Abstract
This study proposes an approach for process-focused assessment (PFA) utilizing the concept of deep neural networks with a sequence of facial images. Recently, process-based assessment has received significant attention compared to result-based assessment in the field of education. Continuously evaluating and quantifying student engagement, as well as understanding and interacting with teachers in study activities are considered important factors. However, to achieve PFA, from the technical and systematic perspectives, the real-time monitoring of the learning process of students is desired, which requires time consumption and extremely high attention to each student. This study proposes an approach to develop an efficient method for evaluating the process of learning and studying students in real time using facial images. We developed a method for PFA by learning facial expressions using a deep neural network model. The model learns and classifies facial expressions into three categories: easy, neutral, and difficult. Because the demand for online learning is increasing, PFA is required to achieve efficient, convenient, and confident assessment. This study chiefly considers a sequence of 2D image data of students solving some exam problems. The experimental results demonstrate that the proposed approach is feasible and can be applied to PFA in classrooms.
Highlights
The recognition of objects, expressions, and emotions using visual images is a challenging task in computer vision and pattern recognition
Local linear embedding (LLE), self-organizing map (SOM), curvilinear component analysis (CCA), curvilinear distance analysis (CDA), and other approaches have been used for nonlinear dimensionality reduction in recognition and classification
We aim to accomplish a framework for process-focused assessment (PFA) by employing the concept of the classification of facial expressions using a deep neural network (DNN) model
Summary
The recognition of objects (e.g., faces), expressions, and emotions using visual images is a challenging task in computer vision and pattern recognition. In the field of face recognition, information, such as using landmarks, 3D curves representing the geometric shape of faces, intensity, and eigenfaces, has provided successful recognition results This area is fundamental and central in practical fields such as certification, surveillance, security, and finance. A major challenge of face (or any object) recognition is to classify objects that are usually defined in high-dimensional space and represent them in a lower dimensional space. Such an excessive number of pixels leads to the degradation of processing speed, hindering application to real practices. Because the original data are usually defined in high-dimensional space, in practice, feature extraction and representation in a non-linear manner are of interest. The details of the review on emotion recognition can be found in [23,24]
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