PEAC MATH INSET 2023
HO 5.4 SLRP FOR ACQUISITION TEMPLATE
2023 PEAC JHS SUMMER INSET
STANDARDS-BASED LEARNING RECOVERY PLAN (SLRP) TEMPLATE*
Directions: Make a plan for undertaking learning recovery in your school by completing the table below. Check your plan for
alignment across columns and review other indicators given for the rubric of this plan.
SUBJECT: MATHEMATICS
TOPIC: STATISTICS AND PROBABILITY
GRADE: 10
TEACHER(S):
QUARTER: 4
1
Missed
Standard
and LCs
2
Current
Standard
and LCs
3
Existing
Curricular
Materials
4
Stand
Alone or
Layered
In
5
Mastery
Expectations &
Skill
Breakdown
*Standards/
LCs that are
stand alone
(as stated in
column 1)
6
Mastery
Expectations &
Skill
Breakdown
*Standards/
LCs that are
merged
(as stated in
column 4)
7
Rubric
Focus
8
Interventio
n or
Remediatio
n Strategies
and Action
9
Plan For
Curricular
Materials
10
Timeline
for
Teaching
Content
Standard:
The learner
demonstrates
understandin
g of key
concepts,
uses, and
importance of
Statistics,
data
collection/gat
hering and
the different
forms of data
representatio
n, measures
of central
tendency,
measures of
Content
Standard:
The learner
demonstrates
understanding
of key
concepts of
measures of
position.
(G10Q4)
In order for
students to
calculate a
specified
measure of
position, they
need to be
able to
calculate first
the different
measures of
central
tendency and
variability of
an ungrouped
data. Hence, it
is important
that the
missed LC is
done well by
The previous
grade level’s
missed LC
may be
merged in
the teaching
of the
current
grade level’s
LC.
N/A
The learner is
expected to be able
to:
Distinguished
:
I can evaluate
the
appropriatenes
s of the
statistical
method used in
analyzing and
interpreting
ungrouped
data; and I can
evaluate and
compare the
interpretation
of the
calculated
values and
accuracy of the
specified
Tier 1 Universal
Instruction
involving the use
of the Chunking
Complexity type
of scaffolding
and procedural
and
metacognitive
forms of
scaffolding.
Existing materials
will be updated
with the inclusion
of the missed
learning
competency
involving the
chunking
complexity type
of scaffolding
and procedural
form of
scaffolding.
Differentiation
will be done by
environment and
content.
The partially
covered
learning
competency will
be covered in
Week 2 of the
4th Quarter in 8
class meetings.
Performance
Standard:
The learner is
able to
conduct
systematically
a mini
research
applying the
1.
Possible
merged LC:
The learner
calculates
the
measures
of central
tendency,
2.
calculate
the
measure
of
central
tendenc
y,
variabilit
y, and
position
of
ungroup
ed data.
Use the
appropri
ate
Differentiation in
the environment
and content will
also be involved.
Students will be
checked on their
mastery by
Meeting 1:
Modeling of the
process of
calculating the
measures of
central
tendency of an
ungrouped
data.
Meeting 2:
variability,
and
probability.
(G7Q4)
Performanc
e Standard:
The learner is
able to collect
and organize
data
systematically
and compute
accurately
measures of
central
tendency and
variability and
apply these
appropriately
in data
analysis and
interpretation
in different
fields.
(G7Q4)
Learning
Competenc
y:
The learner:
1. calculates
the measures
of central
tendency of
ungrouped
and grouped
data. (M7SPIVf-g-1)
2. calculates
the measures
of variability
(range,
average
deviation,
variance,
standard
deviation) of
different
statistical
methods (a)
measures of
central
tendency, b)
measures of
variability, and
c) measures of
position)
(G10Q4)
Learning
Competency:
The learner
calculates a
specified
measure of
position (e.g.,
90th
percentile) of
a set.
(M10SP-IVb1)
the students.
variability,
and
position of
ungrouped
data; and
use
appropriat
e measures
of central
tendency
and
variability
in
analyzing
and
interpretin
g
ungrouped
data, and
compare
the
calculated
values of
the
specified
measures
of position.
3.
measure
of
central
tendenc
y
and
variabilit
y
in
analyzin
g
and
interpret
ing
ungroup
ed data.
