Digital Learning Environment/Learning Analytics

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Introduction Learner Analytics and Learner Support[edit | edit source]

Digitization of learning environment can not only refer to the digitization of learning content, but also to the digital "sensors" in a learning environment for collecting data from a learning process. The presence of sensors in the learning environment allows the use of methods from the area of "Learning Analytics"[1] section. The generic approaches can be introduced at a specific geolocation and adapted to other geolocations. On the one hand, the content-related developments and the educational design of different levels of support of learners can be developed and on the one other hand the OER principles allow the collaboratively adaptation of the content to other languages, different skills of learners and other locations, for which the learning resources refer to.

Learning Tasks[edit | edit source]

  • (Standards) Explore existing standards, that can be used for learner analytics?
  • (Privacy Friendlyness) Explain the benefits of generic concepts that can be used in different learning environments and in which each learner could use their own learner profile in a privacy-friendly way. Profile is used to tailor the learning environment according to different requirements and constraints.
  • (BarCode) Explain how Barcodes can be used for Learner Analytics and individualized support in a digital learning environments. E.g. starting an available learning environment with used IT infrastructure at the educational unit. How can barcodes be
  • (Mobile Devices) Explore the differences between LineageOS and a commercial Android operating system. What would be necessary to install LineageOS on mobile devices so that the learner analytics data is accessible in the educational unit (classroom, school, university, ...) only. Root access to a mobile device is general the ability to control all the installed and available software on a device. Root access provides control over the device which is especially required to have full control over the network active components of the Operating System and the learning environment. Identify the obstables to using LineageOS in a privacy friendly digital learning environment that applied learner analytics.
  • (Privacy) Data generated in digital learning environment are relevant to support learners/students. In the privacy by design approach this data is only available student in the learning environment to support adaptive task selection or select a specific help or support tool for guidance of the learner e.g. by an adaptive learning environment. The data is not shared by default with a teacher. Just put yourself in a classroom with specific more complicated learning task and the teacher in standing physically behind you during your whole problem solving activity. Open Source allows beside local adaptation and optimization for the educational unit also transparency of the algorithm that a teacher does not stand "digitally behind you" during the use of the digital learning environment. Learning results might be actively shared e.g. between teacher and student directly if online IT infrastructure is used or shared on a digital white board for discussion of the results with a group of other students. Privacy by design principles will be applied if the generated digital data will not leave the IT infrastructure of the school or even classroom. Identify a basic infrastructure for an educational unit based on Open Source infrastructure. What are existing Open Source application that are used and what is missing? Determine missing infrastructure and what are the required adaptation of the Open Source code, so that the IT infrastructure fulfills the requirements and constraints of your educational unit or institute?
  • (KnitR and Learner Reports) Analyze the concept of an automated analytic report system with KnitR, that provides by data analysis e.g. in the Open Source Statistics Software R a dynamic report or even a tailored selections of learning tasks tailored for the student's profile or requirements and constraints.

See also[edit | edit source]

References[edit | edit source]

  1. Siemens, G., & d Baker, R. S. (2012, April). Learning analytics and educational data mining: towards communication and collaboration. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 252-254). ACM.