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Anticipated Contract Type: The Government anticipates a Cost Plus Fixed Fee (CPFF), 5 Year IDIQ Delivery Order type contract. SECTION 1: SYNOPSIS The Government is issuing this Request for Information... Anticipated Contract Type: The Government anticipates a Cost Plus Fixed Fee (CPFF), 5 Year IDIQ Delivery Order type contract. SECTION 1: SYNOPSIS The Government is issuing this Request for Information (RFI) for future Solicitation No N00178-21-R-4500 for FY22-FY27 Ready Relevant Learning Ecosystems (RRLE) architecture; software; prototype development; systems engineering and design; cyber engineering and cybersecurity (CS); scientific, technical, analytical support and assistance; configuration management; integration and interoperability; test, evaluation, and assessment; life-cycle sustainment engineering and support services. Naval Surface Warfare Center Dahlgren Division Dam Neck Activity (NSWCDD DNA) is requesting assistance from industry to conduct market research in accordance with FAR Part 10. The Government anticipates making future decisions regarding the procurement process utilized for a potential solicitation based on the responses received from industry that both indicate an interest, and show a capability to provide the required production and RRLE services. The basis for the Ready Relevant Learning Ecosystems (RRLE) software development is the Chief of Naval Operations’ (CNO’s) Design 2.0 that focuses on strengthening our Navy team for the future. The Gold Line of Effort (LOE) will improve and modernize military personnel management and training systems through the “Sailor 2025” program. The three pillars of Sailor 2025 are about driving fundamental changes into our approach to Sailor readiness: 1) a modern personnel system, 2) Ready, Relevant Learning (RRL), and 3) career readiness. The Chief of Naval Personnel is concurrently shaping the overall strategy by transforming technology infrastructure and governance of human resources support (personnel, pay and training) along the Navy’s “Hire-to-Retire” lifecycle. My Navy Learning (MNL) is a viable and holistic solution that achieves all of the desired characteristics of this strategy and will be accomplished by stitching disparate manpower, personnel, training, and education systems together into a cohesive learning ecosystem for both users and decision-makers. MNL has integrated with the development of a Surface Training and Readiness Management System (STRMS) capability to demonstrate this capability in an afloat environment, which will serve as a use case for generalization across other warfare communities. STRMS will facilitate the generation and sustainment of individual, team, and unit proficiency, and track individual career milestones and proficiency requirements across all mission areas for all Naval Surface Ships. STRMS provides the Navy with a comprehensive deck plate training management tool that supports event scheduling and performance assessment to include tracking of individual watch stander and watch team qualifications, experience, currency, proficiency and competency. It is NSWCDD DNA’s intent to develop My Navy Learning (MNL)/Surface Training and Readiness Management System (STRMS) integrated and interoperable computer software applications and associated frameworks that create a Sailor-focused interface providing easy access to all Navy learning to empower Sailors’ use of education, training, and professional development resources. This includes delivering a Total Learning Architecture (TLA) that will leverage the Naval Education Training Command (NETC) Learning Stack, authoritative data environments, including MyNavy HR Authoritative Data Environment (ADE), and learning technologies like artificial intelligence and machine learning (AI/ML) in support of a single solution for Sailors to better manage their progression throughout their unique career learning continuums. Other products lines will include Ready Relevant Learning (RRL) focused distributed training systems and associated technology engineering capabilities supporting DoD distributed training requirements. Representative examples include US Navy Fleet Schoolhouse, Shore based, and Sea based distributed training systems and capabilities; Joint, Combatant Command, and Combat Support Agency distributed training systems and capabilities; and other military service distributed training systems and capabilities. SECTION 2: GENERAL INFORMATION Pursuant to FAR 52.215-3 this RFI is for planning purposes only and is issued solely for conducting market research in accordance with FAR Part 10. The Government will not reimburse respondents for any questions submitted or information provided as a result of this notice. This does NOT constitute a Request for Proposal. The Government is NOT seeking or accepting unsolicited proposals. This notice shall not be construed as a contract, a promise to contract, or a commitment of any kind by the Government. Specific responses will not disqualify or have an impact on participation and evaluation on future solicitations. Not responding to this RFI does not preclude participation in any future solicitation. If a solicitation is issued in the future, it will be announced via SAM.GOV and interested parties must comply with that announcement. It is the responsibility of interested parties to monitor SAM.GOV for additional information pertaining to this requirement. The Government’s estimated dollar range for this effort is <$100M. Questions regarding this announcement shall be submitted in writing via e-mail to the Contract Specialist and Contracting Officer's e-mail addresses. Verbal questions will not be accepted. Questions shall not contain classified information. SECTION 3: REQUIREMENTS OVERVIEW My Navy Learning (MNL): MNL software applications and products will contain the following six architecture baselined capabilities/elements: 1. Comprehensive Learner Framework (Competency Management): A