We're students, and that means one of our biggest inspirations (and some of our most frustrating problems) come from a daily ritual - lectures. Some professors are fantastic. But let's face it, many professors could use some constructive criticism when it comes to their presentation skills. Whether it's talking too fast, speaking too quietly or simply not paying attention to the real-time concerns of the class, we've all been there.

Enter LectureBuddy.

What it does

Inspired by lackluster lectures and little to no interfacing time with professors, LectureBuddy allows students to signal their instructors with teaching concerns at the spot while also providing feedback to the instructor about the mood and sentiment of the class.

By creating a web-based platform, instructors can create sessions from the familiarity of their smartphone or laptop. Students can then provide live feedback to their instructor by logging in with an appropriate session ID. At the same time, a camera intermittently analyzes the faces of students and provides the instructor with a live average-mood for the class. Students are also given a chat room for the session to discuss material and ask each other questions. At the end of the session, Lexalytics API is used to parse the chat room text and provides the instructor with the average tone of the conversations that took place.

Another important use for LectureBuddy is an alternative to tedious USATs or other instructor evaluation forms. Currently, teacher evaluations are completed at the end of terms and students are frankly no longer interested in providing critiques as any change will not benefit them. LectureBuddy’s live feedback and student interactivity provides the instructor with consistent information. This can allow them to adapt their teaching styles and change topics to better suit the needs of the current class.

How I built it

LectureBuddy is a web-based application; most of the developing was done in JavaScript, Node.js, HTML/CSS, etc. The Lexalytics Semantria API was used for parsing the chat room data and Microsoft’s Cognitive Services API for emotions was used to gauge the mood of a class. Other smaller JavaScript libraries were also utilised.

Challenges I ran into

The Lexalytics Semantria API proved to be a challenge to set up. The out-of-the box javascript files came with some errors, and after spending a few hours with mentors troubleshooting, the team finally managed to get the node.js version to work.

Accomplishments that I'm proud of

Two first-time hackers contributed some awesome work to the project!

What I learned

"I learned that json is a javascript object notation... I think" - Hazik

"I learned how to work with node.js - I mean I've worked with it before, but I didn't really know what I was doing. Now I sort of know what I'm doing!" - Victoria

"I should probably use bootstrap for things" - Haoda

"I learned how to install mongoDB in a way that almost works" - Haoda

"I learned some stuff about Microsoft" - Edwin

What's next for Lecture Buddy

  • Multiple Sessions
  • Further in-depth analytics from an entire semester's worth of lectures
  • Pebble / Wearable integration!

@Deloitte See our video pitch!

Built With

  • lexalytics
  • microsoft-cognitive-services-api
  • node.js
  • semantria-api
+ 6 more
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