DepEd Learners SY 2023-24: Comprehensive Data Analysis & Key Findings

Explore the detailed analysis of DepEd Learners SY 2023-24 with special focus on Indigenous Peoples (IP), Balik-Aral, ALIVE programs, Alternative Delivery Modes (ADM), monograde vs multigrade structures, gender parity, regional distribution, urban-rural analysis, and enrollment inequality metrics.

Executive Summary

This comprehensive analysis examines the Department of Education's Learners Dataset for School Year 2023-2024, encompassing 60,167 schools and 27,081,292 enrolled learners across the Philippines. The dataset provides unprecedented insights into the nation's basic education landscape, including special learner populations, alternative delivery modes, class structures, and geographic distribution patterns.

Key Highlights:

Total Enrollment: 27,081,292 learners (13,854,090 male; 13,227,202 female)
National Gender Parity Index (GPI): 0.9548 (slightly male-dominated)
Indigenous Peoples (IP) Enrollment: 2,360,305 (8.72% of total)
Balik-Aral Learners: 156,153 (0.58% of total)
ALIVE Program Participants: 343,672 (1.27% of total)
Gini Coefficient (Enrollment Inequality): 0.6102 (moderate-high inequality)
Multigrade Schools: 6,098 schools serving 599,857 learners

Critical Policy Insight: The data reveals significant geographic disparities in educational access, with the Cordillera Administrative Region (CAR) showing 69.77% IP concentration while the National Capital Region (NCR) has the highest enrollment density at 1,055 students per school. The Grade 6 to Grade 7 cross-sectional comparison shows a 82.82% progression index, indicating a potential transition challenge that warrants further cohort-based investigation.

1. National Enrollment Overview

The Philippine basic education system in SY 2023-24 served over 27 million learners distributed across four educational levels: Kindergarten, Elementary (Grades 1-6), Junior High School (Grades 7-10), and Senior High School (Grades 11-12).

Table 1.1: National Enrollment by Educational Level

Level	Enrollment	% of Total
Kindergarten	2,047,356	7.56%
Elementary (G1-G6)	13,154,595	48.57%
Junior High School (G7-G10)	7,749,265	28.61%
Senior High School (G11-G12)	4,130,076	15.25%
TOTAL	27,081,292	100.00%

Elementary education continues to represent the largest segment of enrollment at nearly half (48.57%) of the total student population, followed by Junior High School at 28.61%.

Enrollment by Educational Level showing distribution across Kindergarten, Elementary, JHS, and SHS — Figure 1: Enrollment Distribution by Educational Level - SY 2023-24

Grade-level enrollment showing male and female distribution from Kindergarten through Grade 12 — Figure 2: Grade-Level Enrollment by Gender - SY 2023-24

Table 1.2: Enrollment by Grade Level and Gender

Grade	Male	Female	Total	GPI
K	1,062,541	984,815	2,047,356	0.9268
G1	1,125,385	1,035,986	2,161,371	0.9206
G2	1,143,307	1,062,672	2,205,979	0.9295
G3	1,129,763	1,053,614	2,183,377	0.9326
G4	1,068,023	961,425	2,029,448	0.9002
G5	1,183,823	1,126,004	2,309,827	0.9512
G6	1,118,856	1,064,499	2,183,355	0.9514
G7	929,793	878,419	1,808,212	0.9447
G8	978,385	961,648	1,940,033	0.9829
G9	995,479	996,772	1,992,251	1.0013
G10	998,546	1,006,024	2,004,570	1.0075
G11	1,050,898	1,039,168	2,090,066	0.9888
G12	1,013,256	1,026,754	2,040,010	1.0133
TOTAL	13,854,090	13,227,202	27,081,292	0.9548

The national GPI of 0.9548 indicates a slight male advantage in enrollment, which reverses at the secondary level (Grades 9-10 and Grade 12) where female enrollment slightly exceeds male enrollment.

2. Special Learner Programs Analysis

2.1 Indigenous Peoples (IP) Enrollment

Indigenous Peoples represent one of the most significant special learner populations in the Philippine education system. In SY 2023-24, IP learners numbered 2,360,305, representing 8.72% of total enrollment.

Table 2.1: IP Enrollment by Grade Level

Grade	Male	Female	Total	GPI
K	90,382	83,681	174,063	0.9259
G1	98,461	92,322	190,783	0.9377
G2	99,721	93,487	193,208	0.9375
G3	103,008	96,352	199,360	0.9354
G4	100,897	92,372	193,269	0.9155
G5	109,141	106,546	215,687	0.9762
G6	103,700	102,984	206,684	0.9931
ES-NG	2,569	1,490	4,059	0.5800
G7	84,229	85,287	169,516	1.0126
G8	81,028	87,877	168,905	1.0845
G9	78,576	87,247	165,823	1.1104
G10	77,658	86,346	164,004	1.1119
JHS-NG	85	49	134	0.5765
SHS (All Strands)	148,859	165,951	314,810	1.1148
TOTAL	1,178,314	1,181,991	2,360,305	1.0031

A notable pattern emerges in IP enrollment: while elementary grades show male advantage (GPI < 1), secondary levels (JHS and SHS) show female advantage (GPI > 1), with GPI increasing from 1.01 in Grade 7 to 1.11 at SHS level. This pattern is consistent with improved female retention among IP learners at the secondary level, though cohort-tracked data would be required to confirm this interpretation.

Regional IP density showing CAR with highest concentration at 69.77% — Figure 3: Indigenous Peoples Enrollment Density by Region - SY 2023-24

Table 2.2: IP Concentration by Region (Top 10)

Region	Schools	Total Enrollment	IP Enrollment	IP Density
CAR	2,080	432,266	301,593	69.77%
BARMM	2,932	1,061,213	335,194	31.59%
Region II	2,916	899,159	229,324	25.50%
Region XI	2,704	1,384,153	318,382	23.00%
Region IX	2,868	1,048,341	222,909	21.26%
MIMAROPA	2,684	887,334	170,933	19.26%
Region XII	2,541	1,178,506	217,091	18.42%
CARAGA	2,355	767,014	102,792	13.40%
Region X	3,106	1,330,705	174,070	13.08%
Region I	3,393	1,244,604	60,236	4.84%

Policy Implication: The Cordillera Administrative Region (CAR) has the highest IP concentration at 69.77%, requiring specialized curricular content and culturally-responsive teaching approaches. The six regions with IP density above 15% collectively serve over 1.5 million IP learners.

