�ݺ�ߣshows by User: stevelonn

�ݺ�ߣshows by User: stevelonn / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: stevelonn / Mon, 11 Jan 2016 17:29:27 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: stevelonn Actionable Learning Analytics: Practical Lessons for Teaching and Learning /slideshow/actionable-learning-analytics-practical-lessons-for-teaching-and-learning/56918530 lonnumkckeynotejan2016-160111172928
“Big Data” for education has an aura of sophistication and complexity that is daunting and seemingly intractable for most faculty and academic departments. Those who are creating innovative educational practices for online and blended contexts are desperate to find ways to leverage this virtual mountain of information to find the nuggets of information that can inform how, and for whom, their innovations are working and how to improve them. In an era where nearly every web-based tool is spraying vast amounts of ‘data exhaust’ there is a clear need for information that can readily provide salient information that can help all students achieve their learning goals. Fortunately, we are now approaching an age where digital tools can begin to deliver truly actionable information that can lead to student success. In this presentation, Dr. Steven Lonn discussed the current state of learning analytics including: * How institutions are assessing their own readiness to capitalize on learning analytics * Recent examples of how faculty have utilized learning analytics data to improve learning outcomes and innovate instructional practices * New developments in student-facing analytics including course data discovery, electronic coaching, and various approaches to dashboards * Implications for increased transparency of learning analytics data in higher education today]]>
“Big Data” for education has an aura of sophistication and complexity that is daunting and seemingly intractable for most faculty and academic departments. Those who are creating innovative educational practices for online and blended contexts are desperate to find ways to leverage this virtual mountain of information to find the nuggets of information that can inform how, and for whom, their innovations are working and how to improve them. In an era where nearly every web-based tool is spraying vast amounts of ‘data exhaust’ there is a clear need for information that can readily provide salient information that can help all students achieve their learning goals. Fortunately, we are now approaching an age where digital tools can begin to deliver truly actionable information that can lead to student success. In this presentation, Dr. Steven Lonn discussed the current state of learning analytics including: * How institutions are assessing their own readiness to capitalize on learning analytics * Recent examples of how faculty have utilized learning analytics data to improve learning outcomes and innovate instructional practices * New developments in student-facing analytics including course data discovery, electronic coaching, and various approaches to dashboards * Implications for increased transparency of learning analytics data in higher education today]]> Mon, 11 Jan 2016 17:29:27 GMT /slideshow/actionable-learning-analytics-practical-lessons-for-teaching-and-learning/56918530 stevelonn@slideshare.net(stevelonn) Actionable Learning Analytics: Practical Lessons for Teaching and Learning stevelonn “Big Data” for education has an aura of sophistication and complexity that is daunting and seemingly intractable for most faculty and academic departments. Those who are creating innovative educational practices for online and blended contexts are desperate to find ways to leverage this virtual mountain of information to find the nuggets of information that can inform how, and for whom, their innovations are working and how to improve them. In an era where nearly every web-based tool is spraying vast amounts of ‘data exhaust’ there is a clear need for information that can readily provide salient information that can help all students achieve their learning goals. Fortunately, we are now approaching an age where digital tools can begin to deliver truly actionable information that can lead to student success. In this presentation, Dr. Steven Lonn discussed the current state of learning analytics including: * How institutions are assessing their own readiness to capitalize on learning analytics * Recent examples of how faculty have utilized learning analytics data to improve learning outcomes and innovate instructional practices * New developments in student-facing analytics including course data discovery, electronic coaching, and various approaches to dashboards * Implications for increased transparency of learning analytics data in higher education today <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/lonnumkckeynotejan2016-160111172928-thumbnail.jpg?width=120&height=120&fit=bounds" /> “Big Data” for education has an aura of sophistication and complexity that is daunting and seemingly intractable for most faculty and academic departments. Those who are creating innovative educational practices for online and blended contexts are desperate to find ways to leverage this virtual mountain of information to find the nuggets of information that can inform how, and for whom, their innovations are working and how to improve them. In an era where nearly every web-based tool is spraying vast amounts of ‘data exhaust’ there is a clear need for information that can readily provide salient information that can help all students achieve their learning goals. Fortunately, we are now approaching an age where digital tools can begin to deliver truly actionable information that can lead to student success. In this presentation, Dr. Steven Lonn discussed the current state of learning analytics including: * How institutions are assessing their own readiness to capitalize on learning analytics * Recent examples of how faculty have utilized learning analytics data to improve learning outcomes and innovate instructional practices * New developments in student-facing analytics including course data discovery, electronic coaching, and various approaches to dashboards * Implications for increased transparency of learning analytics data in higher education today