Compar
e
the
calculate
d values
of
the
specified
measure
s
of
position.
Learning
Targets:
1.
2.
I
can
calculate
the
measure
of
central
tendenc
y,
variabilit
y, and
position
of
ungroup
ed data.
I can use
the
appropri
ate
measure
of
central
measure of
central
tendency,
variability, and
position used.
Proficient:
I can use
appropriate
measures of
central
tendency and
variability in
analyzing and
interpreting
ungrouped
data; and
compare the
calculated
values of the
specified
measures of
position.
Developing:
I can determine
the appropriate
measure of
central
tendency,
variability to be
used in
analyzing a
given data; and
interpret the
meaning of the
calculated
value of a
specified
measure of
position.
Emerging:
I can calculate
the specified
measure of
central
tendency,
variability and
position of
looking at the
rubric scores on
exercises
involving groups
and individual
work.
See procedures
below.
(Guided
Practice)
Worksheet 1 on
calculating the
measures of
tendency of an
ungrouped data
to be answered
in groups of
four employing
“Three Stay,
One Stay”.
Meeting 3:
Modeling of the
process of
calculating the
measures of
variability of an
ungrouped
data.
Meeting 4:
(Guided
Practice)
Worksheet 2 on
calculating the
measures of
variability of an
ungrouped data
to be answered
in groups of
four employing
“Three Stay,
One Stay”.
Meeting 5:
(Independent
Practice)
Worksheet 3 on
using the
appropriate
measure of
central
tendency and
variability in
analyzing and
interpreting
grouped an
ungrouped
data. (M7SPIVh-i-1)
3.
tendenc
y
and
variabilit
y
in
analyzin
g
and
interpret
ing
ungroup
ed data.
I can
compare
the
calculate
d values
of the
specified
measure
s of
position.
ungrouped
data.
ungrouped data
to be answered
by two groups.
Meeting 6:
Modeling of the
process of
calculating the
measures of
position of an
ungrouped
data.
Meeting 7:
(Guided
Practice)
Worksheet 4 on
calculating the
measures of
position of an
ungrouped data
to be answered
in groups of
four employing
“Three Stay,
One Stay”.
Meeting 8:
(Independent
Practice)
Worksheet 5 on
comparing the
calculated
values of the
specified
measures of
position to be
answered
individually.
*adapted from National Institute for Excellence in Teaching (NIET)
Rubric for Scoring Criteria:
Performance
Indicators
calculates
measures of central
tendency
and
position of a given
set of data.
1
Emerging
I can calculate the
specified measure of
central
tendency,
variability and position
of ungrouped data.
2
Developing
3
Proficient
I can determine the
appropriate measure
of central tendency,
variability to be used in
analyzing a given
data; and interpret the
meaning
of
the
calculated value of a
specified measure of
position.
I can use appropriate
measures of central
tendency
and
variability in analyzing
and
interpreting
ungrouped data; and
compare
the
calculated values of
the specified measures
of position.
4
Distinguished
I can evaluate the
appropriateness of
the
statistical
method used in
analyzing
and
interpreting
ungrouped
data;
and I can evaluate
and compare the
interpretation of the
calculated
values
and accuracy of the
specified measure of
central
tendency,
variability,
and
position used.
SYSTEMATIC AND EXPLICIT INTERVENTION PROCEDURES WITH SCAFFOLDING AND DIFFERENTIATION:
Write systematic and explicit intervention procedures showing the following:
a. learning targets
b. type and form of scaffolding
c. use of Acquisition strategies
d. use of Differentiation
Below are sample procedures:
MEETING 1: (CALCULATE THE MEASURE OF CENTRAL TENDENCY OF AN UNGROUPED DATA)
MODELING
1. Tell the students that they will learn to calculate the mean, median, and mode of an ungrouped data.
2. Distribute a copy of data that can be calculated using mean, median, and mode of an ungrouped data.
3. Let me demonstrate the first problem that can be calculated using the three measures of central tendency:
“Listed below are the scores of randomly selected 10 Grade 7 students in their 20 – item Mathematics
quiz: 10, 17, 12, 14, 15, 9, 13, 10, 11, and 18. Calculate the mean, median, and mode of the given set
of data.”
