Education - Revision history

Mohammod.irfan at 22:13, 30 November 2020

2020-11-30T22:13:27Z

Mohammod.irfan at 22:00, 30 November 2020

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Wikiadmin at 21:08, 7 September 2018

2018-09-07T21:08:57Z

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	= <span style="font-size:xx-large;">Structure and Agent System: Education</span> =		= <span style="font-size:xx-large;">Structure and Agent System: Education</span> =

	{\| class="tableGrid" style="width: 100%" cellspacing="0" cellpadding="5" border="0"		{\| class="tableGrid" style="width:100%;" cellspacing="0" cellpadding="5" border="1"
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	\| style="width: 50%" \| <div>'''System/Subsystem'''</div>		\| style="width: 50%" \| <div>'''System/Subsystem'''</div>

Wikiadmin at 20:44, 7 September 2018

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KanishkaNarayan at 19:51, 7 September 2018

2018-09-07T19:51:06Z

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	The deeper driver of learning quality in IFs education model is the educational attainment of the adult population. Attainment is strongly correlated with the level of development. Higher educational attainment countries have a good education system and high resource availability for education. It also signals societies to shift educational priorities towards learning quality as the quantity goals are achieved.		The deeper driver of learning quality in IFs education model is the educational attainment of the adult population. Attainment is strongly correlated with the level of development. Higher educational attainment countries have a good education system and high resource availability for education. It also signals societies to shift educational priorities towards learning quality as the quantity goals are achieved.

	Spending in education is a more proximate driver of learning quality. The evidence on the impact of spending on quality is not always strong. Moreover, the strong correlation between spending and attainment tells us any impact of spending needs to be attainment neutral. In our model, spending variables boost (or reduce) quality scores only when they are above (or below) the spending in other societies with a similar level of development.		Spending in education is a more proximate driver of learning quality. The evidence on the impact of spending on quality is not always strong. Moreover, the strong correlation between spending and attainment tells us any impact of spending needs to be attainment neutral. In our model, spending variables boost (or reduce) quality scores only when they are above (or below) the spending in other societies with a similar level of development.

	Other proximate drivers that affect quality scores in our model are governance and security situations. For example, corruption can reduce the effectivity of spending. We attenuate the spending impact through the corruption variable (GOVCORRUPT) forecast in the IFs governance model. The presence of violence and conflicts in the society can impact both enrollment and quality. We have recently added some causal connection from the governance security index (GOVSECURIND) to learning quality and survival rate. Learning quality scores are forecast in three steps:		Other proximate drivers that affect quality scores in our model are governance and security situations. For example, corruption can reduce the effectivity of spending. We attenuate the spending impact through the corruption variable (GOVCORRUPT) forecast in the IFs governance model. The presence of violence and conflicts in the society can impact both enrollment and quality. We have recently added some causal connection from the governance security index (GOVSECURIND) to learning quality and survival rate. Learning quality scores are forecast in three steps:

	a. forecast overall score,		a.forecast overall score,

	b. forecast subject scores using the forecast on overall score, c. compute gendered forecast for all scores forecast in steps a and b. In this section we shall describe these steps for learning quality scores in elementary education (EDQUALPRIALL etc.). The secondary level education quality model follows a similar algorithm using the same driver variables or those that are relevant to secondary. For example, per student spending variable used in secondary education model is EDEXPERSEC, expenditure per secondary student.		b.forecast subject scores using the forecast on overall score, c. compute gendered forecast for all scores forecast in steps a and b. In this section we shall describe these steps for learning quality scores in elementary education (EDQUALPRIALL etc.). The secondary level education quality model follows a similar algorithm using the same driver variables or those that are relevant to secondary. For example, per student spending variable used in secondary education model is EDEXPERSEC, expenditure per secondary student.

	=== Forecasting Overall Score ===		=== Forecasting Overall Score ===

KanishkaNarayan at 19:49, 7 September 2018

2018-09-07T19:49:21Z

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	Population weighted average of the cohort scores determine the overall quality of the educational attainment of the adults.		Population weighted average of the cohort scores determine the overall quality of the educational attainment of the adults.

