What is Tools Of Assessment Strategies : Checklists, Rating Scales and Rubrics

 

Checklists, Rating Scales and Rubrics

Assessment Strategies and Tools

Checklists, rating scales and rubrics are tools that state specific criteria and allow teachers and students to gather information and to make judgements about what students know and can do in relation to the outcomes. They offer systematic ways of collecting data about specific behaviours, knowledge and skills. The quality of information acquired through the use of checklists, rating scales and rubrics is highly dependent on the quality of the descriptors chosen for assessment. Their benefit is also dependent on students’ direct involvement in the assessment and understanding of the feedback provided. The purpose of checklists, rating scales and rubrics is to: provide tools for systematic recording of observations provide tools for self-assessment provide samples of criteria for students prior to collecting and evaluating data on their work record the development of specific skills, strategies, attitudes and behaviours necessary for demonstrating learning clarify students' instructional needs by presenting a record of current accomplishments. Tips for Developing Checklists, Rating Scales and Rubrics Use checklists, rating scales and rubrics in relation to outcomes and standards. Use simple formats that can be understood by students and that will communicate information about student learning to parents. Ensure that the characteristics and descriptors listed are clear, specific and observable. Encourage students to assist with constructing appropriate criteria. For example, what are the descriptors that demonstrate levels of performance in problem solving? Ensure that checklists, rating scales and rubrics are dated to track progress over time. Leave space to record anecdotal notes or comments. Use generic templates that become familiar to students and to which various descriptors can be added quickly, depending on the outcome(s) being assessed. Provide guidance to students to use and create their own checklists, rating scales and rubrics for self-assessment purposes and as guidelines for goal setting. Checklists usually offer a yes/no format in relation to student demonstration of specific criteria. This is similar to a light switch; the light is either on or off. They may be used to record observations of an individual, a group or a whole class. Rating Scales allow teachers to indicate the degree or frequency of the behaviours, skills and strategies displayed by the learner. To continue the light switch analogy, a rating scale is like a dimmer switch that provides for a range of performance levels. Rating scales state the criteria and provide three or four response selections to describe the quality or frequency of student work. Teachers can use rating scales to record observations and students can use them as self-assessment tools. Teaching students to use descriptive words, such as always, usually, sometimes and neverhelps them pinpoint specific strengths and needs. Rating scales also give students information for setting goals and improving performance. In a rating scale, the descriptive word is more important than the related number. The more precise and descriptive the words for each scale point, the more reliable the tool. Effective rating scales use descriptors with clearly understood measures, such as frequency. Scales that rely on subjective descriptors of quality, such as fair, good or excellent, are less effective because the single adjective does not contain enough information on what criteria are indicated at each of these points on the scale. Added value Increase the assessment value of a checklist or rating scale by adding two or three additional steps that give students an opportunity to identify skills they would like to improve or the skill they feel is most important. For example: put a star beside the skill you think is the most important for encouraging others circle the skill you would most like to improve underline the skill that is the most challenging for you. Rubrics use a set of criteria to evaluate a student's performance. They consist of a fixed measurement scale and detailed description of the characteristics for each level of performance. These descriptions focus on the quality of the product or performance and not the quantity; e.g., not number of paragraphs, examples to support an idea, spelling errors. Rubrics are commonly used to evaluate student performance with the intention of including the result in a grade for reporting purposes. Rubrics can increase the consistency and reliability of scoring. Rubrics use a set of specific criteria to evaluate student performance. They may be used to assess individuals or groups and, as with rating scales, may be compared over time. Developing Rubrics and Scoring Criteria Rubrics are increasingly recognized as a way to both effectively assess student learning and communicate expectations directly, clearly and concisely to students. The inclusion of rubrics in a teaching resource provides opportunities to consider what demonstrations of learning look like, and to describe stages in the development and growth of knowledge, understandings and skills. To be most effective, rubrics should allow students to see the progression of mastery in the development of understandings and skills. Rubrics should be constructed with input from students whenever possible. A good start is to define what quality work looks like based on the learning outcomes. Exemplars of achievement need to be used to demonstrate to students what an excellent or