This document discusses system-wide learner analytics projects at Chico State University and across the California State University system. [1] It describes a research study at Chico State that analyzed the relationship between student usage of the learning management system and academic outcomes. [2] It then provides an overview of current learner analytics projects involving the Moodle and Blackboard learning management systems, including establishing common metrics and queries across campuses.
Navigating the Information-scape: Do Information Visualization Activities Imp...mabrowne
Presentation slides from LOEX of the West 2012 in Burbank, California. Abstract: Identifying appropriate keywords is an essential component of information research. However, many students struggle with translating topics into effective search syntax. This session will describe our study exploring the use of information visualization strategies to help students generate terms for database searching. We compared three pedagogies for identifying and displaying keywords with a control condition, and measured their impact on search behaviors in a sample of 50 undergraduate students. We will share the findings of our qualitative and quantitative data analysis, and discuss their implications for library instruction sessions.
Relevance of scholarships for mba students at presentNIET
The article discusses the relevance of scholarships for MBA students. It notes that scholarships provide financial assistance to students pursuing their education. The article outlines some key differences in eligibility criteria and rules for scholarships between diploma programs and MBA degrees. It emphasizes factors like academic performance, leadership experience, and financial need that scholarship providers consider in their selection process.
“Unleashing Analytics in the Classroom”. Session presentation. Florida International University Online, Annual Conference for Online Instructors. April 2015, Miami, FL.
Learning Analytics: What is it? Why do it? And how?Timothy Harfield
This document provides an introduction and overview of learning analytics. It defines learning analytics as the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning. The document outlines what learning analytics is, what its uses are, and how to get started with learning analytics. Learning analytics can benefit both teachers and learners by helping to identify at-risk students, increase engagement, provide feedback on course design, and support increased achievement. While issues around privacy, responsibility, and defining success must be considered, learning analytics tools in learning management systems and do-it-yourself options are available to help educators get started with learning analytics.
Learner Analytics: from Buzz to Strategic Role Academic TechnologistsJohn Whitmer, Ed.D.
This document summarizes a presentation on learner analytics. It discusses using data from learning management systems (LMS) and student information systems to better understand student learning and optimize educational environments. Specifically, it provides two case studies: 1) California State University's data dashboard that tracks graduation rates and aims to close achievement gaps. 2) CSU Chico's analysis of LMS usage data from its Vista system to examine relationships between technology use and student achievement. The presentation calls on academic technologists to lead efforts in learner analytics due to their expertise in educational technology and data. It provides resources to help campuses build capacity for analytics.
TLC2016 - Learning Analytics - One Universities Journey BlackboardEMEA
Presenter: Sandra Stevenson-Revill
Organisation: University of Derby
Description: Over the years there have been lots of discussions on using data to understand learning content. UDOL are taking the next step, using analytics to understand their online provision and the impact that has on learners. This presentation will outline why the Blackboards Analytics tool, how we implemented and timescales involved. Focusing on the use of the tool within UDOL which is responsible for Derby's online provision. We will show you some of the reports and discuss how we are using them. This is continuing our sequence of presentations on UDOL's use of Blackboard Learning Analytics tools.
Introduction to Learning Analytics in BlackboardTimothy Harfield
Learning analytics is the measurement and analysis of student data to understand and optimize learning. Blackboard Analytics allows students to monitor their online course engagement over time compared to the class average. Using activity reports in Blackboard, students can view metrics on course access, time spent, interactions, submissions, and current grade to gain insight on their participation and performance.
This document discusses system-wide learner analytics projects at Chico State University and across the California State University system. [1] It describes a research study at Chico State that analyzed the relationship between student usage of the learning management system and academic outcomes. [2] It then provides an overview of current learner analytics projects involving the Moodle and Blackboard learning management systems, including establishing common metrics and queries across campuses.
Navigating the Information-scape: Do Information Visualization Activities Imp...mabrowne
Presentation slides from LOEX of the West 2012 in Burbank, California. Abstract: Identifying appropriate keywords is an essential component of information research. However, many students struggle with translating topics into effective search syntax. This session will describe our study exploring the use of information visualization strategies to help students generate terms for database searching. We compared three pedagogies for identifying and displaying keywords with a control condition, and measured their impact on search behaviors in a sample of 50 undergraduate students. We will share the findings of our qualitative and quantitative data analysis, and discuss their implications for library instruction sessions.
Relevance of scholarships for mba students at presentNIET
The article discusses the relevance of scholarships for MBA students. It notes that scholarships provide financial assistance to students pursuing their education. The article outlines some key differences in eligibility criteria and rules for scholarships between diploma programs and MBA degrees. It emphasizes factors like academic performance, leadership experience, and financial need that scholarship providers consider in their selection process.
“Unleashing Analytics in the Classroom”. Session presentation. Florida International University Online, Annual Conference for Online Instructors. April 2015, Miami, FL.
