Abstract
A learner profile is key to personalize learning content. Nowadays learners use different applications and tools to learn (Formal and informal types). Indeed, the diversity of profiles, their content, their structure, their operation, and the actors concerned, limits possible interoperability. Hence, the need for a rich and an interoperable learner profile that describes all previous learning achievements or experiences. In this work, after a brief analysis of available standards in this area, an approach is proposed to build an interoperable learner model based on xAPI statements that combine the formal and informal experiences to enhance learning analytic and personalization. Then, we present a tool to transform collected data into our XML model proposed based on the IMS-LIP standard, and in the end, we explore his utility.
Highlights
The interoperability of learners' data is an important issue in the field of IT environments for human learning
We propose an approach that aims at first, to provide a learner model containing a set of educational information on the experiences of a learner evolving in an online learning environment and other helpful information of the tools from the LMS
This data is collected by the xAPI standard which has been massively implemented by publishers of e-learning solutions in order to ensure interoperability between learning systems [3,4]
Summary
Abstract—A learner profile is key to personalize learning content. Nowadays learners use different applications and tools to learn (Formal and informal types). The diversity of profiles, their content, their structure, their operation, and the actors concerned, limits possible interoperability. The need for a rich and an interoperable learner profile that describes all previous learning achievements or experiences. After a brief analysis of available standards in this area, an approach is proposed to build an interoperable learner model based on xAPI statements that combine the formal and informal experiences to enhance learning analytic and personalization. We present a tool to transform collected data into our XML model proposed based on the IMS-LIP standard, and in the end, we explore his utility
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