1

U.S. Department of Education

Office of Planning, Evaluation and Policy Development

Policy and Program Studies Service

Issue Brief: Personalized Learning Plans

December 2017

Introduction

In 2015–16, the high school graduation rate reached a record high of 84 percent (U.S. Department of 

Education 2017). Despite the gains, over half a million students still drop out of high school each year 

(U.S. Department of Education 2015). High schools have adopted various strategies designed to keep 

students who are at risk of not graduating in school and on track for earning the credits required to 

graduate. “At-risk” students are defined as those failing to achieve basic proficiency in key subjects or 

exhibiting behaviors that can lead to failure and/or dropping out of school. Dropout prevention 

strategies are diverse; they vary in type of program, services offered, frequency, intensity, and duration 

of contact with target students.

The U.S. Department of Education (Department) sponsored the National Survey on High School 

Strategies Designed to Help At-Risk Students Graduate (HSS), which aimed to provide descriptive 

information on the prevalence and characteristics of dropout prevention strategies for at-risk students. 

The survey collected data in the 2014–15 school year from a nationally representative sample of 

2,142 public high schools and focused on 13 specific high school improvement strategies1 identified by a 

panel of external experts and senior Department officials. All findings are based on self-reported data 

from school principals. This brief on personalized learning plans is the eleventh in a series of briefs with 

key findings about these high school improvement strategies.

Definition of Personalized Learning Plans

The HSS focused on high schools and defined a personalized learning plan2 as a formalized process that 

involves high school students setting learning goals based on personal, academic and career interests 

with the close support of school personnel or other individuals that can include teachers, school 

counselors, and parents. Personalized learning plans are developed in a way that identifies the types of

skills students need to pursue their academic and career interests and the steps required to build those 

skills, which may be attained through traditional educational pathways or through other innovative 

delivery mechanisms. The HSS focused on one aspect of personalized learning—the development of 

personalized learning plans—rather than on specific “personalized learning” models or interventions

such as personalized learning curricula, learning environment, or blended learning strategies.

 

1 The survey examined 13 strategies that are designed to improve high school outcomes for at-risk students. These strategies 

are: (1) academic support classes, (2) academic tutoring, (3) career-themed curriculum, (4) case management services, 

(5) college-level coursework, (6) competency-based advancement, (7) credit recovery, (8) early warning systems, (9) high school 

transition activities, (10) mentoring, (11) personalized learning plans, (12) social services, and (13) student support teams. See 

http://www2.ed.gov/about/offices/list/opepd/ppss/reports-high-school.html for the series of briefs. Researchers may request 

access to a restricted-use data file by completing an application with the Institute of Education Science’s National Center for 

Education Statistics. Information about the process is also available at this website. 2 Pane, J.F., Steiner, E.D., Baird, M.D., Hamilton, L.S., and Pane, J.D. 2017. Informing Progress: Insights on Personalized Learning 

Implementation and Effects. Santa Monica, CA: RAND Corporation.
2

Research on Personalized Learning Plans

Studies that examine the outcomes of high school students who developed personalized learning plans

have primarily used correlational research designs or described key implementation strategies for 

helping students create their own plans. For example, a recent correlational study suggests that 

personalized learning plans are associated with higher student motivation, sense of belonging and 

connectedness to school (Solberg et al. 2014). These outcomes were particularly pronounced for 

students who developed plans with challenging academic goals, engaged in career exploration activities, 

participated in leadership development opportunities, and had high levels of parental involvement in 

the planning process. While these results seem promising, more research is needed to establish the 

causal effects of personalized learning plans, particularly the effects on academic and graduation 

outcomes. Most of the literature on personalized learning plans focuses on qualitative information such 

as implementation “lessons” or guides (Missouri Department of Elementary and Secondary Education 

2015; New Jersey Department of Education 2014; and Yonezawa et al. 2012).

Survey Findings on Personalized Learning Plans

This survey does not examine the effectiveness of personalized learning plans but instead describes the 

kinds of schools that offer personalized learning plans and their approaches to implementing the 

strategy. All findings are based on self-reported data from school principals. This analysis included an 

examination of four school characteristics: (1) size, (2) poverty, (3) locale, and (4) graduation rate. Only 

statistically significant differences within a school characteristic (at p <. 05) are discussed; nonstatistically significant differences are not reported. School characteristics were defined in the following 

ways.