	:<math>EDQUALAG15PRI_(r,p,t)=(sum^{21}_{c=4}EdqualPriAgeDst_{c,p,r,t}* Agedst_{c,p,r,t })/(sum^~~21_~~{c=4}Agedst_{c,p,r,t} )</math>		:<math>EDQUALAG15PRI_(r,p,t)=(sum^{21}_{c=4}EdqualPriAgeDst_{c,p,r,t}* Agedst_{c,p,r,t })/(sum^{21}_{c=4}Agedst_{c,p,r,t} )</math>

	:<math>EDQUALAG15SEC_(r,p,t)=(sum^{21}_{c=4}EdqualSecAgeDst_{c,p,r,t}* Agedst_{c,p,r,t })/(sum^~~21_~~{c=4}Agedst_{c,p,r,t} )</math>		:<math>EDQUALAG15SEC_(r,p,t)=(sum^{21}_{c=4}EdqualSecAgeDst_{c,p,r,t}* Agedst_{c,p,r,t })/(sum^{21}_{c=4}Agedst_{c,p,r,t} )</math>

	A simple average of the primary and secondary scores gives the overall quality score for the adult population.		A simple average of the primary and secondary scores gives the overall quality score for the adult population.

KanishkaNarayan at 19:48, 7 September 2018

2018-09-07T19:48:42Z

KanishkaNarayan at 19:47, 7 September 2018

2018-09-07T19:47:33Z

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	<span>Cohort attainments by level of education are also used in to build a specialized educational attainment display, commonly referred to as education pyramid in congruence with demographic pyramids used to display population by age cohorts stacked one on top of the other with the men and women cohorts put opposite to each other around a vertical axis. Education pyramid superimposes educational attainment on top of the demographic pyramid. </span>		<span>Cohort attainments by level of education are also used in to build a specialized educational attainment display, commonly referred to as education pyramid in congruence with demographic pyramids used to display population by age cohorts stacked one on top of the other with the men and women cohorts put opposite to each other around a vertical axis. Education pyramid superimposes educational attainment on top of the demographic pyramid. </span>

			=== <span></span>Learning Quality of the Adult Population ===

			IFs education model forecasts average learning quality scores for men and women (''EDQUALAG15''). The variable is an average of two scores: the average score for those who have completed at least primary education (''EDQUALAG15PRI'') and a second average score for those who completed secondary education (''EDQUALAG15SEC'').

			We could not find any cross-country database on the quality score for adults. We decided to use lagged historical test score data to initialize two quality scores- one for primary education and the other for secondary- for the adults. We assumed that the student test scores twenty-five years back is a crude measure of education quality of an adult at the age of forty today. With this assumption we would be able to measure the quality of forty-five year olds using student from thirty years back and so. However, the database on education quality score is very sparse. So, we adopted a second method of spreading the mid-point score across age cohort. However, given the lack of our understanding about how education quality changes over time we adopted the crude technique of attributing same quality score to all of the five-year adult cohorts.

			Here we will describe the initialization process. When there is no data for that prior year, IFs pre-processor attempts the standard hole-filling processes of IFs, i.e., use data from a nearby year, and if there is no data at all use various estimation technique.

			:<math>EDQUALAG15PRI_{p,r,t=1} = EDQUALPRIALL_{p,r,t=-25}</math>

			:<math>EDQUALAG15SEC_{p,r,t=1} = EDQUALSECALL_{p,r,t=-25}</math>

			The average adult score is spread over adult five-year cohorts (Agedst). The scarcity of historical data and the complexity of computations involved compelled us to opt for a naive spread algorithm that adorns each cohort with the same score (EdqualPriAgeDst). We hope to adopt a more sophisticated spread when we get better data.

			:<math>EdqualPriAgeDst_{c=4 to 21,p,r,t=1}= EDQUALAG15PRI_{p,r,t=1}</math>

			:<math>EdqualSecAgeDst_{c=4 to 21,p,r,t=1}= EDQUALAG15SEC_{p,r,t=1}</math>

			In the subsequent years, the cohort scores are updated through the progression of people across the cohort structure carrying along their learning. The learning quality of the current year is combined with the quality score of the youngest of these cohorts (15 to 19-year-olds). We show below the equation for primary level score .

			:<math> EdqualPriAgeDst_{c=4,p,r,t}= (4/5)* EdqualPriAgeDst_{c=4,p,r,t-1}+(1/5)* EDQUALPRIALL_{r,p,t}</math>

			:<math> EdqualPriAgeDst_{c=5 to 21,p,r,t}= (4/5)* EdqualPriAgeDst_{c=4,p,r,t-1}+(1/5)* EdqualPriAgeDst_{c=4,p,r,t-1}</math>

			Population weighted average of the cohort scores determine the overall quality of the educational attainment of the adults.