critical component of MNL is the ability to support competency mapping-based Education & Training. Competency mapping links learning outcomes (knowledge & skills), proficiency, experience, and currency to specific jobs, qualifications and certifications. The system is able to display progression towards those goals by establishing metrics and collecting data evidence when each are attained. The system works by building and maintaining shareable competency frameworks from a common master task list that can include, but is not limited to naval standards, occupational standards, watch-station requirements, navy enlisted classifications, professional development and leadership standards. Competency management systems manage a diversity of constructs that represent human capabilities, including those referred to by Naval Education and Training (NAVEDTRA), such as Knowledge, Skills, Abilities, Tools, and References (KSATRs), performance objectives, learning objectives, tasks, and standards. These constructs capture the intended scope and use of the competency management system without limiting the construct definition to competencies. In addition, several research and pilot efforts across the Navy are looking at incorporating competence-based practices and competency-based education and training, so this requirement prepares for those efforts. Naval Education and Training (NAVEDTRA) 130 instructs developers to create learning objectives from the Course Training Task List (CTTL) and then link content and test materials to those objectives. Future training systems require digital representations of the data held within the CTTL and of the data used to create the Learning Objects (LO). Competency management systems provide a mechanism to capture the information in the CTTL, LOs, and the analysis data for use by training systems that track learners or that adapt based on analysis parameters. The Comprehensive Learner Framework (Competency Management) includes the following: • A shareable competency framework for all DON ratings and career paths. • A shareable competency framework for watch standing positions (OOD, TAO, EOOW, etc.) to include embedded non-technical competencies. • A shareable competency framework for Personnel Qualification Standards (PQS). • All Manpower, Personnel, Training, & Education (MPT&E) systems will be able to reference and use these frameworks as input into adaptive applications and Sailor feedback mechanisms. • Shareable competency frameworks to be scalable and reachable through Representational State Transfer (REST) web-services and not have web-service clients depend on any JavaScript libraries to get data. 2. Learner Record: A Learner Record is verifiable information about a learner’s achievements in education or training processes, formal or informal, classroom-based or workplace-based. Data in the Learner Record is used for Adaptive Learning inner-loop (and some middle-loop) adaptation and therefore is typically more fine-grained than the Learner Profile. A Learner Profile is information about a learner’s credentials, training history, aptitudes, local and global preferences, and local state; all of which can be shared at the enterprise level (leveraging federated identity to protect privacy) to provide a complete portrait of human performance. The Learner Profile is used for Adaptive Learning outer-loop (and some middle-loop) adaptation and therefore typically summarizes or rolls up Learner Record details to a level that can remain useful over a longer period of time. Learner Profiles typically include a broad range of data: such as demographics, student interests, learning preferences, descriptions of the learning environment, inter- and intra-personal skills, and validated competencies. Other information to consider are social behaviors, academic, and career performance. Additionally, Learner Records (such as transcripts and electronic Training Jacket (eTJ) entries) following Sailors through every transition; assignment-to-assignment and school-to-school. Together, both Learner Profiles and Records are sets of data that powers adaptive training capabilities under MNL, allows for timely feedback to Sailors, and enables individuals the opportunity for taking ownership of their learning. Self-managed learning portfolios can be part of the overall capabilities and help students develop the self-awareness required to set their own learning goals; express their own views of their strengths, weaknesses, and achievements; and take responsibility for them. The Learner Record include the following: • Data elements are aggregated to identify who the Sailor is, describe how they learn, and document their progression. • The data elements are generated from interactions with learning systems in various forms and methods including Experience Application Programming Interface (xAPI) statements. • A Sailor’s Learning Record will be used to populate records and transcripts, assist Sailors in self-managing career goals, and generate personalized recommendations. • Standards and protocols will be established to generate, store, and retrieve personal learning data to enable adaptive and personalized training applications that is interoperable with ADE. 3. User Interface and Experience (UI/UX) with Mobile Learning Components: The UI/UX is the Sailor-facing component of the architecture that provides an on-demand, interactive user experience and acts as the single point of access for all training and education needs within the hire-to-retire learning continuum and must also provide distributed training to a Sailor at the point of need (ashore & afloat) for specified technical, education and professional requirements identified in a learning continuum. Mobile learning is a component of the UI/UX. The User Interface and Experience (UI/UX) includes the following: • An intuitive interface that enables a Sailor to gain easy access to and interact with a TLA based distributed learning environment. • The capability enables a Sailor to train at the point of need (Ashore & Afloat) for specified technical, education, and professional requirements identified for each learning continuum. • The solution allows Sailors to interact in a meaningful and productive way with collaborative tools, intelligent tutors and recommender services. • Solution should not tax Sailor mental and cognitive workload, but seek to minimize it. • It also displays useful and targeted feedback to Sailors, instructors, and decision makers. • Integrated set of tools and resources providing a Single, Sign-on/Single Point of Entry concept that allow Sailors to control/manage their own career requirements more easily. 