Grace Briones

The primary achievement of distance and regular students is managing time effectively. Mismanagement disturbs the academic achievements of learners. Time administration plays a significant role in improving learners' performance and accomplishments. It is a skill to manage time and every learner must familiar and command on this skill for the sake of better results. A student can only survive if he/she has ability of time utilization properly. The motive of this study was to establish path relationship of time management and academic achievement of students in distance learning institutions through structural equation modeling. The population of the study was consisted of distance learning students of Virtual and Allama Iqbal Open Universities. Four hundred participants belong to different departments were participated conveniently in this study. Self-developed questionnaires regarding time management and academic achievement on five point likert scale were used to collect data from respondents. Validity was ensured by field experts before using scales. Internal consistency was checked among items by applying Cronbach alpha, rho-a, and composite reliability for reliability confirmation of scales. All three techniques verified that instruments are valid and reliable. Data were analyzed by smartly for path analysis. There was strong positive relationship between time management and academic achievement r = .778 of distance learning students. It is concluded that both variables are associated with each other and time utilization skill affects students learning seriously. Students of web-based learning may more focus on proper time managing ability for better academic success.

Emotion recognition is one of the important highlights of human emotional intelligence and has long been studied to be incorporated with machine intelligence argued to make machines even more intelligent. This paper aims to contribute to this field of study by enabling machines to recognize emotion from facial electromyogram (EMG) signals. This includes a compilation of the groups attempt to recognize basic facial expressions namely happy, angry, and sad through the use of EMG signals from facial muscles. The group extracted features from the three EMG signals from the face of two human subjects, a male and a female, and analyzed these features to serve as feature templates. Using a minimum-distance classifier, recognition rates exceeded the target accuracy - 85 percent - reaching 94.44 percent for both the male and female subjects.