# Action Research and Thesis Topics on Classroom Shortage 20 action research and thesis topics on classroom shortage for teachers and school heads, each with its question, design, test and the variables that answer it. This is the Markdown copy of [Action Research and Thesis Topics on Classroom Shortage](https://nash.com.ph/blog/action-research-topics-classroom-shortage), an article on the NASH Reviewer Blog by Mark Anthony Llego, published Oct 28, 2026, updated Nov 4, 2026. Topics: Action research, School resources. NASH Reviewer is an independent practice platform not affiliated with DepEd, BHROD, or NEAP. > **Note** > > **What these data can and cannot do.** The cleaned EDCOM II classroom data are school records: DepEd's 2023 classroom shortage estimates, 2023 facilities reports, SY 2024-25 enrollment and shifts, 2022 coordinates and the School Building Program (SBP) 2026 list, one row per public school. In DepEd's Action Research Toolkit, records like these are documents that help you *assess the situation*, the first stage of action research. They can show how large a problem is, where it is and what goes with it. They cannot show whether a teaching strategy or a school plan works: for that you collect your own data before and after you try it. Each topic below says which kind of study it is. NASH Reviewer is not affiliated with DepEd or EDCOM II. Teachers and school heads in schools short of classrooms know the problem first-hand: sections of more than 50 learners, a second shift that ends at dusk, a class held in a converted stockroom. By DepEd's 2023 estimates, 25,823 of 47,659 public schools had a classroom shortage, 165,443 classrooms in all, and 2,041 schools held classes in two or more shifts in SY 2024-25. These are also good research problems: urgent, large, close to the classroom, and documented school by school in public data. This post lists 20 research topics on the classroom shortage in three groups: action research you can carry out in your own classroom or school, studies that assess the situation in a school or division, and topics for a master's thesis or doctoral dissertation. Each topic gives a research question, the variables in the data that answer it (with their exact column names), a design and statistical test from DepEd's Action Research Toolkit, and a starting figure from the national data. Two worked examples then run the tests on the national data, step by step, so you can repeat them for your own division. It is written for teachers and school heads planning action research, Learning Action Cell (LAC) groups, division research staff and graduate students in education. For reading, [Action Research and Thesis Topics on Reading (CRLA)](https://nash.com.ph/blog/action-research-topics-reading-crla) does the same with the CRLA data. Every file behind it is listed on the [EDCOM II classroom data page](https://nash.com.ph/edcom-classroom-data). ## Key points - DepEd defines action research as "a process of systematic and reflective inquiry that aims to improve educational practices or resolve problems in the classroom or in the school" (DepEd Order No. 16, s. 2017). When it tests an intervention, its design is experimental or qualitative, and you collect the data yourself. - The classroom data fit the first stage, assessing the situation, through causal-comparative, correlational and survey studies, and graduate theses that compare many schools. Topics 1 to 6 are action research, 7 to 14 assess the situation, and 15 to 20 are thesis topics. - A typical division has 78 public schools in the data, and 50 with a shortage; 63 of the 229 divisions have fewer than 50 schools. Small samples call for exact tests and careful reading of intervals. - Urban schools more often have a shortage than rural ones, 67.2% against 50.3% (chi-square 975.6, p < 0.001), but the association is weak (phi 0.14). With tens of thousands of schools almost every difference is significant, so report the size of the effect, not only the p-value. - Bigger schools are more crowded: Spearman's rho between a school's learners and its learners per classroom is 0.63 nationally. Within single divisions it runs from -0.08 to 0.88, so the national answer may not be your division's. - Some expected links do not hold: schools with a building needing major repair have a shortage less often (51.0% against 60.0%). Testing what "everyone knows" is a good use of the data. - Every topic below lists its variables by column name, so the analysis can be repeated from the free files and cited. ## Action research, studies of the situation and theses: where the classroom data fit DepEd's Research Management Guidelines (DepEd Order No. 16, s. 2017, amended by DepEd Order No. 26, s. 2021) define action research as "a process of *systematic* and *reflective inquiry* that aims to *improve* educational practices or *resolve* problems in the classroom or in the school". The Action Research Toolkit, which DepEd's Policy, Research and Development Division and Bureau of Learning Delivery prepared in 2019 with the Basic Education Sector Transformation (BEST) Program and the support of the Australian Government, describes the work in five stages: assess the situation, ask a question, act to seek answers, acquire information, and analyze and reflect. The research itself follows five steps: identify the problem, design the study, collect data, analyze and interpret the data, and write the report. The toolkit is clear about designs. "There are only two possible research designs when interventions are applied—experimental and qualitative design." Causal-comparative, correlational and survey research are designs for assessing the situation: they describe a problem, compare groups that already differ, or relate variables, before you decide on an intervention. The toolkit lists data from DepEd's own database among the documents such studies can use, and the classroom data are records of this kind. The table shows where each kind of study stands. | Kind of study | What it answers | Data you need | Topics below | |:--|:--|:--|:--| | Action research (experimental or qualitative) | Did my intervention help my learners or my school? | Data you collect before and after the intervention; the classroom data as context and baseline | 1 to 6 | | Assessing the situation (causal-comparative, correlational, survey) | How big is the problem, where is it, and what goes with it? | The classroom data for your school, district or division; your own survey for topic 14 | 7 to 14 | | Thesis or dissertation | What explains the pattern across a region or the country? | The classroom data, often joined to other data by school ID or PSA code | 15 to 20 | A study that assesses the situation often leads to action research. A school head who finds, in topic 9, that crowded schools in the division run more shifts may then try a room-use plan, as in topic 4, and measure what changes. That is the cycle the toolkit describes: reflection on the results "can be the beginning of another study". ## How to choose and frame a topic The toolkit asks you to rank possible topics on four criteria, and the classroom data can help with each. - **Strategic importance:** how many other teachers, subjects or schools would benefit if the intervention works. The data show how many schools share your problem, in your district and beyond. - **Urgency:** how soon a solution is needed. A school running two shifts, or using makeshift rooms, has