10.3.1 eGLE Overview
The GiSHEO eLearning Environment (eGLE)59 has been developed through the GISHEO60 (On Demand Grid Services for High Education and Training in Earth Observation) project aims at setting-up and developing a reliable resource for knowledge and associated instruments for higher education and training, using existing distributed information on Earth Observation and Grid technology. This project is a partnership between enviroGRIDS partner Technical University of Cluj-Napoca, West University of Timisoara, Romanian Space Agency and the Romanian National Institute for Aerospace Research. The project will enable higher exploitation of the potential of database at the European Space Agency (ESA) and will facilitate synergies orientated towards education and training in Earth Observation.
A relevant introduction to the tool eGLE developed in GISHEO is available on YouTube61 and on GiSHEO project site62. This tool could greatly facilitate how non-technical users could understand and develop workflow to be ported onto the Grid.
The GiSHEO eLearning Environment (eGLE)63 aims to offer to the teachers the ability to easily create lessons for different topics. It is based on the gProcess and ESIP platforms (see Chapter 8, Satellite Data Processing Solution) that represents the intermediate level between the eLearning Oriented Level and the
59 Gorgan D., Stefanut T., Bacu V., Grid based Training Environment for Earth Observation. Published by Springer-Verlag in LNCS 5529, pp. 98–109 (2009)
60 http://gisheo.info.uvt.ro/trac/
61 http://www.youtube.com/watch?v=O59jvlWPFPY
62 http://web.info.uvt.ro/~petcu/eGLE.wmv
63 Stefanut T., Bacu V., Gorgan D., eLearning Lesson Development and Execution Based on gProcess Workflow Description Platform and eGLE E-learning Platform. Proceedings of CSCS-17 Conference, Vol.2., ISSN 2066-4451, pp. 431-436, (2009)
Figure 25 Functional levels in the eGLE related architecture.
Patterns, Templates Lessons, Teaching materials
Image and Spatial Data Process
description
Grid Infrastructure ESIP and gProcess Platforms
eLearning Oriented Level GiSHEO eLearning Environment (eGLE)
Grid Infrastructure (Figure 25) by providing a set of services and tools supporting the flexible description, instantiation, scheduling and execution of the workflows.
eGLE provides mechanisms for knowledge presentation and assessment based on Grid processing capabilities, both for teachers and students. The platform implements user interaction tools as well as other components required for the development, execution and management of the teaching materials. Using these tools the teacher has the ability to:
• Search the available sources for existing learning objects and material that could be added to his lesson
• Create new teaching materials through the implementation and execution of new algorithms using gProcess
• Create visual containers for information display and format their appearance
• Manage the acquired learning components and combine them using visual elements in order to create the lesson
• Specify the desired interactivity level for each of the lesson components
• Publish the lesson and make it available to the students
Through eGLE interface and tools the teacher actually uses the Grid capabilities in a transparent manner.
When searching for existing teaching materials (already processed satellite images, algorithm workflow descriptions, etc.) the user is automatically connected to available distributed databases and remote repositories, without his explicit intervention, the results being displayed in a unified manner (as they are provided by the same data source). Similarly, the teacher may use the Grid based execution to process satellite images, to execute specific algorithms through workflow descriptions or to visualize previously created lessons.
The students have only the ability to execute the lessons according to the constraints established by the teacher. Depending on the interaction specified level, they could also be allowed to describe and experiment new workflows (i.e. algorithms, scenarios) or choose different input data (e.g. satellite images, discrete values) for existing ones.
The databases include conceptual and particular workflow based descriptions, teaching materials and lesson resources, satellite and spatial data. A detailed lesson-architecture description can be found in the paper Gorgan et al.64.
Scenario 1 - the teacher describes visually, using gProcess interface, the processing workflow (i.e.
acyclic graph) for a specific algorithm designed to compute some remote sensing data. The
64 Gorgan D., Stefanut T., Bacu V., Grid based Training Environment for Earth Observation. Published by Springer-Verlag in LNCS 5529, pp. 98–109 (2009)
workflow considers as input three channels of the same satellite image and describes in the graph nodes a few specific operators or Grid services already available (e.g. provided by enviroGRIDS system). The teacher builds up the content of the lesson by combining a few available content patterns and specifies a text, list and two images controls for viewing. He describes by text some theoretical notions and then chooses from the available data sources five potential input files. At runtime the student has the ability to select and visualize one by one the images from the list, and to execute the workflow for each one in order to check the resulted image.
Applied to our example lesson, the teacher can describe by a workflow the computation of vegetation indices for a given geographical area. He chooses a specific Landsat satellite image that covers the geographical region. At runtime the student may choose from a list the vegetation indices that make interest for the study. Using this input, the student may now execute the workflow over the Grid and check the results.
Scenario 2. The teacher adds to the previous scenario a few features. For instance, the student may edit the workflow and experiment other processing algorithms for different available input data-sets. For instance, in our lesson, the student may be granted the ability to change the formula for computation of the vegetation index or the pseudo-coloring settings (the color that will be assigned for every pixel’s vegetation index value). The student can choose the values from a list previously specified by the teacher or he can be allowed to build up the algorithm by searching the entire database of available operators and data sources on the Grid.
10.3.3 Lesson Development Phases
As it was already mentioned, eGLE provides tools and functionalities that support the development of the lessons based on Grid computing technology (Figure 26). The target community of users includes mainly
Figure 26 Lesson authoring in eGLE.
non-technical users such as teachers specialized in different domains like Earth Observation, Environmental Studies, and Hydrology.
The lesson development process consists of the following phases:
1. Acquire the lesson content
2. Organize and display the lesson content 3. Data binding and user interaction description 4. Lesson execution
By the first phase the teacher creates and gathers the teaching materials to be included into the lessons. The teaching objects could be:
• already available teaching objects - that have just to be identified and localized
• new teaching objects - created by the teacher himself
Nevertheless, the teacher could use any available tools through which he can author the teaching objects such as: raster images, pictures, movies, texts, tables, sounds, any multimedia objects, etc. One important feature provided by eGLE is the processing over the Grid of some huge data (i.e. Earth Science data) and to get the results (i.e. processed images, classified images, scalar data, movies, streaming data, graphical annotated 3D objects, etc) as teaching objects, in order to be used later in the lessons, as teaching resources.
After the data acquisition phase, the teacher organizes the information and specifies all the visual formatting (i.e. layout, fonts, colors, image size, and video size), and interaction techniques used in the lesson (i.e. slideshow control, data inputs for a workflow, etc.). The following visual components provided by the eGLE platform, display the lesson contents:
Tools - the lesson requires capabilities on displaying text and images as well as displaying and editing the process description graphs. Each tool allows the specification of the data source (i.e. video, text, picture, sound, computation process, and streaming) and the graphics rendering parameters.
Patterns – define a group the tools mentioned by the teacher by a particular visual style.
Figure 27 Grid processing based lesson execution.
Temperature computing
Templates – define structural and visual pattern of a lesson. At the template level, the teacher may set global attributes such as font family, size and color (e.g. Arial, 12px, black) that will be applied to all the lessons’ visual elements in order to achieve a unitary visual presentation.
The third phase consists of binding data and user interaction techniques. After display definition and data acquisition, the teacher must instantiate the tools included into the lesson by specifying the actual data displayed (i.e. specific image, video file, and sound).
Lesson execution includes as well the execution of complex workflows over the Grid (Figure 27). The workflow involves operators, Web services, and spatial data. In the lesson the teacher and the student may visualize the input and output data, the process description, and the process monitoring (Figure 28).
Figure 28 Samples of the Grid based lesson execution.