4. Content Reuse Component: A key component of building adaptive learning programs is the ability to re-purpose learning objects. This capability enables developers to segment that information into bite-sized pieces for reused over-and-over. Supports IFIT, SDIT, SOJT and PQS modalities. Re-Use of Content include the following: • Content which can be re-purposed based on Sailor career goals and learning preferences. • It enables a Sailor to get access to specific content for study and preparation purposes, to support areas of improvement, address skill decay, and provide reps and sets for improved performance. • The ability to get easy access to and re-use content provides significant return on investment opportunity for the education and training enterprise. • The re-use function supports adaptive learning applications to deliver personalized or tailorable training solutions for Sailors. 5. Adaptive Learning: Deliver adaptive (personalized & tailored) and other pervasive learning techniques to a Sailor for specified technical, education and professional requirements identified in a learning continuum. To achieve adaptive training requires data that comes from three common components: Learner profile data, competency & skills system data, and content metadata. These three components are simultaneously pulling and pushing data from Authoritative Data Environment and Learning Stack (specifically Learning Records Store, Learning Management System, and Student Information System). Adaptive Learning includes the following: • Personalized, interactive, and tailored learning techniques delivered to a Sailor at the point-of-need for specified technical, non-technical, education, and professional requirements identified in a learning continuum. • Adaptive capabilities leverage data from multiple sources to generate AI/ML-based output, which supports intelligent tutoring systems, recommender services, and virtual/interactive components of the user interface. 6. Training Effectiveness Measurement Support: This development line focuses in two areas. The first is a system-based measurement, assessment, tracking and reporting tool that integrates with numerous data sources (such as ADE and Learning Record Store (LRS)) to calculate and assess performance from live data streams, providing real-time measurement results and enabling MNL to provide meaningful feedback and effectively manage training events. The second includes an observer-based data collection and display toolset to help instructors, warfighters, analysts, and others capture human performance data in real time. Training Effectiveness Measurement Support includes the following: • Measures training performance of individuals throughout the TLA based distributed learning environment. • The data captured supports larger analytic functions in an enterprise-level training effectiveness program, such as analysis/understanding of training effectiveness based on human performance using the Kirkpatrick Model of Evaluation. • The data captured support larger analytic function and readiness planning tools for operational units. • The data will also provide feedback to the Sailor, supervisor, and/or manager, and inform adaptive learning applications while continually refining recommender services. Surface Training and Readiness Management System (STRMS): STRMS will use a Service-Oriented Architecture (SOA) architectural foundation. A service orientation provides a means for the enterprise to build distributed enterprise systems based on services. These services are discrete units of application logic that expose evocable interfaces to service consumers. It is necessary, if these services are going to be usable by internal and external consumers, that they be built using precise standards to address interoperability, extensibility, reusability and reliability, STRMS will support governing program architecture including the Net Centric Operational warfare reference Model (NCOW-RM), the Global Information Grid Enterprise Architecture (GIG-EA) and Net Centric Enterprise Services (NCES). Interoperability: MNL/STRMS software applications and products should be interoperable and able to integrate with/but not limited to the following systems. a. Learning Management Systems (LMS): The main objective of the LMS is typically to host, deliver, track and report online learning. It is the central component of the Learning Stack providing a virtual hub where learners can access training and associated resources. The Learning Stack Functional Requirements Document (LS FRD) approved on 3 Oct 2018 describes functions of the LMS. MNL intends to leverage the LMS in order to facilitate a blended and adaptive training environment, making training accessible for remote learners and providing a central location to access training services across an institution or organization. Therefore, the LMS is the most critical interoperable component to function/communicate with MNL capabilities. b. Learning Assessment System (LAS): It is understood that assessment functions will be integrated into the future NETC Learning Management System (LMS). However, it is important to ensure certain assessment functions are retained and operate with a total learning architecture. Typically, a LAS offers a suite of tools providing a number of capabilities, including