Digital Learning Environment
The goal of this learning unit is to develop a generic IT infrastructure for digital learning environments relevant to your discipline or content area:
Version[edit | edit source]
- alpha version - Brainstorming possible sub-aspects that could be covered in the learning module - collected some key questions
Target Group[edit | edit source]
- Teachers who want to engage in the design of Digital Learning Environments
- Researchers who want to analyze learning processes with digital learning environments.
Learner Data[edit | edit source]
Explore the following aspects for learner data:
Learning Tasks[edit | edit source]
- What are possible options and your way-forward to collect data about the learning process?
- How would a generic IT implementation of learning environment look like?
- identify requirements and constraints for the application on learning environments in the 2020 COVID-19 epidemics. How can Moodle, Olat, ... and co. be used in conjunction with the 3 applications above. What others alternative do you have, if you want to apply the Open Community Approach and Open Educational Resources for your learning scenarios.
- (Screencasting) Explore the concept of Screencasting and explain how screencast can be used in learning environments. Image the learning task for learner is to create a screencast for a certain topic. What do the learner learn from creating there own screencast (include not only technical skills but also instructional design of the video and the embedding in a learning environment).
- (Real World Lab) Explore the concept of a Real World Lab and explain why tailored Open Educational Resources are required to support multiple different Real World Labs in different regions that need to adapt the learning resources to the location of learning environment.
- (Operating System of Educational Environments) Analyze setting up an OpenSource client server infrastructure for a learning enviroment. What are the basic requirements and components that you need for hosting e.g. a learning management system? How would you design a Linux distribution that has all the required IT service pre-installed on the Linux distribution! What are the benefits and drawbacks of such an approach?
- (Open Source) Identify the requirements and constraints for the use of Open Source software in digital learning environments. What are the drivers for adaptation of the software to the local IT infrastructure and educational requirements?
Digital contextualization of extracurricular learning places[edit | edit source]
Digital Contextualization can be viewed in extracurricular learning settings (e.g., VR, Aframe, AR.js, ...) Basically Mixare (https://www.mixare.org) with Learning elements in the camera image. Mixare, however, is no longer maintained and refactoring in HTML5 application in a corresponding Framework would be useful.
Extracurricular learning sites: https://en.wikiversity.org/wiki/Real_World_Lab
- AR.js: Create 3D Models Using AR.js and location based Augmented Reality
- MixARe: https://www.mixare.org (not maintained anymore - use AR.js/GeoAR instead)
- TrackingJS: https://trackingjs.com/ - see also Markerless Tracking
Libraries like TrackingJS https://trackingjs.com/ - would be for me too from the side of the informatical implementation of particle tracking as also interesting from the spatial geometry. But here is yes Once generic elements of learning environments are involved, such would be Subject-specific not so relevant. Rather, it would be about it to interact with gestures with digital learning environments non-digital and digital elements relate to a learning environment geoJSON is also suitable for spatial contexts as format.
Adaptive help systems and individualized task generation[edit | edit source]
Learner Profiles[edit | edit source]
Privacy[edit | edit source]
Hoel, T., Griffiths, D., & Chen, W. (2017, March). The influence of privacy protection and privacy frameworks on the design of learning analytics systems. In Proceedings of the seventh international learning analytics & knowledge conference (pp. 243-252). ACM. </ref> Transfer data from the learner's device to a server and analyze it and, if necessary, log on the basis of a larger data set Analysis of error patterns, selection of tasks and help. In the sense of the data protection of the learner data this is not absolutely necessary. Ideally, learner data remains on the user's device by default. Only the explicit sending of tasks to the school server or to servers in a research project, this client-server communication can be explicitly approved for a fixed period by the owner. Otherwise, help systems are only parameterized on the client side or on the end device (laptop, tablet, PC, smartphone)
help systems[edit | edit source]
Adaptive help systems e.g. Using weak-AI methods, learner data analysis is used to tailor the digital learning environment to users' needs and learning requirements. So, in a generic approach, consider the components of a digital learning environment that require adaptive feedback, help, and task selection. Aspects from the Known Areas of the Intelligent Tutorial Systems (ITS)  are implemented on the server in a kind of plugin concept (e.g., R-statistic software). The statistical software R serves in this context to use existing methods for the control of the digital learning environment. Through such an approach, large parts of the implementation (eg of clustering, associative networks, ...) by the use of existing statistical analysis of the save aggregated and anonymous learner data.
Dynamic Document Generation[edit | edit source]
Used tools will be the following application:
- KnitR as R/RStudio package
- Statistic Numeric Packages in R for use with Learner Analytics (Machine Learning)
- Output formats - depending on the learning environment:
- Shiny WebApps - result of Learner Analytics, but also to control help system and calculate a principle of minimal help (ie what help is minimal for the learner, which help actually "helps")
- AppLSAC: WebApps with client-side learner profile,
- Web-based presentations: DZSlides, Reveal, ....