Learning Analytics: What is it? Why do it? And how?Timothy Harfield
This document provides an introduction and overview of learning analytics. It defines learning analytics as the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning. The document outlines what learning analytics is, what its uses are, and how to get started with learning analytics. Learning analytics can benefit both teachers and learners by helping to identify at-risk students, increase engagement, provide feedback on course design, and support increased achievement. While issues around privacy, responsibility, and defining success must be considered, learning analytics tools in learning management systems and do-it-yourself options are available to help educators get started with learning analytics.
Learner Analytics: from Buzz to Strategic Role Academic TechnologistsJohn Whitmer, Ed.D.
This document summarizes a presentation on learner analytics. It discusses using data from learning management systems (LMS) and student information systems to better understand student learning and optimize educational environments. Specifically, it provides two case studies: 1) California State University's data dashboard that tracks graduation rates and aims to close achievement gaps. 2) CSU Chico's analysis of LMS usage data from its Vista system to examine relationships between technology use and student achievement. The presentation calls on academic technologists to lead efforts in learner analytics due to their expertise in educational technology and data. It provides resources to help campuses build capacity for analytics.
TLC2016 - Learning Analytics - One Universities Journey BlackboardEMEA
Presenter: Sandra Stevenson-Revill
Organisation: University of Derby
Description: Over the years there have been lots of discussions on using data to understand learning content. UDOL are taking the next step, using analytics to understand their online provision and the impact that has on learners. This presentation will outline why the Blackboards Analytics tool, how we implemented and timescales involved. Focusing on the use of the tool within UDOL which is responsible for Derby's online provision. We will show you some of the reports and discuss how we are using them. This is continuing our sequence of presentations on UDOL's use of Blackboard Learning Analytics tools.
Introduction to Learning Analytics in BlackboardTimothy Harfield
Learning analytics is the measurement and analysis of student data to understand and optimize learning. Blackboard Analytics allows students to monitor their online course engagement over time compared to the class average. Using activity reports in Blackboard, students can view metrics on course access, time spent, interactions, submissions, and current grade to gain insight on their participation and performance.
This study evaluated the relationship between student use of a Learning Management System (LMS), student characteristics, and academic achievement in a large undergraduate hybrid course. The results showed:
1. LMS use, particularly assessment activities, had a stronger correlation with course grades than student characteristics like high school GPA.
2. A regression model combining LMS usage data and student characteristics moderately predicted student success, suggesting refinement could strengthen results.
3. LMS data required extensive filtering to be useful for analysis, and student variables needed screening for missing data.
The conclusions were that LMS behavioral data may better predict outcomes than demographics alone, and combining LMS and student data in a complete model relates to academic achievement
The document discusses learning analytics and educational data mining. It defines key concepts like managerialism, academic analytics, and learning analytics. It also discusses the potential of learning analytics to improve understanding of teaching and learning. However, it notes some potential problems with abstraction losing detail, organizational structures, confusing correlation with causation, and assumptions of causality. Complex adaptive systems are discussed as a framework to understand these issues.
Presentation by John Whitmer, Michael Haskell (Cal Poly SLO), and Hillary Kaplowitz (CSU Northtridge) at US West Coast Moodle Moot 2012.
“Learner Analytics” has captured the attention of the media and is the topic of much debate in professional and academic circles. What lies behind the hype? In this presentation, we will discuss the state and limits to current in research in LMS Learner Analytics. We will then look at examples of Learner Analytics in Moodle, including tools for faculty and reports for reporting across the entire instance.
Learning Analytics: Realizing their Promise in the California State UniversityJohn Whitmer, Ed.D.
This document discusses learner analytics and how they are being used at California State University (CSU) campuses. It begins with an overview of the promise of learner analytics, including how they can provide insights into student behavior and performance. Examples of learner analytics tools are presented, including Signals and SNAPP. The document then shares three case studies from CSU campuses: one discusses how analytics were used to help a teacher and student at CSU Northridge, another reviews the GISMO analytics tool at CSU Northridge, and a final case study describes how Vista analytics were used in a course at CSU Dominguez Hills. The presentation concludes with a call to action around increasing analytics reporting capabilities.
The Achievement Gap in Online Courses through a Learning Analytics LensJohn Whitmer, Ed.D.
Presentation at San Diego State University on April 12, 2013.
Educational researchers have found that students from under-represented minority families and other disadvantaged demographic backgrounds have lower achievement in online (or hybrid) courses compared to face-to-face course sections (Slate, Manuel, & Brinson Jr, 2002; Xu & Jaggars, 2013). However, these studies assume that "online course" is a homogeneous entity, and that student participation is uniform. The content and activity of the course is an opaque "black box", which leads to conclusions that are speculative at best and quite possibly further marginalize the very populations they intend to advocate for.
The emerging field of Learning Analytics promises to break open this black box understand how students use online course materials and the relationship between this use and student achievement. In this presentation, we will explore the countours of Learning Analytics, look at current applications of analytics, and discuss research applying a Learning Analytics research method to students from at-risk backgrounds. The findings of this research challenge stereotypes of these students as technologically unsophisticated and identify concrete learning activities that can support their success.
