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Although there is consensus in the literature that writing skills are important in STEM (Science, Technology, Engineering, and Mathematics) studies, they are often neglected. However, some efforts have been made to correct this deficiency, one of them being the development of assessment rubrics. This study seeks to contribute to the discussion by presenting the results of the application of a rubric designed to assess the writing skills of a group of 3rd year engineering students. This rubric, which includes linguistic and rhetorical-organizational criteria alongside the mathematical and technical, was used to assess a number of written exercises and essays submitted by students in a 15-week course. The main interest of this study was to test the efficacy of the rubric as a diagnostic tool, conceived to detect the areas of improvement in the students’ written performance and, ultimately, to also help them to achieve higher levels of competence. This goal was achieved, as one of the main conclusions of the study is that, although students usually master the technical aspects of the course, they must improve the linguistic and rhetorical aspects of their written communication. It can likewise be said that all the participants involved in the study profited in one way or another from the application of the rubric and contributed to identifying the ways in which the rubric itself can be improved for future application. Full article

Electronic Learning (e-learning) has made a great success and recently been estimated as a billion-dollar industry. The users of e-learning acquire knowledge of diversified content available in an application using innovative means. There is much e-learning software available—for example, LMS (Learning Management System) and Moodle. The functionalities of this software were reviewed and we recognized that learners have particular problems in getting relevant recommendations. For example, there might be essential discussions about a particular topic on social networks, such as Twitter, but that discussion is not linked up and recommended to the learners for getting the latest updates on technology-updated news related to their learning context. This has been set as the focus of the current project based on symmetry between user project specification. The developed project recommends relevant symmetric articles to e-learners from the social network of Twitter and the academic platform of DBLP. For recommendations, a Reinforcement learning model with optimization is employed, which utilizes the learners’ local context, learners’ profile available in the e-learning system, and the learners’ historical views. The recommendations by the system are relevant tweets, popular relevant Twitter users, and research papers from DBLP. For matching the local context, profile, and history with the tweet text, we recognized that terms in the e-learning system need to be expanded to cover a wide range of concepts. However, this diversification should not include such terms which are irrelevant. To expand terms of the local context, profile and history, the software used the dataset of Grow-bag, which builds concept graphs of large-scale Computer Science topics based on the co-occurrence scores of Computer Science terms. This application demonstrated the need and success of e-learning software that is linked with social media and sends recommendations for the content being learned by the e-Learners in the e-learning environment. However, the current application only focuses on the Computer Science domain. There is a need for generalizing such applications to other domains in the future. Full article