Actionable Learning Analytics: Practical Lessons for Teaching and Learning from Steven Lonn

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0 Using Digital Badges to Recognize Co-Curricular Learning /slideshow/using-digital-badges-to-recognize-cocurricular-learning/34950923 digitalbadgesmozillamay212014-140521075758-phpapp01
Presentation about University of Michigan Pilot on Digital Badges for Co-Curricular Learning pilot. Presented to Mozilla Open Badges Research Community Call on May 21, 2014 (Notes available here: https://openbadges.etherpad.mozilla.org/research-calls-May21) Summary: This pilot project studied the recognition of undergraduate engineering students' co-curricular learning experiences using digital badges in one semester, Winter 2014. Using a web environment, students described and reflected upon their experiences in categories of competencies that leaders in industry and education have identified when evaluating the future needs of the global STEM workforce. The objectives of the project were to (1) deploy an online system that served to standardize the recognition of engineering co-curricular learning; (2) understand different motivations students have for seeking recognition for their co-curricular learning and whether digital badges satisfy those motivations; (3) maximize the perceived value of digital badges while minimizing undue burden on the student to collect evidence of their co-curricular learning; (4) examine how students discuss, discover, and share digital badges and their supporting evidence, with their peers and with potential employers; and (5) disseminate findings that inform the use of digital badges designed to represent the wide variety of skills that students can acquire through co-curricular opportunities in higher education.]]>
Presentation about University of Michigan Pilot on Digital Badges for Co-Curricular Learning pilot. Presented to Mozilla Open Badges Research Community Call on May 21, 2014 (Notes available here: https://openbadges.etherpad.mozilla.org/research-calls-May21) Summary: This pilot project studied the recognition of undergraduate engineering students' co-curricular learning experiences using digital badges in one semester, Winter 2014. Using a web environment, students described and reflected upon their experiences in categories of competencies that leaders in industry and education have identified when evaluating the future needs of the global STEM workforce. The objectives of the project were to (1) deploy an online system that served to standardize the recognition of engineering co-curricular learning; (2) understand different motivations students have for seeking recognition for their co-curricular learning and whether digital badges satisfy those motivations; (3) maximize the perceived value of digital badges while minimizing undue burden on the student to collect evidence of their co-curricular learning; (4) examine how students discuss, discover, and share digital badges and their supporting evidence, with their peers and with potential employers; and (5) disseminate findings that inform the use of digital badges designed to represent the wide variety of skills that students can acquire through co-curricular opportunities in higher education.]]> Wed, 21 May 2014 07:57:58 GMT /slideshow/using-digital-badges-to-recognize-cocurricular-learning/34950923 stevelonn@slideshare.net(stevelonn) Using Digital Badges to Recognize Co-Curricular Learning stevelonn Presentation about University of Michigan Pilot on Digital Badges for Co-Curricular Learning pilot. Presented to Mozilla Open Badges Research Community Call on May 21, 2014 (Notes available here: https://openbadges.etherpad.mozilla.org/research-calls-May21) Summary: This pilot project studied the recognition of undergraduate engineering students' co-curricular learning experiences using digital badges in one semester, Winter 2014. Using a web environment, students described and reflected upon their experiences in categories of competencies that leaders in industry and education have identified when evaluating the future needs of the global STEM workforce. The objectives of the project were to (1) deploy an online system that served to standardize the recognition of engineering co-curricular learning; (2) understand different motivations students have for seeking recognition for their co-curricular learning and whether digital badges satisfy those motivations; (3) maximize the perceived value of digital badges while minimizing undue burden on the student to collect evidence of their co-curricular learning; (4) examine how students discuss, discover, and share digital badges and their supporting evidence, with their peers and with potential employers; and (5) disseminate findings that inform the use of digital badges designed to represent the wide variety of skills that students can acquire through co-curricular opportunities in higher education. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/digitalbadgesmozillamay212014-140521075758-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Presentation about University of Michigan Pilot on Digital Badges for Co-Curricular Learning pilot. Presented to Mozilla Open Badges Research Community Call on May 21, 2014 (Notes available here: https://openbadges.etherpad.mozilla.org/research-calls-May21) Summary: This pilot project studied the recognition of undergraduate engineering students' co-curricular learning experiences using digital badges in one semester, Winter 2014. Using a web environment, students described and reflected upon their experiences in categories of competencies that leaders in industry and education have identified when evaluating the future needs of the global STEM workforce. The objectives of the project were to (1) deploy an online system that served to standardize the recognition of engineering co-curricular learning; (2) understand different motivations students have for seeking recognition for their co-curricular learning and whether digital badges satisfy those motivations; (3) maximize the perceived value of digital badges while minimizing undue burden on the student to collect evidence of their co-curricular learning; (4) examine how students discuss, discover, and share digital badges and their supporting evidence, with their peers and with potential employers; and (5) disseminate findings that inform the use of digital badges designed to represent the wide variety of skills that students can acquire through co-curricular opportunities in higher education.