Using Analytics for Institutional Transformation - Dr. Yvette Mozie-Ross - Un...Blackboard APAC
To achieve its strategic goals, UMBC realized it needed to become a more data-driven institution by deploying more sophisticated tools and procedures to help staff find and analyze data in a timely way. Specifically, the university needed ways that users could develop accurate and easily configurable reports to support operational management decisions and strategic analysis, which a data warehouse made possible. In this talk, Dr. Mozie-Ross will describe how UMBC successfully implemented its data warehouse by resolving campus-wide issues with buy-in, IT partnering with IR, governance, and cost.
This document summarizes a presentation on learning analytics given by Simon Buckingham Shum. Some key points:
- Learning analytics aims to unlock student data to improve 21st century learning by analyzing patterns in data to better understand learning processes and identify students who may need help.
- Examples discussed include Purdue University's predictive model that identified 66-80% of struggling students and a system that provides real-time feedback to students.
- Analytics can look beyond grades and course performance to capture data on learning dispositions, engagement, curriculum mastery, and student discourse to provide a more holistic view of the learning process.
- Challenges include ensuring analytics are used ethically and to improve learning rather than
Cultivating Information Literacy Among Students: Lessons Learned from UCF’s I...Kelvin Thompson
This document summarizes Dr. Kelvin Thompson's presentation on information literacy modules developed at the University of Central Florida. The modules are short, self-contained online lessons that teach information literacy skills and can be assigned by instructors or completed voluntarily by students. Over the past 8 years, over 200,000 assessments have been completed by 37,000+ students across 15 module topics. UCF piloted "badging" completed modules to recognize student achievement, with over 40,000 badges issued so far. While funding cuts have paused new development, the existing modules continue to be maintained and updated annually.
Using Learning Analytics to Understand Student AchievementJohn Whitmer, Ed.D.
1) An analysis of student achievement data from a large enrollment hybrid course found that levels of student engagement with the learning management system (LMS), as measured by website hits and tool usage, better predicted student grades than traditional student characteristics.
2) Higher levels of LMS engagement were particularly important for explaining academic outcomes of "at-risk" students from underrepresented or low-income backgrounds.
3) While insightful, the analysis showed room for improvement through enhanced data filtering, variable selection, and analytical methods to better understand factors influencing student success.
Using Analytics to Intervene with Underperforming College Studentsekunnen
Abstract: Data mining is typically associated with business and marketing. For example, Amazon uses people's past purchases to suggest books they might be interested in buying. Similarly, academic analytics can be used to identify and predict students who might be at risk, by analyzing demographic and performance data of former students. However, there is no clear consensus on how to intervene with current students in a way they will accept and not associate with academic "profiling." Why should students think they are exceptions to our rules? This panel presentation will share how three institutions are approaching this problem and provide an overview of related issues.
This document provides a summary report on Blackboard usage at Grand Rapids Community College for the summer of 2009. It finds that over 1,500 course sections are active in Blackboard, representing a 8% increase from the previous fall. Over 500 unique faculty, or 70% of total faculty, used Blackboard in the last fall semester. Student and faculty surveys show strong support for increased Blackboard usage. Upcoming initiatives include deploying new features like the content system, Relay, and Rave Wireless text messaging.
Pedagogy with Technology: getting the horse out in front of the cartSimon Bates
1. A case study examined the use of PeerWise, a web-based student-generated multiple choice question system, in a large introductory physics course at the University of British Columbia with over 1800 students.
2. Students were highly engaged with the system, far exceeding the minimum requirements by writing questions, answering questions, and providing feedback.
3. Scaffolding the use of PeerWise in tutorials helped support student learning and engagement with the system.
Student Selection Based On Academic Achievement System using k-mean AlgorithmNik Ridhuan
This document outlines a proposed student selection system based on academic achievement using K-Means clustering. It includes background information on the problem of selecting students for competitions and outlines the objectives, scope, limitations, and techniques of the proposed system. The system design is shown through context and data flow diagrams, as well as an entity relationship diagram. The document also includes prototypes and discusses the conclusion and references. The overall aim is to analyze student subject scores and lists to group students based on their skills using K-Means clustering.
This document proposes the development of an online mentoring system to facilitate interaction between students and mentors. It aims to address limitations with conventional methods that require physical proximity and knowledge of contact details. The proposed system would allow mentors to post assignments and questions online and students to submit answers and doubts. It would include modules for administrators, mentors and students with features like automatic student grading, online assignment submission and doubt resolution. The system would be developed using Java technologies on a MySQL database and deployed on a Tomcat server.
EduTools 2.0 is an updated comparison tool for learning management systems (LMS). It surveys key aspects of LMSs like portal functionality, communication tools, assessments, and pricing. The document discusses revising EduTools to address changing needs around ubiquitous technology, converging feature sets, and a focus on learning over learner management. It also explores related topics like personal learning environments, semi-permeable system borders, and interoperability. Authentication systems are similarly surveyed based on their functionality, user experience, pricing, support, and integration capabilities. The results will help inform LMS selection and improvement of comparison resources like EduTools.