acceptable performance is. This provides a collection of quality work for students to use as reference points. Once the standard is established, it is easy to define what exemplary levels and less-than-satisfactory levels of performance look like. The best rubrics have three to five descriptive levels to allow for discrimination in the evaluation of the product or task. Rubrics may be used for summative purposes to gauge marks by assigning a score to each of the various levels. When developing a rubric, consider the following: What are the specific outcomes in the task? Do the students have some experience with this or a similar task? What does an excellent performance look like? What are the qualities that distinguish an excellent response from other levels? What do other responses along the performance quality continuum look like? Is each description qualitatively different from the others? Are there an equal number of descriptors at each level of quality? Are the differences clear and understandable to students and others? Begin by developing criteria to describe the Acceptable level. Then use Bloom's taxonomy to identify differentiating criteria as you move up the scale. The criteria should not go beyond the original performance task, but reflect higher order thinking skills that students could demonstrate within the parameters of the initial task. When developing the scoring criteria and quality levels of a rubric, consider the following guidelines. Level 4 is the Standard of excellence level. Descriptions should indicate that all aspects of work exceed grade level expectations and show exemplary performance or understanding. This is a "Wow!" Level 3 is the Approaching standard of excellence level. Descriptions should indicate some aspects of work that exceed grade level expectations and demonstrate solid performance or understanding. This is a "Yes!" Level 2 is the Meets acceptable standard. This level should indicate minimal competencies acceptable to meet grade level expectations. Performance and understanding are emerging or developing but there are some errors and mastery is not thorough. This is a "On the right track, but …". Level 1 Does not yet meet acceptable standard. This level indicates what is not adequate for grade level expectations and indicates that the student has serious errors, omissions or misconceptions. This is a "No, but …". The teacher needs to make decisions about appropriate intervention to help the student improve. Creating Rubrics with Students Learning increases when students are actively involved in the assessment process. Students do better when they know the goal, see models and know how their performance compares to learning outcomes. Learning outcomes are clarified when students assist in describing the criteria used to evaluate performance. Use brainstorming and discussion to help students analyze what each level looks like. Use student-friendly language and encourage students to identify descriptors that are meaningful to them. For example, a Grade 3 class might describe levels of quality with phrases such as the following. Super! Going beyond Meets the mark Needs more work. Use work samples to help students practise and analyze specific criteria for developing a critical elements list. They can also use samples to practise assigning performance levels and compare criteria from level to level. Although rubrics are often used as assessment of learning tools, they can also be used as assessment for learning tools. Students can benefit from using rubrics as they become more competent at judging the quality of their work and examining their own progress. Example: Involve students in the assessment process by having them participate in the creation of a rubric. This process facilitates a deeper understanding of the intended outcomes and the associated assessment criteria. After a rubric has been created, students can use it to guide their learning. Criteria described in a rubric serve to focus student reflection on their work and facilitate the setting of learning goals for a particular performance assessment. Through self-assessment or peer-assessment, students can use a rubric to assess work completed to date and use it to guide their planning for the "next steps"

Read More

How HEC’s Cancellation of 2 Year BA and BSc Programs acourding to Old And New Policy

 How HEC’s Cancellation of 2 Year BA and BSc Programs is Affecting Students

The Higher Education Commission (HEC) of Pakistan has abolished the two-year Bachelor’s degree programs at its affiliated institutions across the country. According to its notification, students will no longer be able to pursue two-year degrees like B.Com and B.Sc. This measure is reportedly expected to be enforced in January 2021.

The HEC’s notification has led to students resorting to social media to voice their concerns and questions regarding their futures

Why Has the HEC Introduced This Policy?

Graduate degree programs around the world are typically either 3-year or 4-year programs. Students with two-year B.Com or B.Sc. degrees cannot qualify for international postgraduate studies with their qualifications. Simply put, students with two-year degrees will not be able to apply for foreign Master’s degree programs that require applicants to have completed 16 years of education. Students enrolled in B.Com or B.Sc. programs will qualify for admission to foreign postgraduate programs only if they have completed both their Bachelor’s and Master’s degrees.

According to the HEC, this is not the first time the policy has been introduced. “We have been working on it for the past two years,