This document discusses using analytics to improve student success and outcomes. It provides an overview of learning analytics and predictive modeling concepts. Several components of an analytic model are described, including gathering data, predicting outcomes, taking action, monitoring results, and refining processes. Case studies of other institutions that have implemented analytic systems are presented. Managing expectations for analytic projects is also addressed, as results may not be immediate and adoption can be challenging. The goal is to use data-driven insights to help target support and resources to enhance student performance.
This presentation provides an overview of the Systematic Inquiry Cycle and Logic Modeling as tools for designing and developing a research study or project/program initiative.
Program evaluation and outdoor education: An overviewJames Neill
This presentation discusses program evaluation in outdoor education. What is it? Why do it? What methods are there? How can data be analysed? How can results be used? We will consider several example program evaluation studies and available tools and resources. There will also be opportunity to workshop your own program evaluation needs.
Main presentation page: http://wilderdom.com/wiki/Neill_2010_Program_evaluation_and_outdoor_education:_An_overview
EDLD808 Program Evaluation Final Project - Online EducationPaul Gruhn
This presentation is a summary of a program evaluation project I performed on the CSC230 Database for Web Applications course, which I teach online, to Community College Students.
This presentation provides an overview of the Systematic Inquiry Cycle and Logic Modeling as tools for designing and developing a research study or project/program initiative.
Program evaluation and outdoor education: An overviewJames Neill
This presentation discusses program evaluation in outdoor education. What is it? Why do it? What methods are there? How can data be analysed? How can results be used? We will consider several example program evaluation studies and available tools and resources. There will also be opportunity to workshop your own program evaluation needs.
Main presentation page: http://wilderdom.com/wiki/Neill_2010_Program_evaluation_and_outdoor_education:_An_overview
EDLD808 Program Evaluation Final Project - Online EducationPaul Gruhn
This presentation is a summary of a program evaluation project I performed on the CSC230 Database for Web Applications course, which I teach online, to Community College Students.
Jennifer Kuschner, Program Development and Evaluation Specialist, UW-Extension
Kerry Zaleski, Monitoring and Evaluation Project Coordinator, UW-Extension
This interactive session provided participants with an overview of what a logic model is and how to use one for planning, implementation, evaluation or communicating about co-curricular community service activities. The session also provided an opportunity to work in teams to create participant’s own logic model.
Do-It-Yourself Logic Models: Examples, Templates, and ChecklistsInnovation Network
Logic models are nonprofit road maps: they help you diagram where you are now and where you hope to be in the future. They are used for program planning, program management, fundraising, communications, consensus-building, and evaluation planning.
Want to make a logic model, but not sure where to start? In this 90-minute webinar, Johanna Morariu and Ann Emery taught about the nuts and bolts of logic models--what they are, how to make them, who should be involved in the process, and how often to update them. We’ll provide you with tools like a logic model template, free online logic model builder, and a logic model checklist. We’ll also share several examples from real nonprofits so that you’re ready to hit the ground running.
To learn more, please visit www.innonet.org.
The Introduction chapter of the Case Study Summary report presents shortly the history of the Alternative work development also called New Ways of Working a.k.a NewWoW. The effect of enablers usually classified as Technological, Physical and Social are in the main focus.
Objectives are 1) perform three complementary approaches of enablers, concept and future of the organization using the same consultative process to engage work practices 2) find quantitative information of the aspects (what?) of the work environment affecting to personal life using a survey 3) find out qualitative information of “How aspects of the social environment enhance or disrupt Knowledge Work – on individual, team, organizational, societal levels. Why?” using focus group discussions in the same three organizations.
The first part of the report is describing the companies (VTT, Granlund and ISS) change plans and the target setting. The Optimaze engagement methodology and the results are described for the three organizations cases. The key work practices in three organizations have remarkable similarities: the need for communication, coordination, sharing, being with customers/partners/colleagues etc.
