Please visit my website for more information: http://www.comparative-education.com/. To cite this presentation, please use the following: Wiseman, A. W. (2012, April). International Cross-Time, Cross-System (XTXS) Database: Research Potential & Example. Presentation given at the CIES workshop, “Comparative and International Education Research Made Easier: How to Use Several Free Online Data Tools,” San Juan, Puerto Rico.
The coronavirus pandemic has impacted global society in many ways, not least education, with schools and universities moving many teaching and learning practices online. This paper examines the response of educational institutions in employing learning analytics, an approach which includes the collection and analysis of student data to understand and optimise teaching and learning. A systematic review of publications is undertaken and key themes identified in an attempt to answer the question: How did learning analytics allow educators to respond to learners’ risks and challenges during the pandemic? This study illustrates issues around the rapid adoption of technological solutions outside of the institution; inequality of internet access; considerations of data privacy and longer term consequences; and the need for an agile, but considered policy response.
7.2 relationship between electric current and potential differenceAdlishah Risal Bili
Malaysia SPM syllabus Form 5 Physics Chapter 7. Part 2: Electric Current and Potential Difference
::Slide-making service available. For more info, contact coolcikgu@gmail.com::
Contact us for your presentation design needs: lesson / teaching, wedding, seminar, workshop, client pitch etc.
California Ocean Science Trust " Building a Sustainable Knowledge Base for ...Tom Moritz
"Building a Sustainable Knowledge Base for the Marine Protected Areas Monitoring Enterprise" a presentation to the California Ocean Science Trust, Oakland, California March 16, 2010
Quantitative data analysis - John RichardsonOUmethods
Your project report should include: a viable research question; a critical literature review; a research proposal; and a work plan for the project. The proposed methods should include methods of data collection and methods of data analysis. Whether you are carrying out qualitative of quantitative research, you should know broadly how you are going to analyse your data before you collect them. And the work plan for your project should include a realistic estimate of the time it will take you to do the analysis. The aim of this presentation is to get you to think creatively about the kinds of analysis that might address your research problem.
The coronavirus pandemic has impacted global society in many ways, not least education, with schools and universities moving many teaching and learning practices online. This paper examines the response of educational institutions in employing learning analytics, an approach which includes the collection and analysis of student data to understand and optimise teaching and learning. A systematic review of publications is undertaken and key themes identified in an attempt to answer the question: How did learning analytics allow educators to respond to learners’ risks and challenges during the pandemic? This study illustrates issues around the rapid adoption of technological solutions outside of the institution; inequality of internet access; considerations of data privacy and longer term consequences; and the need for an agile, but considered policy response.
7.2 relationship between electric current and potential differenceAdlishah Risal Bili
Malaysia SPM syllabus Form 5 Physics Chapter 7. Part 2: Electric Current and Potential Difference
::Slide-making service available. For more info, contact coolcikgu@gmail.com::
Contact us for your presentation design needs: lesson / teaching, wedding, seminar, workshop, client pitch etc.
California Ocean Science Trust " Building a Sustainable Knowledge Base for ...Tom Moritz
"Building a Sustainable Knowledge Base for the Marine Protected Areas Monitoring Enterprise" a presentation to the California Ocean Science Trust, Oakland, California March 16, 2010
Quantitative data analysis - John RichardsonOUmethods
Your project report should include: a viable research question; a critical literature review; a research proposal; and a work plan for the project. The proposed methods should include methods of data collection and methods of data analysis. Whether you are carrying out qualitative of quantitative research, you should know broadly how you are going to analyse your data before you collect them. And the work plan for your project should include a realistic estimate of the time it will take you to do the analysis. The aim of this presentation is to get you to think creatively about the kinds of analysis that might address your research problem.
This presentation describes TeachingWithData.org, a collection of resources for faculty who want to include data in their undergraduate social science courses. The presentation was given at the 2010 Annual Meeting of the American Sociological Association (Atlanta) by John Paul DeWitt (SSDAN) and Lynette Hoelter (ICPSR)
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
Quantitative Literacy: Don't be afraid of data (in the classroom)!ICPSR
This presentation was conducted at the International Conference on College Teaching and Learning, April 11, 2012. It contains several links to interesting data and statistics, not too complex, that can easily be introduced for discussion in the classroom.
