During the transition from computer-based training (CBT) on compact discs (CDs) to e-learning on the web, one of the major challenges with content delivery was interoperability of the content. In addition, the creation and technical development of the content were based on proprietary or unique technical approaches that often resulted in expensive solutions with a short shelf-life. In order to track a learner’s progress the content often had to be uniquely programmed to work in each specific delivery environment.
Training and Learning Management Systems (LMSs) had differing IT infrastructures and server-side support as well. If an enterprise organization wanted to upgrade a system or change vendors, it often meant abandoning very expensive content and starting over from scratch. Conversely, large content vendors often specified their own delivery environment and forced each system to implement different delivery modules for each content vendor. In addition, another key requirement for learning content was the ability to reuse instructional components in multiple applications and environments regardless of the tools used to create them. This requires, among other things, that content be separated from context specific run-time constraints and proprietary systems so that it can be incorporated into and reused in different applications.
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The SCORM® (Sharable Content Object Reference Model) was created to address these interoperability, reusability, and durability challenges. As a reference model it was intentionally designed to leverage standard web technologies as well as existing learning technology specifications that already existed. SCORM® is comprised of a collection of interrelated technical specifications and guidelines designed to meet the DoD’s high-level requirements for creating interoperable, plug-n-play, browser-based e-learning content. It consists of three different technical specification “books” that collectively address challenges associated with interoperability, portability, reusability, and the instructional sequencing of self-paced e-learning content.
Interoperability
The SCORM® Run-time Environment (RTE) book defines a common data model and application program interface (API) for e-learning content. This combination of data model and API allow for standardized communications between client-side content and a system component (called “the run-time environment”), which is commonly provided by a Learning Management System (LMS).
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Portability
The SCORM® Content Aggregation Model (CAM) book defines how to package content for exchange from system to system, in a transferable ZIP file called the Package Interchange Format (PIF). Packaging enables a standardized portability mechanism between various learning environment applications.
Reusability
The SCORM® Content Aggregation Model (CAM) book describes the components used in a learning experience and how to describe those components to enable search and discovery. Therefore, the CAM book promotes reusability of learning content across Learning Management Systems (LMSs) and repositories. The CAM book describes responsibilities and requirements for building content and content organizations (e.g., course, lessons, modules, etc.). It contains instructions for applying metadata to the all the content organization components in the content package. On the server side, the CAM details the format an LMS must be able to “import” for the purpose of providing content to users.
Sequencing
The SCORM® Sequencing and Navigation (SN) book, in combination with the CAM book, describe how SCORM®-conformant content is delivered to learners through a set of learner or system-initiated navigation events. The branching and flow of that content may be described by a predefined set of activities. SCORM® 2004’s sequencing rules allow instructional designers and content developers to specify the order in which sharable content objects (SCOs), the smallest piece of content that tracks progress, are delivered to learners and what navigation controls are present in a SCORM® 2004-conformant LMS.
Version History & Versions
The SCORM® has several version releases dating back to the year 2000 starting with SCORM® 1.0. SCORM® 1.2, released in 2001 is the first version of SCORM® that was widely adopted.Beginning in 2004, SCORM® began to release different editions of SCORM® 2004 based on iterative fixes and improvements.The most recent release (2009) is SCORM® 2004 4th Edition. The ADL Initiative provides and maintains resources for SCORM® 1.2, SCORM® 2004 3rd Edition, and SCORM® 2004 4th Edition.Developers that are implementing other versions are encouraged to modify their work to be in accordance with one of these three specifications.While the ADL Initiative recommends xAPI and cmi5 solutions for new acquisitions and implementations, it is understood that SCORM solutions are still in wide use and retain a great deal of interoperability.While SCORM® 2004 4th Edition has the most robust feature set, the ADL Initiative recommends use of SCORM® 2004 3rd Edition due to a) more widespread use in DoD and b) more recently updated Conformance Test Suite software.The differences between those two versions are slight (99% compatible), and both benefit from more than ten years of community feedback integrated into their design.
Publications
Choosing a Learning Management System (LMS)
2016
Choosing Authoring Tools
2016
Choosing a Learning Record Store (LRS)
2016
The Next Generation of SCORM: Innovation for the Global Force
2012, IITSEC
Sharable Courseware Object Reference Model (SCORM), Version 1.0
2000
One of the primary access methods for data in Azure Data Lake Storage Gen2 is via the Hadoop FileSystem. Data Lake Storage Gen2 allows users of Azure Blob Storage access to a new driver, the Azure Blob File System driver or ABFS. ABFS is part of Apache Hadoop and is included in many of the commercial distributions of Hadoop. Using this driver, many applications and frameworks can access data in Azure Blob Storage without any code explicitly referencing Data Lake Storage Gen2.
Prior capability: The Windows Azure Storage Blob driver
The Windows Azure Storage Blob driver or WASB driver provided the original support for Azure Blob Storage. This driver performed the complex task of mapping file system semantics (as required by the Hadoop FileSystem interface) to that of the object store style interface exposed by Azure Blob Storage. This driver continues to support this model, providing high performance access to data stored in blobs, but contains a significant amount of code performing this mapping, making it difficult to maintain. Additionally, some operations such as FileSystem.rename() and FileSystem.delete() when applied to directories require the driver to perform a vast number of operations (due to object stores lack of support for directories) which often leads to degraded performance. The ABFS driver was designed to overcome the inherent deficiencies of WASB.
The Azure Blob File System driver
The Azure Data Lake Storage REST interface is designed to support file system semantics over Azure Blob Storage. Given that the Hadoop FileSystem is also designed to support the same semantics there is no requirement for a complex mapping in the driver. Thus, the Azure Blob File System driver (or ABFS) is a mere client shim for the REST API.
However, there are some functions that the driver must still perform:
URI scheme to reference data
Consistent with other FileSystem implementations within Hadoop, the ABFS driver defines its own URI scheme so that resources (directories and files) may be distinctly addressed. The URI scheme is documented in Use the Azure Data Lake Storage Gen2 URI. The structure of the URI is: abfs[s]://file_system@account_name.dfs.core.windows.net/<path>/<path>/<file_name>
Using the above URI format, standard Hadoop tools and frameworks can be used to reference these resources:
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Internally, the ABFS driver translates the resource(s) specified in the URI to files and directories and makes calls to the Azure Data Lake Storage REST API with those references.
Authentication
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The ABFS driver supports two forms of authentication so that the Hadoop application may securely access resources contained within a Data Lake Storage Gen2 capable account. Full details of the available authentication schemes are provided in the Azure Storage security guide. They are:
Shared Key: This permits users access to ALL resources in the account. The key is encrypted and stored in Hadoop configuration.
Azure Active Directory OAuth Bearer Token: Azure AD bearer tokens are acquired and refreshed by the driver using either the identity of the end user or a configured Service Principal. Using this authentication model, all access is authorized on a per-call basis using the identity associated with the supplied token and evaluated against the assigned POSIX Access Control List (ACL).
Note
Azure Data Lake Storage Gen2 supports only Azure AD v1.0 endpoints.
Configuration
All configuration for the ABFS driver is stored in the core-site.xml configuration file. On Hadoop distributions featuring Ambari, the configuration may also be managed using the web portal or Ambari REST API.
Details of all supported configuration entries are specified in the Official Hadoop documentation.
Hadoop documentation
The ABFS driver is fully documented in the Official Hadoop documentation
Next steps