Using Digital Badges to Recognize Co-Curricular Learning from Steven Lonn

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0 Learning Analytics 101 /slideshow/learning-analytics-101/13278632 learninganalytics101final-120611081708-phpapp01
Learning Analytics is an emerging topic of interest throughout all levels of education focusing on how to harness the power of data mining, interpretation, and modeling. However, there are several similar terms (academic analytics, predictive analytics, business intelligence, etc.) that can confuse educators and administrators alike. In this session, we will unpack this new area of interest and discuss how institutions can begin to leverage available products and open source communities to utilize analytics to improve understandings of teaching and learning and to tailor education more effectively. We will briefly present an overview of the learning analytics field, drawing from popular examples such as the Signals project at Purdue U. and the Check My Activity tool at U. Maryland, Baltimore County. We will also review the structure of Sakai CLE and OAE user-level metrics and briefly discuss projects to design and implement tools to utilize these metrics in meaningful ways.]]>
Learning Analytics is an emerging topic of interest throughout all levels of education focusing on how to harness the power of data mining, interpretation, and modeling. However, there are several similar terms (academic analytics, predictive analytics, business intelligence, etc.) that can confuse educators and administrators alike. In this session, we will unpack this new area of interest and discuss how institutions can begin to leverage available products and open source communities to utilize analytics to improve understandings of teaching and learning and to tailor education more effectively. We will briefly present an overview of the learning analytics field, drawing from popular examples such as the Signals project at Purdue U. and the Check My Activity tool at U. Maryland, Baltimore County. We will also review the structure of Sakai CLE and OAE user-level metrics and briefly discuss projects to design and implement tools to utilize these metrics in meaningful ways.]]> Mon, 11 Jun 2012 08:17:06 GMT /slideshow/learning-analytics-101/13278632 stevelonn@slideshare.net(stevelonn) Learning Analytics 101 stevelonn Learning Analytics is an emerging topic of interest throughout all levels of education focusing on how to harness the power of data mining, interpretation, and modeling. However, there are several similar terms (academic analytics, predictive analytics, business intelligence, etc.) that can confuse educators and administrators alike. In this session, we will unpack this new area of interest and discuss how institutions can begin to leverage available products and open source communities to utilize analytics to improve understandings of teaching and learning and to tailor education more effectively. We will briefly present an overview of the learning analytics field, drawing from popular examples such as the Signals project at Purdue U. and the Check My Activity tool at U. Maryland, Baltimore County. We will also review the structure of Sakai CLE and OAE user-level metrics and briefly discuss projects to design and implement tools to utilize these metrics in meaningful ways. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/learninganalytics101final-120611081708-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Learning Analytics is an emerging topic of interest throughout all levels of education focusing on how to harness the power of data mining, interpretation, and modeling. However, there are several similar terms (academic analytics, predictive analytics, business intelligence, etc.) that can confuse educators and administrators alike. In this session, we will unpack this new area of interest and discuss how institutions can begin to leverage available products and open source communities to utilize analytics to improve understandings of teaching and learning and to tailor education more effectively. We will briefly present an overview of the learning analytics field, drawing from popular examples such as the Signals project at Purdue U. and the Check My Activity tool at U. Maryland, Baltimore County. We will also review the structure of Sakai CLE and OAE user-level metrics and briefly discuss projects to design and implement tools to utilize these metrics in meaningful ways.

