Applied Atomic Design: A Scalable Curriculum Methodology to Set Goals, Prepare Challenges, and Celebrate Wins to Motivate Students

Introduction

This presentation will model the University of Texas System’s Institute for Transformational Learning distributed activity design, a scalable online curriculum methodology that helps students set goals, prepares them for real-world application challenges, and celebrates their successes, motivating them for a drive toward graduation and industry success. ITL’s methodology is a replicable process used to successfully deliver a variety of content, including biomedical sciences and cybersecurity. We focus on minute learning outcomes over the broad brush of courses, proof of competency over traditional assessments, and persistent iterations toward mastery over punitive grading. The resulting instructional designs are modular, scalable, and collaborative between ITL, faculty, student, and content.

Participants in this sessions will

• learn the process of atomic design,

• engage in interactive question and answer sessions, and

• be provided with applied examples.

 

The Institute for Transformational Learning (ITL) was established by the University of Texas’ Board of Regents in 2012 as a financially sustainable catalyst for innovation. The ITL’s mandate is to support UT System campuses as they design, test, and scale innovative educational models that will make a UT quality education more accessible, affordable, and successful in knowledge and workforce domains that are critical to Texas and the nation.

 

In this process, ITL collaborated with two UT System programs: 1) UT Rio Grande Valley Biomedical Sciences program and 2) UT San Antonio’s cybersecurity program. UTRGV BMED program utilizes a transdisciplinary approach that allows for design innovation across all 120 credit requirements, including synergistic learning pathways across each term, with virtual rounds as a red thread across disciplinary domains. UTSA’s top-tier cybersecurity program collaborated with the ITL to meet the rapid growth in cybersecurity employment opportunities, with the goal of increasing capacity, expanding its current Bachelor of Business Administration in Information Systems and Cyber Security offering, and adding a new degree program, the Bachelor of Arts in Cyber Security.

 

Design Process

The design, development, and production team collaborates with partners across the UT System to create industry-aligned, engaging learning experiences through which students demonstrate competence in identifiable and measurable outcomes. At distinct times in the design process, our team collaborates with university leadership, program leadership, faculty, staff, and vendors.

 

At the forefront of our process is the creation of a knowledge graph. “A knowledge graph defines, at an atomic level, the skills and know-how required for success in any professional field, as well as hypotheses regarding patterns of knowledge flow across a lifetime of study and practice. Each node in the graph represents a self-contained, measurable learning outcome and is tagged with high fidelity content and assessment activities that encourage deep and sustained practice and gather data on progress toward mastery of complex skillsets and competencies. The knowledge graph can serve as the underlying blueprint for a single instructional activity, a module, a microcertificate, or a degree. Over time, knowledge graphing can help illuminate the unique pathways individual learners actually require to develop mastery of complex professional competencies.” (Stein, M., Komarny, P., Rice, C., & Schwarm, R., 2016). The creation of a knowledge graph includes:

1. Identifying Learning Outcomes We engage faculty, industry subject-matter experts, accreditors, and practitioners in in-depth research into industry and academic standards, qualifying exams, professional certifications, job requirements, and evaluation metrics. The resulting learning outcome statements form the initial node structure hypothesis on the knowledge graph. Outcomes will naturally continue to be added or revised to adjust for changes in practice, technology, and policy.

2. Determining Domains and Levels Learning outcomes are linked to domains and levels of knowledge associated with established competency or accreditation frameworks relevant to the field. When multiple standards or frameworks exist in any given professional arena, we work with our team of experts to adapt, synthesize, and refine the list of goals and expectations.

3. Defining Relationships Nodes connect when one learning outcome requires or benefits from knowledge gained from another. Often a clear sequence between nodes emerges. This initial hypothesis about how knowledge is connected can evolve as data reveals how learners actually engage with content and learning experiences. The hypothesis also allows us to intentionally link prerequisite learning experiences at the level of a node to provide just-in-time refresher content and combat learning decay.