The second part describes a survey of totally 255 persons in three organizations addressing question “What factors of the social environment enhance or disrupt Knowledge Work?”. The SPSS™ statistical program was used to analyse the survey data. The differences between the Granlund, ISS and VTT in survey response patterns reflected mainly the fact that they are different type of organizations.
The third part describes the preparation, execution and the data analysis of the Focus group interviews. The transcripts were content coded both manually and by Atlas.ti, a software package for qualitative data analysis. The overall impression from the group discussion is that that most of the participants cared deeply about are issues that personally affect them or their close colleagues. The drivers for Job Crafting arise from three personal needs. Firstly to exert some control over the job to avoid alienation, secondly to build a positive self-image, and thirdly to connect with others. The three aforementioned needs echo the basic psychological needs of Self-Determination Theory: namely Autonomy, Competence and Relatedness.
Evaluation report is the most important part of an evaluation project. Learn the various aspects that need to be included in an evaluation report. Check out our course on program evaluation by clicking into this link - https://www.udemy.com/course/program-evaluation-for-beginners/?referralCode=C8A8FB44E3313F7F3CF0
Evaluating the quality of quality improvement training in healthcareDaniel McLinden
Quality Improvement (QI)in healthcare is an increasingly important approach to improving health outcomes, improving system performance and improving safety for patients. Effectively implementing QI methods requires knowledge of methods for the design and execution of QI projects. Given that this capability is not yet widespread in healthcare, training programs have emerged to develop these skills in the healthcare workforce. In spite of the growth of training programs, limited evidence exists about the merit and worth of these programs. We report here on a multi-year, multi-method evaluation of a QI training program at a large Midwestern academic medical center. Our methodology will demonstrate an approach to organizing a large scale training evaluation. Our results will provide best available evidence for features of the intervention, outcomes and the contextual features that enhance or limit efficacy.
Sdal air education workforce analytics workshop jan. 7 , 2014.pptxkimlyman
The American Institutes for Research (AIR) and Virginia Tech are collaborating to explore and develop new approaches to combining, manipulating and understanding big data. The two are also looking at how big data analytics can help answer questions critical to solving issues in education, workforce, health, and human and social development. They held two workshops on January 7 and 27, 2014- the first on Education and Workforce Analytics and the second on Health and Social Development Analytics.
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
Jennifer Kuschner, Program Development and Evaluation Specialist, UW-Extension
Kerry Zaleski, Monitoring and Evaluation Project Coordinator, UW-Extension
This interactive session provided participants with an overview of what a logic model is and how to use one for planning, implementation, evaluation or communicating about co-curricular community service activities. The session also provided an opportunity to work in teams to create participant’s own logic model.
Do-It-Yourself Logic Models: Examples, Templates, and ChecklistsInnovation Network
Logic models are nonprofit road maps: they help you diagram where you are now and where you hope to be in the future. They are used for program planning, program management, fundraising, communications, consensus-building, and evaluation planning.
Want to make a logic model, but not sure where to start? In this 90-minute webinar, Johanna Morariu and Ann Emery taught about the nuts and bolts of logic models--what they are, how to make them, who should be involved in the process, and how often to update them. We’ll provide you with tools like a logic model template, free online logic model builder, and a logic model checklist. We’ll also share several examples from real nonprofits so that you’re ready to hit the ground running.
To learn more, please visit www.innonet.org.
The Introduction chapter of the Case Study Summary report presents shortly the history of the Alternative work development also called New Ways of Working a.k.a NewWoW. The effect of enablers usually classified as Technological, Physical and Social are in the main focus.
Objectives are 1) perform three complementary approaches of enablers, concept and future of the organization using the same consultative process to engage work practices 2) find quantitative information of the aspects (what?) of the work environment affecting to personal life using a survey 3) find out qualitative information of “How aspects of the social environment enhance or disrupt Knowledge Work – on individual, team, organizational, societal levels. Why?” using focus group discussions in the same three organizations.