The document discusses learning management systems (LMS) and the Indicators project, which aims to analyze LMS usage data to identify patterns relating to student and teacher engagement and success. It notes that LMS have become ubiquitous in universities but have been adopted with little research on their effectiveness. The Indicators project seeks to address this by examining what student and teacher behaviors in LMS data can reveal about engagement and outcomes.
Interpreting Data Mining Results with Linked Data for Learning AnalyticsMathieu d'Aquin
Interpreting Data Mining Results with Linked Data for Learning Analytics:Motivation, Case Study and Directions
Presentation at the LAK 2013 conference - 10-04-2013
In this presentation to NYU-Learn, I discuss my experience applying data science and machine learning in educational technology and assessment industries. I share tips for thinking about the importance of context and potential of scalability.
Collaborative Research: Stealth Assessment of SE Skills w/Learning AnalyticsJohn Whitmer, Ed.D.
Early description of a work in progress between ACT, UMBC, Blackboard and Vitalsource to investigate the relationship between social and emotional skills and learning analytics using machine and deep learning techniques. A few preliminary results.
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This study evaluated the relationship between student use of a Learning Management System (LMS), student characteristics, and academic achievement in a large undergraduate hybrid course. The results showed:
1. LMS use, particularly assessment activities, had a stronger correlation with course grades than student characteristics like high school GPA.
2. A regression model combining LMS usage data and student characteristics moderately predicted student success, suggesting refinement could strengthen results.
3. LMS data required extensive filtering to be useful for analysis, and student variables needed screening for missing data.
The conclusions were that LMS behavioral data may better predict outcomes than demographics alone, and combining LMS and student data in a complete model relates to academic achievement
The document discusses learning analytics and educational data mining. It defines key concepts like managerialism, academic analytics, and learning analytics. It also discusses the potential of learning analytics to improve understanding of teaching and learning. However, it notes some potential problems with abstraction losing detail, organizational structures, confusing correlation with causation, and assumptions of causality. Complex adaptive systems are discussed as a framework to understand these issues.
Presentation by John Whitmer, Michael Haskell (Cal Poly SLO), and Hillary Kaplowitz (CSU Northtridge) at US West Coast Moodle Moot 2012.
“Learner Analytics” has captured the attention of the media and is the topic of much debate in professional and academic circles. What lies behind the hype? In this presentation, we will discuss the state and limits to current in research in LMS Learner Analytics. We will then look at examples of Learner Analytics in Moodle, including tools for faculty and reports for reporting across the entire instance.
Learning Analytics: Realizing their Promise in the California State UniversityJohn Whitmer, Ed.D.
This document discusses learner analytics and how they are being used at California State University (CSU) campuses. It begins with an overview of the promise of learner analytics, including how they can provide insights into student behavior and performance. Examples of learner analytics tools are presented, including Signals and SNAPP. The document then shares three case studies from CSU campuses: one discusses how analytics were used to help a teacher and student at CSU Northridge, another reviews the GISMO analytics tool at CSU Northridge, and a final case study describes how Vista analytics were used in a course at CSU Dominguez Hills. The presentation concludes with a call to action around increasing analytics reporting capabilities.
The Achievement Gap in Online Courses through a Learning Analytics LensJohn Whitmer, Ed.D.
Presentation at San Diego State University on April 12, 2013.
Educational researchers have found that students from under-represented minority families and other disadvantaged demographic backgrounds have lower achievement in online (or hybrid) courses compared to face-to-face course sections (Slate, Manuel, & Brinson Jr, 2002; Xu & Jaggars, 2013). However, these studies assume that "online course" is a homogeneous entity, and that student participation is uniform. The content and activity of the course is an opaque "black box", which leads to conclusions that are speculative at best and quite possibly further marginalize the very populations they intend to advocate for.
The emerging field of Learning Analytics promises to break open this black box understand how students use online course materials and the relationship between this use and student achievement. In this presentation, we will explore the countours of Learning Analytics, look at current applications of analytics, and discuss research applying a Learning Analytics research method to students from at-risk backgrounds. The findings of this research challenge stereotypes of these students as technologically unsophisticated and identify concrete learning activities that can support their success.
Using Analytics for Institutional Transformation - Dr. Yvette Mozie-Ross - Un...Blackboard APAC
To achieve its strategic goals, UMBC realized it needed to become a more data-driven institution by deploying more sophisticated tools and procedures to help staff find and analyze data in a timely way. Specifically, the university needed ways that users could develop accurate and easily configurable reports to support operational management decisions and strategic analysis, which a data warehouse made possible. In this talk, Dr. Mozie-Ross will describe how UMBC successfully implemented its data warehouse by resolving campus-wide issues with buy-in, IT partnering with IR, governance, and cost.
This document summarizes a presentation on learning analytics given by Simon Buckingham Shum. Some key points:
- Learning analytics aims to unlock student data to improve 21st century learning by analyzing patterns in data to better understand learning processes and identify students who may need help.
- Examples discussed include Purdue University's predictive model that identified 66-80% of struggling students and a system that provides real-time feedback to students.
