EECS 101 - a Repository of Crowdsourced Course Advice


As course advising at UC Berkeley has shifted to a predominantly online format, the popularity of the EECS 101 Piazza for course & advising-related inquiries has increased rapidly. Over 4,000 students actively browse the forum, in search of everything from waitlist-related questions to workload discussions and extracurricular opportunities.

One of the biggest bottlenecks on this forum - something that applies to Piazza in general - is the lack of functional search. Searching for even simple queries yields widely inaccurate results. Piazza seems to use some combination of exact word matching and time-based sorting - and it’s incredibly frustrating for anyone trying to search for answers in a repository of over 13,500 Q&A posts.

This past week, I decided to do something about it - I launched, a site that indexes content from EECS 101 and serves it in a clean format with robust search & filter capabilities. Below, I walk through the process of approaching this.

View this project’s source code here.

Evaluating Search Functionality

Search Query: “188 vs 189”



Search Query: “170 Workload”



Search Query: “CS 161 Waitlist”



Extra: How I Built It

1. Acquiring the Dataset: Scraping Piazza

To scrape Piazza, I used this open-source unofficial Piazza API. I let it run for about an hour and a half with a one-second delay after each API call to avoid rate limits. I saved posts to a local TinyDB JSON database as a form of temporary storage.

See the source code here.

2. Tokenizing the Dataset

To assist with rapid search functionality, I decided to extract key tokens from each post ahead of time. For the time being, this includes professors and courses - but more metadata tokens could be extracted at this step.

The logic for this token extraction is simple - it simply uses an extract_tags function, which looks to find and replace keywords (without replacement) in order from longest length to shortest length.

Example: if {CS 189, CS189, 189} are keywords that correspond to token CS189, then we search for CS 189 first, then CS189, then 189 - and if any of those match, we remove them from the search content temporarily and update the post’s tags while scanning for other keywords.

See the source code here.

3. Building a React Frontend

To build a quick frontend, I took inspiration from Berkeleytime’s format & style. For formatting, I used four main react components:

  • TabBar, to hold the navigation bar.
  • FilterView, to hold the search bar and any filters.
  • ListView, to hold a list of cells with search results.
  • DetailView, to display the post content.

Nothing too complex here.

To build a quick backend, I created two endpoints using NodeJS/Express – a /query endpoint, which queries the my cached Piazza database for a list of posts given search content/filters, and a /content endpoint, which queries the database for actual post content.

This is where the majority of the search logic was implemented - I added support for a handful of different search types, and optimized particular types (e.g. auto) to support entity extraction on the search query itself.

5. Connecting the Frontend/Backend with Redux

I used React-Redux to connect my data stream & API calls to React components.

6. Deploying the Site to Heroku

I deployed the site to a Heroku dyno!