Clean nice suit, glasses and linking to other researchers to anchor it into existing research and adding his ideas.
Naif first gave an overview of two different types of analytics
Academic analytics and Learning analytics
two main terms for the efforts made to identify practical ways of making use of higher education data are academic analytics (was first introduced by Goldstein and Katz, 2005) and learning analytics (student, teacher and content, http://www.solaresearch.org/about/)
The written presentation (full paper) of Naif can be found here
Short abstract of the paper:
Learning analytics (LA) is one of the promising techniques that has been developed in recent times to effectively utilise the astonishing volume of student data available in higher education. Despite many difficulties in its widespread implementation, it has proved to be a very useful way to support failing learners. An important feature of the literature review of LA is that LA has not provided a significant benefit in terms of learner mobility to date since not much research has been carried out to determine the importance of LA in facilitating or enhancing the learning experience of mobile learners. Therefore, this paper describes the potential advantages of using LA techniques to enhance learning in mobile and ubiquitous learning environments from a theoretical perspective. Furthermore, we describe our simplified Mobile and Ubiquitous Learning Analytics Model (MULAM) for analysing mobile learners’ data which is based on Campbell and Oblinger’s five-step model of learning analytics. Finally, we answer the question why now might be the most suitable time to consider analysing mobile learners’ data.
Mobile learning analytics
interaction between learners
interaction between learners and learning materials
Both interactions are immediate interactions.
Mobile Leanring Analytics (MLA): focuses mainly on the collection, analysis and reporting of the data of mobile learners, which can be collected from the explicit mobile interactions between learners, mobile devices and availbable learnign materials, it is oalso supported by the preregistered data.
Explicit learner-to-learner interactions
an analysis of the learner
Ubiquitous Learning Analytics (ULA)
Two types of interactions: direct and indirect
Example Zoodles for explicit learner-to-learning materials
Dreamweaver CS6 makes it easy to build mobile apps for learning analytics integration.
Added value of learning analytics for the learner
The learner can analyse their performance with the others
(adding my ideas: enabling meta learning, understanding the benefits of interacting, keeping track of personal barriers (content wise)...)
A big issue with learner analytics is privacy of the data. How to enable this: opt out must be offered at any time for example.
Aljohani, Naif R., Davis, Hugh C. and Loke, Seng W. (2012) A comparison between mobile and ubiquitous learning from the perspective of human-computer interaction. International Journal of Mobile Learning and Organisation
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