subject:(Learning Analytics)

University of Illinois – Chicago

1. Alwadei, Abdurahman H. Adaptive Learning Analytics: Understanding Student Learning Behavior and Predicting Academic Success.

Degree: 2019, University of Illinois – Chicago

URL: http://hdl.handle.net/10027/23710

► A literature search demonstrates a strong interest in adaptive learning technology (ALT), from notable authors, foundations, professional institutions, commercial enterprises, and government agencies (Healy 2015,… (more)

▼ A literature search demonstrates a strong interest in adaptive learning technology (ALT), from notable authors, foundations, professional institutions, commercial enterprises, and government agencies (Healy 2015, Levy 2015, Johnson and Samora 2016, Mavroudi, Giannakos, and Krogstie 2018). As with any potentially effective teaching tool, ALT needs to be designed and implemented with the knowledge, skills and aptitudes of its recipients in mind. The purpose of this study was to examine the impact of instructional technology on student learning, using an adaptive learning system called Realizeit. In this study, the Realizeit Adaptive Learning Platform is referred to interchangeably as the Adaptive Platform or RALP. In addition to comparing student learning performance under traditional (face-to-face) and adaptive-mediated instruction, I applied Learning Analytics (LA) methodology to provide an in-depth analysis of individual student profiles, addressing different demographic, and academic and RALP-related variables to identify behavioral patterns during student learning. This study demonstrates how LA can serve as a methodological foundation for studying the impact of instructional technology on learning, as well as providing actionable recommendations based on the linear regression prediction models that resulted from the data analysis. Track dynamic data from the adaptive platform, combined with static (demographic) and semi-static (prior academic) data from the Student Information System, constituted the main data source for LA in this study. In addition to studying data generated from the adaptive platform, self-reported data from students, using a modified validated survey, provided valuable perspectives regarding students’ perspectives about their learning experiences with the adaptive platform. The results of this study provide empirical evidence that an adaptive learning intervention can have a significant impact on student learning performance. The use of digital traces shed light on other important aspects of the learning process, such as effective learning strategies, amount and type of effort, and engagement processes and time-management skills. Although study variables that reflected prior academic performance (student cumulative grade point average and pre-test score) were used as control variables, past education success was found to be the most significant predictor of success. Student self-reported data, especially, demonstrated the need to carefully consider the type, amount, and quality of education resources available to students to ensure that they are relevant to their perceived learning needs. Students’ responses also indicated that certain aspects of the adaptive platform, with regards to its design and technical issues, need further improvement. I conclude this study with actionable recommendations intended for course stakeholders, based on interpretation of the predictive model outputs coupled, with analyses of students’ responses. Advisors/Committee Members: Harris, Ilene (advisor), Brown, Blasé (committee member), Razfar, Aria (committee member), Park, Yoon Soon (committee member), Tekian, Ara (committee member), Harris, Ilene (chair).

Subjects/Keywords: Adaptive Learning; Learning Analytics; Predection

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Alwadei, A. H. (2019). Adaptive Learning Analytics: Understanding Student Learning Behavior and Predicting Academic Success. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/23710

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Alwadei, Abdurahman H. “Adaptive Learning Analytics: Understanding Student Learning Behavior and Predicting Academic Success.” 2019. Thesis, University of Illinois – Chicago. Accessed January 15, 2021. http://hdl.handle.net/10027/23710.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Alwadei, Abdurahman H. “Adaptive Learning Analytics: Understanding Student Learning Behavior and Predicting Academic Success.” 2019. Web. 15 Jan 2021.

Vancouver:

Alwadei AH. Adaptive Learning Analytics: Understanding Student Learning Behavior and Predicting Academic Success. [Internet] [Thesis]. University of Illinois – Chicago; 2019. [cited 2021 Jan 15]. Available from: http://hdl.handle.net/10027/23710.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Alwadei AH. Adaptive Learning Analytics: Understanding Student Learning Behavior and Predicting Academic Success. [Thesis]. University of Illinois – Chicago; 2019. Available from: http://hdl.handle.net/10027/23710

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

RMIT University

2. Soltanpoor, R. An integrated framework for learning analytics.

Degree: 2018, RMIT University

URL: http://researchbank.rmit.edu.au/view/rmit:162658

► Low retention rates have been an ongoing concern, especially among educational institutions amidst expanding their student base and catering to large and diverse student cohorts.… (more)

▼ Low retention rates have been an ongoing concern, especially among educational institutions amidst expanding their student base and catering to large and diverse student cohorts. Increasing retention rates without lowering academic standards poses many challenges. The traditional teaching techniques using a one-size-fits-all approach appear to be less effective, and the size and diversity of cohorts demand innovative teaching techniques allowing for adaptive and personalized coaching and learning. In this thesis, we propose a novel, adaptive and integrated analytics framework for learning analytics to address the key concerns of educational institutions. The proposed framework comprises three layers: (1) the conceptual layer which is a context-agnostic and generic analytics layer including descriptive, predictive, and prescriptive techniques; (2) the logical layer or the context-specific learning analytics processes layer that specializes the conceptual layer in the context of education; ten key learning analytics processes are formalized, implemented, and linked to the conceptual layer components; finally, (3) the physical layer that is concerned with education-oriented application implementations and is a context-specific components/algorithmic implementation of the logical layer processes. Our proposed framework, however, is not limited only to the learning and teaching environment. As a proof of concept, we chose the education context and applied our framework on it. The three-layered integrated learning analytics framework proposed allows domain-agnostic elements defined in the conceptual layer to be realized by domain-specific processes in the logical layer, and implemented through existing and new components in the physical layer. Please note that the learning analytics is not confined to the education context alone. The framework, therefore, can be customized for different domains making the approach more widely applicable. An adaptive and innovative approach in the physical layer named the personalized prescriptive quiz (PPQ) is introduced as a demonstration of education-oriented applications assisting the educational institutions. The novel agile learning approach proposed combines descriptive, predictive and prescriptive analytics to create a personalized iterative and incremental approach to learning. The PPQ allows students to easily analyze their current problems (especially, identifying their misconceptions), predict future results, and benefit from personalized intervention tasks. The enhanced PPQ incorporating difficulty and discrimination indexes, run-time question selection, and a hybrid iterative predictive model can be more beneficial and effective for personalized learning. The results demonstrate a significant improvement in student academic performance after applying the PPQ approach. In addition, students claimed that the PPQ helped them elevate their self-esteem and improve student experience which may eventually lead to improved retention rates.

Subjects/Keywords: Fields of Research; Learning Analytics; Adaptive Learning; Personalized Learning; Prescriptive Analytics; Analytics Framework

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APA (6^th Edition):

Soltanpoor, R. (2018). An integrated framework for learning analytics. (Thesis). RMIT University. Retrieved from http://researchbank.rmit.edu.au/view/rmit:162658

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Soltanpoor, R. “An integrated framework for learning analytics.” 2018. Thesis, RMIT University. Accessed January 15, 2021. http://researchbank.rmit.edu.au/view/rmit:162658.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Soltanpoor, R. “An integrated framework for learning analytics.” 2018. Web. 15 Jan 2021.

Vancouver:

Soltanpoor R. An integrated framework for learning analytics. [Internet] [Thesis]. RMIT University; 2018. [cited 2021 Jan 15]. Available from: http://researchbank.rmit.edu.au/view/rmit:162658.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Soltanpoor R. An integrated framework for learning analytics. [Thesis]. RMIT University; 2018. Available from: http://researchbank.rmit.edu.au/view/rmit:162658

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

NSYSU

3. Lin, Hsiu-Fen. Design and implementation of a mobile application for personal learning analytics.

Degree: Master, Information Management, 2012, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0118112-120714

► Learning analytics focuses on using existing accumulated learning data through analysis related techniques to provide appropriate information to learners and facilitating learners to adjust their… (more)

Subjects/Keywords: Prototyping Implementation; Personalized Mobile Learning Analytics System; Learning Analytics; Mobile Learning

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APA (6^th Edition):

Lin, H. (2012). Design and implementation of a mobile application for personal learning analytics. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0118112-120714

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Lin, Hsiu-Fen. “Design and implementation of a mobile application for personal learning analytics.” 2012. Thesis, NSYSU. Accessed January 15, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0118112-120714.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Lin, Hsiu-Fen. “Design and implementation of a mobile application for personal learning analytics.” 2012. Web. 15 Jan 2021.

Vancouver:

Lin H. Design and implementation of a mobile application for personal learning analytics. [Internet] [Thesis]. NSYSU; 2012. [cited 2021 Jan 15]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0118112-120714.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lin H. Design and implementation of a mobile application for personal learning analytics. [Thesis]. NSYSU; 2012. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0118112-120714

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

4. Burrows, Joel. The objective ear: a tool for assessing music education progress.

Degree: 2018, Athabasca University

URL: http://hdl.handle.net/10791/271

►

The objective ear is an application that, given a pair of performances of a piece of music, judges the amount of progress made between the… (more)

Subjects/Keywords: Learning Analytics; Music Education

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APA (6^th Edition):

Burrows, J. (2018). The objective ear: a tool for assessing music education progress. (Thesis). Athabasca University. Retrieved from http://hdl.handle.net/10791/271

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Burrows, Joel. “The objective ear: a tool for assessing music education progress.” 2018. Thesis, Athabasca University. Accessed January 15, 2021. http://hdl.handle.net/10791/271.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Burrows, Joel. “The objective ear: a tool for assessing music education progress.” 2018. Web. 15 Jan 2021.

