIFICATION. EDULEARN Proceedings (Internet). 4479-4488. http://hdl.handle.net/10251/95287S4479448

Significant Properties of e-Learning Objects (SPeLOs)

Presentation describing the aims and results of the JISC Digital Preservation and Records Management Programme study into the significant properties for preservation of E-learning objects

Identify Toxin Contamination in Peanuts Using the Development of Machine Vision Based on Image Processing Technique

This research aimed at identifying the use of image processing technique and classifying the use of K-mean clustering of contaminated and uncontaminated peanuts. A machine vision system was made of a small aluminum box, equipped with a camera, a petri for placing the sample, USB connector, UV lamp, and a computer. Image processing methods consisted of analysis of the average color of RGB in the region of interest, convertion of RGB into HSV, segmentation process, as well as convertion image into grayscale and binary objects in order to obtain the total number of pixels value in the object area so that the mean value of the pixels of the area can be calculated. K-means algorithm was used to classify the contaminated and uncontaminated peanuts based on the average pixel value of R,G, B color parameters. The accuracy of a system was 100% meaning that the performance of machine vision can be used to identify the contaminated and uncontaminated peanuts.Keywords: aflatoxin, K-mean clustering, machine vision, image processing, peanuts