School size. School size categories consisted of small schools (fewer than 500 students), medium 

schools (500–1,199 students), and large schools (1,200 or more students) based on 2013–14

Common Core of Data (CCD) student enrollment data.

School poverty. Poverty levels were based on 2013–14 free or reduced-price lunch (FRPL) and 

total CCD school enrollment data. The poverty categories were low-poverty schools (below 

35 percent students with FRPL), medium-poverty schools (35–49 percent students with FRPL), 

and high-poverty schools (50 percent or more students with FRPL).

School locale. School locale included three mutually exclusive locales from the CCD: rural

schools, suburban/town schools, and city schools.

Graduation rate. School classification by graduation rate was based on three categories: lowgraduation rate (67 percent or lower graduation rate), medium-graduation rate (68 to 

89 percent graduation rate), and high-graduation rate (90 percent or higher graduation rate).

Summary of Key Findings

• In 2014–15, 65 percent of high schools nationwide developed personalized learning plans with

students; an estimated 45 percent of all high school students3 developed a personalized learning 

plan, according to school principals.

• High-poverty schools were more likely than low-poverty schools to develop personalized 

learning plans with students; low-graduation-rate schools were more likely than highgraduation-rate schools to develop personalized learning plans with students.

 

3 HSS survey of high school administrators, 2015 (Question 99).
3

• Among high schools that developed personalized learning plans, schools most commonly 

targeted students for the development of these plans based on poor academic performance (65 

percent), followed by discipline or behavioral issues (52 percent), attendance issues (51 

percent), and students at a particular grade level (50 percent) among others.

• The type of information most commonly included in personalized learning plans were students’ 

postsecondary goals (85 percent) and career goals (85 percent), followed by identification of 

courses or programs required to achieve their educational and aspirational goals while also 

fulfilling course requirements for graduation (82 percent) among others.

• High schools can involve different school personnel or other individuals in the development of 

personalized learning plans. The more common school personnel or other individuals involved in 

the development of personalized learning plans were school counselors (84 percent), followed 

by students (72 percent), teachers (72 percent), and parents (66 percent) among others.

What was the prevalence of personalized learning plans in high schools?

In 2014–15, 65 percent of high schools nationwide developed personalized learning plans with students; 

an estimated 45 percent of all high school students4 developed a personalized learning plan, according 

to school principals. The prevalence of personalized learning plans differed by school poverty and 

graduation rate; there were no significant differences by school size or school locale (Exhibit 1). 

Exhibit 1. Percentage of high schools that developed personalized learning plans with students by 

selected school characteristics, 2014–15

Exhibit reads: In 2014─15, 65 percent of high schools nationwide developed personalized learning plans with students.

* p < .05

NOTE: An asterisk indicates statistical significance. The asterisk is placed on one case per comparison. Differences across school 

characteristics with two categories were based on comparisons between the two groups. 

Unweighted n = 1,925 high schools.

SOURCE: HSS survey of high school administrators, 2015 (Question 98).

 

4 Ibid. 3.
4

Differences by school poverty. High-poverty schools were more likely than low-poverty schools 

to develop personalized learning plans with students (69 percent versus schools 59 percent).

Differences by graduation rate. Low-graduation-rate schools were more likely than highgraduation-rate schools to develop personalized learning plans with students (72 percent versus 

62 percent).

How did high schools target students to develop personalized learning plans?

High schools most frequently targeted specific students to develop personalized learning plans based on 

poor academic performance (65 percent), followed by discipline or behavioral issues (52 percent), 

attendance issues (51 percent), and grade level (50 percent) among others. There were significant 

differences by school size, school poverty level, school locale, and graduation rates in the subgroups of 

students that were targeted to develop personalized learning plans (Exhibit 2). 