Explain to the students that each measure of central tendency has its corresponding formula.
4. So, let’s find out the answer to the given problem.
Σ𝑥
 In finding the mean of an ungrouped data, we will make use of the formula 𝑥̅ = 𝑁 . We have to add all the
scores and divide it with the number of respondents. By doing so, we will now have the mean of 12.9.
 Next, we will try to calculate for the median. In calculating the median of an ungrouped data, we have to
arrange first the given set of data in either increasing or decreasing order, 9, 10, 10, 11, 12, 13, 14, 15, 17,
and 18. Then, we have to find the median score. If the number of data points is odd, it means that our median
is the middle value. If the number of data points is even, we have to get the average of the two middle values
of the arranged data. Since our number of data points is even, we have to look for the two (2) middle values,
which are 12 and 13, and divide it by 2. Therefore, we can now say that the median is 12.5.
 Lastly, we will now try to look for the mode. In looking for the mode of a given ungrouped data, we are just
going to look for the most frequently occurring number or score. In this case, the number that occurred most
is 10.
5. Now, let us consider another example in calculating the mean, median, and mode of an ungrouped data.
“In a survey of 12 households, the number of children was found to be 4, 5, 5, 4, 3, 6, 2, 6, 3, 1, 4, and
2. Calculate the mean, median, and mode of the given ungrouped data.”
We will repeat the process that we did earlier.
 In calculating the mean of an ungrouped data, we add all the scores and divide it with the number of
respondents. By doing so, we will come up with the mean of 3.75.
 On the other hand, to calculate for the median, we have to arrange first the set of data in either increasing
or decreasing order, 1, 2, 2, 3, 3, 4, 4, 4, 5, 5, 6, and 6. In this case, our number of respondents is 12. So,
to find the middle most number, we have to add the 6th and 7th data and divide it by 2. So, we have (4 +
4)/2 = 8/2 = 4. Therefore, our median is 4.
 Lastly, to calculate or look for the mode, we have to find the frequently occurring number or data. In this
case, the frequently occurring number is 4. Therefore, our mode is 4.
6. So, to calculate the mean, median, and mode of an ungrouped data, we have to follow the steps below:
a. MEAN: Add all the scores and divide it with the number of respondents.
b. MEDIAN: Arrange the set of data in either increasing or decreasing order. Then, look for the middlemost score.
c. MODE: Look for the most frequently occurring number or score in the given set of data.
7. Are you ready to try to do what I demonstrated? Don’t worry, I will be guiding you and you will be working in
groups.
8. Now, let us try these steps again with another set of problems.
MEETING 2: (CALCULATE THE MEASURES OF CENTRAL TENDENCY OF AN UNGROUPED
DATA)
GUIDED PRACTICE 1: Chunking Complexity and Procedural/Differentiation by Content and
Environment
1. The class will be asked to state the steps in calculating the mean, median, and mode of an ungrouped data.
2. Worksheet 1 will be distributed to students. This worksheet is consisting of two parts with five (5) questions each:
(1) a true or false and (2) multiple choice questions.
3. The students will answer the worksheet in group of four employing “Three Stay, One Stay”. Students will discuss a
question. They will be numbered 1 – 4. After the students are discussing a number, I, the teacher, will call a number
and that student will leave the group and tell the new group what they discussed. Then proceed to the next question.
The process will be repeated until all the items are done.
4. Constantly monitor the students’ work and progress by roaming around the classroom and giving immediate feedback
on how the students are doing the activity.
5. Once done, we have to check the students’ answer in the worksheets and have the students determine whether the
answer is correct or not, and explain why.
6. Address difficulties encountered by the students in answering the worksheet.
MEETING 3: (CALCULATE THE MEASURES OF VARIABILITY OF AN UNGROUPED DATA)
MODELING
1. Tell the students that they will learn to calculate the measures of variability of an ungrouped data.
2. Distribute a copy of data that can be calculated using the measures of variability of an ungrouped data.
3. Let me demonstrate the first problem that can be calculated using measures of variability:
Data Set A: 10, 9, 7, 7, 8, 9, 6, 7, 8
From the given data set, calculate for the range, average deviation, variance, and standard deviation.
Explain to the students that each measure of variability has its corresponding formula.