an urgent problem that the data record. - **Magnitude:** how many learners would benefit. The data give enrollment by level, so you can count them. - **Feasibility:** whether you can carry out the study with your time, skills and resources. This one is yours to judge. A good research question, in the toolkit's terms, is clearly stated, significant, ethical, feasible, and leads to an action. Quantitative questions come with a null hypothesis and a research hypothesis, which may be nondirectional ("the two groups differ") or directional ("the first group scores higher"). Classroom shortage topics fall under two themes of DepEd's Basic Education Research Agenda (DepEd Order No. 39, s. 2016): teaching and learning, for topics on large classes and shifts, and governance, which covers planning, finance and program management, for topics on rooms, repairs and the SBP list. Topics on makeshift rooms and hard-to-reach schools also touch the cross-cutting area of disaster risk reduction and management. The toolkit expects action research training to run through school [Learning Action Cell sessions](https://nash.com.ph/blog/deped-learning-action-cell), and a LAC group is a good place to pick a shared topic, divide the data work and review results. If your school has written a [school improvement plan](https://nash.com.ph/blog/deped-school-improvement-plan), its [root cause analysis](https://nash.com.ph/blog/root-cause-analysis-school-improvement-plan) may already point to a classroom problem worth studying. ## Getting the data for your school and division 1. Download the [Excel workbook](https://data.nash.com.ph/edcom-classroom-data/v1.1/Classroom_Supply_Demand_Cleaned.xlsx), which holds every table and the data dictionary, or the [school table](https://data.nash.com.ph/edcom-classroom-data/v1.1/schools.csv.gz) as CSV. 2. Filter the school table by `deped_region` and `division`, then by `district` if your study covers a district. 3. Find your school by its DepEd school ID in `school_id`. 4. Keep the columns your topic needs; the table below groups the main ones, and the data dictionary describes all of them. 5. Write down the year of each variable. The sources span 2022 to 2026, and a study should say which year each figure describes. 6. Check the quality flags (topic 20) before you report a single school's figure. 7. Cite the data as shown at the end of this post, and name DepEd and the EDCOM II dashboard as the original sources. The table lists the main variables, the table that holds them, what they measure, their year and how many rows carry a value. Columns for one level, such as `enrol_jhs`, are blank for schools without that level. | Variables | Table | What they measure | Year | Rows with a value | |:-------------------------------------------------------------------------------------------------------------------------|:------------------|:--------------------------------------------------------------------------------------------------------|:-----------|--------------------:| | `school_id`, `school_name` | `schools` | The DepEd school ID and name, the key for joining other school data | All | 48,131 | | `deped_region`, `division`, `district`, `congressional_district` | `schools` | DepEd region, division and district, and the congressional district | 2023 | 48,109 | | `province`, `municipality`, `barangay`, `psgc_municipality_code` | `schools` | Place, with PSA codes for joining census and poverty data | PSGC | 48,131 | | `urban_rural`, `last_mile_school` | `schools` | Urban or rural school; Last Mile School | 2023 | 47,659 | | `lgu_type`, `lgu_income_class` | `schools` | City or municipality, and its income class | PSGC | 47,634 | | `curricular_offering`, `offers_es`, `offers_jhs`, `offers_shs` | `schools` | Levels the school offers | 2023 | 47,687 | | `enrol_es`, `enrol_jhs`, `enrol_shs`, `enrol_total` | `schools` | Learners by level | SY 2024-25 | 47,972 | | `es_highest_shift`, `jhs_highest_shift`, `shs_highest_shift`, `multi_shift` | `schools` | Class shifts by level; two or more shifts at any level | SY 2024-25 | 47,972 | | `classrooms_instr_es`, `classrooms_instr_jhs`, `classrooms_instr_shs`, `classrooms_noninstr_*` | `schools` | Instructional and non-instructional classrooms by level | 2023 | 47,148 | | `learners_per_classroom_es`, `_jhs`, `_shs`, `_all` | `schools` | SY 2024-25 learners per 2023 instructional classroom | Derived | 46,888 | | `buildings_good_condition`, `buildings_needs_minor_repair`, `buildings_needs_major_repair`, `buildings_condemn_demolish` | `schools` | School buildings by condition | 2023 | 47,816 | | `es_makeshift_rooms`, `hs_makeshift_rooms`, `es_tls_rooms`, `hs_tls_rooms` | `schools` | Makeshift rooms and temporary learning spaces, and the classes using them | 2023 | 47,816 | | `shortage_k10`, `shortage_shs`, `shortage_total`, `has_shortage`, `excess_k10` | `schools` | DepEd's classroom shortage and excess estimates | 2023 | 47,659 | | `sbp_classrooms`, `sbp_budget_php`, `sbp_modalities` | `schools` | School Building Program classrooms, budget and implementing offices (0 classrooms when not on the list) | 2026 | 48,131 | | `allocation_coverage`, `shortage_unmet`, `sbp_beyond_shortage` | `schools` | 2026 classrooms set against the 2023 shortage | Derived | 47,659 | | `latitude`, `longitude`, `coord_far_from_barangay` | `schools` | School coordinates and a check on them | 2022 | 46,457 | | `implementation_modality`, `modality_group`, `scope_max_storeys`, `budget_per_classroom_php` | `sbp_projects` | Each SBP 2026 line: who builds it, storeys, classrooms and budget | 2026 | 5,652 | | `hs_accessible_*`, `hs_has_road_*` | `facilities_2023` | Modes of transport and roads that reach a school (answered by high schools) | 2023 | 10,610 | | `shortage_total`, `sbp_classrooms`, `lgu_income_class` | `lgu_summary` | The school figures summed for each city and municipality | 2023-2026 | 1,642 | **How many schools a division study has.** The 47,659 schools in the 2023 shortage table fall in 229 divisions. The median division has 78 schools, the middle half of divisions have 46 to 317, and they range from 13 to 1,270. 63 divisions have fewer than 50 schools, and the median division has 50 schools with a shortage. A study of one district may have only a few dozen schools. With samples this small, use exact tests (Fisher's exact test in place of chi-square when an expected count is below 5), report confidence intervals, and do not read much into a difference of a few schools. A thesis can pool several divisions or a whole region. ## 20 research topics on the classroom shortage | # | Topic | Kind of study | Design | |--:|:--|:--|:--| | 1 | Teaching large classes | Action research | Experimental | | 2 | Learning time in shift classes | Action research | Experimental | | 3 | Learning in makeshift rooms | Action research | Qualitative | | 4 | A room-use plan to cut a shift | Action research (school head) | Experimental, before and after | | 5 | Repairs with partners | Action research (school head) | Experimental, before and after | | 6 | Learning continuity in hard-to-reach schools | Action research | Experimental or qualitative | | 7 | Urban and rural schools | Assessing the situation | Causal-comparative | | 8 | Last Mile Schools | Assessing the situation | Causal-comparative | | 9 | Crowding and class shifts | Assessing the situation | Correlational | | 10 | Building condition and the shortage | Assessing the situation | Causal-comparative | | 11 | Senior high: stand-alone and integrated schools | Assessing the situation | Causal-comparative | | 12 | Bigger schools and crowding | Assessing the situation | Correlational | | 13 | Does the 2026 list follow the shortage in your division? | Assessing the situation | Correlational | | 14 | Teachers' views in crowded schools | Assessing the situation | Survey | | 15 | LGU income class and the 2026 classrooms | Thesis | Regression | | 16 | Who builds classrooms, and what they cost | Thesis | Comparative, regression | | 17 | The shortage left after 2026 | Thesis | Regression | | 18 | Where the shortage clusters, and spare rooms nearby | Thesis | Spatial | | 19 | Hard-to-reach schools and the SBP list | Thesis | Regression | | 20 | How reliable are school-reported classroom data? | Thesis | Data quality study | ## Action research topics for your classroom or school These six topics test an intervention. The classroom data describe the setting and give a baseline for the school; the outcome data are yours to collect, with consent, before and after. The toolkit teaches the paired t-test for one group measured twice (a pretest and a posttest) and the independent t-test for two groups. ### 1. Teaching large classes - **Research question:** Does a cooperative learning routine raise the Mathematics scores of Grade 8 learners in a section of more than 50 learners? - **Variables from the data (context):** `learners_per_classroom_jhs`, `enrol_jhs`, `classrooms_instr_jhs` for your school, and the same columns for the other schools in your division for comparison. Use `_es` or `_shs` for other levels. - **Data you collect:** pretest and posttest scores, attendance, and notes on how the routine ran. - **Design and analysis:** experimental. One section with a pretest and a posttest (paired t-test), or two sections, one with the routine and one without (independent t-test on the gain from pretest to posttest). - **Starting point in the data:** junior high rooms are the most crowded of any level. The median school has 32.1 junior high learners per classroom, against 24.9 in elementary and 30.6 in senior high, and 18.5% of schools with junior high learners and classrooms have more than 50 per room. ### 2. Learning time in shift classes - **Research question:** Do weekly take-home learning plans help Grade 4 learners in the afternoon shift reach the same reading level as those in the morning shift? - **Variables from the data (context):** `es_highest_shift`, `jhs_highest_shift`, `shs_highest_shift`, `multi_shift`, `enrol_es`. - **Data you collect:** reading assessment results at the start and end of the quarter, by shift; time on task; learners' and parents' feedback. - **Design and analysis:** experimental, with the morning shift as the comparison group. Because learners are not assigned to shifts at random, compare their pretest scores first, then compare gains (independent t-test). - **Starting point in the data:** 2,041 public schools held classes in two or more shifts in SY 2024-25. They enroll 4,596,527 learners, 20.4% of the total ([Class Shifts and Crowded Classrooms](https://nash.com.ph/blog/class-shifting-crowded-classrooms)). ### 3. Learning in makeshift rooms - **Research question:** How do Grade 5 learners and their teacher experience learning in a makeshift room, and which changes to seating, ventilation and routines make learning easier for them? - **Variables from the data (context):** `es_makeshift_rooms`, `hs_makeshift_rooms`, `es_classes_in_makeshift`, `hs_classes_in_makeshift`, `es_tls_rooms`, `hs_tls_rooms`. - **Data you collect:** classroom observation notes, interviews or focus groups with learners and the teacher, and simple readings of room temperature and noise at set times. - **Design and analysis:** qualitative. Code the notes and interviews into themes, try the changes, and observe again. The toolkit's sections on interviews, observation and qualitative analysis cover each step. - **Starting point in the data:** 13,806 schools, 29.0% of those in the 2023 shortage table with a facilities report, reported at least one makeshift room. Among them, 74.3% had a classroom shortage, against 46.0% of schools without makeshift rooms. ### 4. A room-use plan to cut a shift (for school heads) - **Research question:** Can a room-use plan that schedules classes in the library, laboratory or other non-instructional rooms for part of the day reduce the number of classes in the second shift, without displacing the services those rooms hold? - **Variables from the data (context):** `classrooms_instr_es`, `classrooms_instr_jhs`, `classrooms_instr_shs`, `classrooms_noninstr_es`, `classrooms_noninstr_jhs`, `classrooms_noninstr_shs`, `multi_shift`, the `*_highest_shift` columns. - **Data you collect:** a room-use log, the number of classes in each shift before and after, and teachers' and learners' views. - **Design and analysis:** a before-and-after study at the school level, with a qualitative part on how the change was received. Count the classes moved and the hours gained. - **Starting point in the data:** of 2,026 schools running two or more shifts with a room count, 91.9% reported at least one non-instructional classroom in 2023. Whether any of these can hold a class, and for how long, is what the study finds out: a clinic or a guidance office is needed where it is. ### 5. Repairs with partners (for school heads) - **Research question:** Does a Brigada Eskwela and partnership plan aimed at rooms needing minor repair bring more rooms back into full use within one school year? - **Variables from the data (context):** `buildings_good_condition`, `buildings_needs_minor_repair`, `buildings_needs_major_repair`, `buildings_condemn_demolish`. - **Data you collect:** a room inspection checklist before and after, a repair log, and the partners' contributions. - **Design and analysis:** a before-and-after study. Report rooms repaired and returned to use, the cost and who paid for it. - **Starting point in the data:** in 2023, 64.8% of schools reported at least one building needing minor repair, and 64.9% one needing major repair. The data count buildings, not rooms, so your checklist should count rooms. ### 6. Learning continuity in hard-to-reach schools - **Research question:** Does a learning packet plan for days when boats cannot sail or roads flood reduce the learning lost on those days? - **Variables from the data (context):** in the facilities table, `hs_accessible_boat`, `hs_accessible_habalhabal`, `hs_accessible_walking_hiking` and the `hs_has_road_*` columns; in the school table, `last_mile_school`. - **Data you collect:** daily attendance, the days classes were disrupted and why, and quiz scores on the lessons covered by packets. - **Design and analysis:** experimental (compare scores on packet weeks with ordinary weeks), or qualitative, through interviews with learners, parents and boat or habal-habal operators. - **Starting point in the data:** 999 public high schools, 9.5%, can be reached by boat, and 767 by no four-wheel vehicle at all ([How Learners Reach Public High Schools](https://nash.com.ph/blog/how-learners-reach-public-high-schools)). ## Topics that assess the situation in your school or division These eight topics can be answered with the classroom data alone, except topic 14, which uses them to draw a survey sample. They are causal-comparative or correlational studies in the toolkit's terms: they compare groups that already