Exhibit 2. Percentage of high schools that targeted student subgroups or issues to develop

personalized learning plans, 2014–15

All 

schools 

with 

learning 

plans

Size Poverty Locale Graduation 

rate

Student subgroups 

targeted

Large Small High Low City Suburban Rural Low High

Performing below 

standards/grade 

level

65% 61% 69% 66% 59% 70% 66% 62% 72%* 60%

Discipline or 

behavioral issues

52% 53% 55% 54% 46% 59%* 53% 46% 62%* 46%

Attendance issues 51% 52% 53% 53% 46% 59%* 53% 46% 63%* 47%

Particular grade level 50% 53% 51% 52%* 42% 51% 51% 48% 52% 48%

Recommended by 

high school staff

49% 51% 50% 49% 43% 53% 47% 46% 56%* 47%

Performing above 

standards/grade 

level

37% 40% 39% 38% 33% 44%* 36% 33% 45%* 36%

English learners 31% 42%* 30% 32% 27% 40%* 33% 23% 37% 29%

Reentry students 29% 35% 29% 30% 25% 37%* 29% 23% 42%* 23%

Exhibit reads: Among high schools that developed personalized learning plans with students in 2014─15, 65 percent targeted

students performing below standards or grade level.

*p < .05

NOTE: An asterisk indicates statistical significance. The asterisk is placed on one case per comparison. Differences across school 

characteristics with two categories were based on comparisons between the two groups. Differences across school 

characteristics with three categories were based on goodness-of-fit across all three categories.

Unweighted n = 1,117 high schools.

SOURCE: HSS survey of high school administrators, 2015 (Question 100).

Differences by school size. Large schools were more likely than small schools to target English 

learners (ELs) to develop personalized learning plans (42 percent versus 30 percent). 

Differences by school poverty. High-poverty schools were more likely than low-poverty schools 

to target students in a particular grade level to develop personalized learning plans (52 percent 

versus 42 percent).
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Differences by school locale. More city schools than suburban or rural schools targeted students 

with discipline or behavioral issues (59 percent of city schools versus 53 percent of suburban 

schools and 46 percent of rural schools); attendance issues (59 percent of city schools versus 53 

percent of suburban schools and 46 percent of rural schools); students who performed above 

standards (44 percent of city schools versus 36 percent of suburban schools and 33 percent of 

rural schools); ELs (40 percent of city schools versus 33 percent of suburban schools and 23 

percent of rural schools); and reentry5 students (37 percent of city schools versus 29 percent of 

suburban schools and 23 percent of rural schools) to develop personalized learning plans.

Differences by graduation rate. Low-graduation-rate schools were more likely than highgraduation-rate schools to target students who performed below standards (72 percent versus 

60 percent); students with attendance issues (63 percent versus 47 percent); discipline or 

behavioral issues (62 percent versus 46 percent); students recommended by staff (56 percent 

versus 47 percent); students who performed above standards (45 percent versus 36 percent); 

and reentry students (42 percent versus 23 percent) to develop personalized learning plans.

What type of information was most commonly included in personalized learning plans?

Some educators may view personalized learning plans as a roadmap to the future and the type of 

information they include as critical. The type of information most commonly included in personalized 

learning plans were students’ postsecondary goals and aspirations (85 percent) and career and

employment goals (85 percent), followed by identification of courses or programs required to achieve 

their educational and career goals while also fulfilling course requirements for graduation (82 percent); 

personal goals (73 percent); personal enrichment interests (68 percent); students’ self-assessment of 

learning strengths and weaknesses, such as areas where the student excels or struggles (62 percent); 

and specific knowledge or skills that should be addressed, such as the identification of learning gaps (56 

percent). The type of information least commonly included in personalized learning plans was 

documentation of the student’s major learning accomplishments or milestones (38 percent). There were 

significant differences in the information included in personalized learning plans by school size, school

locale, and graduation rates (Exhibit 3). There were no significant differences by school poverty.

Differences by school size. Large schools were more likely than small schools to include 

students’ postsecondary goals (92 percent versus 83 percent) in personalized learning plans. 

Small schools were more likely than large schools to include a self-assessment of students’ 

learning strengths and weaknesses (63 percent versus 54 percent) and specific knowledge or 

skills that should be addressed (62 percent versus 42 percent) in personalized learning plans.

Differences by school locale. More city schools than suburban or rural schools included 

information about students’ personal goals (79 percent of city schools versus 71 percent of 

suburban schools and 71 percent of rural schools); personal interests (73 percent of city schools 

versus 64 percent of suburban schools and 67 percent of rural schools); a self-assessment of the 

students’ learning strengths and weaknesses (69 percent of city schools versus 60 percent of 

suburban schools and 59 percent of rural schools); the specific knowledge skills that the student 

should address to achieve the specified goals (65 percent of city schools versus 50 percent of 

suburban schools and 56 percent of rural schools); and the student’s major learning milestones 

(44 percent of city schools versus 36 percent of suburban schools and 34 percent of rural 

schools) in personalized learning plans.