- Analytics can look beyond grades and course performance to capture data on learning dispositions, engagement, curriculum mastery, and student discourse to provide a more holistic view of the learning process.
- Challenges include ensuring analytics are used ethically and to improve learning rather than
Cultivating Information Literacy Among Students: Lessons Learned from UCF’s I...Kelvin Thompson
This document summarizes Dr. Kelvin Thompson's presentation on information literacy modules developed at the University of Central Florida. The modules are short, self-contained online lessons that teach information literacy skills and can be assigned by instructors or completed voluntarily by students. Over the past 8 years, over 200,000 assessments have been completed by 37,000+ students across 15 module topics. UCF piloted "badging" completed modules to recognize student achievement, with over 40,000 badges issued so far. While funding cuts have paused new development, the existing modules continue to be maintained and updated annually.
Using Learning Analytics to Understand Student AchievementJohn Whitmer, Ed.D.
1) An analysis of student achievement data from a large enrollment hybrid course found that levels of student engagement with the learning management system (LMS), as measured by website hits and tool usage, better predicted student grades than traditional student characteristics.
2) Higher levels of LMS engagement were particularly important for explaining academic outcomes of "at-risk" students from underrepresented or low-income backgrounds.
3) While insightful, the analysis showed room for improvement through enhanced data filtering, variable selection, and analytical methods to better understand factors influencing student success.
Using Analytics to Intervene with Underperforming College Studentsekunnen
Abstract: Data mining is typically associated with business and marketing. For example, Amazon uses people's past purchases to suggest books they might be interested in buying. Similarly, academic analytics can be used to identify and predict students who might be at risk, by analyzing demographic and performance data of former students. However, there is no clear consensus on how to intervene with current students in a way they will accept and not associate with academic "profiling." Why should students think they are exceptions to our rules? This panel presentation will share how three institutions are approaching this problem and provide an overview of related issues.
This document provides a summary report on Blackboard usage at Grand Rapids Community College for the summer of 2009. It finds that over 1,500 course sections are active in Blackboard, representing a 8% increase from the previous fall. Over 500 unique faculty, or 70% of total faculty, used Blackboard in the last fall semester. Student and faculty surveys show strong support for increased Blackboard usage. Upcoming initiatives include deploying new features like the content system, Relay, and Rave Wireless text messaging.
Pedagogy with Technology: getting the horse out in front of the cartSimon Bates
1. A case study examined the use of PeerWise, a web-based student-generated multiple choice question system, in a large introductory physics course at the University of British Columbia with over 1800 students.
2. Students were highly engaged with the system, far exceeding the minimum requirements by writing questions, answering questions, and providing feedback.
3. Scaffolding the use of PeerWise in tutorials helped support student learning and engagement with the system.
Student Selection Based On Academic Achievement System using k-mean AlgorithmNik Ridhuan
This document outlines a proposed student selection system based on academic achievement using K-Means clustering. It includes background information on the problem of selecting students for competitions and outlines the objectives, scope, limitations, and techniques of the proposed system. The system design is shown through context and data flow diagrams, as well as an entity relationship diagram. The document also includes prototypes and discusses the conclusion and references. The overall aim is to analyze student subject scores and lists to group students based on their skills using K-Means clustering.
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EduTools 2.0 is an updated comparison tool for learning management systems (LMS). It surveys key aspects of LMSs like portal functionality, communication tools, assessments, and pricing. The document discusses revising EduTools to address changing needs around ubiquitous technology, converging feature sets, and a focus on learning over learner management. It also explores related topics like personal learning environments, semi-permeable system borders, and interoperability. Authentication systems are similarly surveyed based on their functionality, user experience, pricing, support, and integration capabilities. The results will help inform LMS selection and improvement of comparison resources like EduTools.
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In this presentation to NYU-Learn, I discuss my experience applying data science and machine learning in educational technology and assessment industries. I share tips for thinking about the importance of context and potential of scalability.
Collaborative Research: Stealth Assessment of SE Skills w/Learning AnalyticsJohn Whitmer, Ed.D.
Early description of a work in progress between ACT, UMBC, Blackboard and Vitalsource to investigate the relationship between social and emotional skills and learning analytics using machine and deep learning techniques. A few preliminary results.
This presentation to the MoodleMoot UK/I 2017 provides an overview of Learning Analytics for VLE/LMS data and lessons learned in practice from using this data to model student risk and other characteristics. The findings come from fundamental research and application of Blackboard's X-Ray Learning Analytics application.
Blackboard’s data science team conducts large-scale analysis of the relationship between the use of our academic technologies and student impact, in order to inform product design, disseminate effective practices, and advance the base of empirical research in educational technologies.
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Archived presentation made to JISC Learning Analytics workgroup on Feb 22, 2017
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Presentation of research findings and implications from a large-scale analysis of LMS activity and grade data from across 927 institutions, 70,000 courses, and 3.3 million students. This webinar will speak to the promise (and potential pitfalls) of large-scale learning analytics research to promote student success.
The Virtuous Loop of Learning Analytics & Academic Technology Innovation John Whitmer, Ed.D.