differ, or relate variables, without an intervention. Their results show associations, not causes, and they are strongest when they lead to a question you can then test in your own school. ### 7. Urban and rural schools - **Research question:** Are urban public schools in our division more likely than rural schools to have a classroom shortage? - **Variables:** `urban_rural`, `has_shortage`, `shortage_total`, `enrol_total`. - **Design and analysis:** causal-comparative. A chi-square test of independence on the two-by-two table, with the risk ratio and phi as effect sizes; Fisher's exact test for small samples. A Mann–Whitney test compares the size of the shortage. - **Starting point in the data:** urban schools more often have a shortage, 67.2% against 50.3% of rural schools. The [first worked example](#worked-example-1-urban-and-rural-schools-causal-comparative) runs this test in full. ### 8. Last Mile Schools - **Research question:** Do Last Mile Schools in our region more often have a classroom shortage, and are they on the SBP 2026 list as often as other schools? - **Variables:** `last_mile_school`, `has_shortage`, `shortage_total`, `enrol_total`, `sbp_classrooms`, `urban_rural`. - **Design and analysis:** causal-comparative. Chi-square tests and risk ratios for having a shortage and being on the list; Mann–Whitney for the size of the shortage. - **Starting point in the data:** 61.5% of the 8,820 Last Mile Schools have a shortage, against 52.5% of other schools, yet 9.9% of them get SBP 2026 classrooms, against 11.0% ([Last Mile Schools](https://nash.com.ph/blog/last-mile-schools-classroom-shortage)). Against schools with the same shortage, enrollment, urban or rural setting and region, Last Mile Schools have slightly higher odds of getting SBP classrooms (odds ratio 1.24), a good lesson in why a thesis adds control variables. ### 9. Crowding and class shifts - **Research question:** How is the number of learners per classroom related to holding classes in two or more shifts? - **Variables:** `learners_per_classroom_all` (or by level), `multi_shift`, `es_highest_shift`, `jhs_highest_shift`, `shs_highest_shift`. - **Design and analysis:** correlational. Compare learners per classroom in schools with and without shifts (Mann–Whitney, with the rank-biserial correlation as effect size), and show the share of schools with shifts in bands of crowding with 95% confidence intervals. A logistic regression suits a thesis. - **Starting point in the data:** among 46,888 schools with enrollment, shifts and classrooms, the median school with two or more shifts has 55.0 learners per classroom, against 25.5 in single-shift schools (rank-biserial r 0.76, p < 0.001). The chart shows how the share with shifts rises with crowding. ![Bar chart of the share of public schools holding classes in two or more shifts in SY 2024-25, by learners per instructional classroom: 0.4% of 29,266 schools with 30 or fewer, 2.9% with over 30 to 40, 11.2% with over 40 to 50, 20.8% with over 50 to 60, 27.5% with over 60 to 80 and 22.0% with over 80.](https://nash.com.ph/blog/_astro/research-topics-shifts-by-crowding.2LvIWrA-_28kVY1.webp) *Figure 1. Share of public schools holding classes in two or more shifts (SY 2024-25), by SY 2024-25 learners per 2023 instructional classroom, with 95% Wilson intervals. Chart: © 2026 Mark Anthony Llego, NASH Reviewer. [Download the full-resolution chart (PNG)](https://nash.com.ph/blog/charts/deped-data/research-topics-shifts-by-crowding.png).* | Learners per classroom | Schools | Two or more shifts | Share | 95% CI | |:-------------------------|----------:|---------------------:|--------:|:---------------| | 30 or fewer | 29,266 | 130 | 0.4% | 0.4% to 0.5% | | Over 30 to 40 | 8,711 | 253 | 2.9% | 2.6% to 3.3% | | Over 40 to 50 | 3,734 | 419 | 11.2% | 10.2% to 12.3% | | Over 50 to 60 | 1,832 | 381 | 20.8% | 19.0% to 22.7% | | Over 60 to 80 | 1,751 | 481 | 27.5% | 25.4% to 29.6% | | Over 80 | 1,594 | 351 | 22.0% | 20.1% to 24.1% | The share falls in the last band, over 80 learners per room. Some of these ratios are data errors, such as a school that reported too few rooms; topic 20 shows how to find them. A good study reports how it handled them. ### 10. Building condition and the shortage - **Research question:** Do schools with a building needing major repair, or one for condemnation or demolition, more often have a classroom shortage? - **Variables:** `buildings_needs_major_repair`, `buildings_for_condemnation`, `buildings_condemn_demolish`, `buildings_good_condition`, `has_shortage`, `enrol_total`. - **Design and analysis:** causal-comparative. Chi-square tests and risk ratios; then compare like with like, for example schools of similar size, because condition and size may go together. - **Starting point in the data:** the expected link does not hold. 51.0% of schools with a building needing major repair have a shortage, against 60.0% of schools without one, and 53.0% of schools with a building for condemnation or demolition, against 54.7%. A study could ask why: for example, whether older and smaller schools have both worn buildings and enough rooms for fewer learners. ### 11. Senior high: stand-alone and integrated schools - **Research question:** Is a senior high classroom shortage more common in integrated schools than in stand-alone senior high schools? - **Variables:** `curricular_offering`, `shortage_shs`, `enrol_shs`, `learners_per_classroom_shs`, `shs_highest_shift`. - **Design and analysis:** causal-comparative across school types: chi-square for having a shortage, Kruskal–Wallis for learners per classroom. - **Starting point in the data:** 40.9% of stand-alone senior high schools have a senior high shortage, against 64.8% of junior high schools with a senior high level and 75.9% of schools offering Kindergarten to Grade 12 ([Senior High School Classroom Shortage](https://nash.com.ph/blog/senior-high-school-classroom-shortage)). ### 12. Bigger schools and crowding - **Research question:** Do larger schools in our division have more learners per classroom? - **Variables:** `enrol_total`, `classrooms_instr_total`, `learners_per_classroom_all`. - **Design and analysis:** correlational. A scatterplot and Spearman's rho, read with the toolkit's guide to strength and direction. - **Starting point in the data:** rho is 0.63 nationally, but it varies widely by division. The [second worked example](#worked-example-2-bigger-schools-and-crowding-correlational) runs it in full. ### 13. Does the 2026 list follow the shortage in your division? - **Research question:** In our division, are schools with larger shortages more likely to receive SBP 2026 classrooms, and how much of each school's shortage do they cover? - **Variables:** `shortage_total`, `has_shortage`, `sbp_classrooms`, `allocation_coverage`, `shortage_unmet`, `division`. - **Design and analysis:** correlational. The share of schools with a shortage that are on the list, with a 95% interval; Spearman's rho between the shortage and the 2026 classrooms; then compare your division with the national figures. - **Starting point in the data:** nationally, 13.3% of schools with a shortage get 2026 classrooms. Among the 201 divisions with at least 20 schools with a shortage, the share runs from 0.0% to 66.7%, with a median of 12.9%; 5 divisions have none on the list. Across the 25,823 schools with a shortage, rho between the shortage and the 2026 classrooms is 0.13 (95% CI 0.10 to 0.16), a weak