Organizational Implications of Data Science Environments in Education, Resear...Victoria Steeves
Data science (DS) poses key organizational challenges for academic institutions. DS is a multidisciplinary field that includes a range of research methodologies and fields of inquiry. DS as a domain is interested in many of the same issues as libraries: data access and curation, reproducibility, the value of ontologies, and open scholarship. At the same time, identifying opportunities to collaborate and deploy unified services can be challenging. The Data Science Environment (DSE) program, co-funded by the Gordon and Betty Moore and Alfred P. Sloan foundations, provides resources to help universities develop collaborations between researchers, develop tools in DS, and create new career paths for data scientists. Working groups within the DSE focus on reproducibility, career paths, education/training, research methods, space issues, and software/tools. This program has introduced new opportunities for libraries to explore how to engage with this community and consider how to bring the expertise in the DS community to bear on library missions and goals. In this panel, program members from each of the three partner universities, the University of Washington, New York University and the University of California, Berkeley, consider the research questions of the DSE and the organizational impact of these groups in the University as a whole and for the libraries specifically. The panel will employ a case-study presentation model framed through three lenses: the role of data sciences in information science, the
potential career paths for data scientists in libraries, and the potential
amplification of information services (e.g. data curation, institutional repositories, scholarly publishing).
CNI Program: Talk Description: https://www.cni.org/topics/digital-curation/organizational-implications-of-data-science-environments-in-education-research-and-research-management-in-libraries
Video of Talk--Vimeo: https://vimeo.com/149713097
Video of Talk--YouTube: https://www.youtube.com/watch?v=L0G9JsPMEXY
Learning as a Complex Phenomenon: Challenges for Learning Analytics suthers
Presentation given at Learning Analytics Summer Institute 2013. Theories of learning postulate multiple agencies (individual, small group, and collective) and epistemologies e.g., acquisition, intersubjective meaning making, participation). Though we may research these separately, learners experience all of these at once, so learning is a complex phenomenon. Need to connect levels of analysis. Also need to bring in multiple "voices" or theoretical and research traditions, and learn how to manage productive multivocality among them. Two efforts towards this end are briefly described. If it takes on these challenges, Learning Analytics can help by enabling us to manage multiple levels of analysis.
Data Mining for Education
Ryan S.J.d. Baker, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
rsbaker@cmu.edu
Article to appear as
Baker, R.S.J.d. (in press) Data Mining for Education. To appear in McGaw, B., Peterson, P.,
Baker, E. (Eds.) International Encyclopedia of Education (3rd edition). Oxford, UK: Elsevier.
This is a pre-print draft. Final article may involve minor changes and different formatting.
Results to be released on December 6
Key issues:
How far are we nurturing a generation of scientifically literate young people?
Are schools adequately preparing young people for adult life?
What kinds of learning environments do we find in high performing systems?
Can schools improve the futures of students from disadvantaged backgrounds?
Data in The Classroom: It's Not Just for Nerds Anymore!ICPSR
These slides provide resources for real, interactive, and fun data faculty can bring into the classroom for great discussions and paper assignments designed to get students thinking critically. You don't need to be a numbers guru to do it! These slides also emphasize the value of data and numbers to students in getting great jobs and in understanding the world around them.
RDAP14 Poster: Ashley Sands Who will manage scientific research data?ASIS&T
Research Data Access & Preservation Summit
March 26-28, 2014
San Diego, CA
Who will manage scientific research data?
Ashley Sands, University of California, Los Angeles
Presentation at the New Zealand Association of Gerontology conference in 2014. Focus on the utility of spatial and visual methods in ageing science and policy domains.
Introductory Keynote: International Symposium of Comparative Sciences, 2013Alexander Wiseman
Watch a screencast of this keynote presentation here:
Part 1: http://www.youtube.com/watch?v=2xB6rPsjIbk
Part 2: http://www.youtube.com/watch?v=PIiDtghFDJk
Part 3: http://www.youtube.com/watch?v=2Q_fNUEHv28
More information about the symposium is here: http://www.comparative-education.com/call-for-papers-international-symposium-on-comparative-sciences-2013-sofia-bulgaria/
Wiseman, A. W. (2013, May). The Global “Crisis” in Education and the US Polic...Alexander Wiseman
Please visit my website for more information: http://www.comparative-education.com/. To cite this presentation, please use the following: Wiseman, A. W. (2013, May). The Global “Crisis” in Education and the US Policy Response. Presentation to the Comparative and International Education Department, East China Normal University, Shanghai, China.