The first part of the report is describing the companies (VTT, Granlund and ISS) change plans and the target setting. The Optimaze engagement methodology and the results are described for the three organizations cases. The key work practices in three organizations have remarkable similarities: the need for communication, coordination, sharing, being with customers/partners/colleagues etc.
The second part describes a survey of totally 255 persons in three organizations addressing question “What factors of the social environment enhance or disrupt Knowledge Work?”. The SPSS™ statistical program was used to analyse the survey data. The differences between the Granlund, ISS and VTT in survey response patterns reflected mainly the fact that they are different type of organizations.
The third part describes the preparation, execution and the data analysis of the Focus group interviews. The transcripts were content coded both manually and by Atlas.ti, a software package for qualitative data analysis. The overall impression from the group discussion is that that most of the participants cared deeply about are issues that personally affect them or their close colleagues. The drivers for Job Crafting arise from three personal needs. Firstly to exert some control over the job to avoid alienation, secondly to build a positive self-image, and thirdly to connect with others. The three aforementioned needs echo the basic psychological needs of Self-Determination Theory: namely Autonomy, Competence and Relatedness.
Evaluation report is the most important part of an evaluation project. Learn the various aspects that need to be included in an evaluation report. Check out our course on program evaluation by clicking into this link - https://www.udemy.com/course/program-evaluation-for-beginners/?referralCode=C8A8FB44E3313F7F3CF0
Evaluating the quality of quality improvement training in healthcareDaniel McLinden
Quality Improvement (QI)in healthcare is an increasingly important approach to improving health outcomes, improving system performance and improving safety for patients. Effectively implementing QI methods requires knowledge of methods for the design and execution of QI projects. Given that this capability is not yet widespread in healthcare, training programs have emerged to develop these skills in the healthcare workforce. In spite of the growth of training programs, limited evidence exists about the merit and worth of these programs. We report here on a multi-year, multi-method evaluation of a QI training program at a large Midwestern academic medical center. Our methodology will demonstrate an approach to organizing a large scale training evaluation. Our results will provide best available evidence for features of the intervention, outcomes and the contextual features that enhance or limit efficacy.
Sdal air education workforce analytics workshop jan. 7 , 2014.pptxkimlyman
The American Institutes for Research (AIR) and Virginia Tech are collaborating to explore and develop new approaches to combining, manipulating and understanding big data. The two are also looking at how big data analytics can help answer questions critical to solving issues in education, workforce, health, and human and social development. They held two workshops on January 7 and 27, 2014- the first on Education and Workforce Analytics and the second on Health and Social Development Analytics.
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
Big Data and Data Intensive Computing: Education and TrainingJongwook Woo
Big Data has been popular as Data becomes tera-/peta-bytes and un-/semi-structured. This slide illustrates the fundamental of Big Data, especially Hadoop solutions. Besides, it introduces some use cases and the way to learn Hadoop technology.
Innovative Teaching in Higher Education: Big Data EraMiftachul Huda
With massive amounts of data created every second across the internet, the concept of big data would give opportunities with the ability to explore data and understand in maximizing the potential of data collection in relation to innovative teaching in an online learning setting. This is to support teachers’ pedagogical skills, mainly in the big data era from multiple sources in maintaining a competitive advantage to give a feedback on innovative teaching performance. This article aims to critically investigate innovative teaching competencies of teachers in the light of big data approach. Critical review using content analysis from both the theoretical and the empirical base was conducted to explore the big data for supporting innovative teaching. This result shows innovative teaching performance with an insightful result to contribute these competencies towards the theories and models of educational innovation into the pedagogical element.
Opening/Framing Comments: John Behrens, Vice President, Center for Digital Data, Analytics, & Adaptive Learning Pearson
Discussion of how the field of educational measurement is changing; how long held assumptions may no longer be taken for granted and that new terminology and language are coming into the.
Panel 1: Beyond the Construct: New Forms of Measurement
This panel presents new views of what assessment can be and new species of big data that push our understanding for what can be used in evidentiary arguments.
