AI Chatbot Service Framework based on Backpropagation Network for Predicting Student's Performance
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Transcript of AI Chatbot Service Framework based on Backpropagation Network for Predicting Student's Performance
AI Chatbot Service Framework Based on Backpropagation Network for Predicting Student’s Performance
James Hsieh P96044168Benny Suryajaya P96057022
52%parentsworried about their children’s progress
70%studentsnot sure about their grade
AI Chatbot Service Framework Based on Backpropagation Network for Predicting Student’s Performance
Mr. Wang Features
Grade prediction by analyzing student’s behavior and their living environment
Prediction comes in 3 options: Poor, Average, Good
Fast reply via Facebook message
Demo video: https://youtu.be/_3xyxJ-ACxM
Process Flow of the System
Cloud Platform
User
via Facebook API
Send Message
Response Message
Receive Message
Response Message
via Facebook API
System Framework on Cloud Platform
Function Model
Neural Network Algorithm Module
Semantic Analysis Crawler
Core
Node.js Mongo DB
Cloud Server ( Ubuntu OS)
Service
Application 1
Application n…
Crawler: Get Any Data from Internet
Semantic Analysis : Analyze the Best Response for User
Neural Network Algorithm Module
Provide neural network algorithm for applications on this system
Get the training data and target data from database, for training model
Calculate the result of input data using NN algorithm
Data Collection
Student Performance Data Set taken from UCI Machine Learning Repository (https://archive.ics.uci.edu/ml/datasets/Student+Performance)
Source: Paulo Cortez, University of Minho, Guimarães, Portugal
Contains student achievement in secondary education in Portuguese school.
Subjects: Mathematics (395 students) & Portuguese (649 students)
Each data consists of 30 data of behaviour & environment, and 3 grade results
Data is in literal description, enumeration needed
Data Collection
Sex Age Address Family Size Parent Status … G1 G2 G3
1 M 17 Urban <= 3 Divorced 9 10 11
2 F 18 Rural > 3 Together 15 14 12
… … … … … … … … … …
649 M 18 Urban > 3 Divorced 16 17 15
Portuguese Dataset
Methodology
Method used: Backpropagation Neural Network
Reason No information in the form of function f(x) Dataset available with sample of inputs and outputs Problem is a forecasting/prediction, not an optimization
Training data Inputs: column 1 – 30 (student behavior and living environment) Targets: column 31 (grade of student in first period)
AI Chatbot Facebook-based, made with Node.js
Methodology
Method used: Backpropagation Network
Number of hidden layers: 2 Layer 1: 3 nodes Layer 2: 4 nodes
Learning rate: 0.6
Performance
Training State
Regression
Limitation
Limited model accuracy
Number of training data too few
Model parameter
Too many input parameters
Questions to be asked by chatbot also become a lot
Less interactive chatbot
Too many questions to be asked
Further Improvements
Add more training data Current training data might be too small & not representative enough
Reduce the amount of input parameters Purpose is to shorten the amount of questions asked Principal Component Analysis may be used
Make full use of the crawler Using the crawler to get translation data, Mr. Wang can be made to speak in multiple
languages
Use another algorithm In case NN is proven to be not the best choice, we can use another algorithm to
enhance the robot ability for solving other kind of problem
Conclusion
Parents & students are worried about the grade
Mr. Wang can predict how students would fare in his study
Mr. Wang uses a BPN model to predict student performance
Mr. Wang is not perfect and there are still rooms for improvement