Results of 2022 PEAC survey on learning recovery in private secondary education schools participating in the ESC program

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PEAC2022 survey on learning recovery
PEAC 2022 survey on learning recovery


Institutional Testing and Evaluation Office

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A study was commissioned by the Private Education Assistance Committee (PEAC) to inquire into how private secondary schools (Junior High School level) participating in the Educational Service Contract (ESC) program are undertaking learning recovery as they re-open in their respective milieus. In particular, this research aimed to obtain a baseline profile of schools’ Learning Recovery Actions or LRA in relation to identified context variables such as geographic location, school size, tuition rates, faculty turnover, student drop-out rates, and certification status. The survey consisted of 51 items spread across several sections starting with demographic data about the school (e.g., geographic location, school size, tuition rates, faculty turnover, student drop-out rates, certification status). Succeeding sections had items pertaining to the different research questions. A total of 1,789 schools answered the survey (the number represents 50.06% of the total number of ESC schools which is 3,574).

Survey results were tabulated and subjected to the appropriate statistical treatment. Data was analyzed using a mix of quantitative and qualitative methods. Data on schools were analyzed using descriptive statistics, such as frequency, percentage, mean, and standard deviation. The following schools’ demographic data served as the independent variable: enrolment, school Type (diocesan, congregation, family-sectarian, family-nonsectarian), location (city limits, outside city-accessible, outside city-remote), certification (none, limited, partial, full, full with innovation, FAAP), and Region Poverty Incidence (below 10%, between 10% to 19%, 20% and above; based on 2021 Poverty Incidence Rates per Region, Philippine Statistics Authority, 2021). The schools’ Learning Recovery actions (indicated as LRA) was treated as the dependent variable. Correlation and linear regression analyses were conducted using the open-source software JASP Version 0.16.3 (2022). For the responses in the open-ended questions, the study utilized computer-assisted software in conducting the qualitative data analysis (QDA) to standardize the process and steps in the analysis. The study mainly used NVIVO 12 Plus. NVIVO is a software that supports qualitative and mixed methods research. Word charts and word clouds were established along with thematic maps that showed patterns and relationships in the various comments given by survey respondents.

The study answered nine research problem questions regarding the following: 1) schools’ challenges with regards learning loss from the start of school closures during the pandemic, 2) the kind of LRA that were undertaken, 3) the system of evaluating the LRA, 4) resources used for LRA, 5) changes in school operations due to LRA, 6) efforts done for vulnerable or at-risk students, 7) influence of context variables, 8) suggestions for sustaining LRA, and 9) directions for program and policy formulation.

In general, the findings and results show that there is widespread perception of learning loss in the schools that participated in the survey based on results of classroom-based assessments, online tasks in the schools’ Learning Management System, and in some schools, in standardized tests. While there is much use of assessments, the top indicators of learning loss that schools focused on as shown in the tables and thematic maps were low quality of student work (incomplete submissions and outputs in performance tasks), low attendance, and low engagement in online classes.

These predominant indicators of learning loss differ from current literature which characterizes learning loss as the “...difference between the overall level of attainment that a student would have achieved by the end of their course of study – if they had not been affected by the pandemic – and the overall level of attainment that they actually achieved in its wake” (Newton, 2021). This definition emphasizes quantifying learning loss by comparing students’ proficiency levels before and during the pandemic. This process of obtaining and comparing specific data about competency gaps was not a general practice. There is a disconnect between this view of learning loss and schools’ actual practices on the ground. The disconnect between what they say about learning loss and what the actual concept provides points to the need to clarify with schools the meaning of learning loss.

Because there was minimal comparison and use by schools of data to establish in quantitative terms the students’ learning gaps, the schools’ focus on developing Learning Recovery Actions or LRA also did not involve much use of data analysis and understanding students’ learning difficulties in accomplishing certain competencies. With regards to curriculum-related LRA, the thematic maps show that various adjustments were made but with little reference to baseline data of actual students’ proficiency. Similarly, for assessments done as part of the LRA, the schools’ discussion in the thematic maps of their design, construction and administration of assessments does not include opportunities to dissect existing school-based data and make granular impact studies or develop a system for continued data collection and use the data for quantifying levels of learning loss and establishing desired achievement levels. On instruction-related LRA, these efforts of schools cited in the thematic maps were more geared towards boosting resources, using research-based practices, and improving students’ performance on the perceived learning gaps. But less attention was given on how the revitalized instruction actually addressed the learning losses across the key subject areas.

In comparison to other studies on schools’ experiences of LRA, the statistical and thematic maps comparative analyses underline the importance of considering school context factors in relation to LRA such as enrolment, school type, location, certification status, region poverty incidence and learning modality. In the case of PEAC Junior High Schools, the factor of enrolment and regional poverty incidence may indicate the school’s capacity to do LRA; certification status may point to the presence of a school’s quality assurance system to support and sustain LRA; and the combination of learning modalities may suggest the school’s ability to provide differentiated forms of LRA.

In line with these results and findings, the study recommends the following: the provision of professional development seminars-workshops for school administrators and teachers that expand current concepts of learning loss and the importance of quantifying learning loss as a step towards design of appropriate LRA; development of customized learning analytics to support schools’ efforts towards data-driven design of LRA; implementation of and flexibility in the use of different and multiple learning modalities to support LRA; intensify certification, especially among partially-compliant schools to reach full certification status; more collaborative interaction among schools, especially those with limited enrolment or in regions of high poverty incidence; and refinement of the study’s methods and further inquiry into effective models of LRA.



Education | Educational Psychology


Running title: PEAC2022 survey on learning recovery


Academic achievement—Philippines—Testing; Learning—Philippines; Academic achievement—Philippines

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