IP enrollment distribution across grade levels — Figure 4: Indigenous Peoples Enrollment by Grade Level - SY 2023-24

2.2 Balik-Aral Program

The Balik-Aral (Return to School) program supports learners who dropped out but are returning to continue their education. In SY 2023-24, 156,153 learners participated in this program across 31,109 schools.

Table 2.3: Balik-Aral Program Statistics

Program	Male	Female	Total	GPI
Balik-Aral	100,642	55,511	156,153	0.5516

The GPI of 0.5516 reveals a significant gender disparity: male returnees outnumber female returnees nearly 2:1. This pattern suggests that male learners are more likely to drop out and subsequently return to school, or that intervention programs are more effective at reaching male dropouts.

2.3 ALIVE (Arabic Language & Islamic Values Education)

The ALIVE program serves Muslim learners by integrating Arabic Language and Islamic Values Education into the regular basic education curriculum. In SY 2023-24, 343,672 learners were enrolled in ALIVE across 4,966 schools.

Table 2.4: ALIVE Program Statistics

Program	Male	Female	Total	GPI
ALIVE	170,682	172,990	343,672	1.0135

The ALIVE program shows near-perfect gender parity (GPI = 1.0135), with a slight female advantage. The program is heavily concentrated in the Bangsamoro Autonomous Region in Muslim Mindanao (BARMM), which accounts for the vast majority of ALIVE enrollment.

Gender Parity Index comparison for Balik-Aral and ALIVE programs — Figure 5: Gender Parity Index by Special Learner Program - SY 2023-24

Summary of special learner types showing IP, Balik-Aral, and ALIVE totals — Figure 6: Special Learner Types Summary - SY 2023-24

Stacked bar chart showing IP, Balik-Aral, and ALIVE distribution by grade level — Figure 7: Learner Type Distribution by Grade Level - SY 2023-24

3. Alternative Delivery Modes (ADM)

Alternative Delivery Modes provide flexible learning options for learners facing geographic, physical, or socio-economic barriers to traditional schooling.

Table 3.1: ADM Program Enrollment

ADM Program	Enrollment	% of Total	Schools
DistEd (Distance Education)	681	0.0025%	242
e-IMPACT	1,244	0.0046%	228
MISOSA	1,568	0.0058%	81
OthrSch (Other School Interventions)	8,403	0.0310%	757
OHSP (Open High School Program)	13,060	0.0482%	586

The Open High School Program (OHSP) has the highest enrollment among ADM programs at 13,060 learners, reflecting the demand for flexible secondary education options.

Regional heatmap showing ADM program prevalence across different regions — Figure 8: ADM Programs Regional Distribution Heatmap - SY 2023-24

Table 3.2: ADM Programs by Region

Region	DistEd	e-IMPACT	MISOSA	OthrSch	OHSP
BARMM	20	114	9	437	645
CAR	29	83	34	628	61
CARAGA	67	36	4	196	79
MIMAROPA	63	186	112	435	1,001
NCR	46	18	4	252	2,393
Region I	42	2	0	159	408
Region II	19	30	40	127	25
Region III	62	84	852	3,000	825
Region IV-A	34	82	351	291	1,356
Region IX	47	133	13	361	572
Region V	32	110	101	993	500
Region VI	29	61	2	274	947
Region VII	69	54	8	265	308
Region VIII	20	37	2	231	816
Region X	10	140	22	344	1,731
Region XI	76	25	8	190	310
Region XII	16	49	6	220	1,083

ADM Specialization Index showing regional reliance on each ADM program — Figure 9: ADM Specialization Index by Region - SY 2023-24

The ADM Specialization Index measures regional reliance on each ADM program relative to the national average. Values greater than 1 indicate higher-than-average utilization:

Region III shows exceptionally high MISOSA specialization (4.96) and Other School Interventions (3.26)
CAR has strong specialization in e-IMPACT (4.18) and Other School Interventions (4.68)
MIMAROPA shows high utilization of e-IMPACT (4.56) and OHSP (2.34)
Region X demonstrates significant OHSP specialization (2.70)

Summary chart of ADM program enrollment numbers — Figure 10: Alternative Delivery Modes Summary - SY 2023-24

4. Class Structure Analysis (Monograde vs Multigrade)

Class structure significantly impacts teaching methodology and learning outcomes. The dataset distinguishes between monograde (single grade level per class) and multigrade (multiple grade levels per class) settings.

Table 4.1: Class Structure Comparison

Metric	Monograde	Multigrade
Number of Schools	48,016	6,098
Total Enrollment	17,311,969	599,857
Mean Enrollment	360.5	98.4
Median Enrollment	188.0	70.0
% Public Schools	79.7%	98.0%

Multigrade schools are overwhelmingly public institutions (98.0%), serving smaller, typically rural communities with an average of only 98.4 students per school compared to 360.5 for monograde schools.

Violin plot comparing enrollment distribution in monograde vs multigrade schools — Figure 11: Monograde vs Multigrade Enrollment Distribution - SY 2023-24

Table 4.2: Enrollment Percentile Distribution by Class Structure

Percentile	Monograde	Multigrade
P10	50	30
P25	100	47
P50	188	70
P75	374	96
P90	771	135
P95	1,260	203
P99	2,986	705

Comparison of efficiency metrics between monograde and multigrade schools — Figure 12: Class Structure Efficiency Comparison - SY 2023-24

Table 4.3: Multigrade Enrollment by Region

Region	Schools	Elementary Total	Multigrade Total	Multigrade %
CAR	2,080	203,198	21,817	10.74%
Region VIII	4,466	601,590	58,509	9.73%
MIMAROPA	2,684	442,273	20,193	4.57%
CARAGA	2,355	382,255	17,132	4.48%
BARMM	2,932	605,414	26,502	4.38%
Region II	2,916	431,997	18,445	4.27%
Region X	3,106	664,445	22,648	3.41%
Region IX	2,868	526,383	17,729	3.37%
Region XII	2,541	578,482	11,596	2.00%
Region I	3,393	580,484	11,472	1.98%
NCR	2,687	1,295,162	1,488	0.11%
PSO	33	11,904	0	0.00%

Regional distribution of multigrade enrollment percentages — Figure 13: Multigrade Enrollment Percentage by Region - SY 2023-24

Key Finding: CAR leads multigrade implementation at 10.74%, reflecting its mountainous terrain and dispersed communities. NCR has virtually no multigrade classes (0.11%), consistent with its urban, high-density school environment.