Learning Analytics 101 from Steven Lonn

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0 Bridging the Gap from Knowledge to Action: Putting Analytics in the Hands of Academic Advisors /slideshow/bridging-the-gap-from-knowledge-to-action-putting-analytics-in-the-hands-of-academic-advisors/12753164 laklonnpresentation-120501014516-phpapp02
Short Paper Presentation at Learning Analytics and Knowledge Conference 2012, May 1. #LAK12 This paper presents current findings from an ongoing design- based research project aimed at developing an early warning system (EWS) for academic mentors in an undergraduate engineering mentoring program. This paper details our progress in mining Learning Management System data and translating these data into an EWS for academic mentors. We focus on the role of mentors and advisors, and elaborate on their importance in learning analytics-based interventions developed for higher education.]]>
Short Paper Presentation at Learning Analytics and Knowledge Conference 2012, May 1. #LAK12 This paper presents current findings from an ongoing design- based research project aimed at developing an early warning system (EWS) for academic mentors in an undergraduate engineering mentoring program. This paper details our progress in mining Learning Management System data and translating these data into an EWS for academic mentors. We focus on the role of mentors and advisors, and elaborate on their importance in learning analytics-based interventions developed for higher education.]]> Tue, 01 May 2012 01:45:14 GMT /slideshow/bridging-the-gap-from-knowledge-to-action-putting-analytics-in-the-hands-of-academic-advisors/12753164 stevelonn@slideshare.net(stevelonn) Bridging the Gap from Knowledge to Action: Putting Analytics in the Hands of Academic Advisors stevelonn Short Paper Presentation at Learning Analytics and Knowledge Conference 2012, May 1. #LAK12 This paper presents current findings from an ongoing design- based research project aimed at developing an early warning system (EWS) for academic mentors in an undergraduate engineering mentoring program. This paper details our progress in mining Learning Management System data and translating these data into an EWS for academic mentors. We focus on the role of mentors and advisors, and elaborate on their importance in learning analytics-based interventions developed for higher education. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/laklonnpresentation-120501014516-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Short Paper Presentation at Learning Analytics and Knowledge Conference 2012, May 1. #LAK12 This paper presents current findings from an ongoing design- based research project aimed at developing an early warning system (EWS) for academic mentors in an undergraduate engineering mentoring program. This paper details our progress in mining Learning Management System data and translating these data into an EWS for academic mentors. We focus on the role of mentors and advisors, and elaborate on their importance in learning analytics-based interventions developed for higher education.

Bridging the Gap from Knowledge to Action: Putting Analytics in the Hands of Academic Advisors from Steven Lonn

