ASSESSMENT OF LEARNING 2
1. RESEARCH-BASED INQUIRY – It is used to conduct systematic, intentional, fieldbased inquiry into the practice of teaching. A range of activities fall under the umbrella of
this approach, including study groups, curriculum writing, case studies, program
evaluation, and trying out new practices. The following examples are ways in which this
approach can be implemented:
 In Rhode Island, a group of ESL teachers initiated a research process to, among
other goals, help learners measure their own progress in literacy.
 In Virginia, groups of teachers develop inquiry projects with the assistance of
locally trained staff development facilitators.
 In California, instructors involved with the CWELL Action Research Center,
engage in research projects that focus on developing a better understanding of a)
their students’ attitudes, beliefs and achievement in and out of the classroom, b)
the children of adult students, and c) the community surrounding the educated
system.
2. STATISTICS – a science of discipline. It is a branch of Mathematics that deals with
collection, organization, presentation, computation and interpretation of data which are
the, outcomes of learning.
Example: 1) For instance, suppose we selected a random sample of 100 students from a
school with 1000 students. The average height of the sampled students would be an
example of statistics.
2) Comparison of data of different subjects, students, teachers, etc.
3. DESCRIPTIVE STATISTICS – uses methods to summarize a collection of data by
describing what was observed using numbers or graphs
Example: You might visit a school and ask a sample of 100 students if they like
Mathematics subject. You could make a bar chart of yes or no answers.
4. INFERENTIAL STATISTICS – also called PREDICTIVE STATISTICS. It uses
methods to draw patterns in the collected data, and then makes conclusions, predictions
or forecasts about a group or about a process being studied.
Example: You might visit a school and ask a sample of 100 students if they like
Mathematics subject. You could use your research to reason out that around 75-80% of
the population like Mathematics subject.
5. STATISTICAL LITERACY – It is the ability to understand and reason with statistics and
data.
Example: Ability to both critically evaluate statistical material and appreciate the
relevance of statistically-based approaches to all aspects of life in general.
6. EXPERIMENTAL STUDIES – These inquiries investigates causes, in addition to
drawing conclusions on the effect of changes in elements (variables) being studied.
Example: 1) Suppose you want to study the effect of smoking on lung capacity of
women.
2) To study the effect of a possible preventive measure on people who do not
yet have a particular disease.
7. INFERENTIAL STUDIES – Data are gathered and the correlations between intervention
(predictors) and the result derived from a single group are investigated.
Example: Suppose there is a training program that claims to improve test scores and an
experimenter wants to verify the claims. She starts with two groups, one taking the
training program and the other not. She measures the test scores in the beginning and at
the end, making sure that the starting test scores are, on an average, the same for both test
groups. The researcher finds that the test scores for those who take the training are indeed
higher now, and this difference is statistically significant. She concludes that the training
program is effective in improving test scores.
8. CONCEPTUALIZATION – It is a process where instructor communicates with concepts
about external realities.
Example: When we see the concept “feminism”, we make a list of phenomena
representing the concept.
9. CONSTRUCT – When measuring behavioral outcomes, the personal characteristic to be
assessed is call construct. The construct is a proposed attribute of a person that often
cannot be measured directly, but can be assessed using number of indicators or manifest
variables.
Example: Intelligence
Life satisfaction
10. INDICATOR – A sign of the presence of a concept (variable) under study.
Example: 1) An intelligence test is used as an indication of intelligence.
2) Percentages of men who state that it is not acceptable to hit, slap, punch
their wives with hands or other objects under any circumstances.
3) If you’re going to study how college students feel about abortion and why,
the first thing you’ll have to specify is what you mean by “the right to abortion”.
11. DIMENSION – A specific aspect of a concept combined into groups or sub-groups.
Example: 1) Compassion toward neighbors/fellow nationals/foreigners/animals/plants.
