Highest Enabling Strategy to Use in Developing
Highest Thingking Skills to Assess
the Highest Thinking Skill to Assess
Performance Standards Learning Comptencies
Content
Content Assessment
Standards RBT Level Enabling General Strategy Teaching Strategy
Technique
Beyond KUD
Minimum Minimum WW QA PC
Minimum Classification
Lump LC1,2,3 Knowing Understanding Representation
The learner Problem-posing/
demonstrates The learner is able to LC4 Knowing Remembering / Representation Problem-based
understanding apply an appropriate
Random
of key random variable for a LC5 Understanding Analyzing / Communication Strategy/CPA
Variables and
concepts of given real life problems Approach
Probability
random (such as in decision LC6 Understanding Applying Connection
Distributions
variables and making and games of (Concrete,
probability chances) LC7 – 9 Understanding Analyzing / Communication Pictorial,
distributions Abstraction)
LC10 Doing Creating Problem Solving
Normal The learner The learner is able to Illustrates a normal Inquiry based,
Distribution demonstrates solve real-life problems in random variables and Knowing Understanding / / / Representation Think-Pair-Share
key concepts different disciplines its characteristics Activity
of normal involving normal
Constructs a normal
probability distribution Understanding Applying / / / Connection
curve
distribution
Identifies regions
under the normal
curve corresponding Understanding Applying / / / Connection
to different standard
normal values.
Converts a normal Understanding Applying / / / Connection
random variable to a
standard normal
variable and vice
versa.
Computes
probabilities and
Understanding Applying / / / Connection
percentiles using
standard normal table
Illustrates random
Knowing Understanding / / / Representation
sampling
Distinguishes between
parameter and Knowing Understanding Representation
statistic
Identifies sampling Inquiry-based
distributions of
Knowing Understanding Representation
statistics (sample
mean)
Finds the mean and
variance of the
The learner Understanding Analyzing Connection
sampling distribution
demonstrates of the sample mean
The learner is able to Discovery
understanding Defines the sampling
apply suitable sampling Approach
of key distribution of the
Sampling and and sampling distributions
concepts of sample mean for
Sampling of the sample mean to
sampling and normal population Knowing Understanding Representation
Distribution solve the real-life
sampling when the variance is:
problems in the different
distributions (i) known, (ii)
disciplines
of sample unknown
means.
Illustrates the Central
Knowing Understanding Representation
Limit Theorem Collaboration
Defines the sampling
distributions of the
sample mean using Knowing Understanding Representation
the Central Limit
Theorem
Problem-posing/
Solves problems
problem-based
involving distributions Doing Creating Problem Solving
strategy/CPA
of the sample mean
approach
Illustrates point and Interactive
Knowing Remembering Representation
interval estimations discussion
Distinguishes between
Interactive
point and interval Knowing Remembering Representation
discussion
estimation
Identifies point Probing/
estimator for the Knowing Understanding Representation Interactive
population mean discussion
The learner
The learner is able to
demonstrates Computes for the
estimate the population Interactive
understanding point estimate of the Understanding Applying / / / Connection
mean and population discussion
Estimation of of key population mean
proportion to make sound
Parameters concepts of
inferences in real-life
estimation of Identifies the
problems in different
population appropriate form of
disciplines.
mean the confidence
interval estimator for
the population mean
when: (a) the Interactive
Knowing Understanding Representation
population variance is discussion
known, (b) the
population variance is
unknown, and (c) the
Central Limit Theorem
is to be used.
Interactive
Illustrates the t-
Understanding Applying Connection discussion/
distribution
Diagrams
Constructs a t –
Understanding Applying / Connection Collaboration
distribution
Identifies regions
under the t-
Interactive
distribution Knowing Applying / / Connection
discussion
corresponding to
different t-values
Experiential
Identifies percentiles learning/
Understanding Applying / / Communication
using the t-table Interactive
discussion
Computes for the
confidence interval
Interactive
estimate based on the
Understanding Applying / / Connection discussion/
appropriate form of
Collaboration
the estimator for the
population mean.
Solves problems
Experiential
involving confidence
Understanding Applying / / Connection Learning/
interval estimation of
Collaboration
the population mean.