Vancouver:

Burrows J. The objective ear: a tool for assessing music education progress. [Internet] [Thesis]. Athabasca University; 2018. [cited 2021 Jan 15]. Available from: http://hdl.handle.net/10791/271.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Burrows J. The objective ear: a tool for assessing music education progress. [Thesis]. Athabasca University; 2018. Available from: http://hdl.handle.net/10791/271

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Edinburgh

5. Matcha, Wannisa. Tracing learning strategies in online learning environments : a learning analytics approach.

Degree: PhD, 2020, University of Edinburgh

URL: https://doi.org/10.7488/era/611 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.818634

► Learning has expanded beyond formal education; yet, students continue to face the challenge of how to effectively direct their learning. Among the processes of learning,… (more)

Subjects/Keywords: learning analytics; learning strategies; educational data mining

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APA (6^th Edition):

Matcha, W. (2020). Tracing learning strategies in online learning environments : a learning analytics approach. (Doctoral Dissertation). University of Edinburgh. Retrieved from https://doi.org/10.7488/era/611 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.818634

Chicago Manual of Style (16^th Edition):

Matcha, Wannisa. “Tracing learning strategies in online learning environments : a learning analytics approach.” 2020. Doctoral Dissertation, University of Edinburgh. Accessed January 15, 2021. https://doi.org/10.7488/era/611 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.818634.

MLA Handbook (7^th Edition):

Matcha, Wannisa. “Tracing learning strategies in online learning environments : a learning analytics approach.” 2020. Web. 15 Jan 2021.

Vancouver:

Matcha W. Tracing learning strategies in online learning environments : a learning analytics approach. [Internet] [Doctoral dissertation]. University of Edinburgh; 2020. [cited 2021 Jan 15]. Available from: https://doi.org/10.7488/era/611 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.818634.

Council of Science Editors:

Matcha W. Tracing learning strategies in online learning environments : a learning analytics approach. [Doctoral Dissertation]. University of Edinburgh; 2020. Available from: https://doi.org/10.7488/era/611 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.818634

University of Ontario Institute of Technology

6. Weagant, Riley. Supporting student success with machine learning and visual analytics.

Degree: 2019, University of Ontario Institute of Technology

URL: http://hdl.handle.net/10155/1110

► Post secondary institutions have a wealth of student data at their disposal. This data has recently been used to explore a problem that has been… (more)

Subjects/Keywords: Visual analytics; Machine learning; Student retention; Education; Predictive analytics

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APA (6^th Edition):

Weagant, R. (2019). Supporting student success with machine learning and visual analytics. (Thesis). University of Ontario Institute of Technology. Retrieved from http://hdl.handle.net/10155/1110

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Weagant, Riley. “Supporting student success with machine learning and visual analytics.” 2019. Thesis, University of Ontario Institute of Technology. Accessed January 15, 2021. http://hdl.handle.net/10155/1110.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Weagant, Riley. “Supporting student success with machine learning and visual analytics.” 2019. Web. 15 Jan 2021.

Vancouver:

Weagant R. Supporting student success with machine learning and visual analytics. [Internet] [Thesis]. University of Ontario Institute of Technology; 2019. [cited 2021 Jan 15]. Available from: http://hdl.handle.net/10155/1110.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Weagant R. Supporting student success with machine learning and visual analytics. [Thesis]. University of Ontario Institute of Technology; 2019. Available from: http://hdl.handle.net/10155/1110

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Rochester Institute of Technology

7. Cao, Jingyu. The Role of AI & Big Data in Habit Formation.

Degree: MFA, School of Design (CAD), 2020, Rochester Institute of Technology

URL: https://scholarworks.rit.edu/theses/10632

► Forming habits are not easy for everyone. It requires professional methods and strong perseverance, which people usually feel hard to do by themself. However,… (more)

Subjects/Keywords: AI analytics; Big data analytics; Habit formation; Machine learning

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APA (6^th Edition):

Cao, J. (2020). The Role of AI & Big Data in Habit Formation. (Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/10632

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Cao, Jingyu. “The Role of AI & Big Data in Habit Formation.” 2020. Thesis, Rochester Institute of Technology. Accessed January 15, 2021. https://scholarworks.rit.edu/theses/10632.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Cao, Jingyu. “The Role of AI & Big Data in Habit Formation.” 2020. Web. 15 Jan 2021.

Vancouver:

Cao J. The Role of AI & Big Data in Habit Formation. [Internet] [Thesis]. Rochester Institute of Technology; 2020. [cited 2021 Jan 15]. Available from: https://scholarworks.rit.edu/theses/10632.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Cao J. The Role of AI & Big Data in Habit Formation. [Thesis]. Rochester Institute of Technology; 2020. Available from: https://scholarworks.rit.edu/theses/10632

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Hong Kong University of Science and Technology

8. Ming, Yao CSE. Visualization for explainable machine learning.

Degree: 2019, Hong Kong University of Science and Technology

URL: http://repository.ust.hk/ir/Record/1783.1-104003 ; https://doi.org/10.14711/thesis-991012786067703412 ; http://repository.ust.hk/ir/bitstream/1783.1-104003/1/th_redirect.html

► With the recent advancements of machine learning, especially deep learning, we have seen fast-growing applications of these intelligent systems in various domains. However, the increasing… (more)

Subjects/Keywords: Machine learning ; Information visualization ; Visual analytics

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APA (6^th Edition):

Ming, Y. C. (2019). Visualization for explainable machine learning. (Thesis). Hong Kong University of Science and Technology. Retrieved from http://repository.ust.hk/ir/Record/1783.1-104003 ; https://doi.org/10.14711/thesis-991012786067703412 ; http://repository.ust.hk/ir/bitstream/1783.1-104003/1/th_redirect.html

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Ming, Yao CSE. “Visualization for explainable machine learning.” 2019. Thesis, Hong Kong University of Science and Technology. Accessed January 15, 2021. http://repository.ust.hk/ir/Record/1783.1-104003 ; https://doi.org/10.14711/thesis-991012786067703412 ; http://repository.ust.hk/ir/bitstream/1783.1-104003/1/th_redirect.html.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Ming, Yao CSE. “Visualization for explainable machine learning.” 2019. Web. 15 Jan 2021.

Vancouver:

Ming YC. Visualization for explainable machine learning. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2019. [cited 2021 Jan 15]. Available from: http://repository.ust.hk/ir/Record/1783.1-104003 ; https://doi.org/10.14711/thesis-991012786067703412 ; http://repository.ust.hk/ir/bitstream/1783.1-104003/1/th_redirect.html.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ming YC. Visualization for explainable machine learning. [Thesis]. Hong Kong University of Science and Technology; 2019. Available from: http://repository.ust.hk/ir/Record/1783.1-104003 ; https://doi.org/10.14711/thesis-991012786067703412 ; http://repository.ust.hk/ir/bitstream/1783.1-104003/1/th_redirect.html

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

IUPUI

9. Li, Huang. Visual Analytics and Interactive Machine Learning for Human Brain Data.

Degree: 2019, IUPUI

URL: http://hdl.handle.net/1805/19923

►

Indiana University-Purdue University Indianapolis (IUPUI)

This study mainly focuses on applying visualization techniques on human brain data for data exploration, quality control, and hypothesis discovery.… (more)

Subjects/Keywords: Visual analytics; Visualization; Machine learning; Neural network

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APA (6^th Edition):

Li, H. (2019). Visual Analytics and Interactive Machine Learning for Human Brain Data. (Thesis). IUPUI. Retrieved from http://hdl.handle.net/1805/19923

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Li, Huang. “Visual Analytics and Interactive Machine Learning for Human Brain Data.” 2019. Thesis, IUPUI. Accessed January 15, 2021. http://hdl.handle.net/1805/19923.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Li, Huang. “Visual Analytics and Interactive Machine Learning for Human Brain Data.” 2019. Web. 15 Jan 2021.

Vancouver:

Li H. Visual Analytics and Interactive Machine Learning for Human Brain Data. [Internet] [Thesis]. IUPUI; 2019. [cited 2021 Jan 15]. Available from: http://hdl.handle.net/1805/19923.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Li H. Visual Analytics and Interactive Machine Learning for Human Brain Data. [Thesis]. IUPUI; 2019. Available from: http://hdl.handle.net/1805/19923

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Universidade do Rio Grande do Norte

10. Brasil, Pedrina Célia. Um processo analítico de dados educacionais: uma abordagem baseada nos dados socioeconômicos e educacionais dos alunos .