			:<math>EDQUALAG15PRI_(r,p,t)=(sum^21_{c=4}EdqualPriAgeDst_{c,p,r,t}* Agedst_{c,p,r,t })/(sum^21_{c=4}Agedst_{c,p,r,t} )</math>

			:<math>EDQUALAG15SEC_(r,p,t)=(sum^21_{c=4}EdqualSecAgeDst_{c,p,r,t}* Agedst_{c,p,r,t })/(sum^21_{c=4}Agedst_{c,p,r,t} )</math>


			A simple average of the primary and secondary scores gives the overall quality score for the adult population.

			:<math>EDQUALAG15_{p,r,t} = (EDQUALAG15PRI_{p,r,t}+ EDQUALAG15SEC_{p,r,t})/2 </math>

	= <span style="font-size:xx-large;">Knowledge Systems</span> =		= <span style="font-size:xx-large;">Knowledge Systems</span> =

KanishkaNarayan at 19:34, 7 September 2018

2018-09-07T19:34:34Z

@@ Line 311: / Line 311: @@
 :<math>Calcscore_{r,t}=f(EDYRSAG25_{p=3,r,t})</math>
 The regression forecast is adjusted for country specific deviations to compute he final value of the quality score (EDQUALPRIALL). These deviations diminish and disappear in the long run as all countries merge with the function. This is done using the shift convergence algorithm that we use elsewhere in the model. Countries that are below the function merge at a faster pace than those that are above.
-:<math> EdQualPriAllShift_{r}= EDQUALPRIALL_{p=3,r,t=1}-Calscore_{r,t=1}</math>
+:<math>EdQualPriAllShift_{r}= EDQUALPRIALL_{p=3,r,t=1}-Calscore_{r,t=1}</math>
-:<math> If EdQualPriAllShift_{r}=< 0,    EDQUALPRIALL_{p=3,r,t}= Calcscore_{r,t}+ ConvergeOverTime(EdQualPriAllShift_{r},0,50)</math>
+:<math>If EdQualPriAllShift_{r}=< 0, EDQUALPRIALL_{p=3,r,t}= Calcscore_{r,t}+ ConvergeOverTime(EdQualPriAllShift_{r},0,50)</math>
-:<math> If EdQualPriAllShift_{r}>0,    EDQUALPRIALL_{p=3,r,t} = Calcscore_{r,t}+ConvergeOverTime(EdQualPriAllShift_{r},0,85)</math>
+:<math>If EdQualPriAllShift_{r}>0, EDQUALPRIALL_{p=3,r,t} = Calcscore_{r,t}+ConvergeOverTime(EdQualPriAllShift_{r},0,85)</math>
-Next we compute the contribution of educational spending (SpendingContrib) for countries that are above or below the level of spending per student that is expected of a country given its level of development. The expected value is obtained from a regression function plotted with most recent data on per student spending in primary education expressed as a percentage of per capita income (EDEXPERPRI). Per capita income (GDPPCP) is used as a proxy for the level of development. The expected value (edexperstudcomp) is adjusted for country effects by adding a country-specific shift factor (edexperPriShift). The shift factor is computed as the gap between the actual historical/estimated spending data and the computed value in the initial year.  In normal situation, the computed expected value should converge to the expected function and the shift factor would converge to zero.
+Next we compute the contribution of educational spending (SpendingContrib) for countries that are above or below the level of spending per student that is expected of a country given its level of development. The expected value is obtained from a regression function plotted with most recent data on per student spending in primary education expressed as a percentage of per capita income (EDEXPERPRI). Per capita income (GDPPCP) is used as a proxy for the level of development. The expected value (edexperstudcomp) is adjusted for country effects by adding a country-specific shift factor (edexperPriShift). The shift factor is computed as the gap between the actual historical/estimated spending data and the computed value in the initial year. In normal situation, the computed expected value should converge to the expected function and the shift factor would converge to zero.
 :<math>edexperstudcomp_{r,t=1}= f(GDPPCP_{r,t=1})</math>
-:<math>edexperPriShift_{r}=EDEXPERPRI_{r,t=1}- edexperstudcomp_{r,t=1}
+:<math>edexperPriShift_{r}=EDEXPERPRI_{r,t=1}- edexperstudcomp_{r,t=1}</math>
 == <span style="font-size:x-large;">Equations: Budget Flow</span> ==