Waterfall chart showing multigrade enrollment breakdown by grade level — Figure 14: Multigrade Enrollment Waterfall by Grade Level - SY 2023-24

Table 4.4: Multigrade vs Monograde Enrollment by Elementary Grade

Grade	Monograde	Multigrade	Total	Multigrade %
Grade 1	2,114,783	46,588	2,161,371	2.16%
Grade 2	2,156,554	49,425	2,205,979	2.24%
Grade 3	2,133,836	49,541	2,183,377	2.27%
Grade 4	1,986,659	42,789	2,029,448	2.11%
Grade 5	2,261,893	47,934	2,309,827	2.08%
Grade 6	2,144,715	38,640	2,183,355	1.77%

5. Gender Parity Analysis

Gender Parity Index (GPI) measures the ratio of female to male enrollment, with values below 1 indicating male advantage and above 1 indicating female advantage.

Table 5.1: GPI by Region

Region	Male	Female	Total	GPI
BARMM	515,917	545,296	1,061,213	1.0569
Region IX	531,588	516,753	1,048,341	0.9721
PSO	11,899	11,549	23,448	0.9706
Region XII	598,230	580,276	1,178,506	0.9700
Region X	676,882	653,823	1,330,705	0.9659
Region XI	705,197	678,956	1,384,153	0.9628
NCR	1,445,272	1,388,846	2,834,118	0.9610
MIMAROPA	453,834	433,500	887,334	0.9552
CARAGA	392,514	374,500	767,014	0.9541
Region III	1,520,823	1,445,925	2,966,748	0.9508
CAR	221,983	210,283	432,266	0.9473
Region IV-A	2,029,609	1,922,054	3,951,663	0.9470
Region VIII	627,033	592,345	1,219,378	0.9447
Region II	462,447	436,712	899,159	0.9444
Region VII	1,084,084	1,022,377	2,106,461	0.9431
Region V	892,793	840,458	1,733,251	0.9414
Region I	642,732	601,872	1,244,604	0.9364
Region VI	1,041,253	971,677	2,012,930	0.9332

Regional Gender Parity Index visualization — Figure 15: Gender Parity Index by Region - SY 2023-24

BARMM stands out as the only region with female enrollment advantage (GPI = 1.0569), while Region VI shows the highest male advantage (GPI = 0.9332).

5.1 GPI by Senior High School Strand

Gender patterns vary dramatically across SHS academic and technical-vocational strands:

Table 5.2: GPI by SHS Strand

Strand	Male	Female	Total	GPI	Dominance
ABM	140,402	290,060	430,462	2.0659	Female-dominated
HUMSS	512,325	572,612	1,084,937	1.1177	Female-dominated
STEM	338,289	417,421	755,710	1.2339	Female-dominated
GAS	302,494	310,013	612,507	1.0249	Balanced
TVL	753,403	464,149	1,217,552	0.6161	Male-dominated
SPORTS	5,486	2,069	7,555	0.3771	Male-dominated
ARTS	6,724	9,306	16,030	1.3840	Female-dominated
MARITIME	5,031	292	5,323	0.0580	Male-dominated

Gender Parity Index across SHS strands showing extreme variations — Figure 16: Gender Parity Index by SHS Strand - SY 2023-24

Critical Finding: Extreme gender segregation exists in specific SHS strands. The Maritime strand shows GPI of 0.0580 (~5.5% female), while ABM shows GPI of 2.0659 (more than double the female enrollment). This occupational gender tracking has long-term implications for workforce composition.

6. Regional Distribution

Regional enrollment distribution reveals significant geographic concentration of educational resources.

Table 6.1: Regional Enrollment Analysis

Region	Schools	Enrollment	% of Total	Avg Size	GPI
Region IV-A	6,007	3,951,663	14.59%	658	0.9470
Region III	5,194	2,966,748	10.95%	571	0.9508
NCR	2,687	2,834,118	10.47%	1,055	0.9610
Region VII	4,697	2,106,461	7.78%	448	0.9431
Region VI	5,037	2,012,930	7.43%	400	0.9332
Region V	4,467	1,733,251	6.40%	388	0.9414
Region XI	2,704	1,384,153	5.11%	512	0.9628
Region X	3,106	1,330,705	4.91%	428	0.9659
Region I	3,393	1,244,604	4.60%	367	0.9364
Region VIII	4,466	1,219,378	4.50%	273	0.9447
Region XII	2,541	1,178,506	4.35%	464	0.9700
BARMM	2,932	1,061,213	3.92%	362	1.0569
Region IX	2,868	1,048,341	3.87%	366	0.9721
Region II	2,916	899,159	3.32%	308	0.9444
MIMAROPA	2,684	887,334	3.28%	331	0.9552
CARAGA	2,355	767,014	2.83%	326	0.9541
CAR	2,080	432,266	1.60%	208	0.9473
PSO	33	23,448	0.09%	711	0.9706

Regional enrollment distribution across the Philippines — Figure 17: Regional Enrollment Distribution - SY 2023-24

The top three regions (Region IV-A, Region III, and NCR) account for 36.01% of total enrollment, demonstrating significant concentration in the Greater Manila Area and surrounding provinces.

NCR shows the highest average school size at 1,055 students per school, while CAR has the smallest average at 208 students per school, reflecting urban density versus geographic dispersion.

