Personal environment self-directed student learning

Розглядається спосіб побудови персонального середовища самоспрямованого навчання (ПССН). Розглянуто існуючі підходи створення та застосування персонального навчального середовища (ПНС). Наголошено, що ПНС – це сукупність ресурсів, потрібних людині для того, щоб знайти відповіді на різноманітні питання, створити потрібний контекст для навчання і проілюструвати досліджувані процеси. Персональне навчальне середовище – це не конкретний додаток або служба, а особливий підхід до реалізації навчання. Користувач завжди самостійно формує своє ПНС. Подано авторське бачення суттєвих відмінностей між навчальною самостійністю учня та самоспрямованим навчанням. Аналіз останніх досліджень і публікацій дозволив визначити принципи функціонування та компонентний склад ПССН, рекомендувати принципи щодо формування цифрового освітнього середовища. Описано компоненти ПССН: мережі фундаментальних освітніх об'єктів (ФОО), які є базою для навчальної навігації. перелік навичок, на розвитку яких ми фокусуємось; рекомендації щодо застосування методів самостійного навчання; опис методики ведення інформаційних нотаток Zettelkasten, що дозволяє об’єднувати нотатки в мережу; технології навчальної діяльності у тріадах та відповідні інструменти. Детально описано діючий прототип середовища, який можна взяти за основу для побудови власного ПССН. Він побудований на основі хмаро орієнтованого інструментарію ведення нотаток notion.so. Запропоновано готовий для застосування темплейт ПССН. Описаний покроковий алгоритм діяльності учня. Передбачається, що запропоноване середовище допоможе учням сформувати навичку самоспрямованого навчання та реалізувати її на практиці. Запропоноване середовище може бути застосовано учнями під час дистанційного, змішаного, індивідуального та групового навчання. Особливо актуально для тих, хто будує індивідуальну освітню траєкторію.Abstract. The method of building a personal environment of self-directed learning (peSDL) is considered. The existing approaches to the creation and application of a personal learning environment (PLE) are considered. It is emphasized that PLE is a set of resources needed by a person to find answers to various questions, create the right context for learning and illustrate the research processes. A PLE is not a specific application or service, but a special approach to learning. The user always forms his own PLE. The author's vision of significant differences between self-regulated learning (SRL) and self-directed learning(SDL) is presented. The analysis of the latest researches and publications allowed to define the principles of functioning and component structure of peSDL, to recommend principles concerning formation of the digital educational environment. The components of peSDL are described: networks of fundamental educational objects (FEO), which are the basis for educational navigation; a list of skills we focus on; recommendations for the use of self-study methods; description of the method of keeping information notes Zettelkasten, which allows you to combine notes into a network; technologies of educational activity in triads and corresponding tools. The current prototype of the environment, which can be taken as a basis for building your own peSDL, is described in detail. It is based on cloud-based notion.so note-taking tools. A ready-to-use peSDL template has been proposed. The step-by-step algorithm of student's activity is described. It is assumed that the proposed environment will help students develop the skill of self-directed learning and implement it in practice. The proposed peSDL can be used by students during distance, blended, individual and group learning. Especially relevant for those who build an individual educational trajectory

Development and validation of the Personal Values Dictionary:A theory-driven tool for investigating references to basic human values in text

Estimating psychological constructs from natural language has the potential to expand the reach and applicability of personality science. Research on the Big Five has produced methods to reliably assess personality traits from text, but the development of comparable tools for personal values is still in the early stages. Based on the Schwartz theory of basic human values, we developed a dictionary for the automatic assessment of references to personal values in text. To refine and validate the dictionary, we used Facebook updates, blog posts, essays, and book chapters authored by over 180,000 individuals. The results show high reliability for the dictionary and a pattern of correlations between the value types in line with the circumplex structure. We found small to moderate (rs=.1-.4) but consistent correlations between dictionary scores and self-reported scores for 7 out of 10 values. Correlations between the dictionary scores and age, gender, and political orientation of the author and scores for other established dictionaries mostly followed theoretical predictions. The Personal Values Dictionary can be used to assess references to value orientations in textual data, such as tweets, blog posts, or status updates, and will stimulate further research in methods to assess human basic values from text