Degree: 2019, Universidade do Rio Grande do Norte

URL: http://repositorio.ufrn.br/handle/123456789/28336

► The Federal Institute of Education, Science and Technology of Rio Grande do Norte (IFRN) has as mission the social transformation of the region in which… (more)

▼ The Federal Institute of Education, Science and Technology of Rio Grande do Norte (IFRN) has as mission the social transformation of the region in which it operates, contributing to the economic development of the country. According to its Political-Pedagogical Project (PPP), any action related to IFRN courses must be broadly integrated. However, the heterogeneity and the high number of students in class, allied to the lack of time and tools that help the pedagogical decision-making process, corroborate with a closed curricular organization that ignores the students' social context and favors the achievement. of standardized actions. Thus, this research aims to propose an analytical process that helps the teacher to interpret their learning context from the analysis of socioeconomic and educational data of the students of the institution. This work consists of an explanatory work, and applied results to the context of the IFRN. The Knowledge Discovery in Database (KDD) approach was used as inspiration to define the activities of this research, divided here in three stages: preproduction, production and postproduction. The preproduction stage involved discovery and interpretation activities of the problem. This was a systematic review of the literature in order to identify how the learning analysis is applied in teaching environments of Brazilian institutions and characterized the domain of this proposal. In the production stage a process was created for extracting, transforming and loading IFRN data. Through this process, data from the institution's system (Public Administration Unified System, SUAP) were dynamically preprocessed and loaded onto an online dimensional basis capable of supporting the functionality of an analytical data processing system without compromising the organization's daily transactions. The post-production stage involved evaluation and evolution activities of this work. In this was developed the SUAP-BI tool that promotes teachers descriptive and predictive analysis of student performance, relating this to the social context of students. Advisors/Committee Members: Nunes, Isabel Dillmann (advisor).

Subjects/Keywords: Learning analytics; Ambientes virtuais de aprendizagem; IFRN

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APA (6^th Edition):

Brasil, P. C. (2019). Um processo analítico de dados educacionais: uma abordagem baseada nos dados socioeconômicos e educacionais dos alunos . (Masters Thesis). Universidade do Rio Grande do Norte. Retrieved from http://repositorio.ufrn.br/handle/123456789/28336

Chicago Manual of Style (16^th Edition):

Brasil, Pedrina Célia. “Um processo analítico de dados educacionais: uma abordagem baseada nos dados socioeconômicos e educacionais dos alunos .” 2019. Masters Thesis, Universidade do Rio Grande do Norte. Accessed January 15, 2021. http://repositorio.ufrn.br/handle/123456789/28336.

MLA Handbook (7^th Edition):

Brasil, Pedrina Célia. “Um processo analítico de dados educacionais: uma abordagem baseada nos dados socioeconômicos e educacionais dos alunos .” 2019. Web. 15 Jan 2021.

Vancouver:

Brasil PC. Um processo analítico de dados educacionais: uma abordagem baseada nos dados socioeconômicos e educacionais dos alunos . [Internet] [Masters thesis]. Universidade do Rio Grande do Norte; 2019. [cited 2021 Jan 15]. Available from: http://repositorio.ufrn.br/handle/123456789/28336.

Council of Science Editors:

Brasil PC. Um processo analítico de dados educacionais: uma abordagem baseada nos dados socioeconômicos e educacionais dos alunos . [Masters Thesis]. Universidade do Rio Grande do Norte; 2019. Available from: http://repositorio.ufrn.br/handle/123456789/28336

Virginia Tech

11. Straub, Kayla Marie. Data Mining Academic Emails to Model Employee Behaviors and Analyze Organizational Structure.

Degree: MS, Electrical Engineering, 2016, Virginia Tech

URL: http://hdl.handle.net/10919/71320

► Email correspondence has become the predominant method of communication for businesses. If not for the inherent privacy concerns, this electronically searchable data could be used… (more)

Subjects/Keywords: Data analytics; machine learning; social computing

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APA (6^th Edition):

Straub, K. M. (2016). Data Mining Academic Emails to Model Employee Behaviors and Analyze Organizational Structure. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/71320

Chicago Manual of Style (16^th Edition):

Straub, Kayla Marie. “Data Mining Academic Emails to Model Employee Behaviors and Analyze Organizational Structure.” 2016. Masters Thesis, Virginia Tech. Accessed January 15, 2021. http://hdl.handle.net/10919/71320.

MLA Handbook (7^th Edition):

Straub, Kayla Marie. “Data Mining Academic Emails to Model Employee Behaviors and Analyze Organizational Structure.” 2016. Web. 15 Jan 2021.

Vancouver:

Straub KM. Data Mining Academic Emails to Model Employee Behaviors and Analyze Organizational Structure. [Internet] [Masters thesis]. Virginia Tech; 2016. [cited 2021 Jan 15]. Available from: http://hdl.handle.net/10919/71320.

Council of Science Editors:

Straub KM. Data Mining Academic Emails to Model Employee Behaviors and Analyze Organizational Structure. [Masters Thesis]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/71320

Tampere University

12. Heinsuo, Leo. Smart Ticketing: Continuous learning system for document classification .

Degree: 2020, Tampere University

URL: https://trepo.tuni.fi/handle/10024/122011

► The purpose of this thesis is to showcase how to build a continuous learning system for document classification. A project done at CGI NEAAS, which… (more)

Subjects/Keywords: Machine Learning ; Text analytics ; NLP ; Azure ; MLOps

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APA (6^th Edition):

Heinsuo, L. (2020). Smart Ticketing: Continuous learning system for document classification . (Masters Thesis). Tampere University. Retrieved from https://trepo.tuni.fi/handle/10024/122011

Chicago Manual of Style (16^th Edition):

Heinsuo, Leo. “Smart Ticketing: Continuous learning system for document classification .” 2020. Masters Thesis, Tampere University. Accessed January 15, 2021. https://trepo.tuni.fi/handle/10024/122011.

MLA Handbook (7^th Edition):

Heinsuo, Leo. “Smart Ticketing: Continuous learning system for document classification .” 2020. Web. 15 Jan 2021.

Vancouver:

Heinsuo L. Smart Ticketing: Continuous learning system for document classification . [Internet] [Masters thesis]. Tampere University; 2020. [cited 2021 Jan 15]. Available from: https://trepo.tuni.fi/handle/10024/122011.

Council of Science Editors:

Heinsuo L. Smart Ticketing: Continuous learning system for document classification . [Masters Thesis]. Tampere University; 2020. Available from: https://trepo.tuni.fi/handle/10024/122011

University of Waterloo

13. Li, Yitong. Documentation-Guided Fuzzing for Testing Deep Learning API Functions.

Degree: 2020, University of Waterloo

URL: http://hdl.handle.net/10012/16589

► Widely-used deep learning (DL) libraries demand reliability. Thus, it is integral to test DL libraries’ API functions. Despite the effectiveness of fuzz testing, there are… (more)

Subjects/Keywords: fuzzing; testing; text analytics; deep learning

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APA (6^th Edition):

Li, Y. (2020). Documentation-Guided Fuzzing for Testing Deep Learning API Functions. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/16589

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Li, Yitong. “Documentation-Guided Fuzzing for Testing Deep Learning API Functions.” 2020. Thesis, University of Waterloo. Accessed January 15, 2021. http://hdl.handle.net/10012/16589.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Li, Yitong. “Documentation-Guided Fuzzing for Testing Deep Learning API Functions.” 2020. Web. 15 Jan 2021.

Vancouver:

Li Y. Documentation-Guided Fuzzing for Testing Deep Learning API Functions. [Internet] [Thesis]. University of Waterloo; 2020. [cited 2021 Jan 15]. Available from: http://hdl.handle.net/10012/16589.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Li Y. Documentation-Guided Fuzzing for Testing Deep Learning API Functions. [Thesis]. University of Waterloo; 2020. Available from: http://hdl.handle.net/10012/16589

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Toronto

14. Vogt, Krista Lee. Measuring Student Engagement Using Learning Management Systems.

Degree: PhD, 2016, University of Toronto

URL: http://hdl.handle.net/1807/73213

► In less than two decades, learning management systems (LMS) have gone from being a rarity to having near-ubiquitous presence on Canadian college campuses. This wide-spread… (more)

Subjects/Keywords: academic analytics; learning analytics; learning management systems; LMS; online; student engagement; 0745

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APA (6^th Edition):

Vogt, K. L. (2016). Measuring Student Engagement Using Learning Management Systems. (Doctoral Dissertation). University of Toronto. Retrieved from http://hdl.handle.net/1807/73213

Chicago Manual of Style (16^th Edition):

Vogt, Krista Lee. “Measuring Student Engagement Using Learning Management Systems.” 2016. Doctoral Dissertation, University of Toronto. Accessed January 15, 2021. http://hdl.handle.net/1807/73213.

MLA Handbook (7^th Edition):

Vogt, Krista Lee. “Measuring Student Engagement Using Learning Management Systems.” 2016. Web. 15 Jan 2021.

Vancouver:

Vogt KL. Measuring Student Engagement Using Learning Management Systems. [Internet] [Doctoral dissertation]. University of Toronto; 2016. [cited 2021 Jan 15]. Available from: http://hdl.handle.net/1807/73213.

