Research

We began by completing a review of existing data including a literature review focused on work-based learning and also reviewed the eMentoring focus group and resulting eMentoring Think Tank qualitative data that Smart Futures completed in 2021. We also performed a competitor analysis to help us gain a better understanding about the current market and determine competitor features to be leveraged to meet our project goals. Through these processes, we identified the status quo of work-based learning and key findings of existing research, while discovering the scarcity of research of work-based learning in virtual settings.

Below is a list of core findings and design objectives we've discovered from our research:

Exploring market trends

Our team conducted a competitor analysis of products that are currently in the market. We were able to familiarize ourselves with current competitors while comparing and contrasting the competitors to one another and to Smart Futures. It was also an opportunity to share with our client what potential features we thought may provide value to Smart Futures.

We specifically looked at products that fell into one or more of the following categories:

• Career Planning
• Video-based Content
• Mentor-Mentee Connection
• Online Asynchronous Interaction

Mapping our stakeholders

The exercise of creating the stakeholder map forced our team to consider all of those who are involved and impacted by work-based learning. Determining these participants allowed us to visualize the relationship between Smart Futures, their clients, and their users and how work-based learning fits into their role.

Interviewing potential users

To fully address our research questions, and gain insights into how our stakeholders are engaging in WBL, we drafted our interview protocols for a focus group of school career counselors and WBL organizers, as well as another group of students who are of various ages and career interests. Their opinions shed light on the pain points and trends among professionals and novice students, and how we can address these concerns in our product design.

To synthesize our interview findings, we worked as a group to consolidate ideas using affinity mapping. Starting with transcribing all users’ quotes, we grouped similar ideas together into various clusters to represent typical users’ mental models. With the groups of relevant data, we then determined key identifying factors and discussed these perspectives as a group. Then we combined related clusters into supergroups to find the overarching themes and patterns.