Automated tool for on-the-job training with ongoing workflow improvements

Deliverables

Adjusting to a New Process

Billing and reporting activities had become quite stable, until changes regarding re-enrolled participants appeared into the data system. While the business rules remained the same, the data structure did not. This meant all of the automated systems in place now required workarounds.

The development team was working towards a new code which would allow us to continue using the automated tools I had built without any modification. For two to three months, however, we would have to quickly learn and integrate workarounds in our daily work.

Once the code would be deployed, we would need to revert to the old workflow.

Learning Design Strategy

In order to support this temporary shift in workflows and tasks, as well as to reduce errors and reworks which were constantly appearing, I designed a tool, using an off-the-shelf productivity software.

The first step in designing this new on-the-job learning tool was to identify the functions it should support: (1) Skills, (2) Memory and (3) Attention.

Learner States & Gaps

New skills involved assimilating the new flow. The memory aspect required both memorizing new steps while retaining a written trace of the old flow, as we would revert to it later, once the code fix was implemented. Lastly, attention was scarce since along with this project, there were other projects running and requiring our attention. The system had to therefore allow for multiple interruptions while performing the new flow, without incurring errors in the reports.

Design Choices

I opted for a tool that would support learning by providing all steps and context needed at first. It would gradually reduce the information provided to include only critical, non-memorized steps. This would lead towards autonomy.

The tool also retained the old workflows, flagging new parts which could later be removed as the bug fix would be implemented.

I opted for a learning experience centered around the use of dynamic checklists. One of the early questions, therefore, was what constitutes a good checklist. A further design question was how the checklist might be able to adapt to a user’s context, and how it may evolve with a user’s knowledge.

Final Design

The learning tool was part of a broader, four-components based tool I built, to assist with the new workflow. It incorporated the knowledge and skills necessary to generate the analysis report, do the quality assurance steps and test edge cases. The adaptive learning tool was constructed as a loop, taking in user feedback to adjust its level of support over time.

The final ouput was a simple checklist, generated by a popular off-the-shelf software. The innovation was in the logic feeding the list.

Using this tool, the user was able to view more or less details, depending on his level of concentration and interruption. The system also adjusted the steps presented to reflect the user’s knowledge gap. This prevented information from being ignored due to its redundancy or lack of pertinence. The system also allowed documentation at various stages of the process changes, allowing us to revert to a given flow in time depending on the bug fix provided by the development team.

GitHub Project (Public)

Repository (Private)

My Role:

• LXP Designer