KanishkaNarayan at 17:59, 7 September 2018

2018-09-07T17:59:35Z

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	:<math>EDTERGRSCIEN_{r,t}=f(GDPPCP_{r,t})+edterscienshradd_{r,t}</math>		:<math>EDTERGRSCIEN_{r,t}=f(GDPPCP_{r,t})+edterscienshradd_{r,t}</math>

			=== Education Equations: Learning Quality  ===

			The deeper driver of learning quality in IFs education model is the educational attainment of the adult population. Attainment is strongly correlated with the level of development. Higher educational attainment countries have a good education system and high resource availability for education. It also signals societies to shift educational priorities towards learning quality as the quantity goals are achieved.

			Spending in education is a more proximate driver of learning quality. The evidence on the impact of spending on quality is not always strong. Moreover, the strong correlation between spending and attainment tells us any impact of spending needs to be attainment neutral. In our model, spending variables boost (or reduce) quality scores only when they are above (or below) the spending in other societies with a similar level of development.

			Other proximate drivers that affect quality scores in our model are governance and security situations. For example, corruption can reduce the effectivity of spending. We attenuate the spending impact through the corruption variable (GOVCORRUPT) forecast in the IFs governance model. The presence of violence and conflicts in the society can impact both enrollment and quality. We have recently added some causal connection from the governance security index (GOVSECURIND) to learning quality and survival rate. Learning quality scores are forecast in three steps:

			a. forecast overall score,

			b. forecast subject scores using the forecast on overall score, c. compute gendered forecast for all scores forecast in steps a and b. In this section we shall describe these steps for learning quality scores in elementary education (EDQUALPRIALL etc.). The secondary level education quality model follows a similar algorithm using the same driver variables or those that are relevant to secondary. For example, per student spending variable used in secondary education model is EDEXPERSEC, expenditure per secondary student.

			=== Forecasting Overall Score ===

			In the first step we forecast the overall (i.e., all subjects combined) scores (''EDQUALPRIALL'') using a regression model driven by educational attainment of adults twenty-five-years and older (''EDYRSAG25)''. We use available historical data and various estimation techniques to build a full cross-section of EDQUALPRIALL for the base year. These base year values are used to plot the regression function.

			The regression model is used to compute the initial forecast of the overall score

			:<math>Calcscore_{r,t}=f(EDYRSAG25_{p=3,r,t})</math>

			The regression forecast is adjusted for country specific deviations to compute he final value of the quality score (EDQUALPRIALL). These deviations diminish and disappear in the long run as all countries merge with the function. This is done using the shift convergence algorithm that we use elsewhere in the model. Countries that are below the function merge at a faster pace than those that are above.

			:<math> EdQualPriAllShift_{r}= EDQUALPRIALL_{p=3,r,t=1}-Calscore_{r,t=1}</math>

			:<math> If EdQualPriAllShift_{r}=< 0, EDQUALPRIALL_{p=3,r,t}= Calcscore_{r,t}+ ConvergeOverTime(EdQualPriAllShift_{r},0,50)</math>

			:<math> If EdQualPriAllShift_{r}>0, EDQUALPRIALL_{p=3,r,t} = Calcscore_{r,t}+ConvergeOverTime(EdQualPriAllShift_{r},0,85)</math>

			Next we compute the contribution of educational spending (SpendingContrib) for countries that are above or below the level of spending per student that is expected of a country given its level of development. The expected value is obtained from a regression function plotted with most recent data on per student spending in primary education expressed as a percentage of per capita income (EDEXPERPRI). Per capita income (GDPPCP) is used as a proxy for the level of development. The expected value (edexperstudcomp) is adjusted for country effects by adding a country-specific shift factor (edexperPriShift). The shift factor is computed as the gap between the actual historical/estimated spending data and the computed value in the initial year. In normal situation, the computed expected value should converge to the expected function and the shift factor would converge to zero.