This document discusses the potential for learning analytics to provide insights into student learning and outcomes from educational technology usage data. It provides examples from two studies conducted at a university. The first study found that LMS access data predicted student grades better than demographic variables and identified an "over-working gap" for lower-income students. The second study tested learning analytics triggers and interventions but found no significant impact on grades. The document argues for expanding learning analytics efforts, addressing challenges around data quality and governance, and integrating analytics into core applications.
Using Learning Analytics to Assess Innovation & Improve Student Achievement John Whitmer, Ed.D.
Presentation about Learning Analytics for JISC network event; discussion of research findings and implications for individual and institutions considering a Learning Analytics project. Also discuss implications for my work with Blackboard on "Platform Analytics."
Using Learning Analytics to Create our 'Preferred Future'John Whitmer, Ed.D.
One certainty about the future of higher education is that online technologies will play an increasingly central role in the creation and delivery of learning experiences, whether through mobile apps, MOOCs, open content, ePortfolios, and other resources. As adoption increases, the ‘digital exhaust’ recording technology use has increasing potential to understand student learning. The emergent field of Learning Analytics analyzes this data to provide actionable insights for students, for faculty, and for administrators. What have we learned in Learning Analytics to date? What challenges remain? How should we apply Learning Analytics to create our ‘preferred’ future’ that supports deep and meaningful learning
Improving Student Achievement with New Approaches to DataJohn Whitmer, Ed.D.
Presentation delivered at WASC ARC conference on April 11, 2013 on the CSU Data Dashboard and Chico State Learning Analytics case study.
Chico State Case Study: Academic technologies collect highly detailed student usage data. How can this data be used to understand and predict student performance, especially of at-risk students? This presentation will discuss research on a high-enrollment undergraduate course exploring the relationship between LMS activity, student background characteristics, current enrollment information, and student achievement.
CSU Data Dashboard: By monitoring on-track indicators institutional leaders can better understand not only which milestones students are failing to reach, but why they are not reaching them. It can also help campuses to design interventions or policy changes to increase student success and to gauge the impact of interventions.
Learner Analytics Panel Session: Deja-Vu all over again? John Whitmer, Ed.D.
Panel presentation at the DET/CHE 2012 conference on November 28, 2012 by Kathy Fernandes (Chico State), James Frazee (San Diego State), Andrew Roderick (SFSU), and Deone Zell (CSU Northridge).
Many Hands Makes Light Work: Collaborating on Moodle Services and DevelopmentJohn Whitmer, Ed.D.
Presentation by Kathy Fernandes, Andrew Roderick, and John Whitmer at the US West Coast MoodleMoot 2012 on August 2, 2012.
Learning Management Systems have evolved from faculty sandboxes to complex enterprise learning environments. Meanwhile, budgets have plummeted and the LMS market has been undergoing rapid change. Many campuses have moved to Moodle to help stabilize their business and application environments. An important criteria behind this transition for many campuses has been the ability to ‘control their own destiny’ and collaborate with colleagues.
In this presentation, we will discuss the experience of campuses in the California State University system collaborating on Moodle technical development, user services, and support. Among the 10 campuse currently using or in transition to Moodle, we have developed a shared governance model with separate groups to administer policy-related issues and technical / UI issues. We will discuss the creation of a Moodle Shared Code base that is being used by several campuses, and the current migration of SCB features into Moodle v2.0. Moodle techincal expertise is shared between campuses, and training resources have been leveraged across the CSU system. We will discuss the process and features that have led to successful (and not so successful) colllaborative activities, as well as the services that have been created.
Learner Analytics and the “Big Data” Promise for Course & Program AssessmentJohn Whitmer, Ed.D.
Presentation delivered at the San Diego State University "One Day in May" conference on May 22, 201 by John Whitmer, Hillary Kaplowitz, and Thomas J. Norman
Universities archive massive amounts of data about students and their activities. Students also generate significant amounts of “digital exhaust” as they use academic technologies. How can faculty and administrators use automated analysis of this data to save time and conduct targeted interventions to improve student learning?
The emerging discipline of Learner Analytics conducts analysis of this data to learn about student behaviors, predict students at-risk of failure, and identify potential interventions to help those students. In this presentation, we will discuss the contours of this discipline and review the state of research conducted to date. We will then look at several examples of Learner Analytics services and hear from California State University educators who are using these tools to help their students. Finally, we will suggest some immediate ways that Analytics can be conducted at San Diego State.
Presenters:
John Whitmer, California State University, Chico
Hillary Kaplowitz, California State University, Northridge
Thomas J. Norman, CSU Dominguez Hills
Learning Analytics: Realizing the Big Data Promise in the CSUJohn Whitmer, Ed.D.
The word “analytics” has become a buzzword in current educational technology conversations, applied to everything from analysis of student work to LMS usage reporting to institutional analysis of ERP data. Broadly speaking, Learner Analytics refers to the analysis of student data using statistical techniques to improve decision-making. In the context of educational technology, Learner Analytics promises to improve our understanding of effective (and ineffective) student learning and technology usage. What progress have we seen in realizing this promise? This session offers a discussion of the promise of Learner Analytics, current research findings and tools, and explores examples from CSU Chico and the CSU Office of the Chancellor.