Council of Science Editors:

Vogt KL. Measuring Student Engagement Using Learning Management Systems. [Doctoral Dissertation]. University of Toronto; 2016. Available from: http://hdl.handle.net/1807/73213

University of Florida

15. Grant, Christan Earl. Query-Driven Text Analytics for Knowledge Extraction, Resolution, and Inference.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2015, University of Florida

URL: https://ufdc.ufl.edu/UFE0049144

► With the precipitous increase in data, performing text analytics using traditional methods has become increasingly dificult. From now until 2020 the world's data is predicted… (more)

Subjects/Keywords: Analytics; Canopy; Databases; Datasets; Inference; Knowledge bases; Machine learning; Scheduling; SQL; Text analytics; analytics – coreference – database – large-scale – query – query-driven

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Grant, C. E. (2015). Query-Driven Text Analytics for Knowledge Extraction, Resolution, and Inference. (Doctoral Dissertation). University of Florida. Retrieved from https://ufdc.ufl.edu/UFE0049144

Chicago Manual of Style (16^th Edition):

Grant, Christan Earl. “Query-Driven Text Analytics for Knowledge Extraction, Resolution, and Inference.” 2015. Doctoral Dissertation, University of Florida. Accessed January 15, 2021. https://ufdc.ufl.edu/UFE0049144.

MLA Handbook (7^th Edition):

Grant, Christan Earl. “Query-Driven Text Analytics for Knowledge Extraction, Resolution, and Inference.” 2015. Web. 15 Jan 2021.

Vancouver:

Grant CE. Query-Driven Text Analytics for Knowledge Extraction, Resolution, and Inference. [Internet] [Doctoral dissertation]. University of Florida; 2015. [cited 2021 Jan 15]. Available from: https://ufdc.ufl.edu/UFE0049144.

Council of Science Editors:

Grant CE. Query-Driven Text Analytics for Knowledge Extraction, Resolution, and Inference. [Doctoral Dissertation]. University of Florida; 2015. Available from: https://ufdc.ufl.edu/UFE0049144

Arizona State University

16. Pandhalkudi Govindarajan, Sesha Kumar. Bridging Cyber and Physical Programming Classes: An Application of Semantic Visual Analytics for Programming Exams.

Degree: Computer Science, 2016, Arizona State University

URL: http://repository.asu.edu/items/38667

► With the advent of Massive Open Online Courses (MOOCs) educators have the opportunity to collect data from students and use it to derive insightful information… (more)

▼ With the advent of Massive Open Online Courses (MOOCs) educators have the opportunity to collect data from students and use it to derive insightful information about the students. Specifically, for programming based courses the ability to identify the specific areas or topics that need more attention from the students can be of immense help. But the majority of traditional, non-virtual classes lack the ability to uncover such information that can serve as a feedback to the effectiveness of teaching. In majority of the schools paper exams and assignments provide the only form of assessment to measure the success of the students in achieving the course objectives. The overall grade obtained in paper exams and assignments need not present a complete picture of a student’s strengths and weaknesses. In part, this can be addressed by incorporating research-based technology into the classrooms to obtain real-time updates on students' progress. But introducing technology to provide real-time, class-wide engagement involves a considerable investment both academically and financially. This prevents the adoption of such technology thereby preventing the ideal, technology-enabled classrooms. With increasing class sizes, it is becoming impossible for teachers to keep a persistent track of their students progress and to provide personalized feedback. What if we can we provide technology support without adding more burden to the existing pedagogical approach? How can we enable semantic enrichment of exams that can translate to students' understanding of the topics taught in the class? Can we provide feedback to students that goes beyond only numbers and reveal areas that need their focus. In this research I focus on bringing the capability of conducting insightful analysis to paper exams with a less intrusive learning analytics approach that taps into the generic classrooms with minimum technology introduction. Specifically, the work focuses on automatic indexing of programming exam questions with ontological semantics. The thesis also focuses on designing and evaluating a novel semantic visual analytics suite for in-depth course monitoring. By visualizing the semantic information to illustrate the areas that need a student’s focus and enable teachers to visualize class level progress, the system provides a richer feedback to both sides for improvement.

Subjects/Keywords: Educational technology; Educational evaluation; Computer science; Intelligent authoring; Learning Analytics; Orchestration technology; Programming; Semantic Analytics; Visual Analytics

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APA (6^th Edition):

Pandhalkudi Govindarajan, S. K. (2016). Bridging Cyber and Physical Programming Classes: An Application of Semantic Visual Analytics for Programming Exams. (Masters Thesis). Arizona State University. Retrieved from http://repository.asu.edu/items/38667

Chicago Manual of Style (16^th Edition):

Pandhalkudi Govindarajan, Sesha Kumar. “Bridging Cyber and Physical Programming Classes: An Application of Semantic Visual Analytics for Programming Exams.” 2016. Masters Thesis, Arizona State University. Accessed January 15, 2021. http://repository.asu.edu/items/38667.

MLA Handbook (7^th Edition):

Pandhalkudi Govindarajan, Sesha Kumar. “Bridging Cyber and Physical Programming Classes: An Application of Semantic Visual Analytics for Programming Exams.” 2016. Web. 15 Jan 2021.

Vancouver:

Pandhalkudi Govindarajan SK. Bridging Cyber and Physical Programming Classes: An Application of Semantic Visual Analytics for Programming Exams. [Internet] [Masters thesis]. Arizona State University; 2016. [cited 2021 Jan 15]. Available from: http://repository.asu.edu/items/38667.

Council of Science Editors:

Pandhalkudi Govindarajan SK. Bridging Cyber and Physical Programming Classes: An Application of Semantic Visual Analytics for Programming Exams. [Masters Thesis]. Arizona State University; 2016. Available from: http://repository.asu.edu/items/38667

Queensland University of Technology

17. Gibson, Andrew P. Reflective writing analytics and transepistemic abduction.

Degree: 2017, Queensland University of Technology

URL: https://eprints.qut.edu.au/106952/

► This thesis presents a model of Reflective Writing Analytics which brings together two distinct ways of knowing: the human world of individuals in society, and… (more)

Subjects/Keywords: reflective writing analytics; transepistemic abduction; abductive reasoning; learning analytics; epistemology; computational analysis

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APA (6^th Edition):

Gibson, A. P. (2017). Reflective writing analytics and transepistemic abduction. (Thesis). Queensland University of Technology. Retrieved from https://eprints.qut.edu.au/106952/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Gibson, Andrew P. “Reflective writing analytics and transepistemic abduction.” 2017. Thesis, Queensland University of Technology. Accessed January 15, 2021. https://eprints.qut.edu.au/106952/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Gibson, Andrew P. “Reflective writing analytics and transepistemic abduction.” 2017. Web. 15 Jan 2021.

Vancouver:

Gibson AP. Reflective writing analytics and transepistemic abduction. [Internet] [Thesis]. Queensland University of Technology; 2017. [cited 2021 Jan 15]. Available from: https://eprints.qut.edu.au/106952/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Gibson AP. Reflective writing analytics and transepistemic abduction. [Thesis]. Queensland University of Technology; 2017. Available from: https://eprints.qut.edu.au/106952/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Penn State University

18. Lim, Sunghoon. EVENT DETECTION AND PREDICTION USING ONLINE USER GENERATED DATA.

Degree: 2018, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/15041sxl441

► Data-driven event detection and prediction are a fundamental research challenge of the 21st century. Data-driven event detection and prediction provide valuable current and future knowledge… (more)

▼ Data-driven event detection and prediction are a fundamental research challenge of the 21st century. Data-driven event detection and prediction provide valuable current and future knowledge not only to large organizations, such as enterprises and hospitals, but also to individuals, such as customers and patients, respectively. In particular, textual data have been widely used as a primary knowledge source for data-driven event detection and prediction, since 80 percent of the digital data that have been generated by society today originates in unstructured textual form. Unfortunately, existing studies on text-data-driven event detection and prediction typically employ top-down machine learning methods, which are constrained by their need for (1) datasets of examples for training the models or (2) predetermined search keywords. However, in many cases, generating datasets of examples is an expensive process and impractical for many real-world applications. Furthermore, it is also difficult to use predetermined indicators for new event detection and prediction. The objective of this dissertation is to create bottom-up machine learning models, which do not require datasets of examples for training the models or predetermined search keywords, for text-data-driven event detection and prediction. The bottom-up machine learning models reduce type I and type II identification errors during the process of text-data-driven event detection and prediction. Reducing type I identification errors (i.e., false positives) is crucial for decreasing the misidentification of irrelevant data as relevant data, as such errors reduce the quality of necessary data needed for event detection and prediction. Reducing type II identification errors (i.e., false negatives) is also important, because increasing the size of correctly identified data can improve the quantity of necessary data needed for event detection and prediction. In particular, this research uses online user generated data, such as social media data, as a knowledge source for event detection and prediction due to (1) the availability of user opinions related to a wide range of topics (from a user’s perspective); (2) the ability to acquire user feedback in real-time and at a low-cost (from an analyst’s perspective); and (3) the size and heterogeneity of the data. In this dissertation, first, a Bayesian sampling model is presented for determining appropriate search keywords that reduce type I and type II identification errors when detecting events, such as detecting users’ feedback on product features or users’ medical conditions. Second, a clustering-based model using sentiment analysis is proposed for detecting the spread of events, such as detecting the spread of positive/negative online user feedback or the spread of a latent disease(s). Third, a causal analysis model based on word co-occurrence networks and Ganger causality analysis is provided for event prediction, such as predicting the spread of positive/negative user generated content or future enterprise outcomes.… Advisors/Committee Members: Conrad S Tucker, Dissertation Advisor/Co-Advisor, Conrad S Tucker, Committee Chair/Co-Chair, Soundar Kumara, Committee Member, Ling Rothrock, Committee Member, Nilam Ram, Outside Member.

