The performance of some machine learning approaches and a rich context model in student answer prediction

Abstract

Web-based learning systems with adaptive capabilities to personalize content are becoming nowadays a trend in order to offer interactive learning materials to cope with a wide diversity of students attending online education. Learners’ interaction and study practice (quizzing, reading, exams) can be analyzed in order to get some insights into the student’s learning style, study schedule, knowledge, and performance. Quizzing might be used to help to create individualized/personalized spaced repetition algorithm in order to improve long-term retention of knowledge and provide efficient learning in online learning platforms. Current spaced repetition algorithms have pre-defined repetition rules and parameters that might not be a good fit for students’ different learning styles in online platforms. This study uses different machine learning models and a rich context model to analyze quizzing and reading records from e-learning platform called Hypocampus in order to get some insights into the relevant features to predict learning outcome (quiz answers). By knowing the answer correctness, a learning system might be able to recommend personalized repetitive schedule for questions with maximizing long-term memory retention. Study results show that question difficulty level and incorrectly answered previous questions are useful features to predict the correctness of student’s answer. The gradient-boosted tree and XGBoost models are best in predicting the correctness of the student’s answer before answering a quiz. Additionally, some non-linear relationship was found between the reading learning material behavior in the platform and quiz performance that brings added value to the accuracy for all used models.

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

“…ML algorithms are widely used for prediction purposes and applied to provide solutions to questions such as global solar radiation [9], accuracy in determining the mortality rate in COVID-19 patients [10], and efficient processes for manufacturing industries [11]. In addition, ML algorithms assist the educational sector to evaluate student performance [12], forecast student dropout rates in any course [13], and understand students' unique learning styles [14]. Due to ML's vast and dynamic implementation and its capability to learn from any dataset, and predict and classify future transactions, we have selected multiple ML algorithms for this study.…”

Framework for the Adaptive Learning of Higher Education Students in Virtual Classes in Peru Using CRISP-DM and Machine Learning

“…Literature shows that five ML algorithms have been applied for the following purposes: Prediction of the student's performance (Evangelista, 2021;Lincke et al, 2021;Qiu et al, 2022;Sense et al, 2021), recommendation of appropriate actions to improve the quality of courses (Hosny and Elkorany, 2022;Yanes et al, 2020), recommendation of individualized learning resources (Arsovic and Stefanovic, 2020;Cheng and Wang, 2021;Ling and Chiang, 2022), prediction of the best academic engineering program for the student (Ezz and Elshenawy, 2020). Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) have been used to predict the performance of students' evaluation, being DT and RF the most accurate algorithms, exceeding 90 % (Evangelista, 2021).…”

“…To mitigate the problem, various proposals have been made. Lincke et al (2021) analyzed evaluation records to predict learning outcomes for teachers to take action and increase the number of students passing. Singh et al (2022) recommend an individual study plan for each student and define tutoring strategies based on the student's learning style and level of knowledge through the SeisTutor system.…”

“…Yang et al (2021) implemented a summarized class material recommendation system to refer students to specific pages containing relevant information that they have to thoroughly study before class to improve academic performance. However, Lincke et al (2021) do not evaluate relevant variables other than grades to obtain a more accurate prediction. Singh et al (2022), the tool is focused on recommending actions for the teacher to support students, which does not allow their autonomous development.…”

“…Various studies on adaptive learning have been found in the literature, such as the application of Artificial Intelligence (IA) algorithms (Hamim et al, 2021;Lincke et al, 2021;Yanes et al, 2020), pedagogical methodologies applied in adaptive learning (Clark and Kaw, 2020;Clark et al, 2022) and the consideration of different student aspects to individualize learning (Hariyanto and Köhler, 2020;White, 2020).…”