More Related Content
Similar to International Cross-Time, Cross-System (XTXS) Database: Research Potential & Example
This presentation describes TeachingWithData.org, a collection of resources for faculty who want to include data in their undergraduate social science courses. The presentation was given at the 2010 Annual Meeting of the American Sociological Association (Atlanta) by John Paul DeWitt (SSDAN) and Lynette Hoelter (ICPSR)
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
Quantitative Literacy: Don't be afraid of data (in the classroom)!ICPSR
This presentation was conducted at the International Conference on College Teaching and Learning, April 11, 2012. It contains several links to interesting data and statistics, not too complex, that can easily be introduced for discussion in the classroom.
Organizational Implications of Data Science Environments in Education, Resear...Victoria Steeves
Data science (DS) poses key organizational challenges for academic institutions. DS is a multidisciplinary field that includes a range of research methodologies and fields of inquiry. DS as a domain is interested in many of the same issues as libraries: data access and curation, reproducibility, the value of ontologies, and open scholarship. At the same time, identifying opportunities to collaborate and deploy unified services can be challenging. The Data Science Environment (DSE) program, co-funded by the Gordon and Betty Moore and Alfred P. Sloan foundations, provides resources to help universities develop collaborations between researchers, develop tools in DS, and create new career paths for data scientists. Working groups within the DSE focus on reproducibility, career paths, education/training, research methods, space issues, and software/tools. This program has introduced new opportunities for libraries to explore how to engage with this community and consider how to bring the expertise in the DS community to bear on library missions and goals. In this panel, program members from each of the three partner universities, the University of Washington, New York University and the University of California, Berkeley, consider the research questions of the DSE and the organizational impact of these groups in the University as a whole and for the libraries specifically. The panel will employ a case-study presentation model framed through three lenses: the role of data sciences in information science, the
potential career paths for data scientists in libraries, and the potential
amplification of information services (e.g. data curation, institutional repositories, scholarly publishing).
CNI Program: Talk Description: https://www.cni.org/topics/digital-curation/organizational-implications-of-data-science-environments-in-education-research-and-research-management-in-libraries
Video of Talk--Vimeo: https://vimeo.com/149713097
Video of Talk--YouTube: https://www.youtube.com/watch?v=L0G9JsPMEXY
Learning as a Complex Phenomenon: Challenges for Learning Analytics suthers
Presentation given at Learning Analytics Summer Institute 2013. Theories of learning postulate multiple agencies (individual, small group, and collective) and epistemologies e.g., acquisition, intersubjective meaning making, participation). Though we may research these separately, learners experience all of these at once, so learning is a complex phenomenon. Need to connect levels of analysis. Also need to bring in multiple "voices" or theoretical and research traditions, and learn how to manage productive multivocality among them. Two efforts towards this end are briefly described. If it takes on these challenges, Learning Analytics can help by enabling us to manage multiple levels of analysis.
Data Mining for Education
Ryan S.J.d. Baker, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
rsbaker@cmu.edu
Article to appear as
Baker, R.S.J.d. (in press) Data Mining for Education. To appear in McGaw, B., Peterson, P.,
Baker, E. (Eds.) International Encyclopedia of Education (3rd edition). Oxford, UK: Elsevier.
This is a pre-print draft. Final article may involve minor changes and different formatting.
Results to be released on December 6
Key issues:
How far are we nurturing a generation of scientifically literate young people?
Are schools adequately preparing young people for adult life?
What kinds of learning environments do we find in high performing systems?
Can schools improve the futures of students from disadvantaged backgrounds?
Data in The Classroom: It's Not Just for Nerds Anymore!ICPSR
These slides provide resources for real, interactive, and fun data faculty can bring into the classroom for great discussions and paper assignments designed to get students thinking critically. You don't need to be a numbers guru to do it! These slides also emphasize the value of data and numbers to students in getting great jobs and in understanding the world around them.