 

5 Reentry students are those who dropped out of high school and then re-enrolled, as defined by the HSS.
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Differences by graduation rate. High-graduation-rate schools were more likely than lowgraduation-rate schools to include students’ postsecondary goals in personalized learning plans 

(88 percent versus 81 percent).

Exhibit 3. Among high schools with personalized learning plans, the percentage of schools reporting 

the types of information included in the plans, by selected school characteristics, 2014–15

All 

schools 

with 

learning 

plans

Size Locale Graduation rate

Information included Large Small City Suburban Rural Low High

Postsecondary goals 85% 92%* 83% 86% 84% 84% 81%* 88%

Career goals 85% 87% 85% 83% 86% 84% 83% 86%

Courses/programs required 

to achieve goals and fulfill 

graduation requirements

82% 86% 82% 81% 81% 84% 80% 84%

Personal goals 73% 71% 75% 79%* 71% 71% 78% 72%

Personal interests 68% 67% 67% 73%* 64% 67% 66% 68%

Self-assessment of students’ 

learning strengths and 

weaknesses

62% 54%* 63% 69%* 60% 59% 65% 60%

Specific knowledge or skills 

that should be addressed

56% 42%* 62% 65%* 50% 56% 63% 55%

Documentation of major 

learning milestones

38% 33% 40% 44%* 36% 34% 41% 34%

Exhibit reads: Among high schools that developed personalized learning plans with students in 2014─15, 85 percent of 

schools reported that these plans included students’ postsecondary goals.

*p < .05

NOTE: An asterisk indicates statistical significance. The asterisk is placed on one case per comparison. Differences across 

school characteristics with two categories were based on comparisons between the two groups. Differences across school 

characteristics with three categories were based on goodness-of-fit across all three categories.

Unweighted n = 1,226 high schools.

SOURCE: HSS survey of high school administrators, 2015 (Question 102).

Who was involved in the development of personalized learning plans?

High schools can involve different school personnel or other individuals in the development of 

personalized learning plans. The more common school personnel or other individuals involved in the 

development of personalized learning plans were school counselors (84 percent), followed by students

(72 percent), teachers (72 percent), and parents (66 percent) among others (Exhibit 4). There were 

significant differences by school size, school locale, and graduation rate in the types of school personnel 

or other individuals involved in the development of personalized learning plans (Exhibit 4). There were 

no significant differences by school poverty.

Differences by school size. Small schools were more likely than large schools to involve teachers 

(78 percent versus 52 percent), administrative staff (66 percent versus 45 percent), districtemployed staff (10 percent versus 5 percent), and community-based agency staff (10 percent 

versus 4 percent) in the development of personalized learning plans. Large schools were more 

likely than small schools to involve school counselors (95 percent versus 78 percent) in the 

development of personalized learning plans.
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Differences by school locale. More city schools than suburban or rural schools involved social 

workers in the development of personalized learning plans (27 percent of city schools versus 21 

percent of suburban schools and 14 percent of rural schools).

Differences by graduation rate. Low-graduation-rate schools were more likely than highgraduation rate schools to involve teachers (85 percent versus 68 percent), administrative staff 

(67 percent versus 57 percent), social workers (26 percent versus 14 percent), and communitybased agency staff (15 percent versus 5 percent) in the development of personalized learning 

plans. High-graduation-rate schools were more likely than low-graduation-rate schools to 

involve school counselors (91 percent versus 66 percent) in the development of personalized 

learning plans.

