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Call: Applying Data Science and Learning Analytics...
Wednesday, July 14, 2021
Event Details
Call for Chapters: Applying Data Science and Learning Analytics Throughout a Learner’s Lifespan
Editors
Goran Trajkovski, Western Governors University, United States
Marylee Demeter, Western Governors University, United States
Heather Hayes, Western Governors University, United States
Call for Chapters
Proposals Submission Deadline: July 14, 2021
Full Chapters Due: November 11, 2021
Submission Date: November 11, 2021
Goran Trajkovski, Western Governors University, United States
Marylee Demeter, Western Governors University, United States
Heather Hayes, Western Governors University, United States
Call for Chapters
Proposals Submission Deadline: July 14, 2021
Full Chapters Due: November 11, 2021
Submission Date: November 11, 2021
Introduction
This publication examines novel and emerging applications of data science and sister disciplines in gaining insights from data to inform interventions into the learners' journey and interactions with an academic or training institution. Topics will focus on building models of learners for success, using data to inform courseware and assessmentware development, and planning services supporting the learning process, including capturing, understanding, impacting, and implementing changes in learning, teaching, and assessment. Data are collected at various times and places throughout the learners' lifecycles, and the learners and the institution should benefit from the insights and knowledge gained from those data.
Objective
Research in the domains of learning analytics and educational data mining has prototyped an approach where methodologies from data science and machine learning are used to gain insights into the learning process by using large amounts of data. Various experiments have been piloted across learning and training institutions with various degrees of success limited largely to the interest of individuals or small groups in affecting academic and business operations supporting learning activities. As many training and academic institutions are maturing in their data-driven decisioning, useful, scalable, and interesting trends may start emerging, and organizations can benefit from sharing information on those efforts. While training and academic institutions may vary in definition, approach, size, and mission, learning about the learner and providing services that are as closely aligned to their behaviors as needs is the essence of their existence.
Target Audience
The primary target audience for this proposed publication are leaders at academic and training organizations interested in putting the data they have been collecting into action via insights and prescriptions enabled by the application of various methods from data science, machine learning, business intelligence, Big Data, data mining, statistics, and other related disciplines. Product design and development professionals, including instructional designers, assessment developers, instructional technologists, and psychometricians, will use the topics covered in the publication to develop environments for data-driven decision-making in their respective domains. Data scientists will contribute and benefit from the publication by reviewing the application of methodologies in the domain of learning.
Recommended Topics
Adaptive Content Engines, Aggregating, Analytics Databases, Artificial Intelligence, Association Rule Mining, Automated Support, Causal Data Mining, Classification, Clustering, Collecting and Processing Data, Collecting Data from Many Sources, Correlation Mining, Course Readiness, Dashboard, Reporting, And Visualization Tools, Data Mining, Density Estimation, Discovering or Improving Models of a Domain’s Knowledge Structure, Discovery with Models, Distillation of Data for Human Judgment, Educational Data Mining, Educational Systems, Educational Technology, Grades and Test Scores, Improvement of Student Models, Intelligent Tutoring Systems, Intervention Engines, Knowledge and Idea Management, Learning Analytics, Learning Analytics Engines, Learning E-Portfolios, Learning Process Analytics, Metrics, Rubrics and Collaboration Tools, Organizational Systems, Outlier Detection, Prediction, Recommendations, Refining Educational Theories, Regression, Relational Analysis, Relationship Mining, Sequential Pattern Mining, Social Network Analysis, Student and Teacher Surveys, Student Modeling, Studying Pedagogical Support, Text Mining, Tracking Tools in Learning Management Systems., Understanding of Key Factors Impacting Learning, Visualization, Web Analytics, Web Mining.
Submission Procedure
Researchers and practitioners are invited to submit on or before July 14, 2021, a chapter proposal of 1,000 to 2,000 words clearly explaining the mission and concerns of his or her proposed chapter. Authors will be notified by July 28, 2021 about the status of their proposals and sent chapter guidelines.Full chapters are expected to be submitted by November 11, 2021, and all interested authors must consult the guidelines for manuscript submissions at https://www.igi-global.com/publish/contributor-resources/before-you-write/ prior to submission. All submitted chapters will be reviewed on a double-blind review basis. Contributors may also be requested to serve as reviewers for this project.
Note: There are no submission or acceptance fees for manuscripts submitted to this book publication, Applying Data Science and Learning Analytics Throughout a Learner’s Lifespan. All manuscripts are accepted based on a double-blind peer review editorial process.
All proposals should be submitted through the eEditorial Discovery® online submission manager.
Publisher
This book is scheduled to be published by IGI Global (formerly Idea Group Inc.), an international academic publisher of the "Information Science Reference" (formerly Idea Group Reference), "Medical Information Science Reference," "Business Science Reference," and "Engineering Science Reference" imprints. IGI Global specializes in publishing reference books, scholarly journals, and electronic databases featuring academic research on a variety of innovative topic areas including, but not limited to, education, social science, medicine and healthcare, business and management, information science and technology, engineering, public administration, library and information science, media and communication studies, and environmental science. For additional information regarding the publisher, please visit https://www.igi-global.com. This publication is anticipated to be released in 2022.
