The Attribute Universe
The attribute universe is a way of understanding how ‘attributes’ might be processed in a way that supports the understanding and management of learners needs and strengths.
We have worked to clarify what purpose this way of processing attributes has, and how it might be effectively used for the most amount of organizations and their members.
Summary
Our research focused on understanding the goal-setting and up-skilling experiences for employees and managers. The primary aim was to explore how MARi’s platform and information architecture could assist managers in supporting their employees achieve short-term and long-term career goals.
78%
of employees desire reinforcement for their career decisions.
Likewise, they want to have agency in decision-making processes.
So What?
MARi’s AI capabilities can and should work to support both manager and employee goals through data-driven, actionable insights.
Key Research Insights
Over 7 months of research, we have discovered a foundational need for the MARi platform in the form of a need to develop and expand it around employees, in addition to focusing on training manager usage. To that end, we’ve gathered some insights we believe can make future development more successful.
Contextual Employee Goals
Attributes aren’t easy to grasp, especially at scale. To have a system that provides value to users, it must align with their mental models effectively, and include clear displays of their strengths and growth opportunities.
Attribute Visualization
Effective employee support and engagement requires a platform that can generate personalized and data-driven insights while maintaining user autonomy and positive feedback on decisions.
Effective Mentorship and Communication
Stakeholder interviewees, particularly those more junior in their career paths, expressed a need for guidance from qualified mentors to help them navigate their professional development journey.
4
Iterations
30+
Semi-structured Interviews
10+
Competitor analysis
10
Sprints of iteration
Design through research
We utilized many different artifact generation methods to summarize key research findings, make design decisions, and help us explore and understand the complexities of learning management systems and what is further needed from them. Some examples of these artifacts are:
Flow Chart
Diagrams allow us to explore the logical flow of various processes.
Affinity Diagram
We frequently used affinity diagrams to find common patterns and divergence.
Sketch & Storyboard
Through sketches, we generated concepts from diverse perspectives.
Card Sorting
Activity allowing us to draw conclusions and similarities between participants.
Identifying Project Focus
The attribute universe, being a large concept to wrestle with, needed to be scaled to a level our team could understand and utilize it. To do this we, with the assistance of MARi, produced a foundational attribute matrix. Through this matrix, we detailed three different stakeholder group levels (individual, team, and organization), and high-level tasks that they would need to do in MARi’s service. From these, we identified tasks that we would focus on as critical flows.
Understanding Key Stakeholders
Prior to conducting proper user testing and creating prototypes, we identified target stakeholder groups that we wanted to serve. From interviews with stakeholders we identified as critical to our project goals, we produced a set of archetypes to support our project and future development considerations.
Research Becomes Design, Becomes Research
We’ve produced two final iteration design flows, one following a typical session for a Training Manager, and one for an individual Employee.
Iterative Insight Generation
Each iteration of our prototypes flows, we gathered a set of high-level learnings from which we made explorations and refinements in our next. These learnings have primary value within our prototypes in eliciting these refinements, but can be carried forward as considerations as MARi expands. Further details on what was done in the prototype iterations can be found in the respective flow design pages.
V1 Prototype
75%+ of participants indicated an interest in detailed, granular sub-attribute views over higher-level views for more precise identification of improvement opportunities.
Early-career employees value actionable next steps, finding attributes with proficiency levels insufficient.
V2 Prototype
Granular bubble graphs do not clearly align with the attribute mental models of more than 80% of participants.
In addition, almost 90% reported unclear recommendations due to lack of alignment with previous/current practices.
V3 Prototype
Interviewees, especially those with unconventional/disconnected career paths, often experience feelings of uncertainty- they want reassurance that they are making on-track decisions and connections with experienced counterparts/peers.
Employee agency in the learning process is highly valued by employees, who appreciate AI in the supportive/validating role when making data-driven decisions. Early-career employees have expressed preference for skipping goal-setting stages in favor of directly choosing skill improvement areas.
V4 Prototype
Current Iteration, see more in Trainers and Employees pages