			:<math>edexperstudcomp_{r,t=1}= f(GDPPCP_{r,t=1})</math>

			:<math>edexperPriShift_{r}=EDEXPERPRI_{r,t=1}- edexperstudcomp_{r,t=1}

	== <span style="font-size:x-large;">Equations: Budget Flow</span> ==		== <span style="font-size:x-large;">Equations: Budget Flow</span> ==

← Older revision		Revision as of 22:13, 30 November 2020
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	<span>The education model of IFs simulates patterns of educational participation and attainment in 186 countries over a long time horizon under alternative assumptions about uncertainties and interventions (Irfan 2008).  Its purpose is to serve as a generalized thinking and analysis tool for educational futures within a broader human development context. </span>		<span>The education model of IFs simulates patterns of educational participation and attainment in 186 countries over a long time horizon under alternative assumptions about uncertainties and interventions (Irfan 2008).  Its purpose is to serve as a generalized thinking and analysis tool for educational futures within a broader human development context. </span>

	<span>The model forecasts gender- and country-specific access, participation, and progression rates at levels of formal education starting from elementary through lower and upper secondary to tertiary. The model also forecasts costs and public spending by level of education. Dropout, completion, and transition to the next level of schooling are all mapped onto corresponding age cohorts thus allowing the model to forecast educational attainment for the entire population at any point in time within the forecast horizon.</span>		<span>The model forecasts gender- and country-specific access, participation, and progression rates at levels of formal education starting from elementary through lower and upper secondary to tertiary. The model also forecasts costs and public spending by level of education. Dropout, completion, and transition to the next level of schooling are all mapped onto corresponding age cohorts, thus allowing the model to forecast educational attainment for the entire population at any point in time within the forecast horizon.</span>

	<span>From simple accounting of the grade progressions to complex budget-balancing and budget impact algorithm, the model draws upon the extant understanding and standards (e.g., UNESCO's ISCED classification explained later) about national systems of education around the world. One difference between other attempts at forecasting educational participation and attainment (e.g, McMahon 1999; Bruns, Mingat and Rakotomalala 2003; Wils and O’Connor 2003; Delamonica, Mehrotra, and Vandemoortele. 2001; Cuaresma and Lutz 2007) and our forecasting, is the embedding of education within an integrated model in which demographic and economic variables interact with education, in both directions, as the model runs. </span>		<span>From simple accounting of the grade progressions to complex budget-balancing and budget impact algorithm, the model draws upon the extant understanding and standards (e.g., UNESCO's ISCED classification explained later) about national systems of education around the world. One difference between other attempts at forecasting educational participation and attainment (e.g., McMahon 1999; Bruns, Mingat and Rakotomalala 2003; Wils and O’Connor 2003; Delamonica, Mehrotra, and Vandemoortele. 2001; Cuaresma and Lutz 2007) and our forecasting is the embedding of education within an integrated model in which demographic and economic variables interact with education, in both directions, as the model runs. </span>

	<span>In the figure below we display the major variables and components that directly determine education demand, supply, and flows in the IFs system.  We emphasize again the inter-connectedness of the components and their relationship to the broader human development system.  For example, during each year of simulation, the IFs cohort-specific demographic model provides the school-age population to the education model.  In turn, the education model feeds its calculations of education attainment to the population model’s determination of women’s fertility.  Similarly, the broader economic and socio-political systems provide funding for education, and levels of educational attainment affect economic productivity and growth, and therefore also education spending.</span>		<span>In the figure below we display the major variables and components that directly determine education demand, supply, and flows in the IFs system.  We emphasize again the inter-connectedness of the components and their relationship to the broader human development system.  For example, during each year of simulation, the IFs cohort-specific demographic model provides the school-age population to the education model.  In turn, the education model feeds its calculations of education attainment to the population model’s determination of women’s fertility.  Similarly, the broader economic and socio-political systems provide funding for education, and levels of educational attainment affect economic productivity and growth, and therefore also education spending.</span>
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	== <span style="font-size:x-large;">Education Model Coverage</span> ==		== <span style="font-size:x-large;">Education Model Coverage</span> ==

	UNESCO has developed a standard classification system for national education systems called the International Standard Classification of Education, ISCED. ISCED 1997 uses a numbering system to identify the sequential levels of educational systems—namely, pre-primary, primary, lower secondary, upper secondary, post-secondary non-tertiary, and tertiary—which are characterized by curricula of increasing difficulty and specialization as the students move up the levels. IFs education model covers primary (ISCED level 1), lower secondary (ISCED level 2), upper secondary (ISCED level 3), and tertiary education (ISCED levels 5A, 5B and 6).		UNESCO has developed a standard classification system for national education systems called the International Standard Classification of Education, ISCED. ISCED 1997 uses a numbering system to identify the sequential levels of educational systems—namely, pre-primary, primary, lower secondary, upper secondary, post-secondary non-tertiary, and tertiary—which are characterized by curricula of increasing difficulty and specialization as the students move up the levels. IFs education model covers primary (ISCED level 1), lower secondary (ISCED level 2), upper secondary (ISCED level 3), and tertiary education (ISCED levels 5A, 5B, and 6).