link. See [Do 2026 Classrooms Go Where the Shortage Is?](https://nash.com.ph/blog/classroom-allocation-vs-shortage-2026) for the national analysis. ### 14. Teachers' views in crowded schools - **Research question:** What do teachers in schools with more than 50 learners per classroom see as the main effects of crowding on their teaching, and which kinds of support do they ask for? - **Variables (to draw the sample):** `learners_per_classroom_all`, `school_id`, `division`, `district`. - **Data you collect:** a short survey of mostly closed-ended items, with a few open questions. - **Design and analysis:** survey research. Use the data as the sampling frame: list the schools above the threshold, draw a random sample stratified by district, and report the shares answering each way with their intervals. - **Starting point in the data:** 5,177 public schools had more than 50 learners per classroom, in 216 divisions. ## Thesis and dissertation topics These six topics suit a master's thesis or a doctoral dissertation. They cover many schools, use several variables at once, and often join the classroom data to other data by school ID or PSA code. Each has been described in an earlier post of this series, which gives a starting point and a method to build on. ### 15. LGU income class and the 2026 classrooms - **Research question:** Did the 2026 classrooms reach schools in poorer municipalities as often as those in richer ones, once the size of the shortage and of the town are taken into account? - **Variables:** in `lgu_summary`, `lgu_income_class`, `lgu_type`, `shortage_total`, `sbp_classrooms`; in the school table, `lgu_income_class`, `has_shortage`, `shortage_total`, `sbp_classrooms`, `enrol_total`, `urban_rural`, `last_mile_school`, `deped_region`, `division`. - **Design and analysis:** logistic regression of being on the list, with standard errors clustered by division; a trend test across income classes. - **Starting point in the data:** 633 of 694 first-class municipalities (91.2%) have at least one classroom on the SBP list, against 56.9% of fifth-class municipalities, but the gap fades once town size and region are held equal ([Classroom Allocation by LGU Income Class](https://nash.com.ph/blog/classroom-allocation-lgu-income-class)). ### 16. Who builds classrooms, and what they cost - **Research question:** How does the budget per classroom differ by implementing office, number of storeys and region, and what explains the difference? - **Variables:** in `sbp_projects`, `implementation_modality`, `modality_group`, `scope_of_work`, `scope_max_storeys`, `classrooms`, `budget_php`, `budget_per_classroom_php`, with the school's `deped_region`. - **Design and analysis:** Kruskal–Wallis across implementing offices, then a regression of the logarithm of the cost per classroom. - **Starting point in the data:** a new classroom on the SBP 2026 list is budgeted at a median PHP 3.03 million, and PHP 5.57 million on DepEd's own Last Mile School lines, whose scope often adds furniture, solar power, water and toilets ([How Much Does a Classroom Cost?](https://nash.com.ph/blog/cost-to-build-classroom-philippines)). ### 17. The shortage left after 2026 - **Research question:** Which schools will still lack classrooms after the 2026 program, and what school and place characteristics predict it? - **Variables:** `shortage_unmet`, `shortage_total`, `sbp_classrooms`, `sbp_beyond_shortage`, `allocation_coverage`, with the school characteristics from topics 7 to 11. - **Design and analysis:** descriptive analysis of where the unmet shortage lies, then a regression of the unmet share; a two-part model handles the many schools with none. - **Starting point in the data:** counted school by school, the 2026 classrooms cover 10.4% of the 165,443-classroom shortage, leaving 148,276 unmet, and 7,457 of the 2026 classrooms go to schools with no 2023 shortage. The dashboard's own figure for the shortage left is 133,024, because it subtracts every 2026 classroom from the total ([Do 2026 Classrooms Go Where the Shortage Is?](https://nash.com.ph/blog/classroom-allocation-vs-shortage-2026)). ### 18. Where the shortage clusters, and spare rooms nearby - **Research question:** Does the shortage cluster in space, and how far is each school with a shortage from a school with spare classrooms? - **Variables:** `latitude`, `longitude`, `coord_far_from_barangay`, `shortage_k10`, `excess_k10`, `enrol_total`, `district`, `psgc_barangay_code`. - **Design and analysis:** spatial analysis: nearest-neighbour distances, and hot spot statistics such as Moran's I. PSA codes let you join census population, growth and poverty data. - **Starting point in the data:** 16,396 schools have more rooms than DepEd's estimate says they need. Half of the schools with a K-10 shortage and usable coordinates (50.5%) have such a school within 2 km in a straight line ([Schools With More Classrooms Than They Need](https://nash.com.ph/blog/schools-with-more-classrooms-than-needed)). Spare rooms on paper are not always usable or movable, which is itself a question for the study. ### 19. Hard-to-reach schools and the SBP list - **Research question:** Are hard-to-reach high schools less often on the SBP 2026 list, once the size of their shortage is taken into account? - **Variables:** in `facilities_2023`, the `hs_accessible_*` and `hs_has_road_*` columns, joined by `school_id_current` to the school table's `school_id`, `shortage_total`, `sbp_classrooms`, `last_mile_school`, `enrol_total`. - **Design and analysis:** logistic regression with access groups and the shortage as predictors, standard errors clustered by division. - **Starting point in the data:** among high schools with a 2023 shortage, 7.4% of those no motor vehicle reaches by road are on the list, against 17.9% of those a four-wheel vehicle reaches ([How Learners Reach Public High Schools](https://nash.com.ph/blog/how-learners-reach-public-high-schools)). ### 20. How reliable are school-reported classroom data? - **Research question:** How often do school-reported classroom and facility figures look implausible, and how much do the errors change the shortage figures? - **Variables:** `lcr_outlier_es`, `lcr_outlier_jhs`, `lcr_outlier_shs`, `flag_classes_exceed_rooms`, `coord_far_from_barangay`, `psgc_lgu_conflict`, with the cleaning log and the dashboard figures file. - **Design and analysis:** a data quality study: count and map the flags, compare the dashboard's figures with the cleaned ones, and redo a key analysis with and without flagged schools. - **Starting point in the data:** 674 schools have an outlying learner–classroom ratio at some level, 595 report more classes in temporary or makeshift rooms than is plausible, 1,416 schools' coordinates lie more than 10 km from their barangay, 1,674 have no coordinates, and 26 are linked to a barangay in a different LGU from the one DepEd names. [How the EDCOM Classroom Data Was Cleaned](https://nash.com.ph/blog/edcom-classroom-data-cleaned) documents every rule. ## Worked example 1: urban and rural schools (causal-comparative) This example runs topic 7 on the national data, in the toolkit's order: question, hypotheses, data, test, interpretation. **Question.** Is the share of public schools with a classroom shortage different for urban and rural schools? **Hypotheses (nondirectional).** Null hypothesis: the share of schools with a shortage is the same for urban and rural schools. Research hypothesis: the shares differ. **Data.