Subjects/Keywords: Machine Learning; Text Mining; Social Media Analytics; Online Data Analytics; Event Detection; Event Prediction

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Lim, S. (2018). EVENT DETECTION AND PREDICTION USING ONLINE USER GENERATED DATA. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/15041sxl441

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Lim, Sunghoon. “EVENT DETECTION AND PREDICTION USING ONLINE USER GENERATED DATA.” 2018. Thesis, Penn State University. Accessed January 15, 2021. https://submit-etda.libraries.psu.edu/catalog/15041sxl441.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Lim, Sunghoon. “EVENT DETECTION AND PREDICTION USING ONLINE USER GENERATED DATA.” 2018. Web. 15 Jan 2021.

Vancouver:

Lim S. EVENT DETECTION AND PREDICTION USING ONLINE USER GENERATED DATA. [Internet] [Thesis]. Penn State University; 2018. [cited 2021 Jan 15]. Available from: https://submit-etda.libraries.psu.edu/catalog/15041sxl441.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lim S. EVENT DETECTION AND PREDICTION USING ONLINE USER GENERATED DATA. [Thesis]. Penn State University; 2018. Available from: https://submit-etda.libraries.psu.edu/catalog/15041sxl441

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Princeton University

19. Zhang, Haoyu. Resource Management for Advanced Data Analytics at Large Scale .

Degree: PhD, 2018, Princeton University

URL: http://arks.princeton.edu/ark:/88435/dsp019019s5171

► The rapidly growing size of data and the complexity of analytics present new challenges for large-scale data analytics systems. Modern distributed computing frameworks need to… (more)

▼ The rapidly growing size of data and the complexity of analytics present new challenges for large-scale data analytics systems. Modern distributed computing frameworks need to support not only embarrassingly parallelizable batch jobs, but also advanced applications analyzing text and multimedia data using complex queries and machine learning (ML) models. Given the computation and storage costs of advanced data analytics, resource management is crucial. New applications and workloads expose vastly different characteristics which make traditional scheduling systems inadequate, and at the same time offer great opportunities that lead to new system designs for better performance. In this thesis, we present resource management systems that significantly improve cloud resource efficiency by leveraging the specific characteristics of advanced data analytics applications. We present the design and implementation of the following systems: (i) VideoStorm: a video analytics system that scales to process thousands of vision queries on live video streams over large clusters. VideoStorm's offline profiler generates resource-quality profiles for vision queries, and its online scheduler allocates resources to maximize performance in terms of vision processing quality and lag. (ii) SLAQ: a cluster scheduling system for approximate ML training jobs that aims to maximize the overall model quality. In iterative and exploratory training settings, better models can be obtained faster by directing resources to jobs with the most potential for improvement. SLAQ allocates resources to maximize the cluster-wide quality improvement based on highly-tailored model quality predictions. (iii) Riffle: an optimized shuffle service for big-data analytics frameworks that significantly improves I/O efficiency. The all-to-all data transfer (i.e., shuffle) in modern big-data systems (such as Spark and Hadoop) becomes the scaling bottleneck for multi-stage analytics jobs, due to the superlinear increase in disk I/O operations as data volume grows. Riffle boosts system performance by merging fragmented intermediate files and efficiently scheduling the merge operations. Taken together, this thesis demonstrates a novel set of methods in both job-level and task-level scheduling for building scalable, highly-efficient, and cost-effective resource management systems. We have performed extensive evaluation with real production workloads, and our results show significant improvement in resource efficiency, job completion time, and system throughput for advanced data analytics. Advisors/Committee Members: Freedman, Michael J (advisor).

Subjects/Keywords: Big-Data Analytics; Cloud Computing; Distributed Machine Learning; Distributed Systems; Resource Scheduling; Video Analytics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Zhang, H. (2018). Resource Management for Advanced Data Analytics at Large Scale . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp019019s5171

Chicago Manual of Style (16^th Edition):

Zhang, Haoyu. “Resource Management for Advanced Data Analytics at Large Scale .” 2018. Doctoral Dissertation, Princeton University. Accessed January 15, 2021. http://arks.princeton.edu/ark:/88435/dsp019019s5171.

MLA Handbook (7^th Edition):

Zhang, Haoyu. “Resource Management for Advanced Data Analytics at Large Scale .” 2018. Web. 15 Jan 2021.

Vancouver:

Zhang H. Resource Management for Advanced Data Analytics at Large Scale . [Internet] [Doctoral dissertation]. Princeton University; 2018. [cited 2021 Jan 15]. Available from: http://arks.princeton.edu/ark:/88435/dsp019019s5171.

Council of Science Editors:

Zhang H. Resource Management for Advanced Data Analytics at Large Scale . [Doctoral Dissertation]. Princeton University; 2018. Available from: http://arks.princeton.edu/ark:/88435/dsp019019s5171

Open Universiteit Nederland

20. Van Mourik, Daniëlle. The effects of scaffolding and feedback adaptive to the characteristics of the learner on learning progress and performance for the purpose of personalized learning.

Degree: Master, Faculty of Educational Sciences, 2020, Open Universiteit Nederland

URL: http://hdl.handle.net/1820/5c31ee3d-b0c0-430a-a5a0-5f60479e7215

► Learners can differ in a number of ways. However, in current education differences are rarely taken into account, which means that courses are the same… (more)

▼ Learners can differ in a number of ways. However, in current education differences are rarely taken into account, which means that courses are the same for all learners. Differences between individuals are a starting point to personalize a learning trajectory. There is evidence in the literature that fitting support and feedback to the needs of the learner have positive effects on the quality and pace of learning. This study aims to contribute to this line of research by exploring a combination of learning strategies that adapt to the learner's characteristics. The aim of this study is to test the effectiveness of a personalized learning program as a combination of adjusting (a) the difficulty of exercises, and (b) the nature of the feedback. Both are adapted to the learner's level of performance. The learning task in this study is the game “Space Fortress” (Agarwal et al., 2018; Mané & Donchin, 1989). The effects of personalized learning strategies are examined on the learning progress and learning outcome, by comparing the results of a standardized learning program (non-personalized) with those of a personalized learning program. The learning program took five hours extended over two weeks. A quasi-experimental pre-test - training - post-test control group study was conducted among forty participants randomly assigned to the control condition (standardized learning program) and the experimental condition (the personalized learning program). The average age of the participants is 24 years. Participants were recruited through the TNO database. Before the learning program started, participants' self-efficacy was measured using the Motivated Strategies for Learning Questionnaire (Pintrich, Smith, García, & McKeachie, 1991) and the aiming task pretest was administered to participants. After the completion of the learning program, motivation was measured with the Intrinsic Motivation Inventory (Deci & Ryan, 1982) and the learning experience with the Personalized Learning Environment Questionnaire (Waldrip et al., 2014), the aiming task posttest was administered to participants. The learning progress was measured by the performance (pre, mid, post) on the learning tasks, the learning performance was measured by the performance on the complete Space Fortress game (Frederiksen & White, 1989) and the explanatory factor for the performance on the complete Space Fortress game by means of the aiming task on the pretest (Mané & Donchin, 1989). The ANCOVA showed that participants from the experimental condition did not have a higher performance level on the posttest than participants in the control condition. The repeated measures ANOVAs have shown that participants in the experimental condition had no faster progress than participants in the control condition. The T-test showed that no difference was found in how participants in the control condition and the experimental condition assessed the training, according to their learning needs. The Multiple Regression Analysis revealed that both self-efficacy and…

Subjects/Keywords: personalized learning; learning strategies; scaffolding; feedback; adaptive learning environment; learner characteristics; learning analytics

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APA (6^th Edition):

Van Mourik, D. (2020). The effects of scaffolding and feedback adaptive to the characteristics of the learner on learning progress and performance for the purpose of personalized learning. (Masters Thesis). Open Universiteit Nederland. Retrieved from http://hdl.handle.net/1820/5c31ee3d-b0c0-430a-a5a0-5f60479e7215

Chicago Manual of Style (16^th Edition):

Van Mourik, Daniëlle. “The effects of scaffolding and feedback adaptive to the characteristics of the learner on learning progress and performance for the purpose of personalized learning.” 2020. Masters Thesis, Open Universiteit Nederland. Accessed January 15, 2021. http://hdl.handle.net/1820/5c31ee3d-b0c0-430a-a5a0-5f60479e7215.

MLA Handbook (7^th Edition):

Van Mourik, Daniëlle. “The effects of scaffolding and feedback adaptive to the characteristics of the learner on learning progress and performance for the purpose of personalized learning.” 2020. Web. 15 Jan 2021.

Vancouver:

Van Mourik D. The effects of scaffolding and feedback adaptive to the characteristics of the learner on learning progress and performance for the purpose of personalized learning. [Internet] [Masters thesis]. Open Universiteit Nederland; 2020. [cited 2021 Jan 15]. Available from: http://hdl.handle.net/1820/5c31ee3d-b0c0-430a-a5a0-5f60479e7215.