AFEL-Analytics for Everyday Learning

The goal of AFEL is to develop, pilot and evaluate methods and applications, which advance informal/collective learning as it surfaces implicitly in online social environments. The project is following a multi-disciplinary, industry-driven approach to the analysis and understanding of learner data in order to personalize, accelerate and improve informal learning processes. Learning Analytics and Educational Data Mining traditionally relate to the analysis and exploration of data coming from learning environments, especially to understand learners' behaviours. However, studies have for a long time demonstrated that learning activities happen outside of formal educational platforms, also. This includes informal and collective learning usually associated, as a side effect, with other (social) environments and activities. Relying on real data from a commercially available platform, the aim of AFEL is to provide and validate the technological grounding and tools for exploiting learning analytics on such learning activities. This will be achieved in relation to cognitive models of learning and collaboration, which are necessary to the understanding of loosely defined learning processes in online social environments. Applying the skills available in the consortium to a concrete set of live, industrial online social environments, AFEL will tackle the main challenges of informal learning analytics through 1) developing the tools and techniques necessary to capture information about learning activities from (not necessarily educational) online social environments; 2) creating methods for the analysis of such informal learning data, based on combining feature engineering and visual analytics with cognitive models of learning and collaboration; and 3) demonstrating the potential of the approach in improving the understanding of informal learning, and the way it is better supported; 4) evaluate all the former items in real world large scale applications and platforms.The authors would like to thank the rest of the AFEL consortium. This work was supported by the Know-Center Graz, the Science Foundation Ireland (SFI) Insight Centre for Data Analytics and the European-funded project AFEL (GA687916). The Know-Center Graz is funded within the Austrian COMET Program - Competence Centers for Excellent Technologies - under the auspices of the Austrian Ministry of Transport, Innovation and Technology, the Austrian Ministry of Economics and Labor and by the State of Styria. COMET is managed by the Austrian Research Promotion Agency (FFG).non-peer-reviewe

Modern information technologies and innovative teaching methods in training: methodology, theory, experience, problems

У збірнику наукових праць знані дослідники, педагоги-практики середніх загальноосвітніх шкіл, закладів професійно-технічної освіти, працівники коледжів і закладів вищої освіти висвітлюють теоретичні й прикладні аспекти впровадження сучасних інформаційних технологій та інноваційних методик навчання у підготовку кваліфікованих робітників, молодших спеціалістів, бакалаврів і магістрів. Для науковців і педагогів-практиків загальноосвітніх шкіл, коледжів, закладів професійно-технічної та вищої освіти, працівників інститутів післядипломної педагогічної освіти. Статті збірника подано в авторській редакції.The collection of scientific works includes well-known researchers, teachers-practitioners of secondary schools, vocational schools educators, employees of colleges and institutions of higher education cover theoretical and applied aspects of the implementation of modern information technologies and innovative teaching methods in the training of skilled workers, junior specialists, bachelors and masters. For scientists and teachers-practitioners of secondary schools, colleges, vocational and higher education institutions, employees of institutes postgraduate pedagogical education. The articles of the collection are submitted in the author's edition

