Educational Psychology Analysis Method for Extracting Students’ Facial Information Based on Image Big Data

Abstract

At present, most of the research on academic emotions focuses on the concept, current situation, and relevance. There are not many researches on the application of artificial intelligence-based neural network facial expression recognition technology in practical teaching. With reference to image-based big data, this research integrates the application of artificial intelligence facial expression recognition technology with the research on educational theory and applies information technology to the actual teaching process, in order to promote the optimization of the teaching process and improve the learning effect. Method. A Hadoop cluster consisting of 3 nodes is built on the Linux system, and the environment required for Opencv execution is compiled for each node, which provides support for subsequent parallel optimization, feature extraction, feature fusion, and recognition of student facial images. The image data type and input and output format based on MapReduce framework are designed, and the image data is optimized by means of serialized files. The color features, texture features, and Sift features of students’ facial images and common distractors were analyzed. A parallel extraction framework of student facial image features is designed, and based on this, the student facial image feature extraction under Hadoop platform is implemented. This paper proposes a dynamic sequential facial expression recognition method that combines shallow and deep features with an attention mechanism. The relative position of facial landmarks and local area texture features based on FACS represent shallow-level features. At the same time, the structure of ALexNet is improved to extract the deep features of sequence images to express high-level semantic features. The effectiveness of the facial expression recognition system is improved by introducing three attention mechanisms: self-attention, weight-attention, and convolutional attention. Results/Discussion. Through the analysis of the teaching effect, we found that when teachers can obtain the correct student’s academic mood, they can intervene on the students’ positive academic mood. The purpose of the intervention is to improve the positive academic emotions of students. After the students receive the intervention, their academic emotions are also improved and are positively correlated with their academic performance. Through the analysis of teaching effect, the research can achieve the predetermined goal. From the specific teaching effect of this study, it is concluded that in classroom teaching, teachers should devote energy to intervene in students’ positive academic emotions, in order to improve students’ positive academic emotions, which will improve students’ academic performance and teaching.

RoboREIT: An interactive robotic tutor with instructive feedback component for requirements elicitation interview training

“…Facial expressivity plays a crucial communication role as it enables the rapid assessment of the emotional state of the other person in a conversation. In an educational setup like our interview trainer system, the affects that emerge during a learning process belong to a specific subset like frustration, boredom, confusion, and enjoyment 64 by which the learner's cognitive situation is conveyed. Monitoring the learner's facial expressions would help the instructor in shaping the course of the training by adjusting the pace of teaching or balancing the intensity of the provided content.…”

“…63 Facial expressivity plays a crucial communication role as it enables the rapid assessment of the emotional state of the other person in a conversation. In an educational setup like our interview trainer system, the affects that emerge during a learning process belong to a specific subset like frustration, boredom, confusion, and enjoyment 64 Having all these in mind, we performed an exploratory analysis on the facial expressivity of the participants in the condition of RoboREIT where the audio-visual interaction was performed with an embodied robot. Our objective is to investigate whether participants exhibited facial expressions during their interaction with Nao and, if so, to examine any differences in the expression patterns among participants.…”

Retracted: Educational Psychology Analysis Method for Extracting Students’ Facial Information Based on Image Big Data

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”