RDAP14 Poster: Ashley Sands Who will manage scientific research data?ASIS&T
Research Data Access & Preservation Summit
March 26-28, 2014
San Diego, CA
Who will manage scientific research data?
Ashley Sands, University of California, Los Angeles
Presentation at the New Zealand Association of Gerontology conference in 2014. Focus on the utility of spatial and visual methods in ageing science and policy domains.
Introductory Keynote: International Symposium of Comparative Sciences, 2013Alexander Wiseman
Watch a screencast of this keynote presentation here:
Part 1: http://www.youtube.com/watch?v=2xB6rPsjIbk
Part 2: http://www.youtube.com/watch?v=PIiDtghFDJk
Part 3: http://www.youtube.com/watch?v=2Q_fNUEHv28
More information about the symposium is here: http://www.comparative-education.com/call-for-papers-international-symposium-on-comparative-sciences-2013-sofia-bulgaria/
Wiseman, A. W. (2013, May). The Global “Crisis” in Education and the US Polic...Alexander Wiseman
Please visit my website for more information: http://www.comparative-education.com/. To cite this presentation, please use the following: Wiseman, A. W. (2013, May). The Global “Crisis” in Education and the US Policy Response. Presentation to the Comparative and International Education Department, East China Normal University, Shanghai, China.
Wiseman, A.W. (2013, May). The Development and Impact of Youth Political Soc...Alexander Wiseman
Please visit my website for more information: http://www.comparative-education.com/. To cite this presentation, please use the following: Wiseman, A.W. (2013, May). The Development and Impact of Youth Political Socialization through Formal Mass Education Worldwide: Evidence from ICCS 2009. Paper presented at the Sino-American Academic Symposium: Comparative Research on Cultivating Responsibility, Personality and Capability of Youth, Tongji University, Shanghai, China.
Strategic approach to making education policy based on evidence/data in the Arabian Gulf. Please visit my website for more information: http://www.comparative-education.com/.
The Economic Impact of the Achievement Gap in Saudi ArabiaAlexander Wiseman
Please visit my website for more information: http://www.comparative-education.com/. To cite this presentation, please use the following: Wiseman, A. W. (2011, March). The Economic Impact of the Achievement Gap in Saudi Arabia. Paper presented at the International Exhibition and Forum for Public Education, Riyadh, Saudi Arabia.
Comparing National and Non-national Student Achievement in Saudi Arabia: Alexander Wiseman
Please visit my website for more information: http://www.comparative-education.com/. To cite this presentation, please use the following: Wiseman, A. W., & LaRue, B. (2011, April). Comparing National and Non-national Student Achievement in Saudi Arabia by Analyzing Economic Participation disparity Using Educational Indicators. Paper presented at the annual meeting of the American Educational Research Association, New Orleans, LA.
Gendered Roles, Principal-Teacher Relationships, School Climate and Instructi...Alexander Wiseman
Please visit my website for more information: http://www.comparative-education.com/. To cite this presentation, please use the following: Wiseman, A. W., & Jackson, K. (2011, May). Gendered Roles, Principal-Training Relationships, School Climate and Leadership Activity: A Cross-national Analysis using TALIS 2008. Paper presented at the annual meeting of the Comparative and International Education Society, Montreal, Canada.
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.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
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.
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.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Digital Tools and AI for Teaching Learning and Research
International Cross-Time, Cross-System (XTXS) Database: Research Potential & Example
1. International Cross-Time, Cross-
System (XTXS) Database:
Research Potential & Example
Alexander W. Wiseman
Lehigh University
aww207@lehigh.edu
2. Problems with Cross-national Data
• It is “isolated” – many datasets exist, few are
readily compatible or complementary
• It is “inaccessible” – sophisticated statistical
skills are necessary
• It is “flat” – one or two dimensions at best
3. Potential with XTXS
XTXS is XTXS is XTXS is in
“integrated” “accessible” “3D”
8. Space: Comparing Systems
• Comparing across TIME
locations, political or
geographical
boundaries, educatio
SPACE
nal systems
• Bread and butter of
comparative and
international
education research
10. Potential with XTXS
XTXS is XTXS is XTXS is in
“integrated” “accessible” “3D”
11. Sample Research Questions
1. How do health factors associate with
student achievement?