AFEL-Analytics for Everyday Learning

The goal of AFEL is to develop, pilot and evaluate methods and applications, which advance informal/collective learning as it surfaces implicitly in online social environments. The project is following a multi-disciplinary, industry-driven approach to the analysis and understanding of learner data in order to personalize, accelerate and improve informal learning processes. Learning Analytics and Educational Data Mining traditionally relate to the analysis and exploration of data coming from learning environments, especially to understand learners' behaviours. However, studies have for a long time demonstrated that learning activities happen outside of formal educational platforms, also. This includes informal and collective learning usually associated, as a side effect, with other (social) environments and activities. Relying on real data from a commercially available platform, the aim of AFEL is to provide and validate the technological grounding and tools for exploiting learning analytics on such learning activities. This will be achieved in relation to cognitive models of learning and collaboration, which are necessary to the understanding of loosely defined learning processes in online social environments. Applying the skills available in the consortium to a concrete set of live, industrial online social environments, AFEL will tackle the main challenges of informal learning analytics through 1) developing the tools and techniques necessary to capture information about learning activities from (not necessarily educational) online social environments; 2) creating methods for the analysis of such informal learning data, based on combining feature engineering and visual analytics with cognitive models of learning and collaboration; and 3) demonstrating the potential of the approach in improving the understanding of informal learning, and the way it is better supported; 4) evaluate all the former items in real world large scale applications and platforms.The authors would like to thank the rest of the AFEL consortium. This work was supported by the Know-Center Graz, the Science Foundation Ireland (SFI) Insight Centre for Data Analytics and the European-funded project AFEL (GA687916). The Know-Center Graz is funded within the Austrian COMET Program - Competence Centers for Excellent Technologies - under the auspices of the Austrian Ministry of Transport, Innovation and Technology, the Austrian Ministry of Economics and Labor and by the State of Styria. COMET is managed by the Austrian Research Promotion Agency (FFG).non-peer-reviewe

AFEL-Analytics for Everyday Learning

The goal of AFEL is to develop, pilot and evaluate methods and applications, which advance informal/collective learning as it surfaces implicitly in online social environments. The project is following a multi-disciplinary, industry-driven approach to the analysis and understanding of learner data in order to personalize, accelerate and improve informal learning processes. Learning Analytics and Educational Data Mining traditionally relate to the analysis and exploration of data coming from learning environments, especially to understand learners\u27 behaviours. However, studies have for a long time demonstrated that learning activities happen outside of formal educational platforms, also. This includes informal and collective learning usually associated, as a side effect, with other (social) environments and activities. Relying on real data from a commercially available platform, the aim of AFEL is to provide and validate the technological grounding and tools for exploiting learning analytics on such learning activities. This will be achieved in relation to cognitive models of learning and collaboration, which are necessary to the understanding of loosely defined learning processes in online social environments. Applying the skills available in the consortium to a concrete set of live, industrial online social environments, AFEL will tackle the main challenges of informal learning analytics through 1) developing the tools and techniques necessary to capture information about learning activities from (not necessarily educational) online social environments; 2) creating methods for the analysis of such informal learning data, based on combining feature engineering and visual analytics with cognitive models of learning and collaboration; and 3) demonstrating the potential of the approach in improving the understanding of informal learning, and the way it is better supported; 4) evaluate all the former items in real world large scale applications and platforms.The authors would like to thank the rest of the AFEL consortium. This work was supported by the Know-Center Graz, the Science Foundation Ireland (SFI) Insight Centre for Data Analytics and the European-funded project AFEL (GA687916). The Know-Center Graz is funded within the Austrian COMET Program - Competence Centers for Excellent Technologies - under the auspices of the Austrian Ministry of Transport, Innovation and Technology, the Austrian Ministry of Economics and Labor and by the State of Styria. COMET is managed by the Austrian Research Promotion Agency (FFG)

AFEL: Towards measuring online activities contributions to self-directed learning

More and more learning activities take place online in a selfdirected manner. Therefore, just as the idea of self-tracking activities for fitness purposes has gained momentum in the past few years, tools and methods for awareness and self-reflection on one’s own online learning behavior appear as an emerging need for both formal and informal learners. Addressing this need is one of the key objectives of the AFEL (Analytics for Everyday Learning) project. In this paper, we discuss the different aspects of what needs to be put in place in order to enable awareness and self-reflection in online learning. We start by describing a scenario that guides the work done. We then investigate the theoretical, technical and support aspects that are required to enable this scenario, as well as the current state of the research in each aspect within the AFEL project. We conclude with a discussion of the ongoing plans from the project to develop learner-facing tools that enable awareness and selfreflection for online, self-directed learners. We also elucidate the need to establish further research programs on facets of self-tracking for learning that are necessarily going to emerge in the near future, especially regarding privacy and ethics.This work has received funding from the European Union’s Horizon 2020 research and innovation programme as part of the AFEL (Analytics for Everyday Learning) project under grant agreement No 687916