Intervention Study
Goal
The goal of our intervention study was to increase participants’ micro-learning consistency by anchoring micro-learning to an existing habit. We hypothesized that attaching a new behavior to an established habit/routine would improve consistency compared to participants’ baseline behavior and new participants’ self-reported micro-learning frequency prior to the study. As a reminder, micro-learning includes reading books, the news, articles/newsletters, listening to podcasts, and watching educational videos (short-form and long-form included). In particular, this includes any form of active, intentional learning (it cannot just pop up on your feed).
Methods
The study was conducted by recruiting a mix of participants from the baseline study and new participants. For returning participants, we recruited a participant from each persona we had identified from the baseline study: Pure Vibes, Sunshine Scheduler, The Optimizer, and FOMO. Then, we recruited 4 new participants that fit the criteria for our study (ages 18-25, motivated to micro-learn but not currently learning for more than three times per week), for a total of 8 participants. The key question we wanted to address was whether anchoring micro-learning to participants’ existing habits could increase the frequency and/or consistency of micro-learning behavior.
To implement the intervention, participants identified an existing habit/routine (an “anchor”) and reported its duration and typical time of occurrence. Then, participants selected a reinforcement mechanism to help attach micro-learning to the anchor, such as a text reminder, calendar notification, or wallpaper/lockscreen prompt. Most participants elected for a text reminder from us that would happen within 1 hour of the anchor’s time of day. Over a five-day period, participants logged every instance their anchor occurred and every micro-learning session, regardless of whether it occurred alongside the anchor. This enabled us to assess how often micro-learning occurred with the anchor, independently, or how often the anchor occurred without micro-learning.
Results and Key Insights
Our intervention study, however, was less successful than we expected. Although each participant was successfully able to find an anchor to attach micro-learning to, each returning participant’s consistency did not meaningfully increase compared to the baseline, and each new participant’s micro-learning consistency barely increased compared to their reported baseline (from their pre-study interviews). Examples of participants’ anchors included making breakfast, making coffee, or their night-time routine (showering, washing face, brushing teeth, etc.). Notably, many participants indicated that reminders were unnecessary for remembering to micro-learn during anchor moments, suggesting that anchoring successfully created an associative link between the routine and learning behavior. However, micro-learning only occurred with the anchor about half the time, indicating that just remembering to micro-learn didn’t always translate into action.
This reflects our first grounded theory we discovered from our baseline study, where participants struggle with having enough activation energy to engage in micro-learning; just because participants remembered to micro-learn didn’t exactly mean they had enough energy to learn. Our results also suggest the importance of our second grounded theory, where we hypothesized that the biggest barrier to micro-learning was not time, but rather having a cognitive surplus. Our intervention study results suggest that while anchoring effectively reduces the memory friction of micro-learning, it does not overcome the energy barriers needed to learn, meaning that a major obstacle our study failed to overcome was energy.
How this May Change Our Solution Design
This slightly changes our solution design; while we believe anchoring is still a helpful mechanism as it provides a non-invasive, consistent reminder to micro-learn (especially for participants who lack a consistent schedule or tend to learn based off of “vibes” (Pure Vibes persona)), we may need to implement a strategy to help participants find the energy, or find moments of surplus energy, that they can use to knock out micro-learning without feeling too drained.
System Paths
For the “Pure Vibes” persona, who only micro-learns when they feel like it, we started by identifying the existing pattern: learning is driven by mood or motivation, with no consistent trigger. This means some days they learn for 20 minutes, and other days they don’t learn at all. There’s no stable cue, no reinforcement loop, and no identity shift tied to being “someone who learns daily.” From there, we designed the path by anchoring micro-learning to an existing daily routine. The system begins with a normal routine, then an early reminder tied to the anchor activity, which reduces reliance on motivation. The app pulls up curated suggestions to remove friction from deciding what to learn, and the user spends 5–15 minutes engaging with the content. Afterward, they can optionally log their learning, which supports reflection without adding pressure. Over time, this repeated loop is meant to shift identity from “I learn when I feel like it” to “I’m someone who learns every day.”