2. Impact of teacher characteristics on HIV/AIDS
curriculum implementation?
3. How does HIV/AIDS prevalence among young
adults impact the teaching profession?
– Teacher attrition due to health/infection
– Shift in association between teacher
training, education level, years experience
12. 1. How do health factors associate
with student achievement?
• Health Factors
– Health expenditure per capita (current USD), 1995-
2009
– Malnutrition prevalence, weight for age (% children
<5), 1975-2009
– Prevalence of HIV, total (% ages 15-24), 1990-2009
• Student Achievement Indicators
– PISA science literacy (15 year
olds), 2000, 2003, 2006, 2009
– TIMSS science scores (4th graders), 1995, 2003, 2007
– PIRLS reading literacy (4th graders), 2001, 2006
13. 1. How do health factors associate
with student achievement?
The cross-time element is considered first.
How well do the two sets of factors align by year?
14. 1. How do health factors associate
with student achievement?
The cross-system element is considered next.
How good is the location coverage by year for the
health factors?
15. Potential for Statistical Analyses
• Descriptive statistics (frequencies, central
tendency, variability)
• Inferential statistics (differences, relationships)
• Multilevel analysis (w/ supplementary data)
• Structural Equation Modeling (latent
variables)
• Time Series Analysis (patterns over time)
– Event History Analysis (duration or time-to-event)
16.
17.
18. 1. How do health factors associate
with student achievement?
The cross-space element is considered next.
How good is the location coverage by year for the
student achievement indicators?
19.
20.
21.
22. Potential for Statistical Analyses
• Descriptive statistics (frequencies, central
tendency, variability)
• Inferential statistics (differences, relationships)
• Multilevel analysis (w/ supplementary data)
• Structural Equation Modeling (latent
variables)
• Time Series Analysis (patterns over time)
– Event History Analysis (duration or time-to-event)
23. Potential with XTXS
XTXS is XTXS is XTXS is in
“integrated” “accessible” “3D”
24. International Cross-Time, Cross-
System (XTXS) Database:
Research Potential & Example
Alexander W. Wiseman
Lehigh University
aww207@lehigh.edu
Editor's Notes
Definitely a consumer of international comparative education data, sometimes a commentator on this kind of data, too.Been using this kind of data since 1998, often TIMSS but typically supplemented by World Bank, UNESCO, and OECD data.Promising alternative to the hunt and find method of cross-national data collection. Eliminates much of the statistical inconsistencies that pulling together your own international dataset can create.A handful of current and former graduate students who are working on research with me and turning towards this data. Will discuss some of their examples.Tell you why I think XTXS has potential: empirical advantage and dimensionality.Tell you about its potential for empirical analysis of international and comparative education phenomena.
Where to look for information is always the biggest challenge when doing cross-national analysis. Bringing these data together is often a big challenge. Are the units compatible? What about the weighting when aggregating to the national/system level? And, making sure that the character and the dynamics/complexity of a phenomenon are represented by the data is always important. How much depth is there in the data?
XTXS offers international and comparative education researchers an empirical advantage because it is:IntegratedAccessible3 dimensional
It is integrated because it brings together data from many of the most frequent sources of comparative and international education data into one coherent dataset. AchievementBackground Context/CharacteristicsTrends in educational system phenomena (enrollment, resources)Etc.
XTXS is accessible because it provides an ensemble of data in two easy-to-use formats: wide datasets and long datasets.It comes downloadable as Excel, SPSS, and SASS compatible datasets, so anyone from a range of statistical software skill levels can use it.And, the format of the data allows the user to configure it in analyses in order to look across time, system, and level.
To me, the potential dimensionality of XTXS is its greatest feature.We often talk about the benefits of triangulation in empirical research. This is similar in many respects because it gives us the potential to approach any international comparative education phenomenon using cross-system, cross-time, and cross-level analyses.
XTXS provides data for the many variables at specific yearly time points for as many of the variables as possible. These time series are cross-sectional year-by-year, and the participants/data subjects are not necessarily the same from year-to-year, so this is not longitudinal or panel data.But, it does provide multiple time points, and allows us to document and investigate change over time. This is huge, since one of the biggest critiques of a lot of internationally comparative data like the TIMSS and PISA is that it is cross-sectional and so may not represent the full reality of what’s going on.The cross-time element of XTXS provides an way to track this change over time by assembling these data over several decades together in one dataset.
