2.5.1 Usability Testing

Usability testing assesses how easily and intuitively learners can use a product. It involves observing learners as they interact with prototypes or early versions of a solution, noting areas where they encounter difficulties. Usability testing ensures that learning interfaces are accessible, easy to navigate, and designed to support learning goals without causing frustration or cognitive overload.

From the Learning Engineering Toolkit

In the Toolkit, usability testing is one specific form of end-user testing. It means trying a product out with representative end users to study its human-system interface, so as to pinpoint design flaws, find chances to improve or add features, and learn how the target population behaves and what it prefers. [LET-12]

Usually a facilitator has participants work through a set of tasks with the system while the facilitator or other team members watch and record what participants do, flagging any departures from the expected actions. [LET-12] The facilitator may prompt a think-aloud approach, asking participants to voice whatever crosses their mind as they use the system, for instance remarking that they cannot locate a menu option they are after. [LET-12] Facilitators need to take care in how they pose or answer questions so they do not unintentionally bias the outcome. [LET-12]

Usability testing is frequently paired with a reactions survey; the Toolkit points to the ten-item System Usability Scale (SUS), rescaled to a 0–100 range, on which an overall score of roughly 68 counts as above average. [LET-12] Drawing on usability authority Jakob Nielsen, the Toolkit suggests that the strongest results come from testing no more than about five users and running as many modest tests as the budget allows, cautioning that testing nobody yields no insight at all. [LET-12]

These methods carry forward the user-centered design tradition laid out in the human-centered chapter, where usability is defined, measurable criteria for it are set, and data are gathered and assessed so that the design process can tackle and resolve usability problems over successive iterations. [LET-03]

Further Reading

Source: wrgr/lecommons — curated by the learning engineering community. Confidence: medium — lecommons-curated; not yet independently expert-validated in this context. To validate or challenge any item: use the Edit button on this page. Upgrading confidence from medium → high requires expert sign-off.

Landmark Academic Papers

• Design principles for virtual humans in educational technology environments — Scotty D. Craig, Nicolaus L. Schroeder (2018). International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-017-0148-y · ~150 citations · tier: contemporary

  Synthesized human-factors engineering principles for designing pedagogical agents. Showed that voice, appearance, and persona of virtual instructors systematically affect learner cognitive load and perceived credibility — establishing that LE must integrate psychological design alongside algorithmic design. Source: lecommons/landscape/data/papers.json · ID: LE-LS-AP-009 · confidence: medium · expert-validated: false

• The voice quality of pedagogical agent impacts learning and agent perceptions — Ryan F. Siegle, Scotty D. Craig (2024). Journal of Computer Assisted Learning · doi:10.1111/jcal.12997 · ~20 citations · tier: contemporary

  Empirically demonstrated that voice quality (prosody, naturalness, warmth) of a pedagogical agent significantly affects both learning outcomes and learner perception. Illustrates the meticulous human factors engineering required in modern AI-driven learning systems. Source: lecommons/landscape/data/papers.json · ID: LE-LS-AP-010 · confidence: medium · expert-validated: false

Key People

• Scotty D. Craig (profile), Arizona State University (active 2000s–present) — Human factors researcher; virtual humans and pedagogical agents specialist

  Systematically investigated design principles for virtual humans and pedagogical agents in learning environments Source: lecommons/landscape/data/people.json · ID: LE-LS-PP-014 · confidence: medium · expert-validated: false

Organizations, Conferences & Journals

• International Journal of STEM Education (journal) · link

  Source: lecommons/landscape/data/organizations.json · ID: LE-LS-JO-007 · confidence: medium · expert-validated: false

Programs & Initiatives

• LENS @ JHU — Learning Engineering for Next-Generation Systems (PC) · link

  Concentration within JHU MEd in Learning Design & Technology. Targets practitioners in complex organizations: defense, healthcare, large-scale education. Grounded in human systems integration and learning engineering expertise. Capstone produces evidence dashboard, reproducible report, and governance/ethics plan. Unique JHU ecosystem: APL + Medicine + IEEE/ICICLE partnership. Source: lecommons/site/src/data/programs_people_registry.json · ID: LE-PP-001 · confidence: medium · expert-validated: false

• Learning Engineering Fellowship (CMU OLI) (PC) · link

  Nine-week intensive for educators and designers to apply learning science and data-informed methods to real educational products and contexts; part of OLI professional learning. Source: lecommons/site/src/data/programs_people_registry.json · ID: LE-PP-075 · confidence: medium · expert-validated: false

• ASU Learning Engineering Institute & Graduate Certificate (PC) · link

  Graduate certificate and research network fusing human systems engineering, design, and evidence to improve educational systems; connects students with Learning Engineering Research Network partners. Source: lecommons/site/src/data/programs_people_registry.json · ID: LE-PP-079 · confidence: medium · expert-validated: false

• Purdue School of Engineering Education (ENE) (PC) · link

  First-in-the-nation school of engineering education; graduate offerings include the online M.S. in Engineering Education, Ph.D. in engineering education research, and the stackable Teaching and Learning in Engineering graduate certificate—explicit “learning engineering” language appears in certificate and course titles. Source: lecommons/site/src/data/programs_people_registry.json · ID: LE-PP-089 · confidence: medium · expert-validated: false

• International Journal of STEM Education (CO) · link

  Discipline-based education research (DBER): problem-based learning, flipped classrooms, educational robotics, STEM learning outcomes at scale. Source: lecommons/site/src/data/programs_people_registry.json · ID: LE-PP-130 · confidence: medium · expert-validated: false

• Human Factors and Ergonomics Society (CO) · link

  Professional society for human factors and ergonomics. Publishes Human Factors journal and organizes the annual HFES conference. The Training Systems technical group directly addresses LE-relevant human factors in instructional systems. Source: lecommons/site/src/data/programs_people_registry.json · ID: LE-PP-133 · confidence: medium · expert-validated: false

Lecommons enrichment applied 2026-04-17. All items pending expert validation. See wrgr/lecommons for source data and curation methodology.