 

Starting with separate, achievable, action-based learning outcomes, subject-matter experts and teaching & learning experts collaborate to refine and revise outcomes to meet academic and industry needs with a goal of collecting a massive web of linked knowledge and skills. For example, in our work with UTSA’s cybersecurity program, the primary competency framework is an amalgamation of domains and competencies provided by the:

• National Initiative for Cybersecurity Education (NICE)

• National Security Administration Centers for Academic Excellence in Cyber Operations (NSA CAE CO)

• UTSA BBA Common Body of Knowledge (BBA CBK)

 

This framework is augmented with competencies from the Accreditation Board for Engineering and Technology (ABET) and industry certifications as appropriate. The knowledge graph sets the stage to begin working collaboratively with faculty on the design and development of their course. Faculty designers are assigned to work with ITL lead instructional designers to create a detailed design of the assessments and learning experiences that enable students to demonstrate the outcomes associated with each experience. For example, UTSA faculty were consulted to confirm and refine the learning outcomes for their courses. They then are repeatedly engaged and consulted during the course design process through a series of interactive workshops where instructional design models are used to frame the learning outcomes and modules.

 

Collaboration

Atomic design is key to the ITL mission. Each student activity is aligned to learning outcomes and teachable content. For example, precise assessment questions are aligned to exact outcomes that are linked to specific content. The resulting data informs the faculty if a student is, in fact, accomplishing the learning outcomes. During the design phase, faculty are asked to sign off on the final design components.

 

In the design meetings, the lead instructional designer and faculty collaborate on pinpointing and refining their course’s learning outcomes. Once the programmatic competencies and outcomes are aligned, the lead instructional designer and the design faculty collaborate on the following design components:

• Assessment Strategy: A decision is made on the overall strategy for assessing students.

• Activity Pattern: A rhythm is set for how student will experience the content, modules, and course as a whole. The rhythm provides students with consistency and allows students to anticipate the activities they have to complete, ultimately assisting them with time management.

• Learning Arc: The thematically structured learning experience is driven by evenly spaced content chunks.

• Grading Scale: The grading scale outlines specific assessment details and achievable points.

• Rubrics: Rubrics are used to define the criteria, levels, and points for how students are assessed.

• Syllabus: Ultimately student-facing, the syllabus documents the expectations of the course, including an outline of learning outcomes, overall description, and an assessment calendar.  

 

Modularity

Because courses are divided into distinct modules, they can be disaggregated for non-degree-seeking students and combined for a full-course experience. This provides innovative use-case scenarios for traditional students, professional development, industry training, and more.

 

Scalability

Since these courses are built for scalability, open educational resources (OER) are leveraged for assessment and content. All content utilized is aligned to the learning outcomes regardless of the source. This feeds modularization, allowing “chunks” of content to be utilized as standalone content or as refreshers for future courses.

 

To inform scalability, a workload analysis is conducted on student activity workload and faculty grading workload, creating a feedback loop which refines the instructional design of the student activity pattern, ultimately improving both the student and faculty experience.

 

Benefits

Based on this atomic design and activity pattern, students are guided to achieve course goals. For example, iterative challenges provide students with an awareness of their capabilities and needs for improvement on the road toward mastery.

 

Recognizing the need to reward and celebrate achievements, cybersecurity students earn nanocertificates and microcertificates as they master particular learning outcomes. These certificates are included in their visible transcript and properly prepare them for industry certifications.

 

As a competency-based program, application-oriented assessments prove students’ ability to, in UTSA’s case, succeed in real-life cybersecurity threats and situations. Similarly, UTRGV biomedical science students were challenged with team-based learning activities that revolved around a patient scenario. They were then asked to work in groups to understand, diagnose, and propose treatment for the patient. During this activity, students were presented with a chart in which they had to document facts, propose hypotheses, outline learning issues, and provide additional information needed for a diagnosis. This provided students with a team-based application activity where they collaborated and brainstormed with their peers.

 

Embodying the understanding that online learning should be personalized to the user’s experience, cybersecurity students possessing industry skills can immediately take a module’s challenge, shortcutting the traditional pathways that more novice students may need.

 

Future Research Aspirations

Our next steps include an analysis of the collected millions of data points which we predict will inform and improve current activity patterns. We also predict the data will inform personalized and recommended course experiences based on student profiles.