Exhibit 4. Percentage of high schools that reported involving the types of school personnel or other 

individuals in the development of personalized learning plans, by selected school characteristics,

2014–15

Size Locale Graduation rate

Personnel or other 

individuals involved

All schools with 

learning plans

Large Small City Suburban Rural Low High

School counselors 84% 95%* 78% 82% 84% 85% 66%* 91%

Students 72% 69% 72% 73% 72% 70% 75% 74%

Teachers 72% 52%* 78% 74% 68% 74% 85%* 68%

Parents 66% 66% 67% 62% 66% 70% 64% 68%

Administrative staff 60% 45%* 66% 62% 57% 61% 67%* 57%

School psychologists 23% 23% 22% 26% 23% 20% 21% 21%

Social workers 20% 17% 20% 27%* 21% 14% 26%* 14%

District-employed staff whose 

job is to mentor 9% 5%* 10% 9% 9% 9% 11% 8%

Community-based agency 

staff 8% 4%* 10% 11% 8% 7% 15%* 5%

Exhibit reads: Among high schools that developed personalized learning plans with students in 2014–15, 84 percent of schools 

reported that school counselors were involved in the development of these plans. 

*p < .05

NOTE: An asterisk indicates statistical significance. The asterisk is placed on one case per comparison. Differences across school 

characteristics with two categories were based on comparisons between the two groups. Differences across school 

characteristics with three categories were based on goodness-of-fit across all three categories.

Unweighted n = 1,231 high schools.

SOURCE: HSS survey of high school administrators, 2015 (Question 101).

Methodology

The National Survey on High School Strategies Designed to Help At-Risk Students Graduate was a 

survey of 13 high school strategies designed to improve graduation rates among students at risk of 

dropping out and was administered in the 2014–15 school year. The 13 strategies are: (1) academic 

support classes, (2) academic tutoring, (3) career-themed curriculum, (4) case management, (5) collegelevel coursework, (6) competency-based advancement, (7) credit recovery, (8) early warning systems, 

(9) high school transition activities, (10) mentoring, (11) personalized learning plans, (12) social services, 

and (13) student support teams.
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The purpose of the survey was to inform education practitioners and policymakers about the

prevalence, characteristics, and students served by these strategies in U.S. public high schools. The

descriptive study did not measure the effectiveness of particular strategies but instead examined

implementation factors in high schools across the country. The study team identified the 13 strategies

and designed survey items for each strategy with input from a panel of external experts in the field and

senior Department officials. All findings are based on self-reported data from school principals.

The researchers selected a nationally representative sample of high schools6 using a random sampling 

approach, stratifying high schools based on graduation rate (from EDFacts)

7 and locale code (from NCES 

2013–14 Common Core of Data). The survey collected data from high school principals (or designees 

knowledgeable about programs and strategies) at sampled schools. The survey response rate was 

90 percent. The survey responses, after cleaning and processing, were analyzed in SAS and Stata using 

descriptive techniques that apply the appropriate statistical population weights to account for 

stratification by graduation rate and locale. 

Results reported in this brief reflect the full survey sample unless otherwise noted and are 

representative of U.S. public high schools nationwide. References in the text to differences between 

subgroups based on sample data refer only to differences that are statistically significant using a 

significance level of 0.05.

 

6 All U.S. public high schools providing instruction to 12th grade students in the fall of 2010 were included in the sampling frame 

unless (1) the lowest offered grade was 11th grade or higher, (2) there were fewer than five students in grades 9 through 12, 

(3) the percentage of students enrolled in grades 9 through 12 was under 20 percent of the total school enrollment and the 

total number of students in grades 9 through 12 was fewer than 20, or (4) the school name contained one of nine keywords 

indicating juvenile detention center or hospital. Of the 103,813 total schools listed in the 2010–11 CCD, 22,447 high schools met 

the criteria to be included in the sampling frame.

7 There were 3,302 schools without graduation rate information in the 2010–11 EDFacts public use data set. The researchers 

used an imputation approach to assign these schools to either the high- or low-graduation-rate stratum. The imputation 

process began by examining the distribution of the high/low graduation rate classification for 19,145 schools by sampling 

locale. The percentage of schools classified as high graduation rate was calculated separately for each locale sampling stratum; 

68.4 percent of rural schools were classified as high graduation rate, 63.0 percent of suburban schools were classified as high 

graduation rate, and 41.0 percent of city schools were classified as high graduation rate. The research team randomly assigned 

each of the 3,302 schools with unknown graduation rates to the high graduation rate stratum with probability 68.4 if the school 

was classified as rural, with probability 63.0 if the school was classified as suburban, and with probability 41.0 if the school was 

classified as urban. The sample size was adjusted upwards to account for potential misclassification due to this method. In 

analysis, the researchers used the restricted-use 2013–14 EDFacts data and graduation rates published on school and district 

websites to fill in this missing data.
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References

Missouri Department of Elementary and Secondary Education. 2015. School Counselor’s Guide to the K12 Individual Student Planning Process. Jefferson City, MO: Missouri Department of Elementary and 

Secondary Education.