Draws conclusion
about the population
Probing/
mean based on its Understanding Applying / / / Connection
Collaboration
confidence interval
estimate.
Test of The learner The learner The learner Illustrates: (a) null Knowing Understanding / Representation Experiential
Hypothesis demonstrates is able to is able to hypothesis (b) Learning Inquiry
understanding perform use the alternative hypothesis based Guided-
of key appropriat appropriate (c) level of significance discovery
concepts of e tests of tools useful (d) rejection region; approach,
tests of hypothesis in and (e) types of errors interdisciplinary
hypotheses on of the processing in hypothesis testing approach,
the population and Calculates the connection,
population mean and managing probabilities of collaboration
mean and proportion numerical Understanding Applying / Representation
committing a Type I
population to make data in and Type II error.
proportion sound order to
inferences describe a
Identifies the
in real-life phenomeno
parameter to be Understanding Analyzing / Connection
problems n.
tested given
in the
different Formulates the
disciplines appropriate null and
alternative Understanding Analyzing / Communication
hypotheses on a
population mean.
Identifies the
appropriate form of
the test-statistic Experiential
when: (a) the Learning Inquiry
population variance is based Guided-
assumed to be known discovery
Knowing Understanding / Representation approach,
(b) the population
variance is assumed to interdisciplinary
be unknown; and (c) approach,
the Central Limit connection,
Theorem is to be collaboration
used.
Identifies the Understanding Analyzing / Communication
appropriate rejection
region for a given
level of significance
when: (a) the
population variance is
assumed to be known
(b) the population
variance is assumed to
be unknown; and (c)
the Central Limit
Theorem is to be
used.
Computes for the test-
statistic value Understanding Applying / Connection
(population mean)
Draws conclusion
about the population
mean based on the Understanding Analyzing / Communication
test-statistic value and
the rejection region.
Solves problems
involving test of
Understanding Evaluating / Reasoning and Proof
hypothesis on the
population mean.
Formulates the
appropriate null and
alternative
Understanding Applying / Connection
hypotheses on a
population
proportion.
Identifies the Experiential
appropriate form of Learning Inquiry
test-statistic when the Understanding Analyzing / / Communication based Guided-
Central Limit Theorem discovery
is to be used. approach,
Identifies the interdisciplinary
appropriate rejection approach,
region for a given connection,
level of significance Understanding Analyzing / / Communication collaboration
when the Central
Limit Theorem is to be
used.
Computes for test-
statistic value
Understanding Applying / / Connection
(population
proportion)
Draws conclusion
about the population Understanding Analyzing / / Communication
proportion based on
the test-statistic value
and the rejection
region.
Solves problems
involving test of
hypothesis on the Understanding Evaluating / Reasoning and Proof
population
proportion.
Illustrates the nature
Knowing Understanding Representation Inquiry-based
of bivariate data
Constructs a scatter
Understanding Applying / / / Connection Collaboration
plot
The learner Describes shape
demonstrates (form), trend
understanding The learner is able to (direction), and Understanding Applying Connection
Correlation variation (strength)
of key perform correlation and
and based on scatter plot.
concepts of regression analyses on
Regression
correlation real life problems in Estimates strength of
Analysis Lecture-
and different disciplines association between
Understanding Applying Connection discussion
regression the variables based on
analyses. a scatter plot.
Calculates the
Pearson’s sample Understanding Applying Connection
correlation coefficient
Small-group
Solves problems
discussion/collab
involving correlation Understanding Applying Connection
oration/lecture-
analysis.
discussion
Identifies the
independent and Understanding Applying Communication
dependent variables.
Draws the best-fit line
Understanding Applying Communication
on a scatter plot.
Small-group
Calculates the slope
discussion/collab
and y-intercept of the Understanding Applying Connection
oration/lecture-
regression line
discussion
Interprets the
calculated slope and
Understanding Analyzing Communication
y-intercept of the
regression line
Predicts the value of
the dependent
variable given the Understanding Analyzing Communication
value of the Small-group
independent variable discussion/collab
oration/lecture-
Solves problems discussion
involving regression Understanding Analyzing Communication
analysis
Prepared by: Checked by:
JUNE ERNEST M. TESORIO CORAZON L. NATIVIDAD
Subject Teacher Principal
