Use machine learning to classify a given question (easy / difficult)
Classification algorithms are machine learning algorithms that assign predefined labels or categories to input data based on its features. These algorithms aim to learn a mapping from input features to the corresponding output labels, allowing them to make predictions on new, unseen data. Several algorithms can be applied in the classification task, such as Decision Tree, Support Vector Machine, k-nearest Neighbor, Naive Bayes among others [1]. Thereby, a classification algorithm can be used to classify the given question into different difficult levels based on the question information available. However, to perform the classification task, it is necessary to establish an input and output relation between the question information.
Example:
In [2] a Learning Management System (LMS ) is proposed. Thereby this LMS can store multiple pieces of information on students through an online database, enabling it to recommend appropriate questions for each student based on an analysis of their previous responses to questions. The LMS manages learners and provides an online platform that can evaluate their skills. Questions need to be classified according to their difficulty level so that the LMS can recommend them to learners appropriately and thereby increase their learning efficiency. The questions were classified according to their difficulty level using various machine learning models, such as logistic regression (LR ), random forest (RF ), and extreme gradient boosting (xgboost )
Reference:
[1] Han, J., Kamber, M. \& Pei, J. Data Mining: Concepts and Techniques. (Morgan Kaufmann Publishers,2012 )
[2] Kim GI, Kim S, Jang B (2023 ) Classification of mathematical test questions using machine learning on datasets of learning management system questions. PLOS ONE 18(10 ): e0286989. https://doi.org/10.1371/journal.pone.0286989
[2] Kim GI, Kim S, Jang B (2023 ) Classification of mathematical test questions using machine learning on datasets of learning management system questions. PLOS ONE 18(10 ): e0286989. https://doi.org/10.1371/journal.pone.0286989
Author of the tip:
Florbela P. Fernandes
Instituto Politécnico de Bragança