- (Libre) Office Documents (Application of the [Open Community Approach|Open Community Approach]])
- e-Books: Tailored to learners' learning requirements
- wtf_wikipedia Tools for downloading collaborative learning units in offline learning environments with adaptive help systems support learners.
- Paper output of individualized tasks and help based on task processing with mobile devices, which can be parameterized and filtered by a task pool
- Geo-Tailored Questionnaires: 
Crowd Sourcing and Citizen Sciences[edit | edit source]
Data Collection Using the Open Data Kit allows collaborative data collection  in a learning group that provides insights into a student's research question in an aggregated state. Crowd Sourcing will be such a component of training and learning of data and methods that can be solved as collaboratively by data collection and evaluation problem :
- '(Problem-oriented access)' How many vehicles drive certain roads in our city? How loud it is at different times (see NoiseTube) George Drosatos, Pavlos S. Efraimidis, Ioannis N. Athanasiadis, Matthias Stevens and Ellie D'Hondt Privacy Preserving Computation of Participatory Noise Maps in the Cloud, Journal of Systems and Software, February 2014. Note, DOI: 10.1016 / j.jss.2014.01.035 </ref>)? If traffic calming is e.g. possible in the school environment? Where were frequent road accidents in the past and why is this place so dangerous for pedestrians / cyclists?
- '(Spatial data evaluation)' Are there patterns in the collected data? What is the cause of the found pattern? Can the type of data collection have led to the pattern, or is there actually an increased occurrence of events, noise levels, ... at a particular location?
Overall, digital learning environments are integrated into the spatial context, and personalized data analysis, along with the client-side learning profile, provides bidirectional data transport. In the Citizen Science concept, data is collected from the learners and at the same time they gain insights into the aggregated data of all users and thus also see the state of the current collaborative data collection. Furthermore, one can also identify missing areas that had not been edited by a user before.
Client-side / server-side learner profiles:[edit | edit source]
In the course of the data protection discussion, client-side storage of learner profiles should also be considered, whereby the client-side learner profile adapts to the learning prerequisites of the learners, but no user data is collected, aggregated and evaluated on the servers. In the case of research projects with a digital learning environment, of course, then the learner data must be encrypted and only then transferred to a RestfulAPI as backend. In essence, this point is about deciding on the client-side or server-side storage of learner data and an abstraction on generic software components for digital learning environments, which may be made available with virtualization as a backend for schools.
Tasks[edit | edit source]
- Search for existing open source software packages that you would like to use for your digital learning environment!
- First try to determine at subject-didactic level whether and which learner data should be collected about the learning process and analyze whether the existing software offers this possibility!
See also[edit | edit source]
- Collaborative Mapping
- Flipped Classroom
- Intelligent Tutorial Systems
- Localization Open Educational Resources
- Markerless Tracking
- 3D Modeling for Digital Learning Environments
- Open Educational Resources
- Real World Lab
- Teaching and Learning Online
- Design Science as a cyclic iterative design process for learning environments as Design Pattern
- Operating Systems
References[edit | edit source]
- UN-Guidelines for Use of SDG logo and the 17 SDG icons (2016/10) - http://www.un.org/sustainabledevelopment/wp-content/uploads/2016/10/UN-Guidelines-for-Use-of-SDG-logo-and-17-icons.October-2016.pdf
- Alexopoulou, T., Michel, M., Murakami, A., & Meurers, D. (2017). Task effects on linguistic complexity and accuracy: A large-scale learner corpus analysis employing natural language processing techniques. Language Learning, 67 (S1), 180-208.
- Pistilli, M.D. (2017). Learner Analytics and Student Success Interventions. New Directions for Higher Education, 2017 (179), 43-52.
- Lester, J., Taylor, R., Sawyer, R., Culbertson, K., & Roberts, C. (2018). MetaMentor: A System Designed to Study, Teach, Train, and Foster Self-regulated Learning for Students and Experts Using Their Multimodal Data Visualizations. In Intelligent Tutoring Systems (page 411). Springer.
- Herselman, M., Niehaus, E., Ruxwana, N., D'Souza-Niehaus, N., Heyne, N., Platz, M., & Wagner, R. (2010). Geo-referenced learning resources can be offered via the GPS sensors of mobile devices depending on the location of the learners.
- Brabham, D.C. (2010). Moving the crowd at Threadless: Motivations for participation in a crowdsourcing application. Information, Communication & Society, 13 (8), 1122-1145.
- Skaržauskaitė, M. (2012). The application of crowd sourcing in educational activities. Social Technologies, 2 (1), 67-76.