7. Division & Provincial Analysis

7.1 Top Divisions by Enrollment

Table 7.1: Top 30 Divisions by Enrollment

Rank	Division	Schools	Enrollment	IP %
1	Cebu	1,346	626,725	0.82%
2	Quezon City	620	617,326	0.52%
3	Bulacan	862	611,099	0.46%
4	Rizal	699	580,511	0.99%
5	Cavite	615	502,729	1.28%
6	Quezon	1,135	491,852	0.91%
7	Camarines Sur	1,281	490,699	2.48%
8	Iloilo	1,383	478,206	6.64%
9	Davao City	656	468,199	11.60%
10	Batangas	913	415,068	0.20%
11	Pampanga	716	396,548	1.29%
12	Leyte	1,363	382,249	0.25%
13	Manila	324	380,503	0.70%
14	Nueva Ecija	893	372,693	1.31%
15	Caloocan City	319	350,653	0.38%
16	Pangasinan I, Lingayen	768	342,581	1.08%
17	Negros Occidental	688	327,089	1.81%
18	Bohol	1,273	321,644	1.45%
19	Bukidnon	754	313,620	27.87%
20	North Cotabato	857	310,915	12.40%
21	Isabela	993	296,005	23.57%
22	Tarlac	632	280,779	2.57%
23	Pangasinan II, Binalonan	661	272,178	2.78%
24	Zamboanga City	293	270,482	12.44%
25	Palawan	882	267,668	40.02%
26	Cagayan	914	263,511	22.02%
27	Laguna	488	259,200	0.55%
28	Taguig City and Pateros	225	252,834	0.60%
29	Cebu City	269	246,748	0.79%
30	Masbate	754	238,390	1.04%

Top divisions by enrollment with IP concentration indicators — Figure 18: Top 30 Divisions by Enrollment - SY 2023-24

Notable divisions with high IP concentration include Palawan (40.02%), Bukidnon (27.87%), Isabela (23.57%), and Cagayan (22.02%).

7.2 Provincial Analysis

Table 7.2: Top 20 Provinces by Enrollment

Rank	Province	Schools	Enrollment	IP %
1	CEBU	2,252	1,359,724	0.69%
2	CAVITE	1,234	1,042,183	0.88%
3	NCR SECOND DISTRICT	997	1,021,844	0.57%
4	BULACAN	1,200	894,496	0.48%
5	NEGROS OCCIDENTAL	1,602	860,432	2.26%
6	RIZAL	923	811,859	0.96%
7	LAGUNA	1,213	809,264	0.52%
8	PANGASINAN	1,743	784,393	1.60%
9	NCR FOURTH DISTRICT	791	769,330	0.88%
10	BATANGAS	1,350	701,829	0.33%
11	NCR THIRD DISTRICT	575	662,441	0.39%
12	PAMPANGA	1,024	661,467	1.16%
13	DAVAO DEL SUR	1,031	642,137	12.49%
14	ILOILO	1,615	614,303	5.31%
15	CAMARINES SUR	1,450	596,373	2.47%
16	QUEZON	1,287	586,528	0.85%
17	LEYTE	1,674	543,284	0.29%
18	ZAMBOANGA DEL SUR	1,293	542,756	15.63%
19	NUEVA ECIJA	1,185	542,063	1.10%
20	MISAMIS ORIENTAL	855	456,816	5.12%

Top provinces by enrollment distribution — Figure 19: Top 20 Provinces by Enrollment - SY 2023-24

8. Municipality-Level Analysis

Table 8.1: Top 30 Municipalities/Cities by Enrollment

Rank	Municipality	Schools	Enrollment	Avg Size
1	QUEZON CITY	620	617,326	996
2	DAVAO CITY	656	468,199	714
3	KALOOKAN CITY	319	350,653	1,099
4	ZAMBOANGA CITY	293	270,482	923
5	CEBU CITY (Capital)	269	246,748	917
6	CITY OF ANTIPOLO	224	231,348	1,033
7	CAGAYAN DE ORO CITY (Capital)	223	201,937	906
8	TAGUIG CITY	177	198,275	1,120
9	CITY OF PASIG	155	193,672	1,249
10	GENERAL SANTOS CITY (DADIANGAS)	166	181,238	1,092
11	CITY OF SAN JOSE DEL MONTE	165	174,416	1,057
12	CITY OF DASMARIÑAS	164	174,050	1,061
13	CITY OF VALENZUELA	137	162,633	1,187
14	BACOLOD CITY (Capital)	197	160,796	816
15	CITY OF CALAMBA	176	136,916	778

Top municipalities by enrollment with average school size — Figure 20: Top 30 Municipalities/Cities by Enrollment - SY 2023-24

Urban concentration is evident: Quezon City alone serves 617,326 learners (2.28% of national enrollment), with City of Pasig having the highest average school size at 1,249 students.

9. School Sector Analysis

Table 9.1: School Sector Distribution

Sector	Schools	% Schools	Enrollment	% Enrollment	GPI
Public	47,818	79.48%	23,225,427	85.76%	0.9434
Private	12,113	20.13%	3,731,936	13.78%	1.0194
SUC/LUC	203	0.34%	100,481	0.37%	1.3398
PSO	33	0.05%	23,448	0.09%	0.9706

Public schools dominate the landscape, accounting for 79.48% of schools and 85.76% of enrollment. Private schools show slight female advantage (GPI = 1.0194), while State Universities and Colleges (SUC/LUC) demonstrate strong female advantage (GPI = 1.3398).

Distribution of schools and enrollment by sector — Figure 21: School Sector Analysis - SY 2023-24

9.1 School Management Type Distribution

Table 9.2: Schools by Management Type

Management Type	Schools	% Schools	Enrollment	% Enrollment
DepEd	47,802	79.45%	23,218,258	85.74%
Non-Sectarian	8,558	14.22%	2,447,099	9.04%
Sectarian	3,549	5.90%	1,283,547	4.74%
SUC	181	0.30%	87,482	0.32%
PSO	33	0.05%	23,448	0.09%
LUC	22	0.04%	12,999	0.05%
DOST	14	0.02%	6,946	0.03%
International School	6	0.01%	1,290	0.00%
Other GA	2	0.00%	223	0.00%

School management type distribution pie chart — Figure 22: School Management Type Distribution - SY 2023-24

10. School Characteristics

10.1 Annex Status Analysis

Table 10.1: School Annex Status Distribution

Annex Status	Schools	% Schools	Enrollment	Avg Size
Standalone School	57,296	95.23%	24,910,424	435
Mother School	2,015	3.35%	1,833,409	910
Annex/Extension School	843	1.40%	335,548	398
Mobile School/Center	13	0.02%	1,911	147

Distribution of schools by annex status — Figure 23: School Annex Status Distribution - SY 2023-24

Mother schools average 910 students, indicating they serve as central hubs with satellite annex/extension schools.

