Online and Collaborative Learning Design model based on IMS-LD to Stimulate Collaborative Learning in E-learning Environments

In the e-learning field, there is an urgent need for the sharing, reuse and design of online courses as learning objects. However, in the vast majority of cases, e-learning courses are built in a manner that not stimulating cooperation, interaction, and collaborative learning. The primary aim of this paper is to develop a strategy for constructing learning objects, strategy targeted at supporting instructors in designing educational contents in order to promote collaborative learning in e-learning environments. A key challenge in this work is the definition of a new method of learning design of e-learning contents to stimulate collaborative learning. In addition, we introduce a general model of online and collaborative learning design. Model is based on the methods of instructional design and Educational Modeling Languages, particularly the IMS-LD specification. Firstly, the paper presents the online and collaborative design process of a content based on a life cycle adapted. Then, the paper describes the steps of the modeling process of content. Finally, the paper exposes the adopted technical choices and a first prototype is set up to provide a subjective evaluation of the new framework

Using an Intelligent Tutoring System to Support Learners’ WMC in e-learning: Application in Mathematics Learning

Learning is a complex process linked to a number of individual, cognitive and affective characteristics of the learner. In this context, the learner’s Working Memory (WM) concept has also been identified as a preacher of learning performance. In the learning process, solving a problem is an essential part of the learner's WM. Its capacity and performance are predictive factors of academic success. The success of the given problem depends on a good selection, processing and transformation of this information. This mental work within the WM requires a good WM Capacity (WMC). The improvement of the WMC needs some strategies which focus on the learning content and the use of educational technologies in the learning process. Encouraged by the results of previous research, this work aims to harness the potential of educational technologies to enhance the learners' WM ability in learning mathematics in an e-learning environment. In order to help learners with low WMC in e-learning, this paper proposes to set up an e-learning platform integrating an Intelligent Tutorial System (ITS). The purpose of this ITS is to manage the limited ability of the learner's WM by adopting strategies that improve the transition of mathematical knowledge from short-term memory to long-term memory. The proposed ITS generates e-learning content that will serve to improve the learner's WMC, and subsequently improve his learning performance

Using an Intelligent Tutoring System to Support Learners’ WMC in e-learning: Application in Mathematics Learning

Learning is a complex process linked to a number of individual, cognitive and affective characteristics of the learner. In this context, the learner’s Working Memory (WM) concept has also been identified as a preacher of learning performance. In the learning process, solving a problem is an essential part of the learner's WM. Its capacity and performance are predictive factors of academic success. The success of the given problem depends on a good selection, processing and transformation of this information. This mental work within the WM requires a good WM Capacity (WMC). The improvement of the WMC needs some strategies which focus on the learning content and the use of educational technologies in the learning process. Encouraged by the results of previous research, this work aims to harness the potential of educational technologies to enhance the learners' WM ability in learning mathematics in an e-learning environment. In order to help learners with low WMC in e-learning, this paper proposes to set up an e-learning platform integrating an Intelligent Tutorial System (ITS). The purpose of this ITS is to manage the limited ability of the learner's WM by adopting strategies that improve the transition of mathematical knowledge from short-term memory to long-term memory. The proposed ITS generates e-learning content that will serve to improve the learner's WMC, and subsequently improve his learning performance

An Evaluation Model of Digital Educational Resources

Abstractâ??Today, the use of digital educational resources in teaching and learning is considerably expanding. Such expansion calls educators and computer scientists to reflect more on the design of such products. However, this reflection exposes a number of criteria and recommendations that can guide and direct any teaching tool design be it campus-based or online (e-learning). Our work is at the heart of this issue. We suggest, through this article, examining academic, pedagogical, didactic and technical criteria to conduct this study which aims to evaluate the quality of digital educational resources. Our approach consists in addressing the specific and relevant factors of each evaluation criterion. We will then explain the detailed structure of the evaluation instrument used : â??evaluation gridâ?. Finally, we show the evaluation outcomes based on the conceived grid and then we establish an analytical evaluation of the state of the art of digital educational resources

A Deep Learning Approach to Manage and Reduce Plastic Waste in the Oceans

The accumulation of plastic objects in the Earth’s environment will adversely affect wildlife, wildlife habitat, and humans. The huge amount of unrecycled plastic ends up in landfill and thrown into unregulated dump sites. In many cases, specifically in the developing countries, plastic waste is thrown into rivers, streams and oceans. In this work, we employed the power of deep learning techniques in image processing and classification to recognize plastic waste. Our work aims to identify plastic texture and plastic objects in images in order to reduce plastic waste in the oceans, and facilitate waste management. For this, we use transfer learning in two ways: in the first one, a pre-trained CNN model on ImageNet is used as a feature extractor, then an SVM classifier for classification, the second strategy is based on fine tuning the pre-trained CNN model. Our approach was trained and tested using two (02) challenging datasets one is a texture recognition dataset and the other is for object detection, and achieves very satisfactory results using two (02) deep learning strategies