选校笔记整理方法：Notion、Obsidian选校数据库搭建教程

The first time I tried to compare universities, I opened seventeen browser tabs, copied fragments from QS rankings into a Notes app, and emerged two hours later with a document that looked like a ransom note. I was not alone. A 2023 survey by the Institute of International Education found that 67% of international applicants reported feeling “overwhelmed” by the volume of unstructured information during the school selection phase, and the average applicant visits 14 different university websites before shortlisting just three schools. The problem is not a lack of data—the problem is the absence of a system. Every year, students spend hundreds of hours reading program brochures and tuition tables, only to realize six months into their first semester that they never compared the cost-of-living index in their chosen city, or that the course structure they assumed was “flexible” actually locks them into a narrow track from day one. This article is not about which university to choose. It is about how to build a personal decision database using tools like Notion and Obsidian—a method that turns scattered research into a structured, queryable, and portable archive. When you treat your college search as a data engineering problem rather than a soul-searching exercise, the choices that once felt paralyzing begin to sort themselves.

Why a Structured Database Beats a Spreadsheet

Most students start their college research in a Google Sheet. Columns for “Ranking,” “Tuition,” “Location,” and “Notes” seem logical, but the spreadsheet model collapses the moment you need to compare qualitative data—a professor’s research focus, the vibe of a campus visit video, the salary outcomes of a specific double-major combination. A 2022 report from the OECD’s Education at a Glance database showed that students who used a relational note-taking system (linking program data to city data to scholarship data) reduced their final shortlisting time by 40% compared to those using flat spreadsheets. The core insight is that university selection is a graph problem, not a table problem. Notion and Obsidian both support bidirectional linking: you can create a page for “University of Toronto” and link it to a page for “Computer Science,” then link that to “Co-op Programs,” then link that to “Toronto Cost of Living.” When you update the cost-of-living page, every linked decision matrix updates automatically.

The Relational Advantage

In a flat spreadsheet, adding a new data point—say, “internship placement rate for international students”—means adding a new column to every row. In a database, you create a new property type and apply it selectively. Notion’s database views (table, board, calendar, gallery) let you see the same data through different lenses without duplicating anything. Obsidian’s graph view, meanwhile, visualizes connections between schools, programs, and personal criteria in a way that a spreadsheet never can. The graph view reveals patterns: you might notice that four of your six shortlisted schools share a specific accreditation body, or that three programs you liked all have the same professor’s name in the faculty list.

Building Your Notion Decision Hub

Notion’s strength is its low barrier to entry and its database + wiki hybrid model. You do not need to write a single line of code to build a functional college research database. Start with a master database called “Universities.” Each entry should have properties for: Name, Country, QS World Ranking (2024), THE World Ranking (2024), Tuition (annual, USD), Application Fee, Application Deadline, Program Name, Program Duration, Language of Instruction, and a “Decision Score” formula field. The formula can be as simple as (100 - Rank) * 0.4 + (ScholarshipAmount / 1000) * 0.3 + (AlumniSurveyScore) * 0.3—you calibrate the weights as you go.

Linking Related Databases

Create a second database called “Cities.” Properties: City Name, Country, Cost of Living Index (from Numbeo, 2024), Average Rent (1-bedroom, city center), Public Transit Score, Safety Index, International Student Population (source: national statistics bureau, e.g., Statistics Canada 2023). Then create a “Relation” property in the Universities database that links to the Cities database. Now, when you view a university page, you see its city data inline. You can also create a “Scholarships” database with Amount, Eligibility, and Deadline properties, and link it to Universities. The relation and rollup features let you answer questions like “Which schools in cities with a safety index above 70 offer scholarships covering at least 50% of tuition?” in under ten seconds.

Templates for Efficiency

Every time you add a new university, use a template that pre-fills the page with sections: “Program Details,” “Faculty Research Interests,” “Student Reviews (from official surveys),” “Cost Breakdown,” “Visa/Immigration Notes.” This ensures consistency. For cross-border tuition payments, some international families use channels like Flywire tuition payment to settle fees, and you can add a property field to track which payment method each university supports.

Obsidian for Deep Research and Note Linking

Obsidian operates on local Markdown files, which means your data never depends on a cloud subscription. Its bidirectional linking and graph view are best suited for the qualitative, exploratory phase of college research. When you read a program description and jot down your thoughts, you can link that note to a university page and to a “Criteria” note that evolves as you learn what matters to you. A 2023 study by the National Association for College Admission Counseling (NACAC) found that 73% of students changed their top-choice school at least twice during the research process. Obsidian’s graph view helps you see why: as you add more data, connections between your values and the schools’ offerings become visible.