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0 Just Clicking Through: How Instructors Use Sakai as a Web Portal /slideshow/just-clicking-through-how-instructors-use-sakai-as-a-web-portal/8338927 lonnsakai11webportalslides-110617115621-phpapp01
It is well known that the "typical" instructor uses online management systems to distribute course materials and make announcements. What has not been explored is how instructors use these systems to link to the vast array of educational content and resources available through the Internet. Which types of websites are most common? For which disciplines? How does an instructor choose which content is the "best" or most relevant for their course? This session will present how instructors at residential and commuter campuses of the University of Michigan used the Web Content and Resources tools in Sakai to link to Internet resources. The different categories of resources as well as differences between campuses, disciplines, and departments will be discussed. Session participants will be asked to reflect on these findings and discuss implications for connecting instructors to "reliable" resources, and how to potentially scaffold instructors' selections of external resources in Sakai OAE.]]>
It is well known that the "typical" instructor uses online management systems to distribute course materials and make announcements. What has not been explored is how instructors use these systems to link to the vast array of educational content and resources available through the Internet. Which types of websites are most common? For which disciplines? How does an instructor choose which content is the "best" or most relevant for their course? This session will present how instructors at residential and commuter campuses of the University of Michigan used the Web Content and Resources tools in Sakai to link to Internet resources. The different categories of resources as well as differences between campuses, disciplines, and departments will be discussed. Session participants will be asked to reflect on these findings and discuss implications for connecting instructors to "reliable" resources, and how to potentially scaffold instructors' selections of external resources in Sakai OAE.]]> Fri, 17 Jun 2011 11:56:17 GMT /slideshow/just-clicking-through-how-instructors-use-sakai-as-a-web-portal/8338927 stevelonn@slideshare.net(stevelonn) Just Clicking Through: How Instructors Use Sakai as a Web Portal stevelonn It is well known that the "typical" instructor uses online management systems to distribute course materials and make announcements. What has not been explored is how instructors use these systems to link to the vast array of educational content and resources available through the Internet. Which types of websites are most common? For which disciplines? How does an instructor choose which content is the "best" or most relevant for their course? This session will present how instructors at residential and commuter campuses of the University of Michigan used the Web Content and Resources tools in Sakai to link to Internet resources. The different categories of resources as well as differences between campuses, disciplines, and departments will be discussed. Session participants will be asked to reflect on these findings and discuss implications for connecting instructors to "reliable" resources, and how to potentially scaffold instructors' selections of external resources in Sakai OAE. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/lonnsakai11webportalslides-110617115621-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> It is well known that the "typical" instructor uses online management systems to distribute course materials and make announcements. What has not been explored is how instructors use these systems to link to the vast array of educational content and resources available through the Internet. Which types of websites are most common? For which disciplines? How does an instructor choose which content is the "best" or most relevant for their course? This session will present how instructors at residential and commuter campuses of the University of Michigan used the Web Content and Resources tools in Sakai to link to Internet resources. The different categories of resources as well as differences between campuses, disciplines, and departments will be discussed. Session participants will be asked to reflect on these findings and discuss implications for connecting instructors to "reliable" resources, and how to potentially scaffold instructors' selections of external resources in Sakai OAE.

Just Clicking Through: How Instructors Use Sakai as a Web Portal from Steven Lonn

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0 Analytics Collaboration Session at Sakai 2011 /stevelonn/analytics-collaboration-session-at-sakai-2011 combined-110616121623-phpapp01
Academic Analytics is a hot topic in Higher Education. Institutions are seeking to use analytics to understand student success and academic performance, maximize retention. Increasingly, regulatory and accreditation bodies require this information to help measure effectiveness. This block session will report on a number of analytics initiatives within the Sakai Community, and higher education generally. Opportunities will be provided to interact with individual presenters, and to synthesise information available across the session.]]>
Academic Analytics is a hot topic in Higher Education. Institutions are seeking to use analytics to understand student success and academic performance, maximize retention. Increasingly, regulatory and accreditation bodies require this information to help measure effectiveness. This block session will report on a number of analytics initiatives within the Sakai Community, and higher education generally. Opportunities will be provided to interact with individual presenters, and to synthesise information available across the session.]]> Thu, 16 Jun 2011 12:16:19 GMT /stevelonn/analytics-collaboration-session-at-sakai-2011 stevelonn@slideshare.net(stevelonn) Analytics Collaboration Session at Sakai 2011 stevelonn Academic Analytics is a hot topic in Higher Education. Institutions are seeking to use analytics to understand student success and academic performance, maximize retention. Increasingly, regulatory and accreditation bodies require this information to help measure effectiveness. This block session will report on a number of analytics initiatives within the Sakai Community, and higher education generally. Opportunities will be provided to interact with individual presenters, and to synthesise information available across the session. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/combined-110616121623-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Academic Analytics is a hot topic in Higher Education. Institutions are seeking to use analytics to understand student success and academic performance, maximize retention. Increasingly, regulatory and accreditation bodies require this information to help measure effectiveness. This block session will report on a number of analytics initiatives within the Sakai Community, and higher education generally. Opportunities will be provided to interact with individual presenters, and to synthesise information available across the session.

