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	<title>multidimensional educational evaluation &#8211; Science</title>
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	<title>multidimensional educational evaluation &#8211; Science</title>
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		<title>Valley Versus Hills: New Index Reveals Stark Educational Divide Across Manipur&#8217;s Districts</title>
		<link>https://scienmag.com/valley-versus-hills-new-index-reveals-stark-educational-divide-across-manipurs-districts/</link>
		
		<dc:creator><![CDATA[Courtney Benton]]></dc:creator>
		<pubDate>Sat, 03 Oct 2026 16:27:00 +0000</pubDate>
				<category><![CDATA[Social Science]]></category>
		<category><![CDATA[Composite Educational Development Index (CEDI)]]></category>
		<category><![CDATA[composite index]]></category>
		<category><![CDATA[district-level analysis]]></category>
		<category><![CDATA[dropout rates]]></category>
		<category><![CDATA[education policy]]></category>
		<category><![CDATA[educational development]]></category>
		<category><![CDATA[Educational disparity in Manipur]]></category>
		<category><![CDATA[educational participation metrics]]></category>
		<category><![CDATA[hill districts]]></category>
		<category><![CDATA[impact of geography on educational outcomes]]></category>
		<category><![CDATA[institutional efficiency]]></category>
		<category><![CDATA[institutional efficiency in education]]></category>
		<category><![CDATA[Manipur]]></category>
		<category><![CDATA[multidimensional educational evaluation]]></category>
		<category><![CDATA[regional educational development gaps]]></category>
		<category><![CDATA[regional inequality]]></category>
		<category><![CDATA[school infrastructure]]></category>
		<category><![CDATA[school infrastructure assessment]]></category>
		<category><![CDATA[spatial disparity]]></category>
		<category><![CDATA[standardized educational indicators]]></category>
		<category><![CDATA[UDISE+]]></category>
		<category><![CDATA[UDISE+ database 2024–25]]></category>
		<category><![CDATA[valley districts]]></category>
		<category><![CDATA[valley-hill educational divide]]></category>
		<guid isPermaLink="false">https://scienmag.com/?p=230870</guid>

					<description><![CDATA[A new composite index study of Manipur's sixteen districts reveals a sharp valley–hill divide in school infrastructure, participation, and institutional efficiency, with infrastructure emerging as the strongest statistical correlate of educational performance.]]></description>
										<content:encoded><![CDATA[<p>A new district-level analysis of school education in Manipur, a state tucked into the far north-east of India, has quantified what many residents have long sensed: a child&#8217;s educational prospects in the state depend heavily on which side of the valley–hill divide they happen to live on. Researchers at Manipur University constructed three composite indices covering educational participation, school infrastructure, and institutional efficiency, then combined them into a Composite Educational Development Index, or CEDI, for all sixteen districts. The results, drawn from the Ministry of Education&#8217;s UDISE+ database for the 2024–25 academic year, show a developmental gap so wide that the lowest-ranked district scores nearly two full standard deviations below the highest.</p>
<p>The study&#8217;s methodology is grounded in the idea that educational development is inherently multidimensional. Rather than relying on enrolment counts or literacy rates alone, the researchers treated infrastructure as an input, participation as a process, and institutional efficiency as an output, echoing the input–process–performance framework common in educational assessment. Every indicator was standardized using Z-score transformation so that variables measured in different units could be compared, and all indicators received equal weights, a choice the authors acknowledge may influence rankings. Districts were then classified into five categories, from Very Low to Very High, using standard deviation thresholds.</p>
<p>The headline finding is a pronounced spatial pattern. Imphal West topped the composite index with a score of 0.902, followed closely by Kakching at 0.888, with Imphal East, Thoubal, and Bishnupur rounding out the high performers. Every one of these districts lies in the central Imphal Valley, a compact alluvial plain that occupies barely a tenth of the state&#8217;s 22,327 square kilometres yet concentrates most of its population, roads, and institutions. At the other extreme, Pherzawl, a remote hill district, recorded a composite score of −1.126, the only district in the Very Low category, with hill districts Ukhrul, Kamjong, Noney, Tengnoupal, and Kangpokpi close behind.</p>
<p>Infrastructure emerged as the most unequal dimension of all. The Infrastructure Index showed a standard deviation of 0.698 and a range of 2.535, the widest spread among the indices examined, suggesting that uneven resource allocation is the single largest driver of educational inequality in the state. The details are striking. Nearly every school in Manipur has drinking water, and most have toilets and playgrounds, but only 35.19 percent have computer facilities, 36.63 percent have internet connectivity, and a mere 12.90 percent have functional smart classrooms. In Kakching, 88.30 percent of schools enjoy internet access, while in Kamjong the figure is just 6.79 percent. Pherzawl&#8217;s schools report library facilities in only 4.62 percent of cases, and electricity coverage there stands at 23.08 percent.</p>
