Case study
Purpose: turn learning activity into useful next steps. The implemented system read xAPI statements from an LRS, mapped that activity to the relevant skills and competencies, and used the resulting skill matrix to recommend what to do next.
Assessment feedback carries full weight (100%). Work products evaluated against clear rubrics carry 85%. External credentials carry 75%. Self-attested skills carry 45%.
These high-impact, lower-effort actions would give you the most significant Skill EQ improvement in the shortest time.
Based on current trajectory, your Skill EQ is projected to reach 82-88 at the end of the learning path.
The system read xAPI statements stored in a learning record store (LRS) to capture learning activity and evidence as it occurred.
It mapped each statement to the relevant skills and competencies, then assembled those mappings into an individual skill matrix.
The dashboard made that matrix visible so learners and staff could see current coverage, gaps, and the next opportunities to build capability.
The recommendation engine checked the learner's skill matrix for gaps and next eligible actions.
It surfaced a relevant next step at the point of need, giving learners a practical route to build the skills and competencies their activity had not yet covered.
Instructor view includes ability to submit assessment evidence, flag skills for review, adjust inferred proficiency levels, and view aggregate cohort analytics.