10.2 Curricular Offering Combinations

Table 10.2: Schools by Curricular Offering

Offering	Schools	% Schools	Enrollment	% Enrollment
ES	40,715	67.67%	13,524,704	49.94%
JHS+SHS	7,774	12.92%	8,056,294	29.75%
ES+JHS+SHS	3,377	5.61%	2,678,687	9.89%
ES+JHS	3,217	5.35%	1,100,594	4.06%
Kinder Only	1,887	3.14%	39,677	0.15%
JHS	1,777	2.95%	1,034,351	3.82%
SHS	1,400	2.33%	642,022	2.37%
ES+SHS	20	0.03%	4,963	0.02%

Distribution of schools by curricular offering combinations — Figure 24: Curricular Offering Combinations - SY 2023-24

Elementary-only schools represent the majority (67.67%), while integrated schools (ES+JHS+SHS) account for only 5.61% but serve 9.89% of enrollment.

10.3 School Size Distribution

Table 10.3: Schools by Size Category

Size Category	Schools	% of Schools	Total Enrollment
Very Small (1-100)	14,045	23.34%	778,673
Small (101-300)	23,481	39.03%	4,315,179
Medium (301-500)	8,980	14.93%	3,472,392
Large (501-1000)	7,388	12.28%	5,117,264
Very Large (>1000)	6,155	10.23%	13,394,781

Distribution of schools by enrollment size category — Figure 25: School Size Distribution - SY 2023-24

While "Very Large" schools represent only 10.23% of total schools, they serve 49.45% of all learners (13.4 million), highlighting the concentration of students in large institutions.

11. Enrollment Inequality Metrics

11.1 Lorenz Curve Analysis

The Lorenz curve measures the inequality in enrollment distribution across schools.

National Inequality Metrics:

Gini Coefficient: 0.6102
Theil Index: 0.7374
Palma Ratio: 6.40

Lorenz curve showing enrollment inequality across schools — Figure 26: Lorenz Curve - Enrollment Inequality - SY 2023-24

Table 11.1: Lorenz Curve Coordinates (Deciles)

Cumulative % Schools	Cumulative % Enrollment
0%	0.00%
10%	0.60%
20%	2.14%
30%	4.49%
40%	7.65%
50%	11.77%
60%	17.16%
70%	24.40%
80%	34.58%
90%	51.05%
100%	100.00%

The bottom 50% of schools serve only 11.77% of students, while the top 10% serve 48.95% of enrollment—a stark illustration of concentration in large schools.

Table 11.2: Inequality Metrics by Sector

Sector	Schools	Gini	Theil	Palma
Public	47,818	0.5939	0.7020	5.54
Private	12,113	0.6681	0.8717	11.51
SUC/LUC	203	0.4802	0.4055	3.24

Private schools show higher inequality (Gini 0.6681, Palma 11.51) compared to public schools, reflecting a wider range of school sizes in the private sector.

11.2 Regional Concentration Metrics

Table 11.3: Enrollment Concentration by Region

Region	Schools	Gini	HHI	Theil
NCR	2,687	0.6825	0.0013	0.8546
Region IV-A	6,007	0.6345	0.0006	0.7545
Region VI	5,037	0.6049	0.0008	0.7192
CAR	2,080	0.5890	0.0021	0.7120
Region X	3,106	0.5921	0.0012	0.6905
Region IX	2,868	0.5828	0.0014	0.6949
Region VIII	4,466	0.5875	0.0009	0.6890
Region XI	2,704	0.5891	0.0013	0.6755
Region VII	4,697	0.5826	0.0007	0.6445
Region I	3,393	0.5550	0.0011	0.6327
Region XII	2,541	0.5565	0.0014	0.6211
CARAGA	2,355	0.5554	0.0015	0.6028
Region III	5,194	0.5663	0.0006	0.5915
Region II	2,916	0.5491	0.0011	0.5828
Region V	4,467	0.5366	0.0008	0.5733
MIMAROPA	2,684	0.5241	0.0012	0.5521
BARMM	2,932	0.4768	0.0008	0.4292
PSO	33	0.4837	0.0751	0.4067

Regional concentration metrics showing Gini, HHI, and Theil Index — Figure 27: Enrollment Concentration Metrics by Region - SY 2023-24

NCR has the highest Gini (0.6825), reflecting the widest range of school sizes in the capital region. BARMM shows the most equitable distribution (Gini 0.4768).

12. Effect Size Analysis

Effect size metrics (Cohen's d) quantify the practical significance of differences between groups.

Table 12.1: Cohen's d Effect Sizes

Comparison	N (Group 1)	N (Group 2)	Cohen's d	Interpretation
Public vs Private	47,818	12,113	0.229	Small
Monograde vs Multigrade	48,016	6,098	0.469	Small
With IP vs Without IP	38,086	22,081	0.521	Medium
With Balik-Aral vs Without	31,109	29,058	0.714	Medium

Cohen's d effect size analysis for various group comparisons — Figure 28: Effect Size Analysis (Cohen's d) - SY 2023-24

Schools with Balik-Aral programs show medium effect size (d = 0.714), indicating meaningful differences in enrollment patterns compared to schools without such programs.

13. Data Quality Assessment

13.1 Data Quality Flags

Table 13.1: Data Quality Summary

Flag	Count	% of Total
Zero Enrollment Schools	115	0.19%
IP Exceeds Total (Error)	0	0.00%
Statistical Outliers (>3σ)	1,289	2.14%
Very Large Schools (>5000)	309	0.51%

Data quality flags and anomaly detection results — Figure 29: Data Quality Flags - Anomaly Detection - SY 2023-24

The 115 zero-enrollment schools represent newly opened or closed institutions requiring follow-up verification.

13.2 Monograde/Multigrade Discrepancy

Table 13.2: Mono+Multi vs K-6 Discrepancy Analysis

Metric	Value
Total Elementary Schools	47,329
Mean Discrepancy	2.82
Median Discrepancy	1.00
Std Dev	11.23
Min	1
Max	456

Table 13.3: Discrepancy Severity Distribution

Category	Schools	Percentage
Perfect Match (≤10)	45,483	96.10%
Minor (11-50)	1,372	2.90%
Moderate (51-200)	455	0.96%
Major (>200)	19	0.04%

Distribution of monograde/multigrade enrollment discrepancies — Figure 30: Monograde/Multigrade Discrepancy Analysis - SY 2023-24

96.10% of schools show perfect or near-perfect match between mono+multi counts and K-6 enrollment, indicating strong data integrity.