Council of Science Editors:

Van Mourik D. The effects of scaffolding and feedback adaptive to the characteristics of the learner on learning progress and performance for the purpose of personalized learning. [Masters Thesis]. Open Universiteit Nederland; 2020. Available from: http://hdl.handle.net/1820/5c31ee3d-b0c0-430a-a5a0-5f60479e7215

University of California – Berkeley

21. Scott, John Michael. Configurations of Community and Collaboration in Online Learning: An Assemblage Approach.

Degree: Education, 2018, University of California – Berkeley

URL: http://www.escholarship.org/uc/item/5kh5b964

► Delivering engaging and rigorous learning experiences in online environments has become a key priority for higher education institutions, driving prolific innovation in tools, pedagogy, and… (more)

Subjects/Keywords: Educational technology; Educational philosophy; assemblage; collaborative learning; learning analytics; online learning; remix; social learning

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APA (6^th Edition):

Scott, J. M. (2018). Configurations of Community and Collaboration in Online Learning: An Assemblage Approach. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/5kh5b964

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Scott, John Michael. “Configurations of Community and Collaboration in Online Learning: An Assemblage Approach.” 2018. Thesis, University of California – Berkeley. Accessed January 15, 2021. http://www.escholarship.org/uc/item/5kh5b964.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Scott, John Michael. “Configurations of Community and Collaboration in Online Learning: An Assemblage Approach.” 2018. Web. 15 Jan 2021.

Vancouver:

Scott JM. Configurations of Community and Collaboration in Online Learning: An Assemblage Approach. [Internet] [Thesis]. University of California – Berkeley; 2018. [cited 2021 Jan 15]. Available from: http://www.escholarship.org/uc/item/5kh5b964.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Scott JM. Configurations of Community and Collaboration in Online Learning: An Assemblage Approach. [Thesis]. University of California – Berkeley; 2018. Available from: http://www.escholarship.org/uc/item/5kh5b964

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Universiteit Utrecht

22. Leeuwen, A. van. Teacher regulation of CSCL: Exploring the complexity of teacher regulation and the supporting role of learning analytics.

Degree: 2015, Universiteit Utrecht

URL: http://dspace.library.uu.nl:8080/handle/1874/313223

► During computer-supported collaborative learning (CSCL), students collaboratively solve tasks while being supported by computers. CSCL is beneficial for collaboration because it offers students a platform… (more)

▼ During computer-supported collaborative learning (CSCL), students collaboratively solve tasks while being supported by computers. CSCL is beneficial for collaboration because it offers students a platform for communication and a joint working space for solving tasks. All student activities are automatically logged and are available for review to teachers, offering them the opportunity to follow and diagnose the progress of all groups synchronously. Furthermore, they can send messages to the whole class or specifically intervene in one or more collaborating groups as they see fit, for example to give feedback or explanations. CSCL settings therefore offer unique affordances for teacher regulation, but also makes teacher regulation a challenging task because of all the available information. This thesis aimed to answer the questions how teachers regulate CSCL in terms of diagnosing and intervening strategies, and whether automated analyses and visualizations of student activities (learning analytics) can help teachers in this task of regulation. The studies in Part I of the thesis illustrated how teachers diagnose student activities and how they explain the choice for particular interventions. Central concepts during this investigation were synchronicity and adaptivity, which means that teachers during CSCL try to tailor their support to the characteristics and needs of multiple collaborating groups that all engage in multiple types of activities. Teachers continuously monitor and diagnose the students’ activities, focusing their attention sometimes on the group level and other times on the class level again to make an announcement or to see whether any group needs additional help. The affordances of CSCL can help the teacher during this process. However, these affordances must be seen in light of the challenges that teachers face as well. In particular, the opportunity to monitor the collaborative process in real-time also means there is a challenge to maintain an overview of all the available information. With too much information available, adaptive teaching is hindered and teachers have less time to make informed decisions about interventions and about whether or not to intervene. Learning analytics (LA) may support teachers because they analyze and summarize information about students. The studies in Part II of the thesis examined the effects of LA that either visualized students’ social activities (such as the occurrence of disagreement) or students’ cognitive activities (such as task progress). In the two studies, different effects of the LA were found, for example more frequent identification of the groups that experienced problems and in another study LA tools led to significantly more teacher interventions. Three mechanisms were proposed for these findings, namely that LA can aggregate information to a manageable level and thereby provide teachers with a quick overview of the situation, LA steer the teachers’ attention and make them aware of the shown information, and LA tools provide additional… Advisors/Committee Members: Brekelmans, J.M.G., Erkens, G., Janssen, J.J.H.M..

Subjects/Keywords: teaching; computer-supported collaborative learning; learning analytics; secondary education

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APA (6^th Edition):

Leeuwen, A. v. (2015). Teacher regulation of CSCL: Exploring the complexity of teacher regulation and the supporting role of learning analytics. (Doctoral Dissertation). Universiteit Utrecht. Retrieved from http://dspace.library.uu.nl:8080/handle/1874/313223

Chicago Manual of Style (16^th Edition):

Leeuwen, A van. “Teacher regulation of CSCL: Exploring the complexity of teacher regulation and the supporting role of learning analytics.” 2015. Doctoral Dissertation, Universiteit Utrecht. Accessed January 15, 2021. http://dspace.library.uu.nl:8080/handle/1874/313223.

MLA Handbook (7^th Edition):

Leeuwen, A van. “Teacher regulation of CSCL: Exploring the complexity of teacher regulation and the supporting role of learning analytics.” 2015. Web. 15 Jan 2021.

Vancouver:

Leeuwen Av. Teacher regulation of CSCL: Exploring the complexity of teacher regulation and the supporting role of learning analytics. [Internet] [Doctoral dissertation]. Universiteit Utrecht; 2015. [cited 2021 Jan 15]. Available from: http://dspace.library.uu.nl:8080/handle/1874/313223.

Council of Science Editors:

Leeuwen Av. Teacher regulation of CSCL: Exploring the complexity of teacher regulation and the supporting role of learning analytics. [Doctoral Dissertation]. Universiteit Utrecht; 2015. Available from: http://dspace.library.uu.nl:8080/handle/1874/313223

23. Guarcello, Maureen Augusta. Blended Learning and Bottlenecks in the California State University System: An Empirical Look at the Importance of Demographic and Performance Analytics.

Degree: PhD, Leadership Studies, 2015, University of San Diego

URL: https://digital.sandiego.edu/dissertations/1

► In Fall 2014 over 460,000 students enrolled in the 23-campus California State University system; unfortunately, more than 20,000 qualified applicants were denied admission due… (more)

▼ In Fall 2014 over 460,000 students enrolled in the 23-campus California State University system; unfortunately, more than 20,000 qualified applicants were denied admission due to capacity and budgetary constraints. In response to continued overcrowding, the Chancellor's Office and Board of Trustees are investigating "bottlenecks," defined as anything limiting students' ability to graduate in a timely manner. Blended learning, a pedagogy combining face-to-face and computer-mediated instruction, presents a potential solution to alleviate overcrowding and bottleneck problems. In an effort to investigate the extent to which student demographics and performance analytics explain student success outcomes in a popular blended learning psychology course, an explanatory sequential design was used to study 18,254 students enrolled in the course between 2006 and 2014. In the initial quantitative part of the design, logistic regression and traditional regression analysis were used to determine the predictors of those who chose to drop the course, those who ultimately passed the course, and then to investigate why some students received higher grades than others. Results revealed that race, gender, age, socioeconomic status, and early course participation were key predictors of success. Some of the most significant findings – which included the fact that Mexican American, African American, and Filipino students were less successful in the course than their White counterparts – were examined in more detail in the qualitative part of the study that followed. Specifically, students who self-identified within these race/ethnicities provided a nuanced look at their own course experiences by completing questionnaires and interviews for the study. Thematic findings revealed socioeconomic status, time management, parents' education, and students' campus community as factors contributing to course performance. This study represents one of few large-scale analyses of a blended learning environment focused upon learner outcomes, and it serves to inform the evaluative work surrounding student success interventions, including the ability to predict and understand student risk characteristics for dropping, failing, or performing poorly within a blended learning environment. Understanding the many reasons students engage in less successful behavior may inform student success strategies and alleviate bottlenecks, especially as the prevalence of blended learning courses increases within the California State University system.

Subjects/Keywords: blended learning; learning analytics; student success; higher education

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APA (6^th Edition):

Guarcello, M. A. (2015). Blended Learning and Bottlenecks in the California State University System: An Empirical Look at the Importance of Demographic and Performance Analytics. (Doctoral Dissertation). University of San Diego. Retrieved from https://digital.sandiego.edu/dissertations/1

Chicago Manual of Style (16^th Edition):

Guarcello, Maureen Augusta. “Blended Learning and Bottlenecks in the California State University System: An Empirical Look at the Importance of Demographic and Performance Analytics.” 2015. Doctoral Dissertation, University of San Diego. Accessed January 15, 2021. https://digital.sandiego.edu/dissertations/1.

MLA Handbook (7^th Edition):

Guarcello, Maureen Augusta. “Blended Learning and Bottlenecks in the California State University System: An Empirical Look at the Importance of Demographic and Performance Analytics.” 2015. Web. 15 Jan 2021.