Marcia Linn, Lydia Liu from UC Berkeley and ETS discuss continuous assessment of science and new kinds of constructs that relate to collaboration and student reasoning.
John Byrnes from SRI International discusses text and other semi-structured data sources and different methods of analysis.
Kristin Dicerbo from Pearson discusses hidden assessments and the different student interactions and events that can be used in inferential processes.
Panel 2: The Test is Just the Beginning: Assessments Meet Systems Context
This panel looks at how assessments are not the end game, but often the first step in larger big-data practices at districts/state/national levels.
Gerald Tindal from the University of Oregon discusses State data systems and special education, including curriculum-based measurement across geographic settings.
Jack Buckley Commissioner of the National Center for Educational Statistics discussing national datasets where tests and other data connect.
Lindsay Page, Will Marinell from the Strategic Data Project at Harvard discussing state and district datasets used for evaluating teachers, colleges of education, and student progress.
Panel 3: Connecting the Dots: Research Agendas to Integrate Different Worlds
This panel will look at how research organizations are viewing the connections between the perspectives presented in Panels 1 and 2; what is known, what is still yet to be discovered in order to achieve the promised of big connected data in education.
Andrea Conklin Bueschel Program Director at the Spencer Foundation
Ed Dieterle Senior Program Officer at the Bill and Melinda Gates Foundation
Edith Gummer Program Manager at National Science Foundation
Data-driven cognitive technologies will enable personalised education and improve outcomes for students, educators and administrators. Ultimately, education experiences will be transformed and improved when data can accompany the students throughout their life-long learning journey.
What is the future of education? Find out soon from our next #IBMfuturEd study.
Many believe Big Data is a brand new phenomenon. It isn't, it is part of an evolution that reaches far back history. Here are some of the key milestones in this development.
A brief introduction to Pattern Recognition. Slides were used for a Seminar at the Interactive Art PhD at School of Arts of the UCP, Porto, Portugal (http://artes.ucp.pt)
This presentation, by big data guru Bernard Marr, outlines in simple terms what Big Data is and how it is used today. It covers the 5 V's of Big Data as well as a number of high value use cases.
Improving and Demonstrating Impact for Youth Using Qualitative DataDetroitYDRC
This workshop provided an overview of how to use qualitative data for improving and demonstrating the impact of youth development programs. Tips for collecting, analyzing and using qualitative data are provided. Examples of creative ways to visualize qualitative data are also shared.
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
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.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
1. USING ANALYTICS TO
IMPROVE STUDENT SUCCESS:
A PRIMER ON LEVERAGING
DATA TO ENHANCE STUDENT
PERFORMANCE
March 23, 2014 Matthew D. Pistilli, PhD
2. Plan for the day
Introductions and Purpose
Conceptual Overview
Other Institutions’ Analytics
Five Components of Analytics
Individual/Group Work & Planning
Managing Expectations in Next Steps
3. Who are we?
Where are we from?
Why are we here?
Introductions and Purpose
6. Definitions of Learning Analytics
The measurement, collection, analysis and reporting
of data about learners and their contexts, for
purposes of understanding and optimizing learning
and the environments in which it occurs (SoLAR)
Evaluating large data sets to provide decision
makers with information that can help determine the
best course of action for an organization, with a
specific goal of improving learning outcomes
(EDUCAUSE, 2011)
7. Definitions Continued
Using analytic techniques to help target
instructional, curricular, and support resources to
support the achievement of specific learning goals
(van Bareneveld, Arnold, & Campbell, 2012)
the process of developing actionable insights
through problem definition and the application of
statistical models and analysis against existing
and/or simulated future data (Cooper, 2012)
8. Definitions Continued
Using data to inform decision-making; leveraging
data to identify students in need of academic
support; and allowing direct user interaction with a
tool to engage in some form of sensemaking that
supports a subsequent action (Krumm, Washington,
Lonn, & Teasley)
The use of data, statistical analysis, and
explanatory and predictive models to gain insights
and act on complex issues (Bichsel, 2012)
15. Analytics is about…
Actionable intelligence
Moving research to practice
Basis for design, pedagogy, self-
awareness
Changing institutional culture
Understanding the limitations and
risks
17. Student Involvement Theory
Alexander Astin - UCLA
Involvement:
The amount of physical and psychological
energy that the student devotes to the
academic experience. (1985, p. 134)
Exists on a continuum, with students investing varying
levels of energy
Is both quantitative and qualitative
Direct relationship between student learning and
student involvement
Effectiveness of policy or practice directly related to
their capacity to increase student learning
(Astin, 1999)
19. Inputs
The personal, background, and
educational characteristics that students
bring with them to postsecondary
education that can influence educational
outcomes (Astin, 1984).