13.3 Geocoding Readiness

Table 13.4: Address Field Completeness

Field	Complete	Completeness %
region	60,167	100.00%
division	60,167	100.00%
province	60,167	100.00%
municipality	60,167	100.00%
barangay	60,097	99.88%
street_address	54,964	91.35%

Table 13.5: Geocoding Readiness Levels

Level	Schools	Percentage
Full Address	54,901	91.25%
Partial (Muni+Brgy)	5,196	8.64%
Minimal (Muni only)	70	0.12%

Geocoding readiness assessment showing address completeness — Figure 31: Geocoding Readiness - Address Field Analysis - SY 2023-24

91.25% of schools have full address data suitable for precise geocoding.

13.4 Legislative District Field Validation

Table 13.6: Legislative District Field Issues

Metric	Value
Unique Values	9
Expected	~250 (Congressional Districts)
Missing Values	0

Table 13.7: Legislative District Value Distribution

Value	Schools	Percentage
2nd District	18,108	30.10%
1st District	17,514	29.11%
Lone District	9,412	15.64%
3rd District	7,089	11.78%
4th District	4,196	6.97%
5th District	2,269	3.77%
6th District	1,239	2.06%
7th District	307	0.51%
PSO	33	0.05%

Legislative district field validation showing data quality issues — Figure 32: Legislative District Field Validation - SY 2023-24

⚠️ Data Quality Warning: The legislative_district field contains ordinal values ("1st District," "2nd District") rather than unique district identifiers. With only 9 unique values instead of the expected ~250 congressional districts, this field is NOT suitable for district-level analysis.

14. Grade Transition Analysis

Table 14.1: Grade 6 to Grade 7 Cross-Sectional Comparison

Metric	Value
Grade 6 Enrollment	2,183,355
Grade 7 Enrollment	1,808,212
Enrollment Difference (G6-G7)	375,143
Cross-Sectional Progression Index	82.82%
Cross-Sectional Non-Progression Estimate	17.18%

Grade 6 to Grade 7 transition analysis showing retention and apparent dropout — Figure 33: Grade 6 to Grade 7 Transition Analysis - SY 2023-24

Important Methodological Note: The 17.18% non-progression estimate is derived from comparing Grade 6 and Grade 7 enrollments within the same school year. This cross-sectional approach does not track the same cohort of learners across time. The 375,143 learner difference may reflect a combination of factors including actual dropout, transfers to private schools or Alternative Learning System (ALS), out-migration, differences in birth cohort sizes between years, and data collection timing variations. A true retention rate analysis requires longitudinal cohort tracking across consecutive school years. Nevertheless, this cross-sectional indicator warrants further investigation to understand the underlying causes of the enrollment differential at this critical transition point.

15. Urban vs Rural Analysis (PSGC-Enriched)

Using Philippine Standard Geographic Code (PSGC) integration, schools were classified by urban/rural status.

Table 15.1: Urban vs Rural School Distribution

Category	Schools	Share
Rural	33,330	55.4%
Urban	16,759	27.9%
Unknown	10,078	16.8%

Table 15.2: Enrollment by Urban/Rural Classification

Area	Total Enrollment	IP Enrollment
Urban	12,589,680	718,360
Rural	9,753,593	1,333,286

Table 15.3: IP Concentration by Area

Area	IP Learners (%)
Urban	5.7%
Rural	13.7%

Urban vs rural school distribution and enrollment comparison — Figure 34: Urban vs Rural Distribution - SY 2023-24

Rural areas have more than double the IP concentration (13.7%) compared to urban areas (5.7%), highlighting the geographic distribution of indigenous communities.

Table 15.4: Urban Schools Percentage by Region

Region	Urban Schools	Rural Schools	Urban %
Region VIII	238	3,372	6.6%
Region II	344	2,068	14.3%
CAR	318	1,671	16.0%
BARMM	434	2,177	16.6%
Region V	680	3,181	17.6%
Region IX	514	2,046	20.1%
Region I	726	2,365	23.5%
MIMAROPA	438	1,425	23.5%
CARAGA	430	1,298	24.9%
Region VI	1,328	2,846	31.8%
Region VII	1,338	2,619	33.8%
Region X	951	1,679	36.2%
Region XII	1,006	1,300	43.6%
Region XI	1,018	1,067	48.8%
Region III	2,258	2,076	52.1%
Region IV-A	2,980	2,140	58.2%
NCR	1,758	0	100.0%

Region VIII has the lowest urban school percentage (6.6%), while NCR is entirely urban (100.0%).

16. LGU Income Class Analysis

Table 16.1: Schools and Enrollment by LGU Income Class

Income Class	Schools	Enrollment	Avg School Size	IP %
1st	38,713	20,030,540	517	8.1%
2nd	9,772	3,464,332	355	9.2%
3rd	7,012	2,298,657	328	11.8%
4th	3,942	1,098,580	279	10.1%
5th	448	92,546	207	18.9%

School and enrollment distribution by LGU income class — Figure 35: LGU Income Class Analysis - SY 2023-24

A generally inverse relationship exists between income class and IP concentration: 5th class (lowest income) LGUs have 18.9% IP enrollment compared to 8.1% in 1st class LGUs. Average school size also correlates with income class: 517 students in 1st class vs 207 in 5th class.

17. School Density Analysis

Table 17.1: School Density by Income Class

Income Class	Schools	Population (sum muni)	Schools per 100K
1st	38,713	109,064,089	35.50
2nd	9,772	12,053,449	81.07
3rd	7,012	8,626,838	81.28
4th	3,942	4,101,699	96.11
5th	448	412,831	108.52

Table 17.2: School Density by Urban/Rural

Area	Schools	Population (sum muni)	Schools per 100K
Urban	16,759	91,062,955	18.40
Rural	33,330	83,806,893	39.77

School density analysis by income class and urban/rural classification — Figure 36: School Density per 100K Population - SY 2023-24

Lower-income LGUs have significantly higher school density (108.52 schools per 100K in 5th class vs 35.50 in 1st class), reflecting the proliferation of smaller schools serving dispersed populations. Rural areas (39.77 per 100K) have more than double the school density of urban areas (18.40), though urban schools serve more students per institution.

