This project investigates the benefits and practicality of applying Zimmerman's (1994) dimensions of self-regulated learning to co-curricular activities so as to increase students' willingness and opportunities to communicate in English in the hallways of intensive English programs. Three of these dimensions (social environment, motivation, and physical environment) work together to create a semi-structured liaison between in and out of class communicative environments and give students an occasion, location, and motivation to speak English with one another. To evaluate the effectiveness of such activities and conceptualize a means in which to assist intensive English programs effectively incorporate co-curricular activities in their curricula, the principal researcher designed and conducted a co-curricular activity based self-regulated learning. She obtained student feedback using surveys and interviews and found that the majority of students spoke mostly English, made new friends, practiced listening and speaking skills, and enjoyed themselves at this activity. As a result of this data, the principal researcher created a booklet to assist in the planning of future co-curricular activities. While only a preliminary study, this data and resulting booklet have great potential to fill the hallways of intensive English programs with English and thus create a holistic learning environment.

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The purpose of this study is a critical examination of this Committee with emphasis on its methods, procedure, and worth.

The problem is to study the aims of the University Interscholastic League of Texas and evaluate them in the light of the aims of education as set up by authorities in the fields of education and to study the present method of conducting the League activities in Johnson County and evaluate the activities in the light of the aims of education and the data obtained from superintendents in the Conference B schools of Johnson County.

The purposes of this study are: (1) to determine the popularity of extra-curricular organizations existing in the school as a means of indicating the successful and the unsuccessful groups in the school, (2) to establish the relationships of certain factors to the popularity of the activities so that efforts may be made to improve less successful activities, (3) to discover the extent and the popularity of social activities provided by the city of Cleburne, Texas, for the high school students in order that administrators of the school may have more insight into the extent of the need of high school students for social activities sponsored by the school.

This study of the extra-curricular activities of the Woodrow Wilson High School of Dallas, Texas, was made to determine who and why each of the activities developed, the life span of each, the percentage of the student body enrolled in the various groups, and the value of each extra-curricular activity from the viewpoint of the teachers, of the students, and of authorities in the field.

Wearable activity classifiers, so far, have been able to perform well with simple activities, strictly-scripted activities, and application-specific activities. In addition, current classification systems suffer from using impractical tight-fitting sensor networks, or only use one loose-fitting sensor node that cannot capture much movement information (e.g., smartphone sensors and wrist-worn sensors). These classifiers either do not address the bigger picture of making activity recognition more practical and being able to recognize more complex and naturalistic activities, or try to address this issue but still perform poorly on many fronts. This dissertation works toward having practical, in-the-wild, and reusable wearable activity classifiers by taking several steps that include the four following main contributions. The dissertation starts by quantifying users' needs and expectations from wearable activity classifiers to set a framework for designing ideal wearable activity classifiers. Data collected from user studies and interviews is gathered and analyzed, then several conclusions are made to set a framework of essential characteristics that ideal wearable activity classification systems should have. Afterwards, this dissertation introduces a group of datasets that can be used to benchmark different types of activity classifiers and can accommodate for a variety of goals. These datasets help comparing different algorithms in activity classification to assess their performance under various circumstances and with different types of activities. The third main contribution consists of developing a technique that can classify complex activities with wide variations. Testing this technique shows that it is able to accurately classify eight complex daily-life activities with wide variations at an accuracy rate of 93.33%, significantly outperforming the state-of-the-art. This technique is a step forward toward classifying real-life natural activities performed in an environment that allows for wide variations within the activity. Finally, this dissertation introduces a method that can be used on top of any activity classifier that allows access to its matching scores in order to improve its classification accuracy. Testing this method shows that it improves classification results by 11.86% and outperforms the state-of-the-art, therefore taking a step forward toward having reusable activity classification techniques that can be used across users, sensor domains, garments, and applications. / Ph. D. / Wearable activity classifiers are wearable systems that can recognize human activities. These systems are needed in many applications. Nowadays, they are mainly used for fitness purposes – e.g., Fitbits and Apple Watches – and in gaming consoles – e.g., Microsoft Kinect. However, these systems are still far from being ideal. They still miss many characteristics that would make them practical and usable for different purposes, such as in medical applications, industrial applications, and other types of applications where recognizing human activities can be useful. This dissertation works toward having practical wearable activity classifiers that can be reused for different purposes in real-life scenarios. Four contributions are introduced in this dissertation. The dissertation starts by quantifying users’ needs and expectations from wearable activity classifiers and sets a framework for designing them. Afterward, this dissertation introduces a group of datasets that can be used to benchmark and compare different types of activity classifiers. The third main contribution consists of a technique that enables activity classifiers to recognize complex activities having a wide range of variations within each activity. Finally, this dissertation introduces a method that can be used to improve the recognition accuracy of activity classifiers.