4. So, let’s find out the answer to the given problem.
 In finding the range, we have to arrange the data in ascending order. Then find the highest and lowest score.
Subtract the smallest score from the highest score.
Data Set A = 6, 7, 7, 7, 8, 8, 9, 9, 10
Range = highest score – lowest score
= 10 – 6 = 4
Therefore, our range is 4.
Σ𝑥
 In finding the average deviation, we have to find the mean of the given data set using the formula, 𝑥̅ = 𝑛 .
Σ𝑥
𝑥̅ =
𝑛
Σ𝑥 71
𝑥̅ =
=
= 𝟕. 𝟖𝟗
𝑛
9
Therefore, our mean is 7.89.
After finding the mean, we have to find the deviation.
𝑑 = |6 − 7.89| = 1.89
𝑑 = |7 − 7.89| = 0.89
𝑑 = |7 − 7.89| = 0.89
𝑑 = |7 − 7.89| = 0.89
𝑑 = |8 − 7.89| = 0.11
𝑑 = |8 − 7.89| = 0.11
𝑑 = |9 − 7.89| = 1.11
𝑑 = |9 − 7.89| = 1.11
𝑑 = |10 − 7.89| = 2.11
Then find the sum of all the deviations and divide it by the number of data points to get the average
deviation.
Therefore, our average deviation is 1.01.
Σ𝑑𝑖
AD =
𝑛
9.11
AD =
= 𝟏. 𝟎𝟏
9
̅)𝟐
𝚺(𝒙−𝒙
 Lastly, we are going to calculate for the variance using the formula, 𝑠 2 = 𝑛+1 . To look for the variance, we
have to find the mean of the given data set. Since, it was already calculated on the average deviation, we
will just copy it, 7.89.
Now, we proceed to calculating for Σ(𝑥 − 𝑥̅ )2 . To get the sum of squares, subtract the mean from each
value, square it, and add them all.
(𝑥 − 𝑥̅ )2 = (6 − 7.89)2 = 3.57
(𝑥 − 𝑥̅ )2 = (7 − 7.89)2 = 0.79
(𝑥 − 𝑥̅ )2 = (7 − 7.89)2 = 0.79
(𝑥 − 𝑥̅ )2 = (7 − 7.89)2 = 0.79
(𝑥 − 𝑥̅ )2 = (8 − 7.89)2 = 0.01
(𝑥 − 𝑥̅ )2 = (8 − 7.89)2 = 0.01
(𝑥 − 𝑥̅ )2 = (9 − 7.89)2 = 1.23
(𝑥 − 𝑥̅ )2 = (9 − 7.89)2 = 1.23
(𝑥 − 𝑥̅ )2 = (10 − 7.89)2 = 4.45
̅)𝟐 = 𝟏𝟐. 𝟖𝟕
𝚺(𝒙 − 𝒙
Then, calculate for the variance using the formula.
̅) 𝟐
𝚺(𝒙 − 𝒙
𝑠2 =
𝑛+1
𝑠2 =
12.87 12.87
=
= 1.287
9+1
10
Therefore, our variance is 1.287.
 Finally, we are now going to calculate the standard deviation. To look for the standard deviation, we are
going to get the square root of the variance.
𝑠 = √1.287 = 1.13
Therefore, our calculated standard deviation is 1.13.
5. So, to calculate the measures of variability, we have to follow the steps below.
 RANGE: Arrange the data in ascending order. Then, subtract the smallest score from the highest score.
 AVERAGE DEVIATION: Find the mean. Find the deviation. Then find the sum of all the deviations and divide
it by the number of data points to get the average deviation.
 VARIANCE: Find the mean. Find the deviation from the mean, square it all, add all, and divide it with (n +
1).
 STANDARD DEVIATION: Find the square root of the variance.
 Are you ready to try to do what I demonstrated? Don’t worry, I will be guiding you and you will be working
in groups.
 Now, let us try these steps again with another set of problems.
MEETING 4: (CALCULATE THE MEASURES OF POSITION OF AN UNGROUPED DATA)
GUIDED PRACTICE 3: Chunking Complexity and Procedural/Differentiation by Content and
Environment
1. The class will be asked to state the steps in calculating the measures of position of an ungrouped data.
2. Worksheet 2 will be distributed to students. This worksheet is consisting of two parts with five (5) questions each:
(1) a true or false and (2) multiple choice questions.
