Matching Items (16)
Filtering by
- All Subjects: Machine Learning
- All Subjects: Evaluation
- Creators: Bansal, Ajay
Description
Academia is not what it used to be. In today’s fast-paced world, requirements
are constantly changing, and adapting to these changes in an academic curriculum
can be challenging. Given a specific aspect of a domain, there can be various levels of
proficiency that can be achieved by the students. Considering the wide array of needs,
diverse groups need customized course curriculum. The need for having an archetype
to design a course focusing on the outcomes paved the way for Outcome-based
Education (OBE). OBE focuses on the outcomes as opposed to the traditional way of
following a process [23]. According to D. Clark, the major reason for the creation of
Bloom’s taxonomy was not only to stimulate and inspire a higher quality of thinking
in academia – incorporating not just the basic fact-learning and application, but also
to evaluate and analyze on the facts and its applications [7]. Instructional Module
Development System (IMODS) is the culmination of both these models – Bloom’s
Taxonomy and OBE. It is an open-source web-based software that has been
developed on the principles of OBE and Bloom’s Taxonomy. It guides an instructor,
step-by-step, through an outcomes-based process as they define the learning
objectives, the content to be covered and develop an instruction and assessment plan.
The tool also provides the user with a repository of techniques based on the choices
made by them regarding the level of learning while defining the objectives. This helps
in maintaining alignment among all the components of the course design. The tool
also generates documentation to support the course design and provide feedback
when the course is lacking in certain aspects.
It is not just enough to come up with a model that theoretically facilitates
effective result-oriented course design. There should be facts, experiments and proof
that any model succeeds in achieving what it aims to achieve. And thus, there are two
research objectives of this thesis: (i) design a feature for course design feedback and
evaluate its effectiveness; (ii) evaluate the usefulness of a tool like IMODS on various
aspects – (a) the effectiveness of the tool in educating instructors on OBE; (b) the
effectiveness of the tool in providing appropriate and efficient pedagogy and
assessment techniques; (c) the effectiveness of the tool in building the learning
objectives; (d) effectiveness of the tool in document generation; (e) Usability of the
tool; (f) the effectiveness of OBE on course design and expected student outcomes.
The thesis presents a detailed algorithm for course design feedback, its pseudocode, a
description and proof of the correctness of the feature, methods used for evaluation
of the tool, experiments for evaluation and analysis of the obtained results.
are constantly changing, and adapting to these changes in an academic curriculum
can be challenging. Given a specific aspect of a domain, there can be various levels of
proficiency that can be achieved by the students. Considering the wide array of needs,
diverse groups need customized course curriculum. The need for having an archetype
to design a course focusing on the outcomes paved the way for Outcome-based
Education (OBE). OBE focuses on the outcomes as opposed to the traditional way of
following a process [23]. According to D. Clark, the major reason for the creation of
Bloom’s taxonomy was not only to stimulate and inspire a higher quality of thinking
in academia – incorporating not just the basic fact-learning and application, but also
to evaluate and analyze on the facts and its applications [7]. Instructional Module
Development System (IMODS) is the culmination of both these models – Bloom’s
Taxonomy and OBE. It is an open-source web-based software that has been
developed on the principles of OBE and Bloom’s Taxonomy. It guides an instructor,
step-by-step, through an outcomes-based process as they define the learning
objectives, the content to be covered and develop an instruction and assessment plan.
The tool also provides the user with a repository of techniques based on the choices
made by them regarding the level of learning while defining the objectives. This helps
in maintaining alignment among all the components of the course design. The tool
also generates documentation to support the course design and provide feedback
when the course is lacking in certain aspects.
It is not just enough to come up with a model that theoretically facilitates
effective result-oriented course design. There should be facts, experiments and proof
that any model succeeds in achieving what it aims to achieve. And thus, there are two
research objectives of this thesis: (i) design a feature for course design feedback and
evaluate its effectiveness; (ii) evaluate the usefulness of a tool like IMODS on various
aspects – (a) the effectiveness of the tool in educating instructors on OBE; (b) the
effectiveness of the tool in providing appropriate and efficient pedagogy and
assessment techniques; (c) the effectiveness of the tool in building the learning
objectives; (d) effectiveness of the tool in document generation; (e) Usability of the
tool; (f) the effectiveness of OBE on course design and expected student outcomes.