Analytics Collaboration Session at Sakai 2011 from Steven Lonn

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0 Investigating Changes in Integrative and Lifelong Learning: Students’ Use of ePortfolios /slideshow/investigating-changes-in-integrative-and-lifelong-learning-students-use-of-eportfolios/8317386 portfoliosurvey-110615110939-phpapp02
For the past two academic years, the ePortfolio team at the University of Michigan has administered pre and post surveys to students that were using ePortfolios for the purpose of fostering integrative, lifelong, and lifewide learning. This session will present results from this longitudinal research, focusing on specific trends, significant findings and implications of this research for conducting integrative assessments of students' academic and co-curricular learning. ]]>
For the past two academic years, the ePortfolio team at the University of Michigan has administered pre and post surveys to students that were using ePortfolios for the purpose of fostering integrative, lifelong, and lifewide learning. This session will present results from this longitudinal research, focusing on specific trends, significant findings and implications of this research for conducting integrative assessments of students' academic and co-curricular learning. ]]> Wed, 15 Jun 2011 11:09:36 GMT /slideshow/investigating-changes-in-integrative-and-lifelong-learning-students-use-of-eportfolios/8317386 stevelonn@slideshare.net(stevelonn) Investigating Changes in Integrative and Lifelong Learning: Students’ Use of ePortfolios stevelonn For the past two academic years, the ePortfolio team at the University of Michigan has administered pre and post surveys to students that were using ePortfolios for the purpose of fostering integrative, lifelong, and lifewide learning. This session will present results from this longitudinal research, focusing on specific trends, significant findings and implications of this research for conducting integrative assessments of students' academic and co-curricular learning. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/portfoliosurvey-110615110939-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> For the past two academic years, the ePortfolio team at the University of Michigan has administered pre and post surveys to students that were using ePortfolios for the purpose of fostering integrative, lifelong, and lifewide learning. This session will present results from this longitudinal research, focusing on specific trends, significant findings and implications of this research for conducting integrative assessments of students' academic and co-curricular learning.

Investigating Changes in Integrative and Lifelong Learning: Students’ Use of ePortfolios from Steven Lonn

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0 Learning Analytics: More Than Data-Driven Decisions /slideshow/learning-analytics-more-than-datadriven-decisions/7435091 lonnlearninganalytics2011-110329131947-phpapp01
An overview of learning analytics as well as recent examples from higher education and current projects underway at the University of Michigan. From The Horizon Report, 2011: "Learning analytics promises to harness the power of advances in data mining, interpretation, and modeling to improve understandings of teaching and learning, and to tailor education to individual students more effectively. Still in its early stages, learning analytics responds to calls for accountability on campuses across the country, and leverages the vast amount of data produced by students in day-to-day academic activities. While learning analytics has already been used in admissions and fund-raising efforts on several campuses, “academic analytics” is just beginning to take shape." ]]>
An overview of learning analytics as well as recent examples from higher education and current projects underway at the University of Michigan. From The Horizon Report, 2011: "Learning analytics promises to harness the power of advances in data mining, interpretation, and modeling to improve understandings of teaching and learning, and to tailor education to individual students more effectively. Still in its early stages, learning analytics responds to calls for accountability on campuses across the country, and leverages the vast amount of data produced by students in day-to-day academic activities. While learning analytics has already been used in admissions and fund-raising efforts on several campuses, “academic analytics” is just beginning to take shape." ]]> Tue, 29 Mar 2011 13:19:43 GMT /slideshow/learning-analytics-more-than-datadriven-decisions/7435091 stevelonn@slideshare.net(stevelonn) Learning Analytics: More Than Data-Driven Decisions stevelonn An overview of learning analytics as well as recent examples from higher education and current projects underway at the University of Michigan. From The Horizon Report, 2011: "Learning analytics promises to harness the power of advances in data mining, interpretation, and modeling to improve understandings of teaching and learning, and to tailor education to individual students more effectively. Still in its early stages, learning analytics responds to calls for accountability on campuses across the country, and leverages the vast amount of data produced by students in day-to-day academic activities. While learning analytics has already been used in admissions and fund-raising efforts on several campuses, “academic analytics” is just beginning to take shape." <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/lonnlearninganalytics2011-110329131947-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> An overview of learning analytics as well as recent examples from higher education and current projects underway at the University of Michigan. From The Horizon Report, 2011: "Learning analytics promises to harness the power of advances in data mining, interpretation, and modeling to improve understandings of teaching and learning, and to tailor education to individual students more effectively. Still in its early stages, learning analytics responds to calls for accountability on campuses across the country, and leverages the vast amount of data produced by students in day-to-day academic activities. While learning analytics has already been used in admissions and fund-raising efforts on several campuses, “academic analytics” is just beginning to take shape."