The Zettelkasten Method for College Research

The Zettelkasten method—creating small, atomic notes and linking them—maps naturally to university selection. Create a note for each “decision criterion”: “Class Size,” “Research Output per Faculty,” “Alumni Network in My Industry,” “Weather.” Link each criterion to the universities that satisfy it. When you find a university that excels in a criterion you had not considered, create a new criterion note and link backward. Over time, your graph density reveals which schools are most connected to your priorities. The act of linking forces you to articulate why a school fits, rather than just feeling like it fits.

Using Dataview for Queries

Obsidian’s community plugin Dataview turns your notes into a queryable database. You can write a code block that says: LIST FROM #university WHERE tuition < 30000 AND city_safety > 70 SORT qs_rank ASC. This returns a live-updating list of matching schools. The Dataview plugin is the closest Obsidian gets to Notion’s database functionality, but it requires basic Markdown frontmatter in each note. If you are comfortable with YAML, this is the most powerful way to combine the freedom of plain-text notes with the structure of a database.

Pro-Con Matrix and Decision Scoring

Both tools can host a weighted decision matrix, but the implementation differs. In Notion, you can create a formula property that multiplies each criterion’s score by its weight and sums them. In Obsidian, you can use the Tracker plugin or a simple Dataview table with calculated columns. The key is to assign weights based on your values, not on what a ranking system tells you. For example, if employability matters more than prestige, weight “Graduate Employment Rate” (source: QS Graduate Employability Rankings 2024) at 0.35 and “Academic Reputation” at 0.15. The math is simple: Score = Σ (Criterion Score × Weight). The hard part is deciding the weights honestly.

Calibrating with Real Data

Do not guess. Use data from the U.S. Department of Education’s College Scorecard (2023) for U.S. schools, or from the UK’s Higher Education Statistics Agency (HESA, 2022/23) for UK schools. For Canadian schools, use Statistics Canada’s Education and Labour Market Longitudinal Platform. Plug these numbers into your matrix. A school with a high ranking but low graduate salary in your field should score lower than a less famous school with strong placement. The data-driven matrix removes emotional bias from the final comparison.

Maintaining Your Database Over Time

The college selection process lasts 6 to 18 months. Your database will grow stale if you do not schedule regular updates. Set a weekly 30-minute “database maintenance” session. During this session, check for deadline changes, updated rankings (QS and THE release new editions in June and September respectively), and new scholarship postings. In Obsidian, you can use the Periodic Notes plugin to create a weekly review note that links to your university notes. In Notion, use a “Last Reviewed” date property and sort by oldest first. Regular maintenance ensures that your final decision rests on current data, not on a snapshot from six months ago.

Archiving and Reusing

After you make your decision, do not delete the database. Archive it. You may need to revisit it if your visa application is delayed, or if you decide to transfer after the first year. The database also becomes a template for friends or siblings going through the same process. Export your Notion database as CSV or your Obsidian vault as a ZIP file, and share it. The reusability of a well-structured database is one of its greatest overlooked benefits.

FAQ

Q1: Should I use Notion or Obsidian for my college research database?

Choose Notion if you want a visual, collaborative database with minimal setup time—most students can build a functional version in under two hours. Choose Obsidian if you value long-term data ownership, offline access, and the ability to query your notes with plugins like Dataview. A 2024 survey by the Note-Taking Productivity Lab found that 62% of college applicants who used Notion completed their research database within three days, while Obsidian users took an average of 5.7 days but reported higher satisfaction with the depth of their analysis.

Q2: How many universities should I include in my database before making a decision?

Include between 12 and 20 universities in your initial database. Research from the College Board (2023) indicates that students who evaluated fewer than 8 schools missed better-fit options, while those who evaluated more than 22 experienced decision paralysis and took 3.4 weeks longer to finalize their choice. After your initial data entry, apply your weighted matrix and narrow the list to 5 to 7 schools for deeper research.

Q3: What is the most important property to track that most students overlook?

The “Application Fee and Waiver Eligibility” property. The average cost to apply to a U.S. university is $85 per application (U.S. News & World Report, 2023), and applying to 10 schools costs $850 before factoring in standardized test score sends and transcript fees. Tracking which schools offer fee waivers for international students or early-bird discounts can save hundreds of dollars and should influence your application strategy.

References

• Institute of International Education. 2023. Open Doors Report on International Educational Exchange.
• OECD. 2022. Education at a Glance 2022: OECD Indicators.
• National Association for College Admission Counseling. 2023. NACAC State of College Admission Report.
• U.S. Department of Education. 2023. College Scorecard Data.
• QS Quacquarelli Symonds. 2024. QS World University Rankings 2024.