Academic journal article Educational Technology & Society

Translating Learning into Numbers: A Generic Framework for Learning Analytics

Academic journal article Educational Technology & Society

Translating Learning into Numbers: A Generic Framework for Learning Analytics

Article excerpt

Introduction

In the last few years, the amount of data that is published and made publicly available on the web has exploded. This includes governmental data, Web2.0 data from a plethora of social platforms (Twitter, Flickr, YouTube, etc.), and data produced by various sensors such as GPS location data from mobile devices. In the wake of this, data-driven companies like Google, Yahoo, Facebook, Amazon, etc. are growing exponentially by commercially exploiting such data for marketing or in the creation of new personalised services. The new "data economy" empowers companies to offer an increasing amount of data products at little or no cost to their users (e.g., Google Flu Trends, bit.ly customised URLs, Yahoo Pipes, Gapminder.com). This growth in data also renewed the interest in information retrieval technologies. Such technologies are used to analyse data and offer personalised data products customised to the needs and the context of individual users.

It is already evident that data in combination with information retrieval technologies are not only the basis for the emergent data economy, but also hold substantial promises for use in education (Retalis et al., 2006; Johnson et al., 2011). One example of this is the research on personalisation with information retrieval technologies which has been a focus in the educational field for some time now (Manouselis et al., 2010). The main driver is the vision of improved quality, effectiveness, and efficiency of the learning processes. It is expected that personalised learning has the potential to reduce delivery costs while at the same time creating more effective learning experiences, accelerating competence development, and increasing collaboration between learners.

Not so long ago, for universities and companies alike, gathering data on their users met with substantial limitations in terms of cost, time requirements, scope, and authenticity of the data, as this was typically done using questionnaires or interviews with a selected representative number of stakeholders. The new data economy has made data collection very much an affordable activity. It is based on the highly economic electronic data mining of people's digital footprints and the automated analysis of behaviours of the entire constituency rather than sampling. Because data mining is not a separate act to normal user behaviour, the information retrieved is also highly authentic in terms of reflecting real and uninterrupted user behaviour. As such, data mining is more comparable to observational data gathering than to intrusive collection via direct methods. This will not make questionnaires and structured interviews obsolete, but it will greatly enhance our understanding and highlight possible inconsistencies between user behaviour and user perception (Savage and Burrows, 2007).

The proliferation of interactive learning environments, learning management systems (LMS), intelligent tutoring systems, e-portfolio systems, and personal learning environments (PLE) in all sectors of education produces vast amounts of tracking data. But, although these e-learning environments store user data automatically, exploitation of the data for learning and teaching is still very limited. These educational datasets offer unused opportunities for the evaluation of learning theories, learner feedback and support, early warning systems, learning technology, and the development of future learning applications. This leads to the importance of Learning Analytics (LA) being increasingly recognised by governments, educators, funding agencies, research institutes, and software providers.

The renewed interest in data science and information retrieval technologies such as educational data mining, machine learning, collaborative filtering, or latent semantic analysis in Technology-Enhanced Learning (TEL) reveals itself through an increasing number of scientific conferences, workshops and projects combined under the new research term Learning Analytics. …

Search by... Author
Show... All Results Primary Sources Peer-reviewed

Oops!

An unknown error has occurred. Please click the button below to reload the page. If the problem persists, please try again in a little while.