Learning by teaching is one of the most prevalent contemporary educational practices, but we still don't understand when it works, and why.
A successful Interactive data visualization is not only aiming to illustrate data and information, but it encourages users to participate and contribute new ideas and data visualizations using exciting data or even their own new data. So,
What are the data visualization and communication competences?
Education in its broader sense is the process of becoming and identity formation. Developing a professional identity, as an engineer, teacher, or medical practitioner, lies at the core of higher education practice. However, previous research illustrates a lack of an integrated framework that conceptualizes the process to explain how learners practice professional identities and what are the relations between educational design and such practices.
Here is a short offering on my endeavours in the recent past...PhD wise that is.
Under the supervision of Prof. Peter Reimann, my research is investigating current practices and sense making of student data presented as Learning Analytics Visualisations (LAV) in the teaching of English as a Second Language (ESL). Existing knowledge of data use in this context will be advanced by examining and tracking teacher learning. This will be achieved by observing teachers’ ability to interpret, engage with and make sense of student data via the concept of expansive learning trajectories (Barab, Hay & Yamagata-Lynch, 1999; Cobb, 1996) in correlation with descriptive frameworks measuring the interplay of ESL pedagogy, technology, and the sense making process.
Widely discussed in the literature is the current and potential influence of digital technology in mainstream education. Opportunities now exist to enhance the teaching and learning experience through an expansive data trail generated by digital device users. In language instruction, the development of Computer Assisted Language Learning (CALL) as a field of research is indicative of this. At an institutional level, the collection and analysis of data is referred to as Academic Analytics, and through learning and teaching spaces, Learning Analytics.
Over recent years, teams have emerged as a crucial vehicle for doing various projects. Teamwork offers both the organisations and individuals the ability to become more familiar with each other, and learn new skills and draw on other team members’ talents, experiences and perspectives. Working in a team enables team members to be more effective in their work, as compared to those who work on their projects on their own. Yet many different types of teams exist ranging from temporarily problem-solving teams to long-term project teams in order to respond to the teamwork demand.