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The CS Education community has developed many educational tools in recent years, such as interactive exercises. Often the developer makes them freely available for use, hosted on their own server, and usually they are directly accessible within the instructor's LMS through the LTI protocol. As convenient as this can be, instructors using these third-party tools for their courses can experience issues related to data access and privacy concerns. The tools typically collect clickstream data on student use. But they might not make it easy for the instructor to access these data, and the institution might be concerned about privacy violations. While the developers might allow and even support local installation of the tool, this can be a difficult process unless the tool carefully designed for third-party installation. And integration of small tools within larger frameworks (like a type of interactive exercise within an eTextbook framework) is also difficult without proper design. This paper describes an ongoing containerization effort for the OpenDSA eTextbook project. Our goal is both to serve our needs by creating an easier-to-manage decomposition of the many tools and sub-servers required by this complex system, and also to provide an easily installable production environment that instructors can run locally. This new system provides better access to developer-level data analysis tools and potentially removes many FERPA-related privacy concerns. We also describe our efforts to integrate Caliper Analytics into OpenDSA to expand the data collection and analysis services. We hope that our containerization architecture can help provide a roadmap for similar projects to follow 

We present the initial version of a “live catalog” of LTI enabled smart learning objects that instructors and educators are able to preview and test before deciding whether to integrate these tools in their own courses. The catalog is available on the public Instructure Canvas site and currently showcases LTI tools from multiple educational institutions. 

The first major goal of this project is to build a state-of-the-art information storage, retrieval, and analysis system that utilizes the latest technology and industry methods. This system is leveraged to accomplish another major goal, supporting modern search and browse capabilities for a large collection of tweets from the Twitter social media platform, web pages, and electronic theses and dissertations (ETDs). The backbone of the information system is a Docker container cluster running with Rancher and Kubernetes. Information retrieval and visualization is accomplished with containers in a pipelined fashion, whether in the cluster or on virtual machines, for Elasticsearch and Kibana, respectively. In addition to traditional searching and browsing, the system supports full-text and metadata searching. Search results include facets as a modern means of browsing among related documents. The system supports text analysis and machine learning to reveal new properties of collection data. These new properties assist in the generation of available facets. Recommendations are also presented with search results based on associations among documents and with logged user activity. The information system is co-designed by five teams of Virginia Tech graduate students, all members of the same computer science class, CS 5604. Although the project is an academic exercise, it is the practice of the teams to work and interact as though they are groups within a company developing a product. The teams on this project include three collection management groups -- Electronic Theses and Dissertations (ETD), Tweets (TWT), and Web-Pages (WP) -- as well as the Front-end (FE) group and the Integration (INT) group to help provide the overarching structure for the application. This submission focuses on the work of the Integration (INT) team, which creates and administers Docker containers for each team in addition to administering the cluster infrastructure. Each container is a customized application environment that is specific to the needs of the corresponding team. Each team will have several of these containers set up in a pipeline formation to allow scaling and extension of the current system. The INT team also contributes to a cross-team effort for exploring the use of Elasticsearch and its internally associated database. The INT team administers the integration of the Ceph data storage system into the CS Department Cloud and provides support for interactions between containers and the Ceph filesystem. During formative stages of development, the INT team also has a role in guiding team evaluations of prospective container components and workflows. The INT team is responsible for the overall project architecture and facilitating the tools and tutorials that assist the other teams in deploying containers in a development environment according to mutual specifications agreed upon with each team. The INT team maintains the status of the Kubernetes cluster, deploying new containers and pods as needed by the collection management teams as they expand their workflows. This team is responsible for utilizing a continuous integration process to update existing containers. During the development stage the INT team collaborates specifically with the collection management teams to create the pipeline for the ingestion and processing of new collection documents, crossing services between those teams as needed. The INT team develops a reasoner engine to construct workflows with information goal as input, which are then programmatically authored, scheduled, and monitored using Apache Airflow. The INT team is responsible for the flow, management, and logging of system performance data and making any adjustments necessary based on the analysis of testing results. The INT team has established a Gitlab repository for archival code related to the entire project and has provided the other groups with the documentation to deposit their code in the repository. This repository will be expanded using Gitlab CI in order to provide continuous integration and testing once it is available. Finally, the INT team will provide a production distribution that includes all embedded Docker containers and sub-embedded Git source code repositories. The INT team will archive this distribution on the Virginia Tech Docker Container Registry and deploy it on the Virginia Tech CS Cloud. The INT-2020 team owes a sincere debt of gratitude to the work of the INT-2019 team. This is a very large undertaking and the wrangling of all of the products and processes would not have been possible without their guidance in both direct and written form. We have relied heavily on the foundation they and their predecessors have provided for us. We continue their work with systematic improvements, but also want to acknowledge their efforts Ibid. Without them, our progress to date would not have been possible.