** The 47,495 schools in the 2023 shortage table with an urban or rural type, from `urban_rural` and `has_shortage`. | School | Has a shortage | No shortage | Schools | Share with a shortage | |:---------|-----------------:|--------------:|----------:|------------------------:| | Urban | 7,341 | 3,578 | 10,919 | 67.2% | | Rural | 18,383 | 18,193 | 36,576 | 50.3% | **Test.** A chi-square test of independence on the table gives chi-square = 975.6, df = 1, p < 0.001, so the null hypothesis is rejected. Phi, the effect size for a two-by-two table, is 0.14, a weak association. The risk ratio says the same in plain terms: a rural school is 0.75 times as likely as an urban one to have a shortage (95% CI 0.74 to 0.76). **Interpretation.** Urban schools more often have a shortage, and theirs are larger: a median of 6.0 classrooms among urban schools with a shortage, against 2.0 among rural ones, or 8.4 classrooms per 1,000 learners against 6.1. Two lessons follow. First, with 47,495 schools, a weak association is still highly significant, so report phi or the risk ratio, not only the p-value. Second, this is an ex post facto comparison: a school's being urban cannot be manipulated, and urban schools are also bigger, so the difference may come from size rather than place. A thesis would add enrollment to a logistic regression to separate the two. In your own division, with far fewer schools, the same test may not reach significance, and Fisher's exact test is safer when an expected count is below 5. The same comparison can be made for other characteristics. The chart and table show five more, using the same 2023 shortage table. ![Dot chart of the share of public schools with a 2023 classroom shortage, among schools with and without each characteristic: rural 50.3% against 67.2% for urban; Last Mile Schools 61.5% against 52.5%; two or more shifts 93.0% against 52.5%; makeshift rooms 74.3% against 46.0%; a building needing major repair 51.0% against 60.0%; a building for condemnation or demolition 53.0% against 54.7%.](https://nash.com.ph/blog/_astro/research-topics-shortage-by-school-type.CHGH9c_k_1RxAFA.webp) *Figure 2. Share of public schools with a 2023 classroom shortage, among schools with and without each characteristic. Chart: © 2026 Mark Anthony Llego, NASH Reviewer. [Download the full-resolution chart (PNG)](https://nash.com.ph/blog/charts/deped-data/research-topics-shortage-by-school-type.png).* | Schools with the characteristic | Schools | Share with a shortage | Share without the characteristic | Risk ratio (95% CI) | |:----------------------------------------|----------:|------------------------:|-----------------------------------:|:----------------------| | Rural (against urban) | 36,576 | 50.3% | 67.2% | 0.75 (0.74 to 0.76) | | Last Mile School (against others) | 8,820 | 61.5% | 52.5% | 1.17 (1.15 to 1.19) | | Two or more shifts (against one) | 2,021 | 93.0% | 52.5% | 1.77 (1.75 to 1.80) | | Uses makeshift rooms | 13,806 | 74.3% | 46.0% | 1.62 (1.59 to 1.64) | | Building needing major repair | 30,880 | 51.0% | 60.0% | 0.85 (0.84 to 0.86) | | Building for condemnation or demolition | 13,750 | 53.0% | 54.7% | 0.97 (0.95 to 0.99) | Shifts and makeshift rooms go with a shortage most strongly: 93.0% of schools with two or more shifts have one. That is close to a definition, since schools add shifts and makeshift rooms when they lack classrooms, so the comparison confirms the data hang together more than it reveals a cause. The two building rows run the other way, the puzzle that topic 10 invites you to study. ## Worked example 2: bigger schools and crowding (correlational) This example runs topic 12: do schools with more learners also have more learners per classroom? **Data.** The 46,846 schools with enrollment, instructional classrooms and a learner–classroom ratio, from `enrol_total` and `learners_per_classroom_all`. **Test.** Spearman's rho, which works on ranks and suits skewed counts such as enrollment, is 0.63 (95% CI 0.60 to 0.66, from a bootstrap that resamples whole divisions). On the toolkit's guide, where 0.1 to 0.3 is weak, 0.4 to 0.6 moderate and 0.7 to 0.9 strong, this is a moderate to strong, positive (direct) relationship: as enrollment rises, so do learners per classroom. The table shows it in bands. | Learners in the school | Schools | Median learners per classroom | |:-------------------------|----------:|--------------------------------:| | 100 or fewer | 8,629 | 15.0 | | 101 to 250 | 16,531 | 22.8 | | 251 to 500 | 10,659 | 29.0 | | 501 to 1,000 | 5,966 | 32.6 | | 1,001 to 2,500 | 3,740 | 39.1 | | Over 2,500 | 1,321 | 50.9 | **The same question, division by division.** A teacher or division researcher would run this test within one division, and the answer depends on which. Computed separately in each of the 229 divisions, rho has a median of 0.56 and runs from -0.08 to 0.88. 37 divisions have a rho below 0.4, 161 between 0.4 and 0.7, and 31 at 0.7 or above; in 2 it is negative. ![Histogram of Spearman's rho between a school's learners and its learners per classroom, computed within each of 229 divisions: most divisions fall between 0.4 and 0.7, with a median of 0.56, a range from -0.08 to 0.88, and the national value of 0.63 marked.](https://nash.com.ph/blog/_astro/research-topics-division-correlations.DyAw69oF_ZGNyyM.webp) *Figure 3. Spearman's rho between a school's SY 2024-25 learners and its learners per 2023 instructional classroom, computed within each division, with the national value marked. Chart: © 2026 Mark Anthony Llego, NASH Reviewer. [Download the full-resolution chart (PNG)](https://nash.com.ph/blog/charts/deped-data/research-topics-division-correlations.png).* **Interpretation.** Rooms do not keep pace with enrollment in larger schools, in most divisions. But a national result is not a local one: run the test for your own division before you cite it. One caution applies to any correlation with learners per classroom: it is built from enrollment, so the two share a term. The correlation here is meaningful, because classrooms could have grown in step with learners and did not, but a correlation between the shortage and learners per classroom would mostly show how DepEd computes the shortage, not a finding. ## Common mistakes to avoid - **Mixing years without saying so.** Coordinates are from 2022, the shortage, rooms and facilities from 2023, enrollment and shifts from SY 2024-25, and the SBP list from 2026. Learners per classroom divide SY 2024-25 learners by 2023 rooms. Name the year of every figure. - **Treating the shortage as a count of missing rooms on the ground.** It is DepEd's 2023 estimate, computed from learners and rooms. Correlating it with learners per classroom or enrollment mostly recovers the formula. - **Treating allocations as buildings.** The SBP 2026 list is a plan; classrooms on it may be built later than planned, or not at all. - **Reading associations as causes.** Causal-comparative and correlational results show what goes together. Say "is associated with", not "causes", and look for third variables such as school size. - **Reporting only p-values.** With thousands of schools nearly every difference is significant. Report effect sizes (risk ratio, phi, rho, rank-biserial r) and confidence intervals. - **Drawing conclusions about learners from school data.** The data describe schools. A link between crowding and shifts across schools does not show how any single learner is affected; that needs learner data, which action research collects. - **Ignoring the flags.