New Jersey Department of Education. 2014. A Guide for Implementing Personalized Student Learning 

Plan (PSLP) Programs. Trenton, NJ: New Jersey Department of Education.

Pane, J.F., Steiner, E.D., Baird, M.D., Hamilton, L.S., and Pane, J.D. 2017. Informing Progress: Insights on 

Personalized Learning Implementation and Effects. Santa Monica, CA: RAND Corporation.

Solberg, V.S., Wills, J., Redmond, K., and Skaff, L. 2014. Use of Individualized Learning Plans as a 

Promising Practice for Driving College and Career Readiness Efforts: Findings and Recommendations 

from a Multi-Method, Multi-Study Effort. Washington, DC: National Collaborative on Workforce and 

Disability for Youth, Institute for Educational Leadership.

U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics. 

2017. “Table 1. Public High School 4-Year Adjusted Cohort Graduation Rate (ACGR).” 

https://nces.ed.gov/ccd/tables/ACGR_RE_and_characteristics_2015-16.asp

U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics. 

2015. “Table 219.57. Population of 15- to 24-Year Olds Enrolled in Grades 10 through 12, Number 

Who Dropped Out (Event Dropouts), Percentage Who Dropped Out (Event Dropout Rate), and 

Percentage Distribution, by Selected Characteristics: 2014.” 

https://nces.ed.gov/programs/digest/d15/tables/dt15_219.57.asp

Yonezawa, Susan, Larry McClure, & Makeba Jones. 2012. Personalization in Schools. Boston, MA: Jobs 

for the Future.
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Appendix: Personalized Learning Plans (Survey Excerpt)

National Survey on High School Strategies Designed to Help At-Risk Students Graduate

98. In the 2014-15 school year, does your school develop personalized learning 

plans for students?

(Please select only one) 

{Only allow one selection} Yes No

 

If the user responds “Yes” to Q98, ask Q99 through Q103. Otherwise, skip to Q104.

99. On average, approximately what percentage of 

high school students in your school receives a 

personalized learning plan in the 2014-15 school 

year?

{Slide bar for 0% to 100%}

100. Are any of the following subsets of students targeted for receiving a 

personalized learning plan? 

(Check all that apply)

Students with attendance issues (e.g., truancy) 

Students with discipline or behavioral issues 

Students performing below standards or grade level 

Students performing above standards or grade level 

Students in a particular grade level, regardless of performance 

Students recommended by high school staff (e.g., counselor or 

teacher) 

Reentry students 

English Language Learners 

Other

(Please Specify________________)



This section asks about Personalized Learning Plans. For the purposes of this survey, personalized 

learning plans are based on a student’s academic and career objectives and personal interests. Plans 

sequence content and skill development to help students graduate on time, college- and careerready, and are updated based on information about student performance toward goals.
11

101. Who is involved in developing personalized learning 

plans? 

(Check all that apply)

Teachers 

School/guidance counselors 

School psychologists 

Social workers 

Administrative staff 

Community-based agency staff 

District-employed staff whose job is to mentor students 

Parents 

Students 

Other

(Please specify: __________________)



102. What type of information is most commonly included in personalized learning 

plans? 

(Check all that apply)

Students’ postsecondary/college goals 

Students’ career goals (including career exploration) 

Students’ personal goals 

Self-assessment of students’ learning strengths and weaknesses (e.g., areas where 

they excel or struggle, learning style)



Specific knowledge or skills that should be addressed (e.g., identification of 

learning gaps, interventions or supports required)



Students’ personal interests (e.g., areas of interest, hobbies) 

Identification of the courses/programs required to allow the student to achieve 

their educational and aspirational goals while also fulfilling school credit and 

course requirements for graduation



Documentation of major learning accomplishments or milestones 

Other

(Please specify: __________________)



The full survey is available at: http://www2.ed.gov/about/offices/list/opepd/ppss/reports-highschool.html