The thesis presents a detailed algorithm for course design feedback, its pseudocode, a
description and proof of the correctness of the feature, methods used for evaluation
of the tool, experiments for evaluation and analysis of the obtained results.
ContributorsRaj, Vaishnavi (Author) / Bansal, Srividya (Thesis advisor) / Bansal, Ajay (Committee member) / Mehlhase, Alexandra (Committee member) / Arizona State University (Publisher)
Created2018
Description
In this project, the use of deep neural networks for the process of selecting actions to execute within an environment to achieve a goal is explored. Scenarios like this are common in crafting based games such as Terraria or Minecraft. Goals in these environments have recursive sub-goal dependencies which form a dependency tree. An agent operating within these environments have access to low amounts of data about the environment before interacting with it, so it is crucial that this agent is able to effectively utilize a tree of dependencies and its environmental surroundings to make judgements about which sub-goals are most efficient to pursue at any point in time. A successful agent aims to minimizes cost when completing a given goal. A deep neural network in combination with Q-learning techniques was employed to act as the agent in this environment. This agent consistently performed better than agents using alternate models (models that used dependency tree heuristics or human-like approaches to make sub-goal oriented choices), with an average performance advantage of 33.86% (with a standard deviation of 14.69%) over the best alternate agent. This shows that machine learning techniques can be consistently employed to make goal-oriented choices within an environment with recursive sub-goal dependencies and low amounts of pre-known information.
ContributorsKoleber, Derek (Author) / Acuna, Ruben (Thesis director) / Bansal, Ajay (Committee member) / W.P. Carey School of Business (Contributor) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
There is a serious need for early childhood intervention practices for children who are living at or below the poverty line. Since 1965 Head Start has provided a federally funded, free preschool program for children in this population. The City of Phoenix Head Start program consists of nine delegate agencies, seven of which reside in school districts. These agencies are currently not conducting local longitudinal evaluations of their preschool graduates. The purpose of this study was to recommend initial steps the City of Phoenix grantee and the delegate agencies can take to begin a longitudinal evaluation process of their Head Start programs. Seven City of Phoenix Head Start agency directors were interviewed. These interviews provided information about the attitudes of the directors when considering longitudinal evaluations and how Head Start already evaluates their programs through internal assessments. The researcher also took notes on the Third Grade Follow-Up to the Head Start Executive Summary in order to make recommendations to the City of Phoenix Head Start programs about the best practices for longitudinal student evaluations.
ContributorsArmfield, Jessica Ann (Author) / Duggan, Mary Anne (Thesis director) / Dumka, Larry (Committee member) / Barrett, The Honors College (Contributor) / T. Denny Sanford School of Social and Family Dynamics (Contributor) / School of International Letters and Cultures (Contributor)
Created2014-05
Description
The purpose of this study was to provide a foundation for a plan for evaluations of the impact of the Learning Center on elementary school children with respect to academic achievement and school-related behaviors. Exploratory pre- and posttest data were collected and analyzed and recommendations were provided for a broader evaluation plan to be used in the future. The experience from the exploratory evaluation, limitations and the recommendations in this study can be used by Chicanos Por La Causa to strengthen the Learning Center and thereby optimize the benefit to the children served within the San Marina residential community.