2) Dimensions of religiosity: belief, ritual, devotional, knowledge
12. NOMINAL DEFINITION – assigned to term, not the real entity
Example: The term “social adjustments” might be “appropriate performance of one’s
major roles in life” – as parent, student, employee, spouse, and so on.
13. OPERATIONAL DEFINITION – specifies how a concept is measured
Example: The term “weight” of an object would be something like this: “weight refers to
the numbers that appear when an object is placed on a weighing scale.
14. REAL DEFINITION – better clarified status of a real thing
1) Zoologist’s definition of “tiger”.
2) To discover the real definition of a term X one needs to investigate the
thing or things denoted by X.
15. VARIABLES – Logical set of attributes
Example: 1) Gender
2) Age
3) Class Grades
16. ATTRIBUTE – A quality or characteristics of something. It may represent any of the
four levels of measurement.
Example: 1) Under the study, like habit of smoking, or drinking. So ‘smoking’ and
‘drinking’ are attributes.
2) Republican, Democratic, Green Party, Independent, Other to measure
Political Affiliation
17. NOMINAL MEASURE – A level of measurement describing a variable that has
attributes which are different.
Example: 1) When classifying people students according to their favorite color, there is
no sense in which green is placed “ahead of” blue. Responses are merely categorized.
2) Males = 1, Females = 2
3) Sales Zone A = Islamabad, Sales Zone B = Rawalpindi
4) Drink A = Pepsi Cola, Drink B = 7-Up, Drink C = Pop Cola
18. ORDINAL MEASURE – A level of measurement describing a variable with attributes
that can be in a rank-order along some dimension.
Example: 1) Our satisfaction ordering makes it meaningful to assert that one student is
more satisfied than another with their new uniform.
2) Career Opportunities = Moderate, Good, Excellent
3) Investment Climate = Bad, Inadequate, Fair, Good, Very Good
4) Merit = A grade, B grade, C grade, D grade
19. INTERVAL MEASURE – A level of measurement describing a variable whose attributes
are rank-ordered and have equal distances between adjacent attributes.
Example: 1) Fahrenheit scale of temperature. The difference between 30 degrees and 40
degrees represents the same temperature difference as the difference between each 80
degrees and 90 degrees. This is because each 10-degree interval has the same physical
meaning.
2) Consumer Price Index
20. RATIO MEASURE – A level of measurement describing a variable with attributes that
have all the qualities of nominal, ordinal and interval and based on a “true zero” point.
Example: 1) Amount of money you have in your pocket right now. Money is measured
on a ratio scale because, in addition to having the properties of interval scale, it has true
zero point: if you have zero money, this implies absence of money.
2) Weight
3) Distance
4) Temperature on the Kelvin Scale
21. INDEX – In an index, scores for individual attributes are constructed.
Example: We might measure religiosity by adding up the number of religious events the
respondent engages in during an average month.
22. SCALE – In a scale, scores are assigned to patterns of ideas. A scale is constructed by
assigning scores to patterns of responses according to higher and lower degrees of civic
participation.
Example: If we are constructing a scale of political activism, we might score “running for
office” higher than simply “voting in the last election”.
23. TYPOLOGY – A classification of observations in terms of attributes on two or more
variables.
Example: The evolution and nuances of language can be better understood when
approached by looking at various similar languages with common traits rather than by
broadly attempting to compare and contrast all languages simultaneously.
24. UNIVARIATE ANALYSIS – This is an analysis of a single variable for purposes of
description.
Example: One example of a variable in univariate analysis might be “age”. Another
might be “height”. Univariate analysis would not look at these two variables at the same
time, nor would it look at the relationship between them.
25. BIVARIATE ANALYSIS – It involves analysis of two variables, for the purpose of
determining empirical relationship between them.
Example: Creating a scatterplot by plotting one variable against another on a Cartesian
plane (think X and Y axis) can sometimes give you a picture of what the data is trying to
tell you. If the data seems to fit a line or curve then there is a relationship or correlation
between two variables.
Submitted by:
NESLY ANN T ANGULO
BSED Student