The State and Future of Learning Management Systems Panel PresentationJohn Whitmer, Ed.D.
This document summarizes a panel discussion on the state and future of learning management systems (LMS). The panel discussed topics such as high adoption rates of LMS in higher education, the role of social media in the future of LMS, and how technologies like mobile and analytics could impact LMS. One question addressed what a "next generation" LMS might look like and what problems it could solve. Another question discussed why LMS reporting and analytics are currently immature and how they could be improved. The document provides data on LMS usage trends in the California State University system.
State of the CSU Learning Management Systems and ServicesJohn Whitmer, Ed.D.
The document discusses the past, present, and future of the CSU Moodle learning management system. It notes that 10 CSU campuses currently use Moodle, and the shared code base version 1.9 will be completed in June 2011. It also outlines plans to migrate the shared code base to Moodle versions 2.0/2.1 and develop a distributed development model. Regarding Blackboard, the last year of a 3-year agreement is ending and Blackboard has proposed new volume licensing discounts. The Chancellor's Office will provide a formal response to Blackboard's proposals.
Current CSU LMS Activities: Campus and Systemwide StrategiesJohn Whitmer, Ed.D.
In this webinar from April 2010, Dr. David Levin from CSU Northridge and Dr. Linda Scott from CSU San Marcos spoke about their campus migrations from Blackboard to Moodle. They discussed the decision-making process on their campus, their timeline, course migrations, implementations, training and support resources, and lessons learned.
Kathy Fernandes and John Whitmer spoke about the Chancellor’s Office Initiative to provide systemwide LMS Services. These services began with the LMS RFP and CSU Sandboxes, and were expanded to provide an LMS “safety net” and a “superset” of LMS services that include systems, integrations, migrations, support services, and educational practices.
Participants will learn about these current efforts and plans for the implementation of the LMS recommendations approved by the CSU Academic Technology Steering Committee in December 2009.
This document summarizes a presentation given to the CATS group about learning management systems (LMS) in the California State University (CSU) system. It discusses the current LMS landscape across CSU campuses, including the types of LMSs used and costs. It then outlines recommendations from the CSU LMS Futures Workgroup for a more coordinated approach, including a centrally-hosted "safety net" LMS and shared governance model. The presentation concludes by discussing coordination strategies for Blackboard and Moodle, next steps, and opportunities for CATS participation.
Faculty Development across the California State University SystemJohn Whitmer, Ed.D.
In this presentation from the US West Coast Moodle Moot 2011, leaders from California State University campuses discuss their efforts to support the increased use of Moodle on their campus. The speakers represent campuses new to Moodle and mature deployments, and discuss the needs of new users and those further along in the adoption process. Issues to be dicussed include: training resources, effective training modalities, critical training issues in Moodle, and more.
Prsenters:
Cherie Blut, CSU San Marcos
Brett Christie, Sonoma State University
Maggie Beers, San Francisco State University
Deone Zell, CSU Northridge
Moderator: John Whitmer, CSU Office of the Chancellor
Partnership & Collaboration in Moodle Development: Making it WorkJohn Whitmer, Ed.D.
Presentation by Kathy Fernandes (CSU Office of the Chancellor), Andrew Roderick (San Francisco State University), and John Whitmer (CSU Office of the Chancellor)
US West Coast MoodleMoot 2011 (July 2011, Rohnert Park, CA)
As an open source application, Moodle has strong potential for collaborative partnerships, support services, and code development. This presentation will describe one year in the life of California State University Moodle Collaborations. Over the past year, the CSU has developed a governance process and established a new organizational culture while working on code development, training materials, migration tool, and expertise collaboration. We will discuss the balance of central coordination and campus leadership, technical issues and opportunities, and plans for the future.
Migrating to Moodle: Lessons Learned from Recent CSU MigrationsJohn Whitmer, Ed.D.
In this presentation from the US West Coast Moodle Moot 2011, leaders from California State University that have recently migrated to Moodle discuss their campus decision-making process, the processes and technologies used to migrate content, and their process of implementation. The speakers represent campuses migrating from both Blackboard and WebCT, and a mix of small and large FTE campuses. Activities that benefited from multi-campus coordination and resource sharing are also be discussed.
