Digitalization of Education: Application and Best Practices Learning Analytics

Digitalization of Education: Application and Best Practices
Learning Analytics -means as “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs”.
Learning analytics evidence for learning has been presented according to 4 propositions:
- Learning analytics improve learning outcomes
- Learning analytics improve learning support and teaching
- Learning analytics are taken up and used widely, including deployment at scale
- Learning analytics are used in ethical way

1. 
   Learning Management Systems(LMS)
   

Learning Management Systems are virtual learning environments that are used in many different educational settings. It helps lecturers and teachers to create and integrate course materials, align content and assessments, and create customized tests for students. In addition, they can articulate learning goals and track studying progress. Studies show that 90% of students prefer e-learning to classroom learning, which makes them prime LMS users. The use of LMS technologies improves students’ engagement in the learning process. Also, it helps overcome shyness and low self-esteem, which are the stumbling blocks to active engagement and participation. It increases engagement and interactivity.
Massive Open Online Courses
Massive Open Online Courses (MOOCs) are available to thousands of students via some web-based platform, increasing access to high-quality material for distance and lifelong learners. MOOCs create large amounts of data can feed the various learning analytics tools. However, most MOOCs still adopt a top-down teaching approach, ignoring the potential for facilitating awareness, self-regulation and personalisation. Drachsler and Kalz (2016) described the potential of learning analytics in the context of MOOCs. In particular, they describe a MOOC Learning Analytics Innovation Cycle which can be applied at the micro, meso, and macro level.

On the micro-level, the data collection and analytics activities are focused on individual reflection and individual prediction. On the meso-level, data from several open courses are combined to support benchmarking and to create insights about behavior of groups of learners rather than the individual. Finally, on the macro-level, cross-institutional learning analytics enables to develop learning and teaching interventions that can be tested in a cluster of educational institutions to analyse the impact of such interventions, beyond contextual factors. In their review of MOOCs and learning analytics, the authors pinpoint that presently most of the conducted studies focused on the micro-level of the MOLAC cycle. A challenge with MOOCS is that, unlike traditional educational systems, measures like detailed demographics and prior knowledge are not collected in order to maintain a low barrier to entry.

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