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Educational Data Science
From Cami Pritchett October 17th, 2016
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LALink: Educational Data Science (Virtual) Demos
Traditional universities are struggling to provide expanding skill sets to increasingly diverse student populations while containing the costs and confronted with competition from technologically novel educational platforms such as MOOCs. Just as precision medicine, supply chain management, and targeted marketing use big data to improve efficiency and effectiveness in respective sectors of the economy, institutions of higher education must apply data-driven decision making to stay competitive and offer affordable, high value added education. This event features presentations on learning sciences research and learning analytics tools by experts from three universities—The University of Texas at Arlington, University of Michigan, and Indiana University.
UTA: Bridging Research and Practice
Large scale data has resulted in increased interest in learning sciences and related research. Much of this research interest is coming from non-traditional education fields as physicists, biologists, and others begin to analyze the data generated by learners in online and blended environments. A second trend has been to incorporate the practices of business intelligence to improve how universities make decisions about student support, recruitment, and institutional resource allocation. In most universities, the research and the practice of analytics are treated as separate silos. At University of Texas Arlington, we have created an integrated model where our learning analytics research (LINK Research Lab) coordinates extensively with our University Analytics department. This discussion will focus on the components of an integrated research/practice system as well as the challenges and ongoing opportunities.
Digital Innovation Greenhouse at University of Michigan
The University of Michigan has engaged in a breadth approach to learning analytics, and is involved in scholarly activity in the field, applied technology development, and institutional infrastructure investment. In this talk, the rich ecosystem of educational innovation initiatives will be surveyed, with a particular focus on (a) investments in scholarly learning analytics work, including two $1.25M interdisciplinary learning analytics grants funded in part through the UM data science initiative, (b) activities in the Digital Innovation Greenhouse (DIG), which serves as an on-campus education technology accelerator to address this challenge (http://ai.umich.edu/about-ai/digital-innovation-greenhouse), and (c) institutional investment in Unizin and the development of a learning analytics architecture to enable data-driven rescission making.
Learning Analytics Initiatives at Indiana University
We will present the initial developments of learning analytics initiatives at Indiana University at an institutional level. We will also give an overview of our experience in processing, analyzing, visualizing, and interpreting the e-textbook reading behavior data that is available from the Unizin Engage e-text reader and discuss the research implications of studying instructional activity data from digital learning environments such as the Learning Management System (LMS).
Bio:
Dr. George Siemens is Professor and Executive Director of the Learning Innovation and Networked Knowledge Research Lab at University of Texas, Arlington and cross-appointed with the Centre for Distance Education at Athabasca University. He has delivered keynote addresses in more than 35 countries on the influence of technology and media on education, organizations, and society. His work has been profiled in provincial, national, and international newspapers (including NY Times), radio, and television. He has served as PI or Co-PI on grants totaling more than $11m, with funding from NSF, SSHRC (Canada), Intel, Bill & Melinda Gates Foundation, and the Soros Foundation. He has received numerous awards, including honorary doctorates from Universidad de San Martín de Porres and Fraser Valley University for his pioneering work in learning, technology, and networks. He holds an honorary professorship with University of Edinburgh and adjust status with University of South Australia. Dr. Siemens is a founding President of the Society for Learning Analytics Research (http://www.solaresearch.org). In 2008, he pioneered massive open online courses (sometimes referred to as MOOCs). He blogs at http://www.elearnspace.org/blog/ and on Twitter: gsiemens
Pete Smith Smith is Chief Analytics Officer and Senior Associate Vice President at the University of Texas at Arlington, where he oversees the office of University Analytics and coordinates with the Learning Innovation and Networked Knowledge (LINK) lab, a learning analytics research laboratory. He previously served as Vice Provost for Digital Teaching and Learning for nearly 20 years. His teaching and research focuses on natural language processing, translation automation, and “big data” in education. A scholar of Russian language and culture, his teaching includes oversight of UTA’s Localization and Translation certificate, offered to students of seven languages (Arabic, Chinese, French, German, Korean Portuguese, Russian) as an introduction to localization and the localization industry. Pete’s recent presentations and publications have centered on the role of big data in education and more comprehensive models of learning in complex environments such as higher education. He was recently recognized by the United States Distance Learning Association with a national award for “Outstanding Leadership by an Individual in the Field of Distance Learning,” for his role leading UTA to become a recognized leader in online learning.
Christopher Brooks is a Research Assistant Professor in the School of Information and Director of Learning Analytics and Research in the Office of Academic Innovation at the University of Michigan. He works with colleagues to design tools to better the teaching and learning experience in higher education and in massive open online courses. His particular research focus is on understanding how learning analytics can be applied to human computer interaction through educational data mining, machine learning, and information visualization. His webpage is at http://christopherbrooks.ca
Serdar Abaci is the Educational Research and Evaluation Specialist for the Learning Technologies division of University Information Technology Services. Dr. Abaci conducts research on and evaluation of teaching and learning technologies. His research interests include feedback, online learning, program evaluation, and teaching and learning technologies in higher education. In his current research, Dr. Abaci examines e-textbook reading activity data to understand student engagement with reading materials, which can be a key determinant for learning and achievement in college. Dr. Abaci received his Ph.D. in Instructional Systems Technology and two Master’s degrees, one in Instructional Systems Technology and one in Educational Inquiry, all from Indiana University.
Joshua Quick is a graduate research assistant for Indiana University UITS Learning Technologies division. He is a doctoral student in the School of Education Learning Sciences program at IU. His research interest center on the use, development, and implementation of learning analytics techniques and tools in higher educational contexts. Prior to pursuing his doctoral degree, Joshua obtained a M.S. in Applied Statistics from the University of Alabama.
