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Special Session in Learning Analytics 2013 : Learning Analytics: From Personal Learning Data to Open Educational Linked Data (SS4)

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Link: http://www.iconip2013.org/index.php?g_page=program&m_page=program03
 
When Nov 3, 2013 - Nov 7, 2013
Where Daegu, Korea
Submission Deadline Jul 30, 2013
Notification Due Aug 30, 2013
Categories    data mining   artificial intelligence   learning   computer science
 

Call For Papers


Abstract

In the last few decades, the number of people connected online for educational purpose is increasing dramatically and consequently a huge quantity of data is being generated. This data is mainly the “traces” or “digital breadcrumbs” that students leave as they interact with the online learning environments. Confident that this data can teach us about learners’ behaviors and help us enhancing learning experience, there has been a growing interest in the automatic analysis of such data. A research area referred to as Learning Analytics (LA) is identified. According to Johnson and al. (2011), LA "refers to the interpretation of a wide range of data produced by and gathered on behalf of students in order to assess academic progress, predict future performance, and spot potential issues".

At the same time, the quantity of published linked data is increasing on the web. Many institutions adhere to Open Linked Data community making available another type of data which can benefit LA. LA can be seen as a twofold approach, acting on :i) Personal Learning data (data from individual and private learning experience) and ii) open educational linked data (public data published on the web).

The main objective of the session is to provide a venue for scientific discourse to exchange opinions and ideas on how to advance theory and practice in Learning Analytics. How learning experiences can benefit from the huge quantity of published and unpublished data? How can we combine open data and private in a way to empower advanced educational services, such as recommendation of suitable educational resources to individual learners or program and institutions benchmarking? Which concepts, approaches and algorithms are appropriate to consume the two types of data?


List of Suggested topics (but not limited to):


- The semantic web and linked data: meaning in connections

- Data mining in learning analytics

- Artificial intelligence in learning analytics

- Techniques and Algorithms used in Learning Analytics

- Learning Analytics for Learning Management Systems

- Open Linked Data and Learning Analytics

- Tools using and exploiting educational Linked Open Data?

- Two views of Learning analytics : Private data and Open data



Session organizers

o Henda Chorfi, King Saud University, Saudi Arabia

o Hend Al-Khalifa, King Saud University, Saudi Arabia

o Maha Al-Yahya, King Saud University, Saudi Arabia

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