** Check the outlier and quality flags (topic 20) and say how you handled them. - **Assuming spare rooms can be used.** A school's excess is an estimate on paper; distance, condition and the levels it serves decide whether a room can help another school. ## Ethics and approval The classroom data are public school-level records with no learner names, so using them needs no consent. Your own data are different. The toolkit sets five protocols for action research: reduce the risk of harm, secure informed consent (from parents, with the learners' assent), safeguard confidentiality and anonymity, avoid deceptive practices, and ensure justice and fidelity. Justice means fairness, for example giving the comparison group the intervention later if it works; fidelity means keeping your promises to participants, such as their anonymity. When you name schools from the data, report them fairly: a school with a large shortage is not a badly run school. Submit your proposal through your school and division under the Research Management Guidelines; action research proposals may be funded through the Basic Education Research Fund (BERF), whose rules are part of those guidelines (DepEd Order No. 16, s. 2017, as amended), so check your region's call each year. [BERF: How to Get DepEd Action Research Funded](https://nash.com.ph/blog/basic-education-research-fund-berf) explains the grant, and [DepEd Action Research Ethics: Consent, Assent and Privacy](https://nash.com.ph/blog/action-research-ethics-deped) covers consent forms and learners' data. ## How these numbers were made The data are the school table, the SBP 2026 lines, the city and municipality table and the 2023 facilities table from the EDCOM II classroom construction dashboard, cleaned for this series. The analysis was run in Python with pandas, NumPy and SciPy, and the charts drawn with Matplotlib. Figures cited from other posts in the series are linked where they appear. - **Variables table.** For each group of columns, the rows with a value in at least one of the main columns of the group. - **Divisions.** The 47,659 schools in the 2023 shortage table, grouped by region and division. - **Worked example 1 and Figure 2.** Schools in the 2023 shortage table with a known value for each characteristic. A shortage means `shortage_total` above zero (`has_shortage`). Makeshift rooms: elementary and high school makeshift rooms add up to at least one, among schools with a facilities report. Building conditions: at least one building in the category. Chi-square without continuity correction; phi as the square root of chi-square divided by the number of schools; risk ratios with 95% intervals from the log method (Katz et al., 1978). - **Figure 1.** Schools with `learners_per_classroom_all` and `multi_shift`, in bands of learners per classroom, with 95% Wilson score intervals (Wilson, 1927). Mann–Whitney U test with the rank-biserial correlation. - **Worked example 2 and Figure 3.** Schools with enrollment and instructional classrooms above zero. Spearman's rho with a 95% interval from 1,000 bootstrap samples of whole divisions, and rho computed within each of the 229 divisions. - **Topic 13.** Among schools with a shortage, the share with `sbp_classrooms` above zero in each division with at least 20 schools with a shortage, and Spearman's rho between `shortage_total` and `sbp_classrooms`, with a division bootstrap interval. - **Other starting figures.** Junior high crowding: `learners_per_classroom_jhs` above 50 among schools with a value. Non-instructional rooms: schools with `multi_shift` and at least one room count. Repairs: schools with a building count in the 2023 shortage table. Quality flags: schools with each flag set in the school table. ## Limits The data describe schools, not learners or classrooms in use, and they are DepEd's own estimates and the schools' own reports for the years shown. The shortage is an estimate from a formula; building counts are of buildings, not rooms; the access fields record whether a mode can reach a school, not how learners travel. The toolkit's correlation guide leaves gaps between its bands (0.3 to 0.4 and 0.6 to 0.7); Figure 3 counts values in those gaps with the lower band. The national results in the worked examples are illustrations: a division's results may differ, as Figure 3 shows. All results are associations. The Action Research Toolkit quoted here is the 2019 edition. DepEd may update its research guidelines, agenda and funding rules; check the current issuances with your division's research staff. ## Frequently asked questions ### What are good action research topics on classroom shortage? Topics that test something you can change in your own classroom or school: a cooperative learning routine for large classes, take-home learning plans for afternoon-shift learners, changes that make a makeshift room easier to learn in, a room-use plan that cuts a second shift, a repair plan with partners, or learning packets for days a hard-to-reach school cannot be reached. The classroom data describe the setting; you collect the outcome data before and after. ### Can I use the EDCOM II classroom data in my action research or thesis? Yes. The cleaned data are free to download, but using them in your study needs our written permission first: email [support@nash.com.ph](mailto:support@nash.com.ph), and name DepEd and the EDCOM II dashboard as the original sources. In action research they serve as context and baseline, the first stage of assessing the situation. In a causal-comparative, correlational or thesis study they can be the main data. ### Which variables measure the classroom shortage? `shortage_k10`, `shortage_shs` and `shortage_total` hold DepEd's 2023 estimates of the classrooms a school lacks, and `has_shortage` marks schools with any shortage. `learners_per_classroom_*`, `multi_shift`, the makeshift and temporary room columns, and the building condition columns describe its effects, and `sbp_classrooms` and `shortage_unmet` set the 2026 classrooms against it. ### What statistical test should I use? It depends on the question. For one group with a pretest and a posttest, a paired t-test; for two groups, an independent t-test, as the toolkit teaches. For two categories, such as urban and rural schools with or without a shortage, a chi-square test or Fisher's exact test, with a risk ratio. For two measures, such as enrollment and learners per classroom, Spearman's rho with a scatterplot. For several predictors at once, as in a thesis, logistic regression. ## Download the data and cite this post The cleaned EDCOM II classroom data behind this series are free to download for personal study; to reuse them, email [support@nash.com.ph](mailto:support@nash.com.ph) for written permission first. [How the EDCOM Classroom Data Was Cleaned](https://nash.com.ph/blog/edcom-classroom-data-cleaned) explains every table and cleaning rule. The files are at data.nash.com.ph: start with the [README](https://data.nash.com.ph/edcom-classroom-data/v1.1/README.md), then the [school table](https://data.nash.com.ph/edcom-classroom-data/v1.1/schools.parquet) (Parquet, or [CSV](https://data.nash.com.ph/edcom-classroom-data/v1.1/schools.csv.gz)), the [School Building Program lines](https://data.nash.com.ph/edcom-classroom-data/v1.1/sbp_projects.csv.gz) (CSV), the [city and municipality