Here is the link to our business plan, here are our slides, here is the code demo video, and here is our Framer prototype!
As fellow students, we have firsthand experience with the struggles of trying to keep up with fast-paced lectures. Students are trying to take detailed notes that they can use to study and review while also trying to pay attention to the lecture and understand the key concepts. It is difficult to get down all of the information with this kind of stress, and students often return from lectures with incomplete notes that they are not able to understand because they were not able to learn much during their time in the lecture. We realized that the best way to learn efficiently is to pay attention to what the lecturer is saying and observe the visuals during the lecture so that when you leave the lecture, you are able to further review and study on your own with some basic understanding of the concepts. However, this isn’t ideal because you leave the lecture without any notes. We tried taking audio recordings of the lecture so that we could refer to it later, but that was time-consuming as we would have to listen to the entire lecture again in order to review. This inspired us to create LectureLine, so that students are able to learn as efficiently as possible by paying attention during lectures and reviewing information with the LectureLine notes.
It’s not just us! Students across the globe struggle with note-taking and the lack of absorption in fast-paced lectures. A recent study demonstrated that 72% of students have difficulty in taking adequate notes and can’t record information fast enough. After conducting a survey of 96 individuals this weekend, we discovered that 83.3% had a heavy increase in self-learning due to COVID-19, 90.6% felt rushed in lectures, and an overwhelming 92.7% said that they would love to see an application that creates notes in real-time and adds resources.
What it does
LectureLine is a clean and efficient mobile application that revolutionizes the process of notetaking with real-time transcription and summarization with links and visuals. Features of LectureLine include fluid note-to-note linking, real-time transcription, compatibility across all devices, notes storage and organization, and offline capabilities.
The application utilizes the process of real-time transcription, but also contains the feature of summarizing the information into bullet points that capture key points and concepts, which none of its competitors include. This is vital in crafting efficient and easy-to-study notes that are more helpful for students in high-stress situations. The application goes far beyond the simple transcription and summary. Along with this recording and transcribing process, the application detects and categorizes key terms and concepts in order to generate images and visuals that may help the student. Along with visuals as a resource, the application will also take these key concepts and display helpful links and resources in order to allow the student to delve deeper and explore the concept further.
How we built it
Using Framer, we developed a virtual prototype that demonstrates the UI/UX aspect of LectureLine. This includes a clear process of how the mobile application works, and what the desired interface of LectureLine looks like. Along with simply demonstrating the interface of note-taking, our virtual prototype shows a clear demonstration of how an individual can create an account, organize and store their notes, and change their type of subscription. Small and desired features that we hope to implement in the future are also included to display the full workings of LectureLine.
Additionally, we created a code demo as a proof of concept for LectureLine. This program, written in Python, utilizes the user’s device’s microphone to listen for information and then transcribes it into written notes. The program then identifies the key concepts of what the speaker is saying through natural language processing and includes links and images into the notes. Then, the user can save their notes onto their own device.
Challenges we ran into
Due to the time pressure of this project, we were not able to include all desired aspects of LectureLine into our prototype. Although our code demo demonstrates the basic workings of our product, we hope to fully implement these desired features in the future with more time. One of the biggest challenges we faced was incorporating the use of punctuation into our transcribed notes. It became difficult to analyze verbal diction in order to process punctuation and capitalization. To overcome this challenge, we utilized source code for punctuation and were able to adapt it into our situation and program.
Additionally, we were hoping to incorporate more features into our prototypes such as offline capabilities, note-to-note linking, and textbook recommendations. With more time, we hope to incorporate these features, as well as machine learning so that the app would, over time, be able to recognize a voice and adapt to the accent and speaking style in order to make more accurate notes. We also plan on using machine learning and natural language processing so that LectureLine can identify the subject of a lecture and take more precise and helpful notes based on the subject (for example: in science subjects, LectureLine would include more labeled diagrams).
Accomplishments that we're proud of
Although we had little experience in Python and natural language processing as a team, we were able to work together in order to understand these concepts. We are extremely proud of our working project and the new concepts we were able to learn. In order to incorporate these new concepts, we conducted a lot of research and went through a lot of trial and error.
Additionally, this was our first time using Framer to prototype our mobile application, and we feel accomplished with the professionalism and efficiency of our given model. Although we felt pressured under time, we are super excited to showcase our working prototype and code demo!
Another thing that we are proud of is our business plan and slides. We did our best to create professional and clean materials that showcase our company, product ideas, and strategies.
What we learned
During this process, we were able to learn a lot about emerging technologies such as natural language processing in Python and how they can be implemented into a situation as basic as note-taking. We also conducted research on efficient learning and note-taking strategies in order to maximize the potential of our product, so we were able to learn about how we can improve our own habits and hopefully help others as well!
We experimented and learned the use of UI/UX design as well as the importance of market analysis, which allowed us to better improve LectureLine in comparison to our competitors. Most importantly, we learned the importance of time management and efficiency, which allowed us to successfully complete this project!
What's next for LectureLine
LectureLine is a mobile application as of now, but we plan to expand to browser extensions and other technologies in order to increase compatibility. This provides the user with much more accessibility, as they can use any device to access its features. We hope to partner with educational institutions, such as schools and universities, in order to reach a larger portion of our main target consumers, students. The team will run various marketing strategies and promotions on numerous websites and university platforms in order to promote the use of this time-efficient and user-friendly product.
LectureLine also has the potential to benefit working professionals, and we plan to maximize that potential through an additional work industry version. This version of LectureLine would have features that are specific to taking notes for meetings and informational sessions. For example, LectureLine would take notes during a meeting and automatically send those notes to meeting participants in order to ensure that everyone is on the same page and that there is no confusion. Through further development, LectureLine would be able to create timelines and assign tasks to individuals based on meeting notes.
Further down the road, the LectureLine team hopes to provide new developments and features in order to increase the productivity and efficiency of our application. For further development of LectureLine, technologies such as natural language processing and machine learning will further be implemented to maximize the functions of LectureLine. This includes textbook recommendations, access to multiple languages, and browser compatibility. Additional features will be added as well, such as saving and condensing the lecture audio by increasing the speed and reducing the time when the lecturer is not speaking so that users may listen to it efficiently.