ContributorsLodhi, Osman Sultan (Author) / Roosa, Mark (Thesis director) / Dumka, Larry (Committee member) / Perez, Norma (Committee member) / Barrett, The Honors College (Contributor) / Department of Chemistry and Biochemistry (Contributor) / Department of Psychology (Contributor)
Created2014-05
Description
Alzheimer’s disease (AD), is a chronic neurodegenerative disease that usually starts slowly and gets worse over time. It is the cause of 60% to 70% of cases of dementia. There is growing interest in identifying brain image biomarkers that help evaluate AD risk pre-symptomatically. High-dimensional non-linear pattern classification methods have been applied to structural magnetic resonance images (MRI’s) and used to discriminate between clinical groups in Alzheimers progression. Using Fluorodeoxyglucose (FDG) positron emission tomography (PET) as the pre- ferred imaging modality, this thesis develops two independent machine learning based patch analysis methods and uses them to perform six binary classification experiments across different (AD) diagnostic categories. Specifically, features were extracted and learned using dimensionality reduction and dictionary learning & sparse coding by taking overlapping patches in and around the cerebral cortex and using them as fea- tures. Using AdaBoost as the preferred choice of classifier both methods try to utilize 18F-FDG PET as a biological marker in the early diagnosis of Alzheimer’s . Addi- tional we investigate the involvement of rich demographic features (ApoeE3, ApoeE4 and Functional Activities Questionnaires (FAQ)) in classification. The experimental results on Alzheimer’s Disease Neuroimaging initiative (ADNI) dataset demonstrate the effectiveness of both the proposed systems. The use of 18F-FDG PET may offer a new sensitive biomarker and enrich the brain imaging analysis toolset for studying the diagnosis and prognosis of AD.
ContributorsSrivastava, Anant (Author) / Wang, Yalin (Thesis advisor) / Bansal, Ajay (Thesis advisor) / Liang, Jianming (Committee member) / Arizona State University (Publisher)
Created2017
Description
Ensemble learning methods like bagging, boosting, adaptive boosting, stacking have traditionally shown promising results in improving the predictive accuracy in classification. These techniques have recently been widely used in various domains and applications owing to the improvements in computational efficiency and distributed computing advances. However, with the advent of wide variety of applications of machine learning techniques to class imbalance problems, further focus is needed to evaluate, improve and optimize other performance measures such as sensitivity (true positive rate) and specificity (true negative rate) in classification. This thesis demonstrates a novel approach to evaluate and optimize the performance measures (specifically sensitivity and specificity) using ensemble learning methods for classification that can be especially useful in class imbalanced datasets. In this thesis, ensemble learning methods (specifically bagging and boosting) are used to optimize the performance measures (sensitivity and specificity) on a UC Irvine (UCI) 130 hospital diabetes dataset to predict if a patient will be readmitted to the hospital based on various feature vectors. From the experiments conducted, it can be empirically concluded that, by using ensemble learning methods, although accuracy does improve to some margin, both sensitivity and specificity are optimized significantly and consistently over different cross validation approaches. The implementation and evaluation has been done on a subset of the large UCI 130 hospital diabetes dataset. The performance measures of ensemble learners are compared to the base machine learning classification algorithms such as Naive Bayes, Logistic Regression, k Nearest Neighbor, Decision Trees and Support Vector Machines.
ContributorsBahl, Neeraj Dharampal (Author) / Bansal, Ajay (Thesis advisor) / Amresh, Ashish (Committee member) / Bansal, Srividya (Committee member) / Arizona State University (Publisher)
Created2017
Description
Machine learning methodologies are widely used in almost all aspects of software engineering. An effective machine learning model requires large amounts of data to achieve high accuracy. The data used for classification is mostly labeled, which is difficult to obtain. The dataset requires both high costs and effort to accurately label the data into different classes. With abundance of data, it becomes necessary that all the data should be labeled for its proper utilization and this work focuses on reducing the labeling effort for large dataset. The thesis presents a comparison of different classifiers performance to test if small set of labeled data can be utilized to build accurate models for high prediction rate. The use of small dataset for classification is then extended to active machine learning methodology where, first a one class classifier will predict the outliers in the data and then the outlier samples are added to a training set for support vector machine classifier for labeling the unlabeled data. The labeling of dataset can be scaled up to avoid manual labeling and building more robust machine learning methodologies.
