Predicting Student Performance Using Machine Learning in fNIRS Data

Abstract

Increasing student involvement in classes has always been a challenge for teachers and school managers. In online learning, some interactivity mechanisms like quizzes are increasingly used to engage students during classes and tasks. However, there is a high demand for tools that evaluate the efficiency of these mechanisms. In order to distinguish between high and low levels of engagement in tasks, it is possible to monitor brain activity through functional near-infrared spectroscopy (fNIRS). The main advantages of this technique are portability, low cost, and a comfortable way for students to concentrate and perform their tasks. This setup provides more natural conditions for the experiments if compared to the other acquisition tools. In this study, we investigated levels of task involvement through the identification of correct and wrong answers of typical quizzes used in virtual environments. We collected data from the prefrontal cortex region (PFC) of 18 students while watching a video lecture. This data was modeled with supervised learning algorithms. We used random forests and penalized logistic regression to classify correct answers as a function of oxyhemoglobin and deoxyhemoglobin concentration. These models identify which regions best predict student performance. The random forest and penalized logistic regression (GLMNET with LASSO) obtained, respectively, 0.67 and 0.65 area of the ROC curve. Both models indicate that channels F4-F6 and AF3-AFz are the most relevant for the prediction. The statistical significance of these models was confirmed through cross-validation (leave-one-subject-out) and a permutation test. This methodology can be useful to better understand the teaching and learning processes in a video lecture and also provide improvements in the methodologies used in order to better adapt the presentation content.

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

“…As discussed earlier, virtual learning environments highlight unique challenges, especially engagement and communication between students and teachers. The same challenge was discussed in a recently published article focusing on the level of student involvement by analyzing live interactive sessions [8]. During the sessions, 18 students were asked various questions by the moderator.…”

“…The main purpose of that research was to identify student engagement during various tasks required during the virtual session. Random forest and logistic regression were two models used in that study that obtained 66% and 63% accuracy, respectively [8]. One of the key features of ML is a training model using different dependent and independent attributes, which further depends on the type of learning algorithm (supervised, semisupervised, or unsupervised).…”

Predicting Students’ Academic Performance with Conditional Generative Adversarial Network and Deep SVM

“…An accuracy of 88.3% was achieved with an optimized RF. Oku et al [19] used two machine learning algorithms to determine student engagement in performing multiple tasks during an online session. The LR and RF models were adopted and achieved accuracy scores of 66% and 63%, respectively.…”

Priming Engineers to Think About Sustainability: Cognitive and Neuro-Cognitive Evidence to Support the Adoption of Green Stormwater Design

“…It provides a more direct measure compared to self-evaluation instruments like the NASA-TLX survey questions ( Hart, 2006 ). There is high level of accuracy when classifying the level of cognitive load using the area under the oxy-Hb curve ( Gao et al, 2020 ; Oku and Sato, 2021 ).…”