Ресурсоэффективные системы в управлении и контроле: взгляд в будущее: сборник научных трудов VII Международной конференции школьников, студентов, аспирантов, молодых ученых, 8 -13 октября 2018 г., г. Томск

В сборнике представлены материалы VII Международной конференции школьников, студентов, аспирантов, молодых ученых "Ресурсоэффективные системы в управлении и контроле: взгляд в будущее". Более 500 авторов из 35 вузов, предприятий и научных исследовательских университетов России, ближнего и дальнего зарубежья представили тезисы своих докладов, в которых рассматриваются актуальные проблемы неразрушающего контроля и технической диагностики, внедрения систем менеджмента, качества образования, управления в современной экономике. Материалы предназначены для специалистов, преподавателей, аспирантов и студентов вузов, а также для всех интересующихся проблемами ресурсоэффективных технологий

Not Yet Ready for Everyone: An Experience Report about a Personal Learning Environment for Language Learning

Abstract. A Personal Learning Environment (PLE) is a mash-up of learning services. It enables students and teachers to assemble a work environment that is adapted to a domain and specific individual needs. In this article, we report on our experiences on using a PLE for Lan-guage Learning in five French lectures at the Shanghai Jiao Tong Uni-versity Continuing Education School. We found that while a PLE has the potential to simplify access to and usage of Web sites and services for language learning, students will use it only if properly motivated. Fur-thermore, at the time being, difficulties that result from the user interface and technical implementation make the interactions with PLEs difficult. The problems need to be overcome in order for PLEs to become adopted by the average, not technically highly literate students and teachers. Key words: PLE, mash-up, experience report

Usage Pattern Recognition in Student Activities

Proceedings of: 6th European Conference of Technology Enhanced Learning, EC-TEL 2011, Palermo, Italy, September 20-23, 2011.This paper presents an approach of collecting contextualized attention metadata combined from inside as well as outside a LMS and analyzing them to create feedback about the student activities for the teaching staff. Two types of analyses were run on the collected data: first, key actions were extracted to identify usage patterns and tendencies throughout the whole course and then usage statistics and patterns were identified for some key actions in more detail. Results of both analyses were visualized and presented to the teaching staff for evaluation.The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007- 2013) under grant agreement no 231396 (ROLE project). Work was also partially funded by the Learn3 project (TIN2008-05163/TSI), the eMadrid project (S2009/TIC-1650), and the Acción Integrada DE2009-0051

The 3P learning model

Recognizing the failures of traditional Technology Enhanced Learning (TEL) initiatives to achieve performance improvement, we need to rethink how we design new TEL models that can respond to the learning requirements of the 21st century and mirror the characteristics of knowledge and learning which are fundamentally personal, social, distributed, ubiquitous, flexible, dynamic, and complex in nature. In this paper, we discuss the 3P learning model; a vision of learning characterized by the convergence of lifelong, informal, and personalized learning within a social context. The 3P learning model encompasses three core elements: Personalization, Participation, and Knowledge-Pull. We then present the social software supported learning framework as a framework that illustrates the 3P learning model in action, based on Web 2.0 concepts and social software technologies

NetLearn: Social network analysis and visualizations for learning

The most valuable and innovative knowledge is hard to find, and it lies within distributed communities and networks. Locating the right community or person who can provide us with exactly the knowledge that we need and who can help us solve exactly the problems that we come upon, can be an efficient way to learn forward. In this paper, we present the details of NetLearn; a service that acts as a knowledge filter for learning. The primary aim of NetLearn is to leverage social network analysis and visualization techniques to help learners mine communities and locate experts that can populate their personal learning environments