ContributorsBatra, Salil (Author) / Femiani, John (Thesis advisor) / Amresh, Ashish (Thesis advisor) / Bansal, Ajay (Committee member) / Arizona State University (Publisher)
Created2017
Description
There is a widespread inequality in health care access and insured rates suffered by the Latino, Spanish-speaking population in Arizona, resulting in poor health measures and economic burden. The passage of the Affordable Care Act in 2010 provided mechanisms to alleviate this disparity, however, many Latino communities lack accessible information and means to gain access to health insurance enrollment. Chicanos Por La Causa (CPLC) is a community based organizing that provides many services to low-income communities across Arizona, one of which is the CPLC Insurance Program. In collaboration with the Community Action Research Experiences (CARE) at Arizona State University, the program was studied to help address the need of a LOGIC model and evaluation plan to determine its effectiveness. Interviews with three executives within CPLC were conducted in conjunction with a literature review to determine the inputs, strategies, outputs, and outcomes of the LOGIC model that drive CPLC Insurance's mission. Evaluation measures were then created to provide the necessary quantitative data that can best show to what degree the program is achieving its goals. Specifically, the results indicated the key outcomes that drive the LOGIC model, and an evaluation plan designed to provide indicators of these outcomes was produced. The implications of this study are that the suggested data collection can verify how effectively the program's actions are creating positive change, as well as show where further improvements may be necessary to maximize effectiveness.
ContributorsCunningham, Matthew Lee (Author) / Fey, Richard (Thesis director) / Dumka, Larry (Committee member) / School of Molecular Sciences (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor) / T. Denny Sanford School of Social and Family Dynamics (Contributor)
Created2016-05
Description
Preventing heat-associated morbidity and mortality is a public health priority in Maricopa County, Arizona (United States). The objective of this project was to evaluate Maricopa County cooling centers and gain insight into their capacity to provide relief for the public during extreme heat events. During the summer of 2014, 53 cooling centers were evaluated to assess facility and visitor characteristics. Maricopa County staff collected data by directly observing daily operations and by surveying managers and visitors. The cooling centers in Maricopa County were often housed within community, senior, or religious centers, which offered various services for at least 1500 individuals daily. Many visitors were unemployed and/or homeless. Many learned about a cooling center by word of mouth or by having seen the cooling center’s location. The cooling centers provide a valuable service and reach some of the region’s most vulnerable populations. This project is among the first to systematically evaluate cooling centers from a public health perspective and provides helpful insight to community leaders who are implementing or improving their own network of cooling centers.
ContributorsBerisha, Vjollca (Author) / Hondula, David M. (Author) / Roach, Matthew (Author) / White, Jessica R. (Author) / McKinney, Benita (Author) / Bentz, Darcie (Author) / Mohamed, Ahmed (Author) / Uebelherr, Joshua (Author) / Goodin, Kate (Author)
Created2016-09-23
Description
In this thesis, several different methods for detecting and removing satellite streaks from astronomic images were evaluated and compared with a new machine learning based approach. Simulated data was generated with a variety of conditions, and the performance of each method was evaluated both quantitatively, using Mean Absolute Error (MAE) against a ground truth detection mask and processing throughput of the method, as well as qualitatively, examining the situations in which each model performs well and poorly. Detection methods from existing systems Pyradon and ASTRiDE were implemented and tested. A machine learning (ML) image segmentation model was trained on simulated data and used to detect streaks in test data. The ML model performed favorably relative to the traditional methods tested, and demonstrated superior robustness in general. However, the model also exhibited some unpredictable behavior in certain scenarios which should be considered. This demonstrated that machine learning is a viable tool for the detection of satellite streaks in astronomic images, however special care must be taken to prevent and to minimize the effects of unpredictable behavior in such models.
ContributorsJeffries, Charles (Author) / Acuna, Ruben (Thesis director) / Martin, Thomas (Committee member) / Bansal, Ajay (Committee member) / Barrett, The Honors College (Contributor) / Software Engineering (Contributor)
Created2023-05