3. The students will answer the worksheet in group of four employing “Three Stay, One Stay”. Students will discuss the
questions. They will be numbered 1 – 4. After the students are done discussing one question at a time, the teacher,
will call a number and that student will leave the group and tell the new group what they discussed. Then they will
proceed to the next question. The process will be repeated until all the items are done.
4. Constantly monitor the students’ work and progress by roaming around the classroom and giving immediate feedback
on how the students are doing the activity.
5. Once done, we have to check the students’ answer in the worksheets and have the students determine whether the
answer is correct or not, and explain why.
6. Address difficulties encountered by the students in answering the worksheet.
MEETING 5: (USE THE APPROPRIATE MEASURE OF CENTRAL TENDENCY AND VARIABILITY
IN ANALYZING AND INTERPRETING UNGROUPED DATA)
INDEPENDENT PRACTICE – Metacognitive and Procedural Scaffolding/Differentiation by
Content and Environment
1. Distribute Worksheet 3 with a problem on using the appropriate measure of central tendency and variability in
analyzing and interpreting ungrouped data.
2. The class will be divided into two groups in answering the worksheet. The 1st group will answer the problem using
the measures of central tendency. The other group will answer the problem by calculating the measures of variability.
3. After answering the problem, the two groups will discuss their answer to come up with a sound conclusion.
4. Students will be reminded whether they were able to complete each step in calculating the measures of central
tendency and position of an ungrouped data in their given problem.
5. The students will be asked about the significant learnings they gained while answering the different worksheets given.
MEETING 6: (CALCULATE THE MEASURES OF POSITION OF AN UNGROUPED DATA)
MODELING
1. Tell the students that they will learn to calculate the quartiles, deciles, or percentiles of an ungrouped data.
2. The students will define the three measures of position using the Vocabulary Map and identify its corresponding
formula.
3. Distribute a copy of data that can be calculated using measures of position of an ungrouped data.
4. Let me demonstrate the first problem that can be calculated using the three measures of position:
“Consider the data set B = {48, 39, 57, 32, 28, 63, 51, 54, 36}. Find the lower quartile, median and 75 th
percentile.”
Explain to the students that each measure of position has its corresponding formula.
5. So, let’s find out the answer to the given problem. In finding the measures of an ungrouped data, it all follows the
same exact steps. The only difference is the formula that we’ll be using in each measure of position.
6. First thing to do is to arrange the data in an increasing order. Next, is to find the position of the k th quartile, decile,
or percentile. Last step is to locate the position of the kth quartile, decile, or percentile in the given set of data.
7. To arrange the set of data in an ascending order, we have 28, 32, 36, 39, 48, 51, 54, 57, and 63.
8. Next is to find the position of the kth quartile, decile, or percentile using its corresponding formula.
𝑘(𝑛+1)
QUARTILE: 𝑄𝑘 = 4
DECILE: 𝐷𝑘 =
𝑘(𝑛+1)
10
PERCENTILE: 𝑃𝑘 =
𝑘(𝑛+1)
100
To continue, we will use the formula 𝑄𝑘 =
𝑘(𝑛+1)
4
. Since we are looking for the lower quartile or 1st quartile, our k will
be 1 and the n or the number of observations is 9.
𝑘(𝑛 + 1)
4
1(9 + 1) 1(10) 10
𝑄1 =
=
=
= 2.5
4
4
4
This only means that our 1st quartile or lower quartile is located on the 2.5th score. And, the 2.5th score lies between
2nd and 3rd score. So, what we will do to get the 2.5th score is to find the value of the 2nd and 3rd score and divide it
32+36
by 2. We have, 2 = 𝟏𝟖. 𝟏𝟔 𝒐𝒓 𝟏𝟖. Therefore, our lower quartile which is located on the 2.5th score is 18.16 or 18.
9. Next, we will try to look for the median. To look for the median of a measures of position of an ungrouped data, we
𝑄𝑘 =
will use the formula for finding the position of the 2nd quartile and we can also use the formula
𝑛+1
2
. Let’s try to use
the two formulas if we will come up with the same correct answer.