	The model covers 186 countries that can be grouped into any number of flexible country groupings, e.g., UNESCO regions, like any other sub-module of IFs. Country specific entrance age and school-cycle length [[Education#Sources_of_Education_Data\|data are collected]] and used in IFs to represent national education systems as closely as possible. For all of these levels, IFs forecast variables representing student flow rates, e.g., intake, persistence, completion, and graduation, and stocks, e.g., enrolment, with the girls and the boys handled separately within each country.		The model covers 186 countries that can be grouped into any number of flexible country groupings, e.g., UNESCO regions, like any other sub-module of IFs. Country specific entrance age and school-cycle length [[Education#Sources_of_Education_Data\|data are collected]] and used in IFs to represent national education systems as closely as possible. For all of these levels, IFs forecast variables representing student flow rates, e.g., intake, persistence, completion, and graduation, and stocks, e.g., enrolment, with the girls and the boys handled separately within each country.
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	Another aggregate measure of educational attainment that we forecast is the average years of education of the adults. We have several measures, EDYEARSAG15, average years of education for all adults aged 15 and above, EDYRSAG25, average years of education for those 25 and older, EDYRSAG15TO24, average years of education for the youngest of the adults aged between fifteen years to twenty-four.		Another aggregate measure of educational attainment that we forecast is the average years of education of the adults. We have several measures, EDYEARSAG15, average years of education for all adults aged 15 and above, EDYRSAG25, average years of education for those 25 and older, EDYRSAG15TO24, average years of education for the youngest of the adults aged between fifteen years to twenty-four.

	IFs education model also covers [[Education#Education_Financial_Flow\|financing of education]]. The model forecast per student public expenditure as a share of per capita income. The model also forecasts total public spending in education and the share of that spending that goes to each level of education.		IFs education model also covers the [[Education#Education_Financial_Flow\|financing of education]]. The model forecast per-student public expenditure as a share of per capita income. The model also forecasts total public spending on education and the share of that spending that goes to each level of education.

	== <span style="font-size:x-large;">What the Model Does Not Cover</span> ==		== <span style="font-size:x-large;">What the Model Does Not Cover</span> ==
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	The quality of the national education system can also vary across countries and over time. The IFs education model does not forecast any explicit indicator of education quality. However, the survival and graduation rates that the model forecasts for all levels of education are implicit indicators of system quality.  At this point, IFs does not forecast any indicator of the cognitive quality of learners. However, the IFs database does have data on cognitive quality.		The quality of the national education system can also vary across countries and over time. The IFs education model does not forecast any explicit indicator of education quality. However, the survival and graduation rates that the model forecasts for all levels of education are implicit indicators of system quality.  At this point, IFs does not forecast any indicator of the cognitive quality of learners. However, the IFs database does have data on cognitive quality.

	The IFs education model does not cover private spending in education.		The IFs education model does not cover private spending on education.

	== <span style="font-size:x-large;">Sources of Education Data</span> ==		== <span style="font-size:x-large;">Sources of Education Data</span> ==
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	([http://datatopics.worldbank.org/education/ http://datatopics.worldbank.org/education/]). EdStats has recently started adding data on educational equality.		([http://datatopics.worldbank.org/education/ http://datatopics.worldbank.org/education/]). EdStats has recently started adding data on educational equality.

