Free Resources Don't Always Tell You What to Do Next

Imagine a student who takes a practice test and discovers that their Math performance needs improvement.

What should they do next?

Should they spend more time studying algebra? Should they review advanced math concepts? Should they revisit questions they answered incorrectly? Or should they focus on understanding why they repeatedly make certain mistakes?

A student with access to an experienced tutor might receive guidance on these questions. A student studying independently may have to figure everything out alone.

Free practice questions solve part of the problem. They give students something to work on.

But students also need ways to understand their progress, identify weaknesses, and decide how to use their limited study time.

For many independent learners, that process can be difficult.

And this leads us to an important distinction: access to information is not the same as knowing how to use it effectively.

We built Mori Prep to help address the first problem. Now we want to investigate how we can address the second.

What If We Used Learning Data to Understand Students Better?

Every time students practice, they generate information about their learning.

A student's answers can reveal which skills they understand and which concepts they may need to revisit. Their practice history can show whether they return to difficult questions, how consistently they study, and which areas they spend the most time working on.

Individually, these observations can help students reflect on their preparation. Across many students, they may reveal broader patterns that are difficult to identify by looking at individual scores alone.

This is where data science becomes valuable.

Rather than simply collecting more questions and adding more resources, we want to explore how educational data can help us understand the learning process itself.

Which practice habits are associated with better performance?

Which SAT skills consistently require more attention?

Do students who review their mistakes make different kinds of progress from students who only complete new questions?

Can a structured study plan help independent learners prepare more consistently?

These are questions we want to investigate, not conclusions we have already reached.

The purpose of learning analytics is not to turn students into numbers. It is to use information to make educational support more relevant to the people who need it.

From a Question Bank to a Guided Learning Experience

We see several ways this research could shape the next stage of Mori Prep.

1. Helping students understand their weaknesses

Instead of looking only at an overall practice score, students could examine their performance across specific skills and identify which areas deserve attention.

A student struggling with a particular algebra concept, for example, could receive a more useful recommendation than a general instruction to study Math harder.

2. Making practice more purposeful

A student's next study session should have a reason behind it.

We want to explore whether recommendations based on skill-level performance, previous mistakes, and study goals can help students make better decisions about what to practice next.

The aim is not to make studying effortless. It is to help students spend their effort more purposefully.

3. Understanding the barriers students share

Learning data may also help us recognize common difficulties across the platform.

If many students struggle with the same skill, that could signal an opportunity to create a clearer lesson or improve the explanations available on Mori Prep.

If students frequently begin practicing but do not return, we should investigate why before assuming they simply lack motivation.

The problem might be the study experience, competing responsibilities, limited internet access, or something else entirely.

Data can help us identify patterns. Listening to students can help us understand them.

4. Measuring whether our improvements work

We do not want to add features simply because they look impressive.

When we introduce a new study tool or recommendation system, we want to examine whether it helps students engage more consistently and improve their understanding.

That requires measuring outcomes, comparing results carefully, and being honest when a feature does not work as expected.

A successful educational platform should not be judged only by how many students register. We should also ask whether students are getting meaningful value from using it.

Why This Matters for Mongolia

Our focus remains on Mongolian students, especially those who cannot afford private SAT tutoring.

We are not suggesting that every student has the same circumstances or that one platform can solve every barrier to education. Students may face different challenges depending on their school, location, access to technology, and available support.

That is precisely why we want to understand those differences rather than assume they do not exist.

A student in Ulaanbaatar and a student in a rural province may use the same question bank but have very different study environments. Some students may need more targeted explanations. Others may benefit from a clearer study plan or resources that are easier to access with limited connectivity.

Our responsibility is to investigate these needs and build accordingly.

We want Mori Prep to become more than a free place to answer questions. We want it to become a platform that learns from how students use it and improves the support it provides.

Throughout this process, student privacy must remain important. Learning data should be collected only when necessary, handled responsibly, and used to improve education—not to label students or publicly rank their circumstances.

The Question We Want to Answer

Mori Prep began with a belief: a student's financial situation should not determine whether they can access high-quality SAT preparation.

We still believe that.

But we now see another question worth exploring.

Can data science help make free educational resources more effective for the students who need them most?

We want to investigate whether a structured, data-informed preparation pathway can help students study more consistently, identify their weaknesses more effectively, and improve their skills without relying on paid tutoring.

We will need to listen to students, examine learning patterns, test ideas, and measure results. We may discover that some of our assumptions are wrong. If that happens, we want the evidence to change what we build.

That is part of the point.

We do not believe that building an educational platform means having every answer from the beginning. It means identifying a problem, developing a thoughtful solution, and continuing to learn from the people the solution is intended to serve.

Bytecode began by making coding education more accessible. Mori Prep extended that mission to university preparation.

Now, we want to investigate how data science can help us take the next step.

Our goal is not simply to make learning free. It is to help make effective learning more accessible, too.

Because a student's potential should not have a price tag—and the information needed to improve should not be reserved for those who can afford a tutor.