10. First, let us use the formula for 2nd quartile.
𝑘(𝑛 + 1)
𝑄𝑘 =
4
2(9 + 1) 2(10)
𝑄2 =
=
=𝟓
4
4
This indicates that the median of the given set of data is located on the 5th score. Let us try to use the other
formula.
median =
𝑛+1
2
=
9+1
2
10
= 2 =𝟓
Since we both came up with the same answer, this only means that the median of the given set of data is located
on the 5th score which is 48.
11. Lastly, let’s try to calculate for the 75th percentile of the given set of data. Since the data are already arranged in an
increasing order, we will proceed to finding the kth position where the 75th percentile belongs. To do this, we will use
𝑘(𝑛+1)
the formula 𝑃𝑘 = 100 .
𝑘(𝑛 + 1)
𝑃𝑘 =
100
75(9 + 1) 75(10) 750
𝑃75 =
=
=
= 𝟕. 𝟓
100
100
100
This only means that the 75th percentile is located on the 7.5th score. And, if you will notice, 7.5th score lies between
54+57
7th and 8th score. So, what we will do is to find the value of the 7th and 8th score and divide it by 2. We have, 2 =
111
2
= 𝟓𝟓. 𝟓. Therefore, our 75th percentile which is located on the 7.5th score is 55.5.
12. Now, let us consider another example in calculating the quartiles, deciles, or percentiles of an ungrouped data.
“Calculate the 6th decile of the following test scores of random samples of ten students: 35, 42, 40,
28, 15, 23, 33, 20, 18, and 28.”
We will repeat the process that we did earlier. In calculating the decile of an ungrouped data, first thing that we
have to do is to arrange the set of data in an increasing order, 15, 18, 20, 23, 28, 28, 33, 35, 40, and 42. After this,
𝑘(𝑛+1)
we will proceed to finding the kth position of the 6th decile using the formula 𝐷𝑘 = 10 .
𝑘(𝑛 + 1)
𝐷𝑘 =
10
6(10 + 1) 6(11) 66
𝐷6 =
=
=
= 6.6 ≅ 𝟕
10
10
10
Therefore, the 6th decile is located on the 7th position which is 33. Our 6th decile is 33.
13. So, to calculate the quartiles, deciles, or percentiles of an ungrouped data, we have to follow the steps below:
a. Arrange the set of data in an increasing order.
b. Find the kth position where the quartile, decile, or percentile belong.
c. Locate the position of the kth quartile, decile, or percentile.
14. Are you ready to try to do what I demonstrated? Don’t worry, I will be guiding you and you will be working in
groups.
15. Now, let us try these steps again with another set of problems.
MEETING 7: (CALCULATE THE MEASURES OF POSITION OF AN UNGROUPED DATA)
GUIDED PRACTICE 3: Chunking Complexity and Procedural /Differentiation by Content and
Environment
1. The class will be asked to state the steps in calculating the measures of position.
2. Worksheet 4 will be distributed to students. This worksheet is consisting of two parts with five (5) questions each:
(1) a true or false and (2) multiple choice questions.
7. The students will answer the worksheet in group of four employing “Three Stay, One Stay”. Students will discuss the
questions. They will be numbered 1 – 4. After the students are done discussing one question at a time, the teacher,
will call a number and that student will leave the group and tell the new group what they discussed. Then they will
proceed to the next question. The process will be repeated until all the items are done.
3. Constantly monitor the students’ work and progress by roaming around the classroom and giving immediate feedback
on how the students are doing the activity.
4. Once done, we have to check the students’ answer in the worksheets and have the students determine whether the
answer is correct or not, and explain why.
5. Address difficulties encountered by the students in answering the worksheet.
MEETING 8: (COMPARE THE CALCULATED VALUES OF THE SPECIFIED MEASURES OF
POSITION)
INDEPENDENT PRACTICE – Metacognitive and Procedural Scaffolding/Differentiation by
Content and Environment
1. Distribute Worksheet 5 with varied problems on comparing the calculated values of the specified measures of position.
2. The students will answer the worksheet individually.
3. Students will be reminded whether they were able to complete each step in calculating the specified measures of
position of an ungrouped data and compare them.
4. The students will be asked about the significant learnings they gained while answering the different worksheets given.