<p>Participation patterns mirror this divide. Imphal West and Kakching posted the highest Participation Index values at 0.887 and 0.968 respectively, while Pherzawl, Kamjong, Kangpokpi, Tengnoupal, Senapati, and Ukhrul all recorded negative scores indicating weaker participation. Transition rates, which track whether students actually progress from one schooling stage to the next, reveal where continuity breaks down. Kamjong managed only a 51.5 percent transition from preparatory to middle level, and Pherzawl just 54.8 percent from middle to secondary, compared with perfect transitions in several valley districts. Children with Special Needs accounted for only 0.63 percent of total enrolment statewide, a figure the authors flag as a signal that inclusive education outreach remains limited across all districts.</p>
<p>Institutional efficiency data tell an equally sobering story. Pherzawl recorded dropout rates of 25.8 percent at the preparatory level and a startling 49.5 percent at the secondary level, meaning roughly half its students leave school before completing secondary education. Kamjong&#8217;s secondary dropout rate reached 20.2 percent. By contrast, Churachandpur, Imphal West, and Thoubal reported zero dropout at preparatory and middle levels. Performance Grading Index scores, a government assessment of institutional quality, ranged from 307.4 in Imphal West down to 191.1 in Noney. Notably, teacher availability is not the problem: pupil–teacher ratios ranged only from 10 to 20 students per teacher across districts, indicating that Manipur&#8217;s inefficiencies lie elsewhere, in retention, facilities, and institutional capacity.</p>
<p>The statistical heart of the paper lies in its correlation and regression analyses, which probe how the three dimensions interact. Participation, infrastructure, and institutional efficiency were strongly intercorrelated, with infrastructure and efficiency showing a coefficient of 0.83. Secondary dropout rates correlated negatively with every index, most strongly with institutional efficiency at −0.76, while middle-to-secondary transition rates and PGI scores correlated positively with all three dimensions. In a multiple regression model explaining about 70.4 percent of the variation in institutional efficiency, infrastructure was the only statistically significant predictor, with a coefficient of 0.515 and a p-value of 0.019. Participation, though positive in direction, did not reach significance. The authors are careful to stress that these are associations, not causal proof, since the cross-sectional design cannot rule out confounding influences such as broader regional development.</p>
<p>That caveat matters, because the study&#8217;s conceptual framing draws on Amartya Sen&#8217;s Capability Approach, which views infrastructure as an enabling condition that expands opportunities rather than a mechanical determinant of outcomes. The authors note that districts with better infrastructure may simply enjoy wider advantages, including greater public investment, stronger administrative capacity, and improved connectivity. They also acknowledge limitations: the analysis uses a single year of administrative data, which may contain reporting inconsistencies, and district-level aggregation risks ecological fallacy, meaning patterns observed across districts may not hold for individual students or schools. Factors such as teacher quality, household poverty, and governance quality were not directly measured.</p>
<p>Even so, the policy implications are pointed. The authors argue that uniform state-wide strategies will not close a gap this structurally embedded, and they call for geographically differentiated interventions. In hill districts such as Pherzawl, Kamjong, and Noney, they recommend prioritizing reliable electricity, internet connectivity, digital learning facilities, libraries, and transport access, with resource allocation formulas that weight remoteness and infrastructural deprivation alongside population size. Teacher deployment policies, including hardship allowances and location-specific recruitment, feature in their recommendations, as does stronger engagement of School Management Committees and parent groups. As India pushes toward the inclusive education goals of SDG-4 and implements the National Education Policy 2020, the study offers a template for diagnosing exactly where, and in what dimension, the system is failing, and a reminder that enrolment drives alone cannot substitute for the classrooms, connections, and institutional muscle that keep children learning.</p>
<p><strong>Subject of Research:</strong> Spatial disparities in educational development across the districts of Manipur, India</p>
<p><strong>Article Title:</strong> Spatial disparities in educational development across districts of Manipur based on participation infrastructure and institutional efficiency</p>
<p><strong>Article References:</strong> Meitei, K. M., &amp; Das, B. (2026). Spatial disparities in educational development across districts of Manipur based on participation infrastructure and institutional efficiency. <em>Discover Global Society, 4</em>(1), Article 262. <a href="https://doi.org/10.1007/s44282-026-00621-1" rel="noopener noreferrer">https://doi.org/10.1007/s44282-026-00621-1</a></p>
<p><strong>Image Credits:</strong> AI Generated</p>
<p><strong>DOI:</strong> <a href="https://doi.org/10.1007/s44282-026-00621-1" rel="noopener noreferrer">10.1007/s44282-026-00621-1</a></p>
<p><strong>Keywords:</strong> educational development, Manipur, spatial disparity, composite index, school infrastructure, institutional efficiency, UDISE+, regional inequality, dropout rates, hill districts, valley districts, education policy</p>
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