Matching Items (5)
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- All Subjects: Machine Learning
- All Subjects: Multitasking
Description
With social technology on the rise, it is no surprise that young students are at the forefront of its use and impact, particularly in the realm of education. Due to greater accessibility to technology, media multitasking and task-switching are becoming increasingly prominent in learning environments. While technology can have numerous benefits, current literature, though somewhat limited in this scope, overwhelmingly shows it can also be detrimental for academic performance and learning when used improperly. While much of the existing literature regarding the impact of technology on multitasking and task-switching in learning environments is limited to self-report data, it presents important findings and potential applications for modernizing educational institutions in the wake of technological dependence. This literature review summarizes and analyzes the studies in this area to date in an effort to provide a better understanding of the impact of social technology on student learning. Future areas of research and potential strategies to adapt to rising technological dependency are also discussed, such as using a brief "technology break" between periods of study. As of yet, the majority of findings in this research area suggest the following: multitasking while studying lengthens the time required for completion; multitasking during lectures can affect memory encoding and comprehension; excessive multitasking and academic performance are negatively correlated; metacognitive strategies for studying have potential for reducing the harmful effects of multitasking; and the most likely reason students engage in media-multitasking at the cost of learning is the immediate emotional gratification. Further research is still needed to fill in gaps in literature, as well as develop other potential perspectives relevant to multitasking in academic environments.
ContributorsKhanna, Sanjana (Author) / Roberts, Nicole (Thesis director) / Burleson, Mary (Committee member) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Stress is one of the critical factors in daily lives, as it has a profound impact onperformance at work and decision-making processes. With the development of IoT
technology, smart wearables can handle diverse operations, including networking and
recording biometric signals. Also, it has become easier for individual users to selfdetect stress with recorded data since these wearables as well as their accompanying
smartphones now have data processing capability. Edge computing on such devices
enables real-time feedback and in turn preemptive identification of reactions to stress.
This can provide an opportunity to prevent more severe consequences that might
result if stress is unaddressed. From a system perspective, leveraging edge computing
allows saving energy such as network bandwidth and latency since it processes data in
proximity to the data source. It can also strengthen privacy by implementing stress
prediction at local devices without transferring personal information to the public
cloud.
This thesis presents a framework for real-time stress prediction using Fitbit and
machine learning with the support from cloud computing. Fitbit is a wearable tracker
that records biometric measurements using optical sensors on the wrist. It also provides developers with platforms to design custom applications. I developed an application for the Fitbit and the user’s accompanying mobile device to collect heart rate
fluctuations and corresponding stress levels entered by users. I also established the
dataset collected from police cadets during their academy training program. Machine
learning classifiers for stress prediction are built using classic models and TensorFlow
in the cloud. Lastly, the classifiers are optimized using model compression techniques
for deploying them on the smartphones and analyzed how efficiently stress prediction
can be performed on the edge.
ContributorsSim, Sang-Hun (Author) / Zhao, Ming (Thesis advisor) / Roberts, Nicole (Committee member) / Zou, Jia (Committee member) / Arizona State University (Publisher)
Created2022
Description
As threats emerge and change, the life of a police officer continues to intensify. To better support police training curriculums and police cadets through this critical career juncture, this thesis proposes a state-of-the-art framework for stress detection using real-world data and deep neural networks. As an integral step of a larger study, this thesis investigates data processing techniques to handle the ambiguity of data collected in naturalistic contexts and leverages data structuring approaches to train deep neural networks. The analysis used data collected from 37 police training cadetsin five different training cohorts at the Phoenix Police Regional Training Academy. The data was collected at different intervals during the cadets’ rigorous six-month training course. In total, data were collected over 11 months from all the cohorts combined. All cadets were equipped with a Fitbit wearable device with a custom-built application to collect biometric data, including heart rate and self-reported stress levels. Throughout the data collection period, the cadets were asked to wear the Fitbit device and respond to stress level prompts to capture real-time responses. To manage this naturalistic data, this thesis leveraged heart rate filtering algorithms, including Hampel, Median, Savitzky-Golay, and Wiener, to remove potentially noisy data. After data processing and noise removal, the heart rate data and corresponding stress level labels are processed into two different dataset sizes. The data is then fed into a Deep ECGNet (created by Prajod et al.), a simple Feed Forward network (created by Sim et al.), and a Multilayer Perceptron (MLP) network for binary classification. Experimental results show that the Feed Forward network achieves the highest accuracy (90.66%) for data from a single cohort, while the MLP model performs best on data across cohorts, achieving an 85.92% accuracy. These findings suggest that stress detection is feasible on a variate set of real-world data using deepneural networks.
ContributorsParanjpe, Tara Anand (Author) / Zhao, Ming (Thesis advisor) / Roberts, Nicole (Thesis advisor) / Duran, Nicholas (Committee member) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2023
Description
The choices of an operator under heavy cognitive load are potentially critical to overall safety and performance. Such conditions are common when technological failures arise, and the operator is forced into multi-task situations. Task switching choice was examined in an effort to both validate previous work concerning a model of task overload management and address unresolved matters related to visual sampling. Using the Multi-Attribute Task Battery and eye tracking, the experiment studied any influence of task priority and difficulty. Continuous visual attention measurements captured attentional switches that do not manifest into behaviors but may provide insight into task switching choice. Difficulty was found to have an influence on task switching behavior; however, priority was not. Instead, priority may affect time spent on a task rather than strictly choice. Eye measures revealed some moderate connections between time spent dwelling on a task and subjective interest. The implication of this, as well as eye tracking used to validate a model of task overload management as a whole, is discussed.
ContributorsZabala, Garrett (Author) / Gutzwiller, Robert S (Thesis advisor) / Cooke, Nancy J. (Committee member) / Gray, Rob (Committee member) / Arizona State University (Publisher)
Created2020
Description
As threats emerge, change, and grow, the life of a police officer continues to intensify. To help support police training curriculums and police cadets through this critical career juncture, this study proposes a state of the art approach to stress prediction and intervention through wearable devices and machine learning models. As an integral first step of a larger study, the goal of this research is to provide relevant information to machine learning models to formulate a correlation between stress and police officers’ physiological responses on and off on the job. Fitbit devices were leveraged for data collection and were complemented with a custom built Fitbit application, called StressManager, and study dashboard, termed StressWatch. This analysis uses data collected from 15 training cadets at the Phoenix Police Regional Training Academy over a 13 week span. Close collaboration with these participants was essential; the quality of data collection relied on consistent “syncing” and troubleshooting of the Fitbit devices. After the data were collected and cleaned, features related to steps, calories, movement, location, and heart rate were extracted from the Fitbit API and other supplemental resources and passed through to empirically chosen machine learning models. From the results of these models, we formulate that events of increased intensity combined with physiological spikes contribute to the overall stress perception of a police training cadet
ContributorsParanjpe, Tara (Author) / Zhao, Ming (Thesis director) / Roberts, Nicole (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05