One key insight from this step was that the main problem wasn’t lack of interest in learning, it was inconsistency caused by the absence of a reliable trigger. By embedding micro-learning into an existing habit, we’re working with the user’s routine instead of against it. Another insight was how much friction small decisions create. Simply suggesting content reduces drop-off significantly. Mapping the full system also helped us see where breakdowns happen, like skipping the reminder, and thinking about how to design gentle re-entry points.
Story Maps
First, we listed out what the must-have features to build our MVP by thinking about what are the highest-impact features that directly solve our user persona’s pain points—becoming more consistent with microlearning. Since our solution involves identifying and using an “anchor,” we knew we had to first include onboarding for identifying an anchor statement and set up that helps users stick to their anchor. Set up includes enabling basic notifications based on item preferences. We also decided to allow simple logging that the user can optionally use to record and keep track of microlearning sessions. While this isn’t as crucial as the onboarding and setup, it can be an additional mechanism to keep some more avid learners motivated by seeing their progress. Lastly, a must-have was the ability for users to reset or regenerate their anchor should their anchor not match their microlearning goals.
The should-have features focused on core functionality and technical enablement, including third-party integrations for calendar syncing, location-based notifications, and basic reflection journaling. These features support contextual triggering and lightweight tracking, ensuring the app can reliably reinforce anchor-based habits.
Lastly, the nice-to-have features centered on user delight and deeper personalization. These included the ability to generate custom wallpapers featuring a user’s anchor statement, contextual setup tips for optimizing anchor effectiveness, and a home-screen widget for quick access. We also envisioned a more personalized reflection experience with guided mindful prompts, in-depth analytics from microlearning history, and social features such as sharing progress with friends or participating in a leaderboard. These enhancements aim to strengthen emotional engagement and long-term retention beyond the core habit loop.
MVP Features
| Feature | User Story
As a user, I want to… |
| Create Account | Create a new account as a new user |
| Sign in | Sign into my existing account so I can get backed to my saved content (anchor statement, logs etc) |
| Onboarding for Anchor Generation | Identify an anchor that aligns with my microlearning goals and environment (context, schedule etc) |
| Notifications Set Up | Set up notifications so that I can microlearn when my anchor occurs, even if I forget |
| Context Set Up | Set up my environment with context cues to remind me to microlearn |
| Log Microlearning Session | Record microlearning sessions |
| View History/Progress | View past microlearning sessions to keep track of progress and address gaps in consistency |
Bubble Map
Our bubble map outlines how we broke the system into two main categories: Management and Personalization and Microlearning. Structuring it this way allowed us to zoom out and see the product as both a content experience and a habit-building framework. It made clear that sustainable microlearning depends just as much on setup and structure as it does on the content itself.
On the management side, identifying your anchor emerged as the core foundation of the experience. Around it, we mapped supporting features such as enabling notifications, selecting notification times, adding widgets, setting wallpapers, editing or regenerating anchors, viewing session history, and tracking microlearning data. This reinforced the idea that helping users intentionally design their environment is key to making the habit stick.
On the microlearning side, we explored how users discover, choose, and engage with content, including selecting suggestions, browsing media, saving content, and recording sessions. Reflection sits between the two areas as an important bridge. It not only reinforces learning, but also helps users evaluate whether their anchor is actually effective. By reflecting on their sessions, users can recognize patterns, see what is working, and decide if adjustments to timing or context are needed. Overall, this step helped clarify priorities, surface gaps, and ensure our design supports both behavior change and continuous improvement.
Assumption Testing
Assumptions
Test Cards
These three assumptions are critical to test because together they determine whether the core loop of the product can function and scale. First, if anchor-based notifications do not reliably trigger sessions, the behavioral engine of the app fails and users will not form a habit. Second, if onboarding is not fast and intuitive, users will drop off before ever experiencing value, preventing meaningful activation. Third, if users are unwilling to integrate external tools, the app may lack depth, personalization, and long-term stickiness, limiting both engagement and defensibility. Testing these assumptions gives us insight into activation (can users get started?), engagement (do reminders drive action?), and expansion (will users deepen their commitment to the ecosystem?). Together, they validate whether the product can meaningfully change behavior rather than simply exist as another learning tool.