Presenters:
David Levin, CSU Northridge
Barbara Taylor, CSU San Marcos
Moderator: John Whitmer, CSU Office of the Chancellor
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How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
How to Build a Module in Odoo 17 Using the Scaffold Method
Learner Analytics Presentation to ATSC Committee
1. System-wide LMS
Learner Analytics Projects
Presenters: Kathy Fernandes and John Whitmer
ATSC Virtual Meeting
December 13, 2012
Slides @
http://goo.gl/DYqJU
2. Agenda
1. Chico State Learner Analytics Research Study
• EDUCAUSE Article (http://goo.gl/tESoi)
2. Current Projects
• Moodle
• Blackboard
2
3. 1. CHICO STATE LEARNER ANALYTICS
RESEARCH STUDY
“Logging on to Improve Achievement” by John Whitmer
EdD. Dissertation (UC Davis & Sonoma State)
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4. Case Study: Intro to Religious Studies
• Redesigned to hybrid delivery through
Academy eLearning
54 F’s
• Enrollment: 373 students
(54% increase on largest section)
• Highest LMS (Vista) usage
entire campus Fall 2010
(>250k hits)
• Bimodal outcomes:
• 10% increased SLO mastery
• 7% & 11% increase in DWF
• Why? Can’t tell with aggregated
reporting data
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5. Driving Conceptual Questions
1. How is student LMS use related to academic
achievement in a single course section?
2. How does that finding compare to the relationship
of achievement with
traditional student characteristic variables?
3. How are these relationships different for
“at-risk” students (URM & Pell-eligible)?
4. What data sources, variables and methods are
most useful to answer these questions?
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6. Variables
Student Characteristic Independent Variables
Gender
Under Represented Minority (URM)
Pell-Eligible
High School GPA
First in Family to Attend College
Student Major (Discipline)
Enrollment Status
Interaction URM & Gender
Interaction URM & Pell-Eligibility
Learning Management System Usage Variables
Total LMS course website hits
Total LMS course dwell time
Administrative tool website hits
Assessment tool website hits
Content tool website hits
Engagement tool website hits
Dependent Variable: Final Course Grade 6
8. Separate Variables: Correlation LMS Use &
Student Characteristic with Final Grade
LMS Student
>
Use Characteristic
Variables Variables
18% Average 4% Average
(r = 0.35–0.48) (r = -0.11–0.31)
Explanation of change Explanation of change
in final grade in final grade
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9. Combined Variables: Regression Final Grade by
LMS Use & Student Characteristic Variables
LMS Student
>
Use Characteristic
Variables Variables
25% +10%
(r2=0.25) (r2=0.35)
Explanation of change Explanation of change
in final grade in final grade
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13. Moodle and Bb Learner Analytics
What do these have in common?
• Multi-campus CSU groups discussing
common analytics questions & query
definitions
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14. Moodle vs. Bb Learner Analytics
Moodle CIG (18 months old) Blackboard Learn Group
Chair: Andrew Roderick, SFSU (just starting)
CIG Chair: Terry Smith, CSUEB
DIY, adopt and evaluate Bb Learn Analytics product
available “off the shelf”;
solutions from other defined and integrated with
Moodlers Peoplesoft
Starting with technical Pre-built Reports and
reporting to build accurate Dashboards to ANYONE on
indicators of use campus (admin. or faculty if
2 rounds of data collection authenticated)
already completed and Charts available inside LMS
discussed for Faculty and Student
Views 14
15. Moodle Reporting & Analytics, Round 1
Prioritized Moodle Queries from S&PG
governance group
Focused on measures of adoption
(% faculty, % students, % course sections)
For expediency, campuses reported using
current queries used for reporting
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16. “How many sections are using the LMS
(out of all sections offered that term)?”
CSU_09 671 2,191
CSU_08 1,098 1,162
CSU_06 2,997 7,064
Active Sections
Inactive Sections
CSU_05 2,492 3,687
CSU_04 553 614
CSU_02 2,270 3,911
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
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17. “How many sections are using the LMS
(out of all sections offered that term)?”
CSU_09 671 2,191
CSU_08 1,098 1,162
CSU_06 2,997
Use = “visible”+”student activity”
7,064
Active Sections
Inactive Sections
CSU_05 2,492 3,687
CSU_04 553
Use = “visible”
614
CSU_02 2,270 3,911
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
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18. Round 2: mCURL
(Moodle Common Usage and Learning Analytics)
8 active CSU & 2 UC campuses
– Co-chaired: John Whitmer, CO ATS and
Mike Haskell, Cal Poly SLO
Starting with same measures of adoption,
prioritizing “wish list” of more advanced analytics
Local database conventions and campus
practices make accurate comps. challenging
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20. mCURL Next Steps
Refine queries for accurate comparative course
and student adoption measures
Select additional queries: depth and breadth of
use
– # tools used
– # students in each section
– frequency of use
Create repositories for campuses to share
unique local queries 20
21. Blackboard Analytics for Learn (A4L)
CSU ATS Co-Lab Agreement – working together
– Functionality: from early alerts/course reporting
to institutional-level analytics
– Up to 4 campuses participating (3 confirmed)
– Period: December 2012-December 2013
– Individual campus Scope of Work for setup of
infrastructure and services
Kick-off meeting next week
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22. Co-Lab Goals
1. Develop methodologies and processes to identify, aggregate,
and transform LMS usage data into information for analytics.
2. Improve campus usage of learning analytics for decision-
making for student success, curriculum improvement, and technical
services.
3. Create shared measures, database reports, and algorithms,
drawing on campus best practices and research innovations.
4. Increase campus awareness of applications and technical tools.
5. Document campus efforts and disseminate to other campuses.
6. Provide professional development in learning analytics.
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