18. Urban vs Rural by Education Level

Table 18.1: Enrollment by Level and Urban/Rural Classification

Level	Urban Enrollment	Rural Enrollment
Kindergarten	889,643	798,036
Elementary	5,660,958	5,178,948
Junior High	3,828,011	2,588,735
Senior High	2,211,068	1,187,874

Urban vs rural enrollment comparison by education level — Figure 37: Urban vs Rural Enrollment by Education Level - SY 2023-24

The urban-rural enrollment gap widens at higher levels: Elementary shows relatively balanced distribution (52.2% urban), but Senior High shows 65.1% urban concentration, suggesting limited SHS access in rural areas.

19. Learner Type Co-occurrence Patterns

Table 19.1: Schools with Learner Type Combinations

Combination	Schools	% of Total
IP Only	13,944	23.18%
Balik-Aral Only	7,470	12.42%
ALIVE Only	285	0.47%
IP + Balik-Aral	19,767	32.85%
IP + ALIVE	809	1.34%
Balik-Aral + ALIVE	306	0.51%
All Three (IP+BA+ALIVE)	3,566	5.93%
None	14,020	23.30%

Venn diagram showing learner type co-occurrence patterns — Figure 38: Learner Type Co-occurrence Patterns - SY 2023-24

Table 19.2: IP × Balik-Aral Cross-tabulation

	No Balik-Aral	Has Balik-Aral
No IP	14,305	7,776
Has IP	14,753	23,333

The most common combination is IP + Balik-Aral (32.85%), suggesting significant overlap between schools serving indigenous communities and those offering returnee programs.

20. Shannon Entropy / Learner Type Diversity

Shannon Entropy measures the diversity of learner types within each region, with higher values indicating more balanced distribution across IP, Balik-Aral, and ALIVE programs.

Table 20.1: Regional Learner Type Diversity

Region	IP	Balik-Aral	ALIVE	Entropy	Normalized
BARMM	335,194	15,605	164,216	1.0567	0.7622
Region IX	222,909	8,607	47,544	0.7360	0.5309
Region XII	217,091	8,224	56,635	0.7002	0.5051
CAR	301,593	1,587	392	0.6388	0.4608
Region XI	318,382	8,568	9,308	0.6138	0.4428
Region II	229,324	3,034	273	0.5921	0.4271
MIMAROPA	170,933	5,946	5,481	0.5650	0.4076
Region X	174,070	9,271	39,055	0.5565	0.4015
CARAGA	102,792	3,860	1,274	0.4369	0.3152
PSO	134	54	2,656	0.4038	0.2913
Region I	60,236	3,354	718	0.2171	0.1566
Region VI	77,884	8,384	786	0.1940	0.1400
Region III	47,517	14,509	1,882	0.1182	0.0853
Region V	28,445	8,214	531	0.1165	0.0840
Region VII	21,653	11,411	3,457	0.1030	0.0743
NCR	17,865	16,276	4,684	0.0858	0.0619
Region IV-A	28,459	22,504	4,308	0.0863	0.0622
Region VIII	5,824	6,745	472	0.0680	0.0490

Shannon entropy showing learner type diversity by region — Figure 39: Regional Learner Type Diversity (Shannon Entropy) - SY 2023-24

BARMM shows the highest normalized entropy (0.7622), indicating the most diverse mix of IP, Balik-Aral, and ALIVE learners. NCR has the lowest diversity (0.0619), with enrollment concentrated primarily in non-special learner categories.

21. SHS Special Learner Programs

21.1 SHS Indigenous Peoples by Strand

SHS-level IP enrollment totals 314,810 learners across all strands, representing approximately 7.6% of total SHS enrollment. TVL and HUMSS strands have the highest IP concentrations.

Table 21.1: SHS IP Enrollment by Strand

Strand	Male	Female	Total	GPI
TVL	60,663	45,195	105,858	0.7450
HUMSS	38,832	49,178	88,010	1.2664
GAS	28,689	35,532	64,221	1.2385
STEM	14,464	23,509	37,973	1.6253
ABM	5,453	12,081	17,534	2.2155
ARTS	251	282	533	1.1235
SPORTS	346	149	495	0.4306
MARITIME	161	25	186	0.1553
TOTAL	148,859	165,951	314,810	1.1148

SHS Indigenous Peoples enrollment by academic strand — Figure 40: SHS Indigenous Peoples by Strand - SY 2023-24

Key Finding: Unlike overall SHS enrollment patterns, IP learners at the SHS level show female advantage (GPI 1.11). The STEM strand shows particularly strong female IP participation (GPI 1.63), while TVL shows male dominance (GPI 0.75). Maritime and Sports strands show extreme male skewing consistent with their overall enrollment patterns.

21.2 SHS Balik-Aral by Strand

SHS-level Balik-Aral enrollment totals 24,650 learners, representing approximately 0.6% of total SHS enrollment. TVL dominates Balik-Aral enrollment, consistent with the strand's focus on technical-vocational skills that may appeal to returning learners seeking employable skills.

Table 21.2: SHS Balik-Aral Enrollment by Strand

Strand	Male	Female	Total	GPI
TVL	6,941	4,201	11,142	0.6052
HUMSS	2,979	2,846	5,825	0.9554
GAS	2,271	2,471	4,742	1.0881
ABM	811	1,024	1,835	1.2626
STEM	503	394	897	0.7833
ARTS	62	69	131	1.1129
SPORTS	46	12	58	0.2609
MARITIME	19	1	20	0.0526
TOTAL	13,632	11,018	24,650	0.8082

SHS Balik-Aral enrollment by academic strand — Figure 41: SHS Balik-Aral by Strand - SY 2023-24

Key Finding: SHS Balik-Aral shows male dominance (GPI 0.81), consistent with the overall Balik-Aral pattern at lower levels. The concentration in TVL (45.2% of SHS Balik-Aral) suggests returning learners prioritize technical-vocational pathways that offer direct employment opportunities.