table](https://data.nash.com.ph/edcom-classroom-data/v1.1/lgu_summary.csv.gz) (CSV), the [facilities table](https://data.nash.com.ph/edcom-classroom-data/v1.1/facilities_2023.csv.gz) (CSV), the [data dictionary](https://data.nash.com.ph/edcom-classroom-data/v1.1/data_dictionary.csv), every table in one [Excel workbook](https://data.nash.com.ph/edcom-classroom-data/v1.1/Classroom_Supply_Demand_Cleaned.xlsx), and every table for [Stata](https://data.nash.com.ph/edcom-classroom-data/v1.1/classroom_tables_stata.zip) and [SPSS](https://data.nash.com.ph/edcom-classroom-data/v1.1/classroom_tables_spss.zip). Every figure computed for this post can be reproduced from the school table, using the columns named in each topic and in the method section. Every file, with its rows, columns and checksum, is listed on the [EDCOM II classroom data page](https://nash.com.ph/edcom-classroom-data). To cite the data: Llego, M. A. (2026). *EDCOM II classroom construction dashboard data, cleaned, version 1.1*. NASH Reviewer. https://nash.com.ph/edcom-classroom-data To cite this post: Llego, M. A. (2026, October 28). *Action research and thesis topics on classroom shortage*. NASH Reviewer Blog. https://nash.com.ph/blog/action-research-topics-classroom-shortage ## References - Creswell, J. W. (2012). *Educational research: Planning, conducting, and evaluating quantitative and qualitative research* (4th ed.). Pearson. - Department of Education. (2016). *Adoption of the Basic Education Research Agenda* (DepEd Order No. 39, s. 2016). - Department of Education. (2017). *Research management guidelines* (DepEd Order No. 16, s. 2017). - Department of Education. (2021). *Amendment to DepEd Order No. 16, s. 2017 (Research management guidelines)* (DepEd Order No. 26, s. 2021). - Department of Education & Basic Education Sector Transformation (BEST) Program. (2019). *Action research toolkit: Teacher's guide*. Department of Education, with support from the Australian Government. - Katz, D., Baptista, J., Azen, S. P., & Pike, M. C. (1978). Obtaining confidence intervals for the risk ratio in cohort studies. *Biometrics, 34*(3), 469–474. - Llego, M. A. (2026). *EDCOM II classroom construction dashboard data, cleaned, version 1.1* [Data set]. NASH Reviewer. https://nash.com.ph/edcom-classroom-data - Wilson, E. B. (1927). Probable inference, the law of succession, and statistical inference. *Journal of the American Statistical Association, 22*(158), 209–212. ## More in this series - [How the EDCOM Classroom Data Was Cleaned](https://nash.com.ph/blog/edcom-classroom-data-cleaned) - [Philippine Classroom Shortage 2023: Where the Gap Is](https://nash.com.ph/blog/classroom-shortage-philippines-2023) - [DepEd School Building Program 2026 in Numbers](https://nash.com.ph/blog/deped-school-building-program-2026) - [Do 2026 Classrooms Go Where the Shortage Is?](https://nash.com.ph/blog/classroom-allocation-vs-shortage-2026) - [Classroom Allocation by LGU Income Class, 2026](https://nash.com.ph/blog/classroom-allocation-lgu-income-class) - [Class Shifts and Crowded Classrooms in DepEd Schools](https://nash.com.ph/blog/class-shifting-crowded-classrooms) - [Last Mile Schools: Classroom Need and 2026 Allocation](https://nash.com.ph/blog/last-mile-schools-classroom-shortage) - Regional reports: [NCR](https://nash.com.ph/blog/classroom-shortage-ncr), [CAR](https://nash.com.ph/blog/classroom-shortage-car), [Region I](https://nash.com.ph/blog/classroom-shortage-region-1-ilocos), [Region II](https://nash.com.ph/blog/classroom-shortage-region-2-cagayan-valley), [Region III](https://nash.com.ph/blog/classroom-shortage-region-3-central-luzon), [Region IV-A](https://nash.com.ph/blog/classroom-shortage-region-4a-calabarzon), [MIMAROPA](https://nash.com.ph/blog/classroom-shortage-mimaropa), [Region V](https://nash.com.ph/blog/classroom-shortage-region-5-bicol), [Region VI](https://nash.com.ph/blog/classroom-shortage-region-6-western-visayas), [NIR](https://nash.com.ph/blog/classroom-shortage-negros-island-region), [Region VII](https://nash.com.ph/blog/classroom-shortage-region-7-central-visayas), [Region VIII](https://nash.com.ph/blog/classroom-shortage-region-8-eastern-visayas), [Region IX](https://nash.com.ph/blog/classroom-shortage-region-9-zamboanga-peninsula), [Region X](https://nash.com.ph/blog/classroom-shortage-region-10-northern-mindanao), [Region XI](https://nash.com.ph/blog/classroom-shortage-region-11-davao), [Region XII](https://nash.com.ph/blog/classroom-shortage-region-12-soccsksargen), [Caraga](https://nash.com.ph/blog/classroom-shortage-region-13-caraga), [BARMM](https://nash.com.ph/blog/classroom-shortage-barmm) - Rankings and deep dives: [Congressional districts](https://nash.com.ph/blog/classroom-shortage-by-congressional-district), [Top 20 cities and municipalities](https://nash.com.ph/blog/cities-municipalities-largest-classroom-shortage), [Rizal](https://nash.com.ph/blog/classroom-shortage-rizal), [Quezon City](https://nash.com.ph/blog/classroom-shortage-quezon-city), [City of Manila](https://nash.com.ph/blog/classroom-shortage-manila), [Tawi-Tawi](https://nash.com.ph/blog/classroom-shortage-tawi-tawi), [Cavite](https://nash.com.ph/blog/classroom-shortage-cavite), [Cost of a classroom](https://nash.com.ph/blog/cost-to-build-classroom-philippines) - Explainers: [PSIP III](https://nash.com.ph/blog/psip-iii-classrooms-explained), [Senior high shortage](https://nash.com.ph/blog/senior-high-school-classroom-shortage), [Junior high shortage](https://nash.com.ph/blog/junior-high-school-classroom-shortage), [Excess classrooms](https://nash.com.ph/blog/schools-with-more-classrooms-than-needed), [Learning Continuity Spaces](https://nash.com.ph/blog/learning-continuity-spaces-sbp-2026), [How learners reach school](https://nash.com.ph/blog/how-learners-reach-public-high-schools) - For teachers and school heads: [Action research and thesis topics](https://nash.com.ph/blog/action-research-topics-classroom-shortage) - Related: [DepEd Classroom and School Building Data, SY 2023-2024](https://nash.com.ph/blog/deped-classroom-school-building-data-sy-2023-2024), [EDCOM II Final Report: Guide for Aspiring School Heads](https://nash.com.ph/blog/edcom-ii-final-report), [National Education Plan 2026-2035: Guide for School Heads](https://nash.com.ph/blog/national-education-plan-2026-2035), [DepEd School Improvement Plan (SIP): Steps, AIP and SRC](https://nash.com.ph/blog/deped-school-improvement-plan), [How to Read DepEd Data: A Guide for Aspiring School Heads](https://nash.com.ph/blog/how-to-read-deped-data), [PPSSH Domain 2: Managing School Operations With DepEd Data](https://nash.com.ph/blog/ppssh-domain-2-managing-school-operations) *Analysis and charts by Mark Anthony Llego for NASH Reviewer. Data: EDCOM II classroom construction dashboard and Department of Education data. NASH Reviewer is not affiliated with EDCOM II or DepEd. Charts may be reused only with written permission from support@nash.com.ph.* --- © 2026 Mark Anthony Llego, NASH Reviewer. All rights reserved. You may quote up to 100 words from this article with the author's name, its title and a link. In research, cite it as: Llego, M. A. (2026, October 28). *Action Research and Thesis Topics on Classroom Shortage*. NASH Reviewer. https://nash.com.ph/blog/action-research-topics-classroom-shortage. Any other reuse of the article, its charts or its data needs written permission: email support@nash.com.ph. Terms of Service: https://nash.com.ph/terms#website-content-and-data