Students have been utilizing IT services such as social media, video games, and streaming services for entertainment, communication and even for coping mechanism to stress and academic workload. However, improper and heavy usage of these services can lead to bad habits and practices that can negatively affect their physical well-being particularly the quality of sleep and academic performance. It is important to investigate how to protect the students from the negative impact of excessive use of technology. This study aims to investigate how high usage rate of IT services such as social media, video games, and streaming services, can negatively affect the academic performance and sleep quality of high school students. Thirty (30) STEM high school students participated in this study over 3 different periods of academic workload. The results of this study show that IT services have no negative impact on the academic performance of the respondents and even beneficial since they are used as coping mechanism when faced with stress and heavy workload. However, most believe that such services have negative impact on their quality of sleep.

Facial landmarks may be used to localize the movement of facial muscles that help identify an emotion. It is important that these points are appropriately represented to achieve a successful emotion recognition rate. In this paper, the extraction of 68 facial landmarks, normalization methods and classification of 7 basic emotions are presented. The Cohn-Kanade Database is used as a test bed for the different emotion recognition tasks. The images are normalized by transforming the inputs based on similarity (CKCT) and the mean shape (CKMS). Forward Search and Principal Component Analysis are used to identify the most important features among the 68 facial points. Decision Tree, Logistic Regression, K-Nearest Neighbor and Multilayer Perceptron algorithms are used in building classifiers on reduced and complete feature set. It is interesting to note that facial points in the mouth area are found to be significant in the classification of emotions.

With an electroencephalogram (EEG) sensor mounted on their head while learning mathematics using two computer-based learning software, EEG signals were collected from fifty six (56) academically-gifted students of ages 11 to 14. The EEG signals are used to predict four academic emotions, namely frustrated, confused, bored, and interested. It is shown that emotion classification accuracy is improved by selective prediction - performed only when a pre-determined proportion of EEG feature values deviate significantly from the baseline mean. The experiments on instances, where 0%, 2%, 4%, and up to 20% of the features are signifi- cantly stronger EEG signals, show that the accuracy rate of decision trees increases from 0.50, 0.59, and 0.45 (for instances with 0% special event features) to 0.74, 0.75, and 0.66 (for in- stances with 20% special event features) for predicting frustrated, confused and bored, respec- tively. Accuracy for predicting interested does not increase like for the other three emotions.

Academic emotions such as confidence, excitement, frustration and interest may be predicted based on brainwaves signals. It is shown that the prediction rate can be improved further when the data from brainwaves signals are complemented by data based on mouse click behavior. Twenty -five (25) undergraduate students were asked to use a math tutoring software while an EEG se nsor was attached to their head to capture their brainwaves sign als throughout the learning session. At the same time, mouse -click features such as the number of clicks, the duration of each click and the distance traveled by the mouse were automatically captured. Using a Multi -Layered Perceptron classifier, classifica tion using brainwaves data alone had accuracy rates of 54 to 88%. Prediction rates based purely on mouse features had accuracy rates of only 32 to 48%. When the two input modalities are combined, accuracy rates increased to up to 92%. Furthermore, the expe riments confirmed that the predication accuracy rate increases as the number of feature values that deviate significantly from the mean increases. In particular, the prediction rates exceed 80% when at least 33% of the features have values that deviate from the mean by more than 1 standard deviation.

Students who performed well in their college mathematics subjects, referred to here as academic achievers, were divided into two groups according to the self-reported level of difficulty faced by them while performing several programming tasks in LOGO - a programming language using turtle-graphics. It is shown that, to some extent, the level of difficulty of tasks faced by academic achievers can be predicted, based on their measured affective levels of excitement, frustration and engagement. These affective states are measured using brainwaves sensors that are attached to the head of the student. Those who assessed the learning experience as easy tend to have higher levels of excitement than those who reported to have experienced difficulty in learning the language. On the other hand, the level of frustration among those having difficulty with the tasks registered slightly higher frustration levels. Three machine learning algorithms were used to predict whether or not a learner finds the tasks to be easy. The average predictive accuracy is 70%.