Learning Analytics: More Than Data-Driven Decisions from Steven Lonn

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0 What Do Users Say? Findings from the Multi-Institutional Survey Initiative (MISI) /slideshow/what-do-users-say-findings-from-the-multiinstitutional-survey-initiative-misi/1696385 misipanel-090708093357-phpapp01
Panel Presentation at the 10th Sakai Conference in Boston, MA This panel session will present an overview of preliminary data from the Sakai Multi-Institutional Survey Initiative (MISI) including perspectives and lessons learned from individual participating institutions. Initial trends, similarities, and differences between institutions regarding instructor and student responses will be discussed.]]>
Panel Presentation at the 10th Sakai Conference in Boston, MA This panel session will present an overview of preliminary data from the Sakai Multi-Institutional Survey Initiative (MISI) including perspectives and lessons learned from individual participating institutions. Initial trends, similarities, and differences between institutions regarding instructor and student responses will be discussed.]]> Wed, 08 Jul 2009 09:33:53 GMT /slideshow/what-do-users-say-findings-from-the-multiinstitutional-survey-initiative-misi/1696385 stevelonn@slideshare.net(stevelonn) What Do Users Say? Findings from the Multi-Institutional Survey Initiative (MISI) stevelonn Panel Presentation at the 10th Sakai Conference in Boston, MA This panel session will present an overview of preliminary data from the Sakai Multi-Institutional Survey Initiative (MISI) including perspectives and lessons learned from individual participating institutions. Initial trends, similarities, and differences between institutions regarding instructor and student responses will be discussed. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/misipanel-090708093357-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Panel Presentation at the 10th Sakai Conference in Boston, MA This panel session will present an overview of preliminary data from the Sakai Multi-Institutional Survey Initiative (MISI) including perspectives and lessons learned from individual participating institutions. Initial trends, similarities, and differences between institutions regarding instructor and student responses will be discussed.

What Do Users Say? Findings from the Multi-Institutional Survey Initiative (MISI) from Steven Lonn

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0 https://cdn.slidesharecdn.com/profile-photo-stevelonn-48x48.jpg?cb=1522843071 . digitaleducation.umich.edu https://cdn.slidesharecdn.com/ss_thumbnails/lonnumkckeynotejan2016-160111172928-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/actionable-learning-analytics-practical-lessons-for-teaching-and-learning/56918530 Actionable Learning An... https://cdn.slidesharecdn.com/ss_thumbnails/digitalbadgesmozillamay212014-140521075758-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/using-digital-badges-to-recognize-cocurricular-learning/34950923 Using Digital Badges t... https://cdn.slidesharecdn.com/ss_thumbnails/learninganalytics101final-120611081708-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/learning-analytics-101/13278632 Learning Analytics 101