Machine Learning and Grading Automation

The video tutorial introduces a real-time automated answer scoring system designed to assist teachers in grading essays. It covers the data set used, preprocessing techniques like stemming and spell correction, and the models implemented, including SVM, random forest, and LSTM. The evaluation metric, quadratic weighted kappa, is explained, highlighting its effectiveness over accuracy. The results show that LSTM with word2vec outperforms other models. A live demonstration illustrates the system's real-time grading capability. The project aims to ease teachers' workload and uncover student thinking patterns.

What was the main problem the presenters aimed to solve?

• Improving student attendance

• Automating the grading process

• Enhancing classroom interaction

• Reducing teaching hours

What system did the presenters introduce?

• A new teaching method

• A real-time automated answer scoring system

• A student management system

• A classroom attendance tracker

What was the source of the dataset used in the project?

• An online survey

• A university research project

• A local school

• The H foundation's automated essay scoring dataset

Which preprocessing technique involves tracing a word back to its root?

• Stop word removal

• Spell correction

• Stemming

• Tokenization

What is the purpose of using word embeddings in the project?

• To illustrate the difference between training on own dataset and pre-trained datasets

• To reduce the size of the dataset

• To improve spelling accuracy

• To enhance the speed of the system

Which basic models were initially used in the project?

• Decision Trees and Naive Bayes

• Logistic Regression and Perceptron

• Radial Basis Function with SVM and Random Forest

• K-Nearest Neighbors and Linear Regression

What type of data do LSTMs work better with?

• Graph data

• Image data

• Sequential data

• Numerical data

What does the quadratic weighted Kappa measure?

• The agreement between two raters

• The speed of the grading process

• The accuracy of the model

• The complexity of the dataset

Which model achieved the highest QWK score in the project?

• Random Forest

• Deep Neural Network

• SVM

• LSTM with Word2Vec

What was the motivation behind the project?

• To make the teacher's job easier with respect to evaluation

• To reduce the number of teachers

• To develop a new curriculum

• To increase the number of students