22. Key Findings & Policy Recommendations

22.1 Critical Findings Summary

Enrollment Concentration: 27.1 million learners across 60,167 schools, with significant concentration in Metro Manila and adjacent regions (36% of enrollment in top 3 regions)
Indigenous Peoples: 2.36 million IP learners (8.72%) with dramatic regional variation—CAR at 69.77% vs NCR at 0.63%
Gender Patterns: National GPI of 0.9548 shows slight male advantage, but extreme variations exist in specific SHS strands (Maritime: 0.058, ABM: 2.066)
Grade Transition Challenge: Cross-sectional data shows 17.18% enrollment differential between Grade 6 and Grade 7 (375,143 learners), representing a potential transition challenge that warrants cohort-based verification
Rural Education Challenge: Rural areas serve 55.4% of schools but only 43.7% of enrollment, with 13.7% IP concentration vs 5.7% urban
Class Structure Disparity: Multigrade schools (98% public) average only 98 students vs 361 for monograde, indicating resource constraints in rural areas
Enrollment Inequality: Gini coefficient of 0.6102 indicates moderate-high inequality; top 10% of schools serve 49% of students
Urban-Rural SHS Gap: Senior High shows 65.1% urban concentration, suggesting limited rural access to secondary education completion
Income-IP Correlation: Lowest-income LGUs (5th class) show 18.9% IP concentration vs 8.1% in highest-income (1st class)
Data Quality: 91.25% geocoding readiness; legislative district field requires correction (ordinal vs identifier issue)

22.2 Policy Recommendations

Immediate Priorities:

Investigate the Grade 6 to Grade 7 enrollment differential through cohort-tracking studies to determine the extent of actual dropout versus other factors (transfers, migration, data timing)
Expand SHS access in rural areas, particularly in regions with <40% urban school concentration
Address extreme gender segregation in specific SHS strands (particularly Maritime, Sports, and ABM) through career guidance and awareness programs

Medium-Term Actions:

Develop culturally-responsive curricula for high-IP regions (CAR, BARMM, Region II, XI, IX)
Increase multigrade teacher training and resources, especially in CAR (10.74% multigrade) and Region VIII (9.73%)
Leverage SHS IP enrollment data (314,810 learners) to design targeted support programs for indigenous learners in senior high school

Long-Term Strategic Considerations:

Address enrollment inequality through equitable resource allocation formulas
Develop integrated school models (ES+JHS+SHS) in underserved areas to improve transition rates
Implement PSGC-based planning tools for evidence-based school infrastructure development

Appendix: Chart Index

Figure	Description	Filename
1	Learner Type Distribution by Grade	chart_01_learner_type_distribution.png
2	Class Structure Violin Plot	chart_02_class_structure_violin.png
3	ADM Regional Heatmap	chart_03_adm_regional_heatmap.png
4	IP Density Report	chart_04_ip_density_report.png
5	Class Structure Efficiency	chart_05_class_structure_efficiency.png
6	GPI by Program	chart_06_gpi_by_program.png
7	Concentration Metrics	chart_07_concentration_metrics.png
8	ADM Specialization Index	chart_08_adm_specialization_index.png
9	Anomaly Detection	chart_09_anomaly_detection.png
10	Enrollment by Level	chart_10_enrollment_by_level.png
11	Enrollment by Grade Gender	chart_11_enrollment_by_grade_gender.png
12	Regional Enrollment	chart_12_regional_enrollment.png
13	Sector Analysis	chart_13_sector_analysis.png
14	Learner Type Summary	chart_14_learner_type_summary.png
15	ADM Summary	chart_15_adm_summary.png
16	School Size Distribution	chart_16_school_size_distribution.png
17	Regional GPI	chart_17_regional_gpi.png
18	IP by Grade	chart_18_ip_by_grade.png
19	Division Analysis	chart_19_division_analysis.png
20	Multigrade by Region	chart_20_multigrade_by_region.png
21	School Management Distribution	chart_21_school_management_distribution.png
22	Annex Status Analysis	chart_22_annex_status_analysis.png
23	SHS IP by Strand	chart_23_shs_ip_by_strand.png
24	SHS Balik-Aral by Strand	chart_24_shs_balikaral_by_strand.png
25	G6-G7 Transition Analysis	chart_25_g6_g7_transition_analysis.png
26	Province Analysis	chart_26_province_analysis.png
27	Municipality Analysis	chart_27_municipality_analysis.png
28	Learner Type Co-occurrence	chart_28_learner_type_cooccurrence.png
29	Lorenz Curve	chart_29_lorenz_curve.png
30	Effect Sizes	chart_30_effect_sizes.png
31	Mono/Multi Discrepancy	chart_31_mono_multi_discrepancy.png
32	Barangay Analysis	chart_32_barangay_analysis.png
33	Legislative District Validation	chart_33_legislative_district_validation.png
34	Curricular Offering Combinations	chart_34_curricular_offering_combinations.png
35	Multigrade Waterfall	chart_35_multigrade_waterfall.png
36	Regional Entropy	chart_36_regional_entropy.png
37	GPI by Strand	chart_37_gpi_by_strand.png
38	Urban vs Rural Distribution	chart_38_urban_rural_distribution.png
39	Income Class Analysis	chart_39_income_class_analysis.png
40	Urban vs Rural by Level	chart_40_urban_rural_by_level.png
41	School Density	chart_41_school_density.png

Data Sources & Methodology Notes

Primary Dataset

Source: Department of Education - Learner Information System (LIS)
School Year: 2023-24 (Beginning of School Year data)
Coverage: 60,167 schools, 27,081,292 learners
Variables: 316 columns including enrollment, special learner types, ADM programs, class structure

PSGC Integration

Source: Philippine Statistics Authority - PSGC Publication Datafile (Q3 2025)
Purpose: Urban/Rural classification, LGU Income Class, Municipality Population
Match Rate: 99.7% municipality match, 83.4% urban/rural classification

Technical Notes

Gender Parity Index (GPI): Calculated as Female/Male enrollment ratio
Gini Coefficient: Calculated using standard formula; 0 = perfect equality, 1 = maximum inequality
Theil Index: Entropy-based measure of inequality
Palma Ratio: Top 10% share / Bottom 40% share of enrollment
Shannon Entropy: Normalized diversity measure across learner types
Cohen's d: Standardized effect size for comparing group means

Data Limitations

Legislative district field contains ordinal values, not unique identifiers
16.8% of schools lack urban/rural classification due to PSGC matching limitations
Grade transition analysis is cross-sectional, not cohort-tracked