During my graduation in 2020, I've built an AI with my friend that can take inputs from students and evaluate them based on Bloom's Taxonomy.
Bloom's taxonomy is a standard model used to evaluate the performance of students in academics on the basis of six categories. The six categories are as follows :
- Create
- Evaluate
- Analyze
- Apply
- Understand
- Remember
Knowing the exact scores in these categories, an instructor can quickly and efficiently help the student improve their understanding.
Bloom's taxonomy is a set of three hierarchical models used to classify educational learning objectives into levels of complexity and specificity. The three lists cover the learning objectives in cognitive, affective and sensory domains. The cognitive domain list has been the primary focus of most traditional education and is frequently used to structure curriculum learning objectives, assessments and activities.
The project was deployed directly on a humanoid robot. The robot in question is "NAO" which is developed by SoftBank robotics.
Prototype demo of the project
In the video, the NAO robot is not using google assistance or Alexa, we had built our own parser to properly incorporate the features we needed.
How we built it
The project was built using Python and Java.
We had also used Heroku cloud database to store information and host a frontend to quickly add/remove questions to the robot. Thanks to GitHub student pack, We had received credits from Heroku to afford the worker dyno.
JetBrains' IntelliJ IDEA and PyCharm has been a HUGE! help throughout the project.
SendGrid was used to email students their respective result cards.
We had also used IBM Watson's cloud and speech engines.
We've used several similarity and semantic indices to check for a question's exact meaning and evaluate it. example, if there is a question that states "What is Java?" and someone answers "Java is a programming language" or "Java is used to program cross-platform applications" then both of these will be evaluated as right and will be given grades. We are using a set of answers which are matched with the answer received from the student based on these indices. We are not doing a keyword match.
Additional Thoughts
At the start of this project we never thought we would be able to do it but thanks to all the support from GitHub student pack it was possible. we had faced multiple obstacles while building this project but solving each of it one by one was very satisfying. It was a very research-oriented project which I think would definitely help us automate exams completely. we had to collect responses from students all over the university so that we can find an optimal threshold for similarity to work, finding proper weights for the questions and points etc. I would say it was a very fun project.
Unfortunately, the code was stored in the university's local computer and it is closed due to the pandemic so we were not able to upload it on GitHub.
Finally, I would like to give a huge shoutout to everyone who had helped me during the project and special thanks to GitHub student pack for providing such amazing tools for free.
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