Perfecting Prismia Clustering via Deep Learning

Background and context:

Prismia is a virtual chat interface that allows students and instructors to have dynamic interactions over course material. It was mainly created to fill a gap in what most other classroom evaluators lack. Alternatives like iClicker and LearningCatalytics are pretty limited to only allowing instructors to ask multiple choice questions. Moreover, giving customizable feedback to individuals is very challenging on those platforms. Prismia offers a solution to those issues by allowing students and instructors to have more of a free-form conversation on the material, and ask questions during any points of confusion.

Current clustering:

Currently, the clustering method for grouping-student responses is quite simple; the current method uses a sentence-BERT model to transform the sentences and encode the messages, and then clusters are made by a simple KMeans model, grouping based on the number of clusters specified by the instructor in Prismia.

Code from Samuel Watson
https://arxiv.org/pdf/1908.10084.pdf
Centroids (in purple) are added to the data
Euclidean Distances are calculated

Selecting and creating a test set:

As previously mentioned, our goal for this project is to improve the clustering, specifically when dealing with more technical responses that require niche knowledge of deep learning. In order to actually assess whether or not our efforts are improving the clustering, we will need to develop a test data set and select an evaluation metric.

Clusters from our test-sentences are represented in the matrix above

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