Vancouver:

Guarcello MA. Blended Learning and Bottlenecks in the California State University System: An Empirical Look at the Importance of Demographic and Performance Analytics. [Internet] [Doctoral dissertation]. University of San Diego; 2015. [cited 2021 Jan 15]. Available from: https://digital.sandiego.edu/dissertations/1.

Council of Science Editors:

Guarcello MA. Blended Learning and Bottlenecks in the California State University System: An Empirical Look at the Importance of Demographic and Performance Analytics. [Doctoral Dissertation]. University of San Diego; 2015. Available from: https://digital.sandiego.edu/dissertations/1

Vanderbilt University

24. Segedy, James René. Adaptive Scaffolds in Open-Ended Computer-Based Learning Environments.

Degree: PhD, Computer Science, 2014, Vanderbilt University

URL: http://hdl.handle.net/1803/12486

► Open-ended computer-based learning environments (OELEs) challenge learners to independently solve complex problems. These environments provide powerful opportunities for learners to develop and utilize strategies for… (more)

▼ Open-ended computer-based learning environments (OELEs) challenge learners to independently solve complex problems. These environments provide powerful opportunities for learners to develop and utilize strategies for self-regulated learning and problem-solving. However, novice learners often struggle in such open-ended environments, therefore, the extent of their effectiveness depends on the capabilities of their computer-based scaffolding agents: software agents embedded within the system that provide adaptive support to struggling learners. To be effective, these agents require systematic methods for effectively modeling and scaffolding (i.e., supporting) learners so that they can provide help that is targeted to addressing weaknesses in their problem-solving capabilities. The research presented in this dissertation has focused on expanding the repertoire of scaffolding agents in OELEs along two fronts. First, an approach to modeling learners called coherence graph analysis (CGA) has been developed. The CGA approach models learners in terms of: (i) the quality of their problem solutions; (ii) their skillfulness in solving open-ended problems; and (iii) the coherence between the actions they perform as part of their problem-solving tasks. Second, a three-stage approach to scaffolding students has been developed and evaluated through classroom studies. This scaffolding strategy actively helps students by: (i) offering to answer their questions; (ii) diagnosing their skill deficiencies; and (iii) requiring them to develop problem-solving skills through guided practice. These approaches were evaluated in a study with two instructional units in four 6th grade classrooms. The results demonstrated the utility of the CGA approach in predicting learners’ performance and learning. Exploratory clustering analyses were employed to explore student behavior. The analyses identified a set of distinct and persistent behavioral profiles among the students. Despite significant changes in students’ behaviors, the set of behavioral profiles identified by the clustering analyses were similar for both instructional units. The analyses also revealed a productive strategy shift: of the 98 students who took part in the study, 60 of them exhibited improved problem-solving behaviors during the second instructional unit. Analyses also provided suggestive evidence for the value of the three-stage scaffolding strategy in helping students learn how to succeed at complex open-ended problem-solving tasks. Advisors/Committee Members: Dr. Julie Adams (committee member), Dr. Robert Bodenheimer (committee member), Dr. Doug Fisher (committee member), Dr. Doug Clark (committee member), Dr. Gautam Biswas (Committee Chair).

Subjects/Keywords: Scaffolding; Open-Ended Learning Environment; Learner Modeling; Adaptive Support; Learning Analytics

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APA (6^th Edition):

Segedy, J. R. (2014). Adaptive Scaffolds in Open-Ended Computer-Based Learning Environments. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/12486

Chicago Manual of Style (16^th Edition):

Segedy, James René. “Adaptive Scaffolds in Open-Ended Computer-Based Learning Environments.” 2014. Doctoral Dissertation, Vanderbilt University. Accessed January 15, 2021. http://hdl.handle.net/1803/12486.

MLA Handbook (7^th Edition):

Segedy, James René. “Adaptive Scaffolds in Open-Ended Computer-Based Learning Environments.” 2014. Web. 15 Jan 2021.

Vancouver:

Segedy JR. Adaptive Scaffolds in Open-Ended Computer-Based Learning Environments. [Internet] [Doctoral dissertation]. Vanderbilt University; 2014. [cited 2021 Jan 15]. Available from: http://hdl.handle.net/1803/12486.

Council of Science Editors:

Segedy JR. Adaptive Scaffolds in Open-Ended Computer-Based Learning Environments. [Doctoral Dissertation]. Vanderbilt University; 2014. Available from: http://hdl.handle.net/1803/12486

Penn State University

25. Tang, Hengtao. EXPLORING SELF-REGULATED LEARNER PROFILES IN MOOCS: A COMPARATIVE STUDY.

Degree: 2018, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/15966hzt111

► Massive Open Online Courses (MOOCs) have received considerable attention with some scholars claiming they have the potential of opening up access to higher education, but… (more)

▼ Massive Open Online Courses (MOOCs) have received considerable attention with some scholars claiming they have the potential of opening up access to higher education, but whether MOOCs will be able to fulfill their educational potential remains uncertain. Learning in MOOCs requires learners to self-regulate their learning process to accomplish their personal goals. Thus, more attention has been paid to investigating how self-regulated learning relates to learner performance in MOOCs. However, existing research has overlooked a person-centered analysis of the difference between online learners in implementing self-regulated learning strategies within MOOCs. Without understanding this difference, educators are unlikely to provide efficient self-regulative interventions relevant to each type of self-regulated learners. To fill this gap, this research applied learning analytics to explore learner profiles of how they performed self-regulated learning in MOOCs. Using K-means clustering analysis, this research revealed three different self-regulated learner profiles: all-around self-regulated learners, less reflective self-regulated learners, and control-oriented self-regulated learners. The subsequent analysis indicated that all-around self-regulated learners were more likely to outperform the other two clusters of learners in course performance, completion, and also forum contributions. In addition, using the comparative method, this research investigated the cultural influence on self-regulated learner profiles and proposed empirical implications for culturally adaptive support to help different types of self-regulated learners succeed in MOOCs. Furthermore, this study compared the profiles of self-regulated learners from the Confucian Heritage Cultural countries and the United States, but no significant difference was found in this aspect. In summary, this research is significant in filling the existing gap of the person-centered view of self-regulated learner profiles. The findings of this research may prove to be important in the effort to reinforce the success of MOOCs, by offering empirical implications on self-regulative intervention design and cultural adaptive support. Additional implications for educators and practitioners are provided at the end of this research. Advisors/Committee Members: Kyle Peck, Dissertation Advisor/Co-Advisor, Kyle Peck, Committee Chair/Co-Chair, Ladislaus Semali, Committee Member, Susan Mary Land, Committee Member, Wei-Fan Chen, Outside Member.

Subjects/Keywords: Self-regulated learning; Learning analytics; MOOCs; Culture; Comparative study

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APA (6^th Edition):

Tang, H. (2018). EXPLORING SELF-REGULATED LEARNER PROFILES IN MOOCS: A COMPARATIVE STUDY. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/15966hzt111

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Tang, Hengtao. “EXPLORING SELF-REGULATED LEARNER PROFILES IN MOOCS: A COMPARATIVE STUDY.” 2018. Thesis, Penn State University. Accessed January 15, 2021. https://submit-etda.libraries.psu.edu/catalog/15966hzt111.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Tang, Hengtao. “EXPLORING SELF-REGULATED LEARNER PROFILES IN MOOCS: A COMPARATIVE STUDY.” 2018. Web. 15 Jan 2021.

Vancouver:

Tang H. EXPLORING SELF-REGULATED LEARNER PROFILES IN MOOCS: A COMPARATIVE STUDY. [Internet] [Thesis]. Penn State University; 2018. [cited 2021 Jan 15]. Available from: https://submit-etda.libraries.psu.edu/catalog/15966hzt111.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Tang H. EXPLORING SELF-REGULATED LEARNER PROFILES IN MOOCS: A COMPARATIVE STUDY. [Thesis]. Penn State University; 2018. Available from: https://submit-etda.libraries.psu.edu/catalog/15966hzt111

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Brigham Young University

26. Henrie, Curtis R. Measuring Student Engagement in Technology-Mediated Learning Environments.

Degree: PhD, 2016, Brigham Young University

URL: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=6948&context=etd

► This is a multiple-article format dissertation that explores methods for measuring student engagement in technology-mediated learning experiences. Student engagement is the committed, focused, and energetic… (more)

Subjects/Keywords: student engagement; learning analytics; measurement; technology-mediated learning; Educational Psychology

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APA (6^th Edition):

Henrie, C. R. (2016). Measuring Student Engagement in Technology-Mediated Learning Environments. (Doctoral Dissertation). Brigham Young University. Retrieved from https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=6948&context=etd

Chicago Manual of Style (16^th Edition):

Henrie, Curtis R. “Measuring Student Engagement in Technology-Mediated Learning Environments.” 2016. Doctoral Dissertation, Brigham Young University. Accessed January 15, 2021. https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=6948&context=etd.

MLA Handbook (7^th Edition):

Henrie, Curtis R. “Measuring Student Engagement in Technology-Mediated Learning Environments.” 2016. Web. 15 Jan 2021.

Vancouver:

Henrie CR. Measuring Student Engagement in Technology-Mediated Learning Environments. [Internet] [Doctoral dissertation]. Brigham Young University; 2016. [cited 2021 Jan 15]. Available from: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=6948&context=etd.