	As said earlier in this document, scores from international assessments ~~are used~~ as a measure of learning quality.  Trends in International Mathematics and Science Study (TIMSS) is a series of international assessments of the mathematics and science knowledge of students at the fourth and eighth grade level in countries around the world conducted once in every four years. The Progress in International Reading Literacy Study (PIRLS) is a reading assessment conducted at the fourth grade level. TIMSS and PIRLS together form the core of the assessments conducted by International Association for the Evaluation of Educational Achievement, a Europe-based international cooperative of national research institutions. OECD conducts Program for International Student Assessment (PISA) to assess the reading, math and science at the fourth grade level in member and some non-member countries. Time series data is available for TIMSS starting from 1995 and for PIRLS from 2001. ~~Spatial~~ coverage of the data is not that great though. Any of this international tests covers around sixty to seventy countries. To overcome this limitation on data coverage researchers, combine international test scores with scores from regional assessments. Some of these regional tests are conducted in Africa (SACMEQ and PASEC) and some in Latin America and the Caribbean (LLECE).		As said earlier in this document, we use scores from international assessments as a measure of learning quality.  Trends in International Mathematics and Science Study (TIMSS) is a series of international assessments of the mathematics and science knowledge of students at the fourth and eighth grade level in countries around the world conducted once every four years. The Progress in International Reading Literacy Study (PIRLS) is a reading assessment conducted at the fourth-grade level. TIMSS and PIRLS together form the core of the assessments conducted by the International Association for the Evaluation of Educational Achievement, a Europe-based international cooperative of national research institutions. OECD conducts the Program for International Student Assessment (PISA) to assess the reading, math, and science at the fourth grade level in member and some non-member countries. Time series data is available for TIMSS starting from 1995 and for PIRLS from 2001. The spatial coverage of the data is not that great though. Any of this international tests covers around sixty to seventy countries. To overcome this limitation on data coverage researchers, combine international test scores with scores from regional assessments. Some of these regional tests are conducted in Africa (SACMEQ and PASEC) and some in Latin America and the Caribbean (LLECE).

	Our learning quality data is a compilation (Angrist, Patrinos and Schlotter 2013) of the international and regional test scores using a methodology that makes data comparable across countries and over time. Hanushek and Kimco (2000) and Altinok and others (2007, 2013) have used similar methodologies. The dataset that we use covers 128 countries over a period extending from 1965 to 2010 and is available at the World Bank Education Statistics databank. A more recent update on the dataset (Altinok, Angrist and Patrinos, 2018) with a better spatial and temporal coverage is yet to be released officially as I am writing this section in March 2018.		Our learning quality data is a compilation (Angrist, Patrinos and Schlotter 2013) of the international and regional test scores using a methodology that makes data comparable across countries and over time. Hanushek and Kimco (2000) and Altinok and others (2007, 2013) have used similar methodologies. The dataset that we use covers 128 countries over a period extending from 1965 to 2010 and is available at the World Bank Education Statistics databank. A more recent update on the dataset (Altinok, Angrist and Patrinos, 2018) with a better spatial and temporal coverage is yet to be released officially as I am writing this section in March 2018.

← Older revision		Revision as of 19:48, 7 September 2018
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	<span>Cohort attainments by level of education are also used in to build a specialized educational attainment display, commonly referred to as education pyramid in congruence with demographic pyramids used to display population by age cohorts stacked one on top of the other with the men and women cohorts put opposite to each other around a vertical axis. Education pyramid superimposes educational attainment on top of the demographic pyramid. </span>		<span>Cohort attainments by level of education are also used in to build a specialized educational attainment display, commonly referred to as education pyramid in congruence with demographic pyramids used to display population by age cohorts stacked one on top of the other with the men and women cohorts put opposite to each other around a vertical axis. Education pyramid superimposes educational attainment on top of the demographic pyramid. </span>

	=== ~~<span></span>~~Learning Quality of the Adult Population ===		=== Learning Quality of the Adult Population ===

	IFs education model forecasts average learning quality scores for men and women (''EDQUALAG15''). The variable is an average of two scores: the average score for those who have completed at least primary education (''EDQUALAG15PRI'') and a second average score for those who completed secondary education (''EDQUALAG15SEC'').		IFs education model forecasts average learning quality scores for men and women (''EDQUALAG15''). The variable is an average of two scores: the average score for those who have completed at least primary education (''EDQUALAG15PRI'') and a second average score for those who completed secondary education (''EDQUALAG15SEC'').
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	Here we will describe the initialization process. When there is no data for that prior year, IFs pre-processor attempts the standard hole-filling processes of IFs, i.e., use data from a nearby year, and if there is no data at all use various estimation technique.		Here we will describe the initialization process. When there is no data for that prior year, IFs pre-processor attempts the standard hole-filling processes of IFs, i.e., use data from a nearby year, and if there is no data at all use various estimation technique.

