Classification of Student Attentiveness on Video Data Using Facial Expressions Extracted from Minimal Image Sequences

Attentiveness is an important indication for student success as it can demonstrate comprehension and the effectiveness of the teaching technique. To facilitate an effective learning environment, tracking the overall attentiveness of each student is very important. However, it becomes challenging for an instructor when many students are physically present in the classroom, when attendance is via video conference, or a combination of both. One way that overall attentiveness can be expressed is as a function of a temporal sequence of facial expressions. This thesis investigates deep learning models\u27 performance on the classification of attentiveness using extracted sequential facial expressions from recorded video data and compares the results. Models are based on long short-term memory networks(LSTM) and convolutional neural networks(CNN). CNN layers are involved in feature extraction, and LSTM layers are involved in sequence prediction. We took 30 minimal image sequences from each 10-second video in the publicly available DAiSEE engagement dataset. We extracted facial emotions from images using an emotion detection algorithm. Our hybrid model(CNN-LSTM) outperformed the LSTMs only model by achieving 89.6% accuracy

Comparing the quality of life of cities that gained and lost population: An assessment with DEA and the Malmquist index

This study compares the quality of life (QoL) of cities that lost population with that of cities that gained population. A unique dataset composed of observations for 11 dimensions of QoL for all mainland Portuguese cities is used. By employing a non-parametric approach (data envelopment analysis), and by using a Malmquist-type index, this study identifies differences in QoL between the group of cities that lost population and the group of cities that gained population, as well as differences within each group. Despite the heterogeneity in cities that shrunk, this group presents, on average, higher QoL than cities that have grown.Fundacao para a Ciencia e a TecnologiaPortuguese Foundation for Science and Technology [UID/ECO/04007/2019 UID/ECO/04007/2019]FEDER/COMPETEEuropean Union (EU) [POCI-01-0145-FEDER-007659]info:eu-repo/semantics/publishedVersio