Important Dates
July 14, 2021: Proposal Submission Deadline
July 28, 2021: Notification of Acceptance
November 11, 2021: Full Chapter Submission
January 9, 2022: Review Results Returned
February 20, 2022: Final Acceptance Notification
March 6, 2022: Final Chapter Submission
Inquiries
Goran Trajkovski
Western Governors University
goran.trajkovski@wgu.edu
Marylee Demeter
Western Governors University
marylee.demeter@wgu.edu
Heather Hayes
Western Governors University
h.hayes@wgu.edu
This publication examines novel and emerging applications of data science and sister disciplines in gaining insights from data to inform interventions into the learners' journey and interactions with an academic or training institution. Topics will focus on building models of learners for success, using data to inform courseware and assessmentware development, and planning services supporting the learning process, including capturing, understanding, impacting, and implementing changes in learning, teaching, and assessment. Data are collected at various times and places throughout the learners' lifecycles, and the learners and the institution should benefit from the insights and knowledge gained from those data.
Objective
Research in the domains of learning analytics and educational data mining has prototyped an approach where methodologies from data science and machine learning are used to gain insights into the learning process by using large amounts of data. Various experiments have been piloted across learning and training institutions with various degrees of success limited largely to the interest of individuals or small groups in affecting academic and business operations supporting learning activities. As many training and academic institutions are maturing in their data-driven decisioning, useful, scalable, and interesting trends may start emerging, and organizations can benefit from sharing information on those efforts. While training and academic institutions may vary in definition, approach, size, and mission, learning about the learner and providing services that are as closely aligned to their behaviors as needs is the essence of their existence.
Target Audience
The primary target audience for this proposed publication are leaders at academic and training organizations interested in putting the data they have been collecting into action via insights and prescriptions enabled by the application of various methods from data science, machine learning, business intelligence, Big Data, data mining, statistics, and other related disciplines. Product design and development professionals, including instructional designers, assessment developers, instructional technologists, and psychometricians, will use the topics covered in the publication to develop environments for data-driven decision-making in their respective domains. Data scientists will contribute and benefit from the publication by reviewing the application of methodologies in the domain of learning.
Recommended Topics
Adaptive Content Engines, Aggregating, Analytics Databases, Artificial Intelligence, Association Rule Mining, Automated Support, Causal Data Mining, Classification, Clustering, Collecting and Processing Data, Collecting Data from Many Sources, Correlation Mining, Course Readiness, Dashboard, Reporting, And Visualization Tools, Data Mining, Density Estimation, Discovering or Improving Models of a Domain’s Knowledge Structure, Discovery with Models, Distillation of Data for Human Judgment, Educational Data Mining, Educational Systems, Educational Technology, Grades and Test Scores, Improvement of Student Models, Intelligent Tutoring Systems, Intervention Engines, Knowledge and Idea Management, Learning Analytics, Learning Analytics Engines, Learning E-Portfolios, Learning Process Analytics, Metrics, Rubrics and Collaboration Tools, Organizational Systems, Outlier Detection, Prediction, Recommendations, Refining Educational Theories, Regression, Relational Analysis, Relationship Mining, Sequential Pattern Mining, Social Network Analysis, Student and Teacher Surveys, Student Modeling, Studying Pedagogical Support, Text Mining, Tracking Tools in Learning Management Systems., Understanding of Key Factors Impacting Learning, Visualization, Web Analytics, Web Mining.
Submission Procedure
Researchers and practitioners are invited to submit on or before July 14, 2021, a chapter proposal of 1,000 to 2,000 words clearly explaining the mission and concerns of his or her proposed chapter. Authors will be notified by July 28, 2021 about the status of their proposals and sent chapter guidelines.Full chapters are expected to be submitted by November 11, 2021, and all interested authors must consult the guidelines for manuscript submissions at https://www.igi-global.com/publish/contributor-resources/before-you-write/ prior to submission. All submitted chapters will be reviewed on a double-blind review basis. Contributors may also be requested to serve as reviewers for this project.
Note: There are no submission or acceptance fees for manuscripts submitted to this book publication, Applying Data Science and Learning Analytics Throughout a Learner’s Lifespan. All manuscripts are accepted based on a double-blind peer review editorial process.
All proposals should be submitted through the eEditorial Discovery® online submission manager.
Publisher
This book is scheduled to be published by IGI Global (formerly Idea Group Inc.), an international academic publisher of the "Information Science Reference" (formerly Idea Group Reference), "Medical Information Science Reference," "Business Science Reference," and "Engineering Science Reference" imprints. IGI Global specializes in publishing reference books, scholarly journals, and electronic databases featuring academic research on a variety of innovative topic areas including, but not limited to, education, social science, medicine and healthcare, business and management, information science and technology, engineering, public administration, library and information science, media and communication studies, and environmental science. For additional information regarding the publisher, please visit https://www.igi-global.com. This publication is anticipated to be released in 2022.
Important Dates
July 14, 2021: Proposal Submission Deadline
July 28, 2021: Notification of Acceptance
November 11, 2021: Full Chapter Submission
January 9, 2022: Review Results Returned
February 20, 2022: Final Acceptance Notification
March 6, 2022: Final Chapter Submission
Inquiries
Goran Trajkovski
Western Governors University
goran.trajkovski@wgu.edu
Marylee Demeter
Western Governors University
marylee.demeter@wgu.edu
Heather Hayes
Western Governors University
h.hayes@wgu.edu