Utilising a Virtual Learning Assistant as a measurement and intervention tool for self-regulation in learning.

Online learning and massive open online courses are widely used in engineering and technology education. Engineering next-generation learning requires overcoming the potential constraints of online learning environments which necessitate higher levels of self-regulation than traditional classroom settings. This particular requirement demands that learners allocate their cognitive, metacognitive, affective and motivational resources to meet this need. Lack of self-regulation can affect learners' engagement with the course content, resulting in sub-optimal learning outcomes or failure to complete the course. This paper reports on the design of a virtual learning assistant and its implementation in online learning activities. This paper outlines the virtual assistant's use as a data collection tool and, further, proposes that the virtual learning assistant has the potential to be used as an assessment tool for self-regulatory skills, and as an intervention tool to support online learners' self-regulation in online learning

Utilising a Virtual Learning Assistant as a Measurement and Intervention Tool for Self-Regulation in Learning

Online learning and massive open online courses are widely used in engineering and technology education. Engineering next-generation learning requires overcoming the potential constraints of online learning environments which necessitate higher levels of self-regulation than traditional classroom settings. This particular requirement demands that learners allocate their cognitive, metacognitive, affective and motivational resources to meet this need. Lack of self-regulation can affect learners' engagement with the course content, resulting in sub-optimal learning outcomes or failure to complete the course. This paper reports on the design of a virtual learning assistant and its implementation in online learning activities. This paper outlines the virtual assistant's use as a data collection tool and, further, proposes that the virtual learning assistant has the potential to be used as an assessment tool for self-regulatory skills, and as an intervention tool to support online learners' self-regulation in online learning

USING NATURAL SHAPE STATISTICS OF URBAN FORM TO MODEL SOCIAL CAPITAL / SOCIALINIO KAPITALO MODELIAVIMAS TAIKANT PASTATŲ FORMOS STATISTINĘ ANALIZĘ

The social aspect is an important but often overlooked part of sustainable development philosophy. In hoping to popularise and show the importance of social sustainable development, this study tries to find a relation between the social environment and urban form. Research in the social capital field provided the methodology to acquire social computational data. The relation between human actions and the environment is noted in many theories, and used in some practices. Human cognition is computationally predictable with natural shape analysis and machine learning methods. In the analysis of shape, a topological skeleton is a proven method to acquire statistical data that correlates with data collected from human experiments. In this study, the analysis of urban form with respect to human cognition was used to acquire computational data for a machine learning model of social capital in counties in the USA Santrauka Tvarios plėtros teorijoje socialinė aplinka yra pripažinta kaip svarbus veiksnys, tačiau trūksta praktinės metodikos. Ryšio tarp urbanistinės formos ir socialinės aplinkos radimas aktualizuotų ir padėtų populiarinti socialinę tvarią plėtrą. Aplinkos įtaka žmonių tarpusavio elgesiui yra ne kartą aptartas reiškinys, tačiau praktikoje retai taikomas. Ankstesniuose socialinio kapitalo tyrimuose pateikiamos metodologijos ir statistiniai duomenys esamos situacijos analizei atlikti. Kaip žmonės suvokia formas, yra nuspėjama taikant statistinę formos analizę ir dirbtinio intelekto metodologiją – sistemos mokymąsi. Klasifikuojant formas topologinio skeleto metodologija gaunami rezultatai koreliuoja su duomenimis, surinktais per eksperimentą, kuriame žmonės klasifikuoja formas. Taikant žinomas formos analizės metodologijas, atspindinčias suvokimą, buvo surinkti duomenys modeliuoti socialinį kapitalą su sisteminio mokymosi modeliu. Sisteminis mokymasis yra dirbtinio intelekto sritis, kurioje remiantis pateiktais duomenimis automatiškai sukalibruojama kompleksinė matematinė formulė. Modeliuojant socialinį kapitalą su formos skeleto statistiniais duomenimis, geriausi rezultatai pasiekti taikant neuroniniais tinklais pagristą sisteminį mokymąsi. Reikšminiai žodžiai: urbanistinė forma,  formos analizė statis­tiniais metodais, socialinis kapitalas, sisteminis mokymasis, daugiasluoksnis perceptronas

Text mining in education

The explosive growth of online education environments is generating a massive volume of data, specially in text format from forums, chats, social networks, assessments, essays, among others. It produces exciting challenges on how to mine text data in order to find useful knowledge for educational stakeholders. Despite the increasing number of educational applications of text mining published recently, we have not found any paper surveying them. In this line, this work presents a systematic overview of the current status of the Educational Text Mining field. Our final goal is to answer three main research questions: Which are the text mining techniques most used in educational environments? Which are the most used educational resources? And which are the main applications or educational goals? Finally, we outline the conclusions and the more interesting future trends

Networked Learning Analytics: A Theoretically Informed Methodology for Analytics of Collaborative Learning

Online social learning is a prevalent pedagogical tool, enabling learners across all ages and cultures to learn together. Educators, policy-makers, and international organizations such as the Organization for Economic Cooperation and Development (OECD) stress the need to assess collaborative learning systematically. However, the systematic assessment of large online groups’ collaboration is still in its infancy. In this chapter, we suggest perceiving social learning through the lens of interaction networks between learners and content. Based on well-accepted learning theories, we demonstrate the harnessing of digital traces of online discussions to the assessment of social learning, at both the individual and the group levels. Practically, our contribution is to suggest a network analysis point of view for the assessment of the performance and design of learning communities. Our proposed methodology can be used by instructors to open-up the black box of collaborative learning, to be able to equip learners with twenty-first-century skill-set