Cross-national education system comparisons are a staple of international comparative education research, and XTXS provides national education systems as a key unit.
A third dimension of XTXS, which requires a bit more statistical manipulation is the potential to use this data for cross-level or cross-context analysis. Conceptually, there are individual-level, classroom-level, and school-level data that are aggregated to the national level for inclusion in the dataset, but we can compare system-level trends with school-level trends as they are aggregated. For example, what do teachers report their highest level of educational attainment is versus what is the national record of teachers’ highest educational attainment. This is one example of why it is important to collect and compare data originally sourced from different levels of analysis.It is also possible to supplement XTXS with data that uses a school or individual unit without aggregation, so that a multilevel regression analysis, for example, might be used. This is especially helpful in contextualizing effects and demonstrating the impact of nested relationships among variables. So for example, we might want student-level science achievement level and gender to be used as the main dependent variable, but use the number of women in parliament as the contextual, system/national level estimation factor to tell us if there is a contextual effect of visible women leaders on girls’ science performance.
So this is the potential: XTXS offers international and comparative education researchers an empirical advantage because it is:IntegratedAccessible3 dimensionalNow, let’s take a look at some of the promise resulting from this potential.
As I mentioned at the beginning, I have several former and current graduate students that I am working with on internationally-comparative studies of health and education.And, as you can see from the 3 sample questions above, they are all related, but take slightly different directions.Explain all three, but focus on #1 for the GCC. (explain Sandi’s experience and former position with the Abu Dhabi Education Council)I’m going to take you through the process rather than show you results per se. The process shows the potential promise for the XTXS data becoming useful and used by researchers more than anything else, I think.
The first thing we had to do was to see which variables were available in XTXS for our health factor and student achievement indicators.There are many variables to choose from, but this is what we initially came up with.
Since XTXS is 3D, we considered the data’s potential for our question from each angle.Across time, we looked at the coverage and alignment of our health and student achievement indicators by year.Great coverage for the health factors, but sparse for the student achievement data.
Next we looked across systems, and again we found that for health factors there was a lot of potential.
At this point, we began to consider our statistical options. Descriptive statistics across time and system is the best place to start because it gives us a feel for what the data looks like, what some potential trends are in the data, and how to approach more sophisticated analyses. Basic inferential statistics that show significant differences between groups and relationships would be the next stop, and give us a way to build our rationale for one of the more complex analyses in response to our main question.Multilevel analyses allow us to look at nested relationships, so for example, we might be able to see if student achievement was a product of health conditions, either by virtue of the health obstacles and challenges students faced in different systems or by virtue of the health infrastructure and its reflection of a wider resource base.Structural equation model is also an option because much of what we would like to eventually investigate is not directly measured, so latent variables become much more important – such as the impact of HIV/AIDS infection rates on teacher attrition.Time series analyses could be especially relevant to us since they identify patterns in the sequence of data over time, test the impact of one or more interventions (such as teaching health-related curricula), forecast future patterns of events or compare series of different kinds of events.We thought that an event history analysis would be especially relevant because it would allow us to measure the increases or decreases in student achievement as health factors changed over time.
As I mentioned before, we are specifically interested in the Gulf Cooperation Council countries for our main question of whether student achievement and health factors are related.So, we began with a look at the changes from year-to-year in health expenditure per capita for the years available in the XTXS data.As the chart shows, overall health expenditures seem to be increasing over time in GCC countries with some specific standouts: Qatar at the high end and Oman at the low end.
To see if the differences over time were shifting as much as we thought for the GCC as a whole (and since we are leaning towards a time series/event history analysis), we plotted the lag 1 first differences in health expenditures for the GCC Mean.Overall, the trend is very much increasing in spending. So, if there is a hypothesized positive association between health expenditure and student achievement, we should see an increase in student achievement as well when we get to that stage.
The problem is that for student achievement the coverage by year and system is much less frequent, and for the GCC countries it is even less.
The good news is that we can supplement this data with specific achievement item data from the Trends in International Mathematics and Science study science assessments for 8th grade equivalent students.These items help us not only get at the health and student achievement relationship, but they show us students’ specific knowledge related to health.