Council of Science Editors:

Henrie CR. Measuring Student Engagement in Technology-Mediated Learning Environments. [Doctoral Dissertation]. Brigham Young University; 2016. Available from: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=6948&context=etd

University of Waterloo

27. Ameli, Soroush. Road Condition Sensing Using Deep Learning and Wireless Signals.

Degree: 2020, University of Waterloo

URL: http://hdl.handle.net/10012/16121

► Similar to human car drivers, future driverless cars need to sense the condition of road surfaces so that they can adjust their speed and distance… (more)

Subjects/Keywords: Deep Learning; Machine Learning; Autonomous Car; Wireless Signal; Data Analytics

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APA (6^th Edition):

Ameli, S. (2020). Road Condition Sensing Using Deep Learning and Wireless Signals. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/16121

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Ameli, Soroush. “Road Condition Sensing Using Deep Learning and Wireless Signals.” 2020. Thesis, University of Waterloo. Accessed January 15, 2021. http://hdl.handle.net/10012/16121.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Ameli, Soroush. “Road Condition Sensing Using Deep Learning and Wireless Signals.” 2020. Web. 15 Jan 2021.

Vancouver:

Ameli S. Road Condition Sensing Using Deep Learning and Wireless Signals. [Internet] [Thesis]. University of Waterloo; 2020. [cited 2021 Jan 15]. Available from: http://hdl.handle.net/10012/16121.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ameli S. Road Condition Sensing Using Deep Learning and Wireless Signals. [Thesis]. University of Waterloo; 2020. Available from: http://hdl.handle.net/10012/16121

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Maryland

28. Ramesh, Arti. A Probabilistic Approach to Modeling Socio-Behavioral Interactions.

Degree: Computer Science, 2016, University of Maryland

URL: http://hdl.handle.net/1903/18964

► In our ever-increasingly connected world, it is essential to build computational models that represent, reason, and model the underlying characteristics of real-world networks. Data generated… (more)

▼ In our ever-increasingly connected world, it is essential to build computational models that represent, reason, and model the underlying characteristics of real-world networks. Data generated from these networks are often heterogeneous, interlinked, and exhibit rich multi-relational graph structures having unobserved latent characteristics. My work focuses on building computational models for representing and reasoning about rich, heterogeneous, interlinked graph data. In my research, I model socio-behavioral interactions and predict user behavior patterns in two important online interaction platforms: online courses and online professional networks. Structured data from these interaction platforms contain rich behavioral and interaction data, and provide an opportunity to design machine learning methods for understanding and interpreting user behavior. The data also contains unstructured data, such as natural language text from forum posts and other online discussions. My research aims at constructing a family of probabilistic models for modeling social interactions involving both structured and unstructured data. In the early part of this thesis, I present a family of probabilistic models for online courses for: 1) modeling student engagement, 2) predicting student completion and dropouts, 3) modeling student sentiment toward various course aspects (e.g., content vs. logistics), 4) detecting coarse and fine-grained course aspects (e.g., grading, video, content), and 5) modeling evolution of topics in repeated offerings of online courses. These methods have the potential to improve student experience and focus limited instructor resources in ways that will have the most impact. In the latter part of this thesis, I present methods to model multi-relational influence in online professional networks. I test the effectiveness of this model via experimentation on the professional network, LinkedIn. My models can potentially be adapted to address a wide range of problems in real-world networks including predicting user interests, user retention, personalization, and making recommendations. Advisors/Committee Members: Getoor, Lise (advisor).

Subjects/Keywords: Computer science; data mining; learning analytics; machine learning; structured prediction

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APA (6^th Edition):

Ramesh, A. (2016). A Probabilistic Approach to Modeling Socio-Behavioral Interactions. (Thesis). University of Maryland. Retrieved from http://hdl.handle.net/1903/18964

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Ramesh, Arti. “A Probabilistic Approach to Modeling Socio-Behavioral Interactions.” 2016. Thesis, University of Maryland. Accessed January 15, 2021. http://hdl.handle.net/1903/18964.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Ramesh, Arti. “A Probabilistic Approach to Modeling Socio-Behavioral Interactions.” 2016. Web. 15 Jan 2021.

Vancouver:

Ramesh A. A Probabilistic Approach to Modeling Socio-Behavioral Interactions. [Internet] [Thesis]. University of Maryland; 2016. [cited 2021 Jan 15]. Available from: http://hdl.handle.net/1903/18964.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ramesh A. A Probabilistic Approach to Modeling Socio-Behavioral Interactions. [Thesis]. University of Maryland; 2016. Available from: http://hdl.handle.net/1903/18964

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Colorado State University

29. Pereira, Aaron. Towards federated learning over large-scale streaming data.

Degree: MS(M.S.), Computer Science, 2020, Colorado State University

URL: http://hdl.handle.net/10217/208427

► Distributed Stream Processing Engines (DSPEs) have seen significant deployment growth along with an increase in streaming data sources such as sensor networks. These DSPEs enable… (more)

Subjects/Keywords: distributed stream processing engines; scalable analytics; federated learning; deep learning

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APA (6^th Edition):

Pereira, A. (2020). Towards federated learning over large-scale streaming data. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/208427

Chicago Manual of Style (16^th Edition):

Pereira, Aaron. “Towards federated learning over large-scale streaming data.” 2020. Masters Thesis, Colorado State University. Accessed January 15, 2021. http://hdl.handle.net/10217/208427.

MLA Handbook (7^th Edition):

Pereira, Aaron. “Towards federated learning over large-scale streaming data.” 2020. Web. 15 Jan 2021.

Vancouver:

Pereira A. Towards federated learning over large-scale streaming data. [Internet] [Masters thesis]. Colorado State University; 2020. [cited 2021 Jan 15]. Available from: http://hdl.handle.net/10217/208427.

Council of Science Editors:

Pereira A. Towards federated learning over large-scale streaming data. [Masters Thesis]. Colorado State University; 2020. Available from: http://hdl.handle.net/10217/208427

Vanderbilt University

30. Martinez Garza, Mario Manuel. Coevolution of Theory and Data Analytics of Digital Game-Based Learning.

Degree: PhD, Learning, Teaching and Diversity, 2016, Vanderbilt University

URL: http://hdl.handle.net/1803/10476

► Learning theory and educational data analytics can be said to coevolve, that is, to refine and improve each other reciprocally, each aspect providing a necessary… (more)

▼ Learning theory and educational data analytics can be said to coevolve, that is, to refine and improve each other reciprocally, each aspect providing a necessary element for the growth and advancement of the other. In this three-paper dissertation, I explore this process of coevolution between learning theory and data analytics in the context of digital game-based learning. From the theoretical side, I describe a framework based on a general theory of cognition (the two-system or dual-system model) that can be applied to digital game environments. The main hypothesis in this framework is that certain patterns of action in the game-space indicate the use of certain epistemic stances that have analogues within the two-system model. The proposed Two Stance/Two Model Framework (2SM) provides (a) improved explanatory power regarding intrapersonal variation in learning from games, (b) more complete theory regarding individual needs, goals, and agency, (c) a more extensive account of collaboration and community, and (d) improved perspective on knowledge-rich interactions in online affinity spaces. From the methodological side, I applied techniques of statistical computing (affinity clustering and sequence mining) to detect the stances of the 2SM as they appear in a physics learning game. The 2SM theorized that slow modes of solution would correlate to higher learning gains; students who use mainly fast iterative solution strategies did achieve lower learning gains than students who preferred slow, elaborated solutions. A second finding was that, as play progresses, students generally improve their performance in game areas that highlight physics concepts, but that this improvement is strongly moderated by their prior knowledge of physics. This dissertation further contributes to the existing knowledge of digital game-based learning by demonstrating how an analysis of the collected actions of players can be applied in a reliable and comprehensive fashion to research questions that are otherwise challenging to investigate. Advisors/Committee Members: Rogers P. Hall (committee member), Daniel T. Levin (committee member), Melissa S. Gresalfi (committee member), Douglas B. Clark (Committee Chair).

Subjects/Keywords: data mining; learning analytics; educational games; science learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Martinez Garza, M. M. (2016). Coevolution of Theory and Data Analytics of Digital Game-Based Learning. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/10476

Chicago Manual of Style (16^th Edition):

Martinez Garza, Mario Manuel. “Coevolution of Theory and Data Analytics of Digital Game-Based Learning.” 2016. Doctoral Dissertation, Vanderbilt University. Accessed January 15, 2021. http://hdl.handle.net/1803/10476.

MLA Handbook (7^th Edition):

Martinez Garza, Mario Manuel. “Coevolution of Theory and Data Analytics of Digital Game-Based Learning.” 2016. Web. 15 Jan 2021.

Vancouver:

Martinez Garza MM. Coevolution of Theory and Data Analytics of Digital Game-Based Learning. [Internet] [Doctoral dissertation]. Vanderbilt University; 2016. [cited 2021 Jan 15]. Available from: http://hdl.handle.net/1803/10476.

Council of Science Editors:

Martinez Garza MM. Coevolution of Theory and Data Analytics of Digital Game-Based Learning. [Doctoral Dissertation]. Vanderbilt University; 2016. Available from: http://hdl.handle.net/1803/10476

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Open Access Theses and Dissertations