Our data found that:
This month marks the first anniversary of AdeptLR's launch. We founded AdeptLR with the mission to make LSAT preparation more efficient – “Drill smarter, improve faster!” Our AI and machine learning algorithm uses real-time data to provide LSAT questions dynamically based on your skill level. Data is core to AdeptLR, and, in the next few weeks, we will share some insights and discovery from our data to help you study better. In this inaugural post, we’d like to share how AdeptLR students harness the power of our platform and how they perform. There are two groups of users who are most successful at using AdeptLR to improve their scores:
We have incredibly dedicated users who drilled extensively across different categories. The top Breadth-first drillers drilled over 1,500 questions, on average improving their logical reasoning scores by 3.8. For example, one of our Breadth-first drillers increased her score from -5 in her initial LR timed section to -1 in her LR timed section by the time she was done with LSAT prep. Our data shows that the more you drill, the more likely you’ll be able to make significant improvements. For anyone struggling with LR, stay calm and keep drilling! In a subsequent post, we will discuss other study patterns and share tips on how to better harness the power of AdeptLR.
.png)
Depth-first drillers make up another group of highly specialized users in our pool. They don’t drill huge quantities of LSAT questions, but they focus on specific categories to tackle their weaknesses. A unique feature of AdeptLR is our 1-click import from LawHub. Depth-first drillers tend to use this feature to import their historical practice data and leverage our analytic dashboard to identify categories that require targeted drilling. With our adaptive algorithm, Depth-first drillers spend the majority of their time tackling moderate to difficult questions in targeted categories. We’ve observed significant improvement in their selection of correct responses.
.jpg)
Our data also sheds a lot of light on study patterns and strategies. In the next post, we will share some interesting observations including whether it’s okay to take a study break! Stay tuned.
Fun fact, breadth-first search (BFS) and depth-first search (DFS) refer to well known search algorithms in Computer Science. The algo-nerds in us couldn't help notice the similarities between our users' LSAT study strategies and these famous algorithms.