	:<math>EDQUALAG15PRI_{p,r,t=1} = EDQUALPRIALL_{p,r,t=-25}</math>		:<math>EDQUALAG15PRI_{p,r,t=1} = EDQUALPRIALL_{p,r,t=-25}</math>

	:<math>EDQUALAG15SEC_{p,r,t=1} = EDQUALSECALL_{p,r,t=-25}</math>		:<math>EDQUALAG15SEC_{p,r,t=1} = EDQUALSECALL_{p,r,t=-25}</math>

	The average adult score is spread over adult five-year cohorts (Agedst). The scarcity of historical data and the complexity of computations involved compelled us to opt for a naive spread algorithm that adorns each cohort with the same score (EdqualPriAgeDst). We hope to adopt a more sophisticated spread when we get better data.		The average adult score is spread over adult five-year cohorts (Agedst). The scarcity of historical data and the complexity of computations involved compelled us to opt for a naive spread algorithm that adorns each cohort with the same score (EdqualPriAgeDst). We hope to adopt a more sophisticated spread when we get better data.

	:<math>EdqualPriAgeDst_{c=4 to 21,p,r,t=1}= EDQUALAG15PRI_{p,r,t=1}</math>		:<math>EdqualPriAgeDst_{c=4 to 21,p,r,t=1}= EDQUALAG15PRI_{p,r,t=1}</math>
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	In the subsequent years, the cohort scores are updated through the progression of people across the cohort structure carrying along their learning. The learning quality of the current year is combined with the quality score of the youngest of these cohorts (15 to 19-year-olds). We show below the equation for primary level score .		In the subsequent years, the cohort scores are updated through the progression of people across the cohort structure carrying along their learning. The learning quality of the current year is combined with the quality score of the youngest of these cohorts (15 to 19-year-olds). We show below the equation for primary level score .

	:<math> EdqualPriAgeDst_{c=4,p,r,t}= (4/5)* EdqualPriAgeDst_{c=4,p,r,t-1}+(1/5)* EDQUALPRIALL_{r,p,t}</math>		:<math>EdqualPriAgeDst_{c=4,p,r,t}= (4/5)* EdqualPriAgeDst_{c=4,p,r,t-1}+(1/5)* EDQUALPRIALL_{r,p,t}</math>

	:<math> EdqualPriAgeDst_{c=5 to 21,p,r,t}= (4/5)* EdqualPriAgeDst_{c=4,p,r,t-1}+(1/5)* EdqualPriAgeDst_{c=4,p,r,t-1}</math>		:<math>EdqualPriAgeDst_{c=5 to 21,p,r,t}= (4/5)* EdqualPriAgeDst_{c=4,p,r,t-1}+(1/5)* EdqualPriAgeDst_{c=4,p,r,t-1}</math>

	Population weighted average of the cohort scores determine the overall quality of the educational attainment of the adults.		Population weighted average of the cohort scores determine the overall quality of the educational attainment of the adults.

	:<math>EDQUALAG15PRI_(r,p,t)=(sum^~~21_~~{c=4}EdqualPriAgeDst_{c,p,r,t}* Agedst_{c,p,r,t })/(sum^21_{c=4}Agedst_{c,p,r,t} )</math>		:<math>EDQUALAG15PRI_(r,p,t)=(sum^{21}_{c=4}EdqualPriAgeDst_{c,p,r,t}* Agedst_{c,p,r,t })/(sum^21_{c=4}Agedst_{c,p,r,t} )</math>

	:<math>EDQUALAG15SEC_(r,p,t)=(sum^~~21_~~{c=4}EdqualSecAgeDst_{c,p,r,t}* Agedst_{c,p,r,t })/(sum^21_{c=4}Agedst_{c,p,r,t} )</math>		:<math>EDQUALAG15SEC_(r,p,t)=(sum^{21}_{c=4}EdqualSecAgeDst_{c,p,r,t}* Agedst_{c,p,r,t })/(sum^21_{c=4}Agedst_{c,p,r,t} )</math>

			A simple average of the primary and secondary scores gives the overall quality score for the adult population.

	~~A simple average of the primary and secondary scores gives the overall quality score for the adult population.~~		:<math>EDQUALAG15_{p,r,t} = (EDQUALAG15PRI_{p,r,t}+ EDQUALAG15SEC_{p,r,t})/2 </math>

	:<math>EDQUALAG15_{p,r,t} = (EDQUALAG15PRI_{p,r,t}+ EDQUALAG15SEC_{p,r,t})/2 </math>

	= <span style="font-size:xx-large;">Knowledge Systems</span> =		= <span style="font-size:xx-large;">Knowledge Systems</span> =