import/export features, testing engines, survey development/delivery and assessments, to support the execution of an enterprise training effectiveness program. This system or its functions should be interoperable and interface with all other systems and tools in the NETC Learning Stack. The system and its associated features will be designed to produce outputs that will help the domain achieve an industry standard of KP levels 1 through 4 assessment capabilities. c. Student Information System (SIS): A management information system for training and education organizations to manage student data. The system provides capabilities for registering students in courses; documenting grading, transcripts, results of student tests and other assessment scores; building student schedules; tracking student attendance; and managing many other student-related data needs in a school. d. Collaborative Learning Environment (CLE): Collaborative learning is an e-learning approach where students are able to socially interact with other students as well as instructors via multiple collaboration tools including but not limited to discussion forums, workgroups, wikis, messaging, chat along with synchronous capabilities such as real-time video conferencing tool that lets you add files, share applications, and use a virtual whiteboard to interact. The CLE tools are used to assist cooperative learning in groups of personnel (otherwise not joined by a common location) a means to solve problems, answer questions, test theories, test ideas, and generate evidence of learning. Some instructors use collaborative learning environments as a way to facilitate workgroups teaching and forum discussions. e. Learning Object Repository (LOR): A learning object and/or content repository is a critical element for adaptive training under MNL. The repository can reside in the LMS or be its own stand-alone component in the Learning Stack. Inconsistent storage solutions are a systemic problem across Navy training. It leads to loss of man-hours in the time developers waste spending to replicate content and curriculum because they do not know it may already exist elsewhere. Additionally, Learning Objects (LOs) such as slide decks, graphic, text, etc… are at risk of loss and at risk of being altered without authorization if stored locally. A centralized learning object and/or content repository for all of Navy training can solve these problems and many more. The storage solution will capture, align, and create the ability to reuse or repurpose learning objects, data, and graphics. This will enable an effective use and reuse of materials which will deliver one standardized training solution and information sharing. f. Learning Record Store (LRS): A LRS is a data store system (essentially a database) serves as a repository for learning activities communicated from authoritative data sources using xAPI; an evolving technology which lets applications share data about human performance along with associated instructional content or performance context information. Traditionally, information collected in a LMS is derived from content launched from that system or is manually entered for tracking. Recent training initiatives identified the need to also capture information from a multitude of other training delivery approaches. This is necessary in order to improve accessibility, better tracking for all training which occurs throughout a Sailor’s career and align training to the skills developed. g. Talent Management (TM) and DON Enterprise Data Environment: Talent Management systems and authoritative data will transform training by working seamlessly with the Total Learning Architecture. Learner profiles, digital records, xAPI statements, credentialing, competencies, and content will include data from multiple sources related to recruitment, training and development, and performance management. Therefore, it is crucial that data to and from MNL components comply with Department of the Navy Data Strategy. Data standards are required to support federated data contracts and services that can integrate data from authoritative sources owned by various DoD organizations. SECTION 4: SUBMISSION OF RESPONSES Responses to this RFI should be submitted by 07 July 2021 ET via e-mail to the Contract Specialist Kimberly Carter, kim.carter@navy.mil with a copy to the Contracting Officer, Kevin Deal, kevin.l.deal@navy.mil. Respondents will NOT be notified of the results of this evaluation. All information will be kept confidential and will not be disseminated to the public. SECTION 4.1: CONTENT Companies responding to this RFI should provide a response not to exceed thirty (30) pages. The Navy has provided the requested format in the attached Market Research Questionnaire to assist Industry with formatting its responses and addressing all the Navy’s questions at this time. Please answer the questions found in the Market Research Questionnaire (Attachment 1) to the best of your ability. Submissions shall be single spaced, typed or printed in Times New Roman font with type no smaller than 12-point font. Paper should be 8 1/2 x 11 inches with a minimum of 1 inch margins around the page. Any text contained within tables, graphs, etc. should be 8-point Times New Roman font or larger. Included files should be created/prepared using Microsoft Office 2016 compatible applications. Graphics, photographs, and other data that may not be compatible with Microsoft Office 2016 should be submitted in Adobe Acrobat format. Submitted electronic files should be limited to the following extensions: .docx Microsoft Word .xlsx Microsoft Excel .pptx Microsoft PowerPoint .pdf Adobe Acrobat .mmpx Microsoft Project Submitted electronic files should not be compressed. The Navy intends to post a sanitized version - removing any indication of the company that submitted the question, as well as any company proprietary information - of all questions and answers resulting from the Industry Day provided under this notice.