20. Inputs
Astin (1993) identified 146 characteristics, including
Demographics
Citizenship
Ethnicity
Residency
Sex
Socioeconomic status
High school academic achievement
Standardized test scores
GPA
Grades in specific courses
Previous experiences & self-perceptions
Reasons for attending college
Expectations
Perceived ability
21. Outcomes
Basic level
Academic Achievement
Retention
Graduation
More abstractly
Skills
Behaviors
Knowledge
The things we are
attempting to
develop in students
22. Environment
Where we have the most control
Factors related to students’ experience while in
college
Astin (1993) identified 192 variables across 8
overarching classifications
Institutional characteristics Financial Aid
Peer group characteristics Major Field Choice
Faculty characteristics Place of residence
Curriculum Student involvement
31. Five Components of Analytic Model
Gather
Predict
ActMonitor
Refine
Components
are cyclical
starting with
gather but
can be
drawn upon
at any point
in the cycle.
33. Gather
Data
In multiple formats
From multiple sources
With insights into students & their success
That can be analyzed & manipulated into formulae
Data is the foundation for this work, and without
good data, the effort may be for naught.
34. Gather
Before gathering, determine what will be gathered.
What question are you trying to answer?
To do so, consider…
Where will your focus be?
What data do you already have (or have access to)?
What else do you need to collect?
Who owns that data?
What will it take to get access to it?
What are the challenges associated with assembling all
the data?
What are the funding implications for data collection and
assembly?
35. Gather
Ultimately, answer the following questions:
1. How will you describe this analytics area to
interested parties?
2. Who are the key stakeholders that need to be
included in discussions?
3. Who should serve as the lead for this area at your
institution?
4. What other considerations are there?
37. Predict
Begins with the question asked in Gather:
What do you want to predict?
How do you identify this as a focus area?
Prediction models built will be driven by
Types of data gathered
Question being answered
What’s currently being predicted?
How?
By whom?
In what realms? Student success?
How can you involve those persons in this effort?
38. Predict
What makes a good model?
Correlation vs. Causation
Expertise required
Data analysis
Statistical
Content
Reliability & Validity
Frequency of updating
Challenges & obstacles
39. Predict
Ultimately, answer the following questions:
1. How will you describe this analytics area to
interested parties?
2. Who are the key stakeholders that need to be
included in discussions?
3. Who should serve as the lead for this area at your
institution?
4. What other considerations are there?
41. Act
Harken back to journalism class…
Who?
What?
Where?
When?
Why?
How?
Add:
Available resources?
Timing
42. Act
Frequency – more is always better
Funding the action
Assessing the impact
What are you assessing?
Were behaviors changed?
How do you know?
Do different actions need to be:
Taken (on your end)?
Suggested (on the students’ end)?
43. Act
Ultimately, answer the following questions:
1. How will you describe this analytics area to
interested parties?
2. Who are the key stakeholders that need to be
included in discussions?
3. Who should serve as the lead for this area at your
institution?