Traditional universities are struggling to provide expanding skill sets to increasingly diverse student populations while containing the costs and confronted with competition from technologically novel educational platforms such as MOOCs. Just as precision medicine, supply chain management, and targeted marketing use big data to improve efficiency and effectiveness in respective sectors of the economy, institutions of higher education must apply data-driven decision making to stay competitive and offer affordable, high value added education. This event features presentations on learning sciences research and learning analytics tools by experts from three universities—The University of Texas at Arlington, University of Michigan, and Indiana University.
UTA: Bridging Research and Practice
Large scale data has resulted in increased interest in learning sciences and related research. Much of this research interest is coming from non-traditional education fields as physicists, biologists, and others begin to analyze the data generated by learners in online and blended environments. A second trend has been to incorporate the practices of business intelligence to improve how universities make decisions about student support, recruitment, and institutional resource allocation. In most universities, the research and the practice of analytics are treated as separate silos. At University of Texas Arlington, we have created an integrated model where our learning analytics research (LINK Research Lab) coordinates extensively with our University Analytics department. This discussion will focus on the components of an integrated research/practice system as well as the challenges and ongoing opportunities.
Digital Innovation Greenhouse at University of Michigan
The University of Michigan has engaged in a breadth approach to learning analytics, and is involved in scholarly activity in the field, applied technology development, and institutional infrastructure investment. In this talk, the rich ecosystem of educational innovation initiatives will be surveyed, with a particular focus on (a) investments in scholarly learning analytics work, including two $1.25M interdisciplinary learning analytics grants funded in part through the UM data science initiative, (b) activities in the Digital Innovation Greenhouse (DIG), which serves as an on-campus education technology accelerator to address this challenge (http://ai.umich.edu/about-ai/digital-innovation-greenhouse), and (c) institutional investment in Unizin and the development of a learning analytics architecture to enable data-driven rescission making.
Learning Analytics Initiatives at Indiana University
We will present the initial developments of learning analytics initiatives at Indiana University at an institutional level. We will also give an overview of our experience in processing, analyzing, visualizing, and interpreting the e-textbook reading behavior data that is available from the Unizin Engage e-text reader and discuss the research implications of studying instructional activity data from digital learning environments such as the Learning Management System (LMS).
Bio:
Dr. George Siemens is Professor and Executive Director of the Learning Innovation and Networked Knowledge Research Lab at University of Texas, Arlington and cross-appointed with the Centre for Distance Education at Athabasca University. He has delivered keynote addresses in more than 35 countries on the influence of technology and media on education, organizations, and society. His work has been profiled in provincial, national, and international newspapers (including NY Times), radio, and television. He has served as PI or Co-PI on grants totaling more than $11m, with funding from NSF, SSHRC (Canada), Intel, Bill & Melinda Gates Foundation, and the Soros Foundation. He has received numerous awards, including honorary doctorates from Universidad de San Martín de Porres and Fraser Valley University for his pioneering work in learning, technology, and networks. He holds an honorary professorship with University of Edinburgh and adjust status with University of South Australia. Dr. Siemens is a founding President of the Society for Learning Analytics Research (http://www.solaresearch.org). In 2008, he pioneered massive open online courses (sometimes referred to as MOOCs). He blogs at http://www.elearnspace.org/blog/ and on Twitter: gsiemens
Pete Smith Smith is Chief Analytics Officer and Senior Associate Vice President at the University of Texas at Arlington, where he oversees the office of University Analytics and coordinates with the Learning Innovation and Networked Knowledge (LINK) lab, a learning analytics research laboratory. He previously served as Vice Provost for Digital Teaching and Learning for nearly 20 years. His teaching and research focuses on natural language processing, translation automation, and “big data” in education. A scholar of Russian language and culture, his teaching includes oversight of UTA’s Localization and Translation certificate, offered to students of seven languages (Arabic, Chinese, French, German, Korean Portuguese, Russian) as an introduction to localization and the localization industry. Pete’s recent presentations and publications have centered on the role of big data in education and more comprehensive models of learning in complex environments such as higher education. He was recently recognized by the United States Distance Learning Association with a national award for “Outstanding Leadership by an Individual in the Field of Distance Learning,” for his role leading UTA to become a recognized leader in online learning.
Christopher Brooks is a Research Assistant Professor in the School of Information and Director of Learning Analytics and Research in the Office of Academic Innovation at the University of Michigan. He works with colleagues to design tools to better the teaching and learning experience in higher education and in massive open online courses. His particular research focus is on understanding how learning analytics can be applied to human computer interaction through educational data mining, machine learning, and information visualization. His webpage is at http://christopherbrooks.ca
Serdar Abaci is the Educational Research and Evaluation Specialist for the Learning Technologies division of University Information Technology Services. Dr. Abaci conducts research on and evaluation of teaching and learning technologies. His research interests include feedback, online learning, program evaluation, and teaching and learning technologies in higher education. In his current research, Dr. Abaci examines e-textbook reading activity data to understand student engagement with reading materials, which can be a key determinant for learning and achievement in college. Dr. Abaci received his Ph.D. in Instructional Systems Technology and two Master’s degrees, one in Instructional Systems Technology and one in Educational Inquiry, all from Indiana University.
Joshua Quick is a graduate research assistant for Indiana University UITS Learning Technologies division. He is a doctoral student in the School of Education Learning Sciences program at IU. His research interest center on the use, development, and implementation of learning analytics techniques and tools in higher educational contexts. Prior to pursuing his doctoral degree, Joshua obtained a M.S. in Applied Statistics from the University of Alabama.
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