4. What other considerations are there?
45. Monitor
Formative & summative in nature
Can present challenges and obstacles
It’s a process
Current process must be understood
New/parallel processes developed as necessary
Involving others… to some extent, the more the
merrier
Availability of resource (time, money, people)
Timing of monitoring
Ability to react
46. Monitor
Review
Data collected and used… was it
Necessary?
Correct?
Sufficient?
Predictions made… were they
Accurate?
Meaningful?
Actions taken… were they
Useful?
Sustainable?
Feedback received to date
47. Monitor
Ultimately, answer the following questions:
1. How will you describe this analytics area to
interested parties?
2. Who are the key stakeholders that need to be
included in discussions?
3. Who should serve as the lead for this area at your
institution?
4. What other considerations are there?
49. Refine
Self-improvement process for
Analytics at the institution
The institution
Enrolled students
Continual monitoring
Small tweaks here and there
Major changes after periods of time
Updating of algorithms and statistical models
Outcome data important as
Assessment
Additional components for inclusion in the model
50. Refine
What was learned from this effort?
Where are the positives?
Where are the deficiencies?
Was the goal realized?
How does the goal/involvement in the project help
meet institutional goals?
Who else needs to be involved to improve/enhance
the process, actions, and outcomes?
How can lessons learned be applied for future use?
51. Refine
Ultimately, answer the following questions:
1. How will you describe this analytics area to
interested parties?
2. Who are the key stakeholders that need to be
included in discussions?
3. Who should serve as the lead for this area at your
institution?
4. What other considerations are there?
52. Elevator Speech for Project
Determine/solidify Institutional Goal
Work on Component Templates
Individual/Group Work
53. What is your goal for this project?
What have you learned?
What are your next steps?
What questions do you still have?
Institution Reporting & Town Hall
56. Expectations Reality
Plug and Play
Immediate results
Solve every problem –
ever!
Universal adoption
Everyone would love it!
Fits, starts, reboots
Mostly long term
outcomes
Solve some problems,
create some new
problems
Lackluster use
Not everyone loved it
57. Institutional Challenges
Data in many places, “owned” by many
people/organizations
Different processes, procedures, and regulations
depending on data owner
Everyone can see potential, but all want something
slightly different
Sustainability – “can’t you just…”
Faculty participation is essential
Staffing is a challenge
58. New Possibilities
Using data that exists on campus
Taking advantages of existing programs
Bringing a “complete picture” beyond academics
Focusing on the “Action” in “Actionable Intelligence”
60. References
Astin, A. W. (1984). Student involvement: A developmental theory for higher education. Journal of College
Student Development, 24, 297-308.
Astin, A. W. (1993). What matters in college? Liberal Education, 79(4).
Astin, A. W. (1994). What matters in college: Four critical years revisited. San Francisco: Jossey-Bass.
Bichsel, J. (2012, August). Analytics in higher education: Benefits, barriers, progress, and recommendations
(Research Report). Louisville, CO: EDUCAUSE Center for Applied Research. Available:
http://net.educause.edu/ir/library/pdf/ERS1207/ers1207.pdf
Cooper, A. (2012). What is Analytics? Definition and Essential Characteristics. CETIS Analytics Series, 1(5).
Available: http://publications.cetis.ac.uk/2012/521
EDUCAUSE Learning Initiative. (2011). 7 things you should know about first-generation learning analytics.
Louisville, CO: EDUCAUSE. Available: http://www.educause.edu/library/resources/7-things-youshould-
know-about-first-generation-learning-analytics
Krumm, A. E., Waddington, R. J., Lonn, S., & Teasley, S. D. (n.d.). Increasing academic success in undergraduate
engineering education using learning analytics: A design based research project. Available:
https://ctools.umich.edu/access/content/group/research/papers/aera2012_krumm_learning_analytics.
pdf
Oblinger, D. G. and Campbell, J. P. (2007). Academic Analytics, EDUCAUSE White Paper.
Society of Learning Analytics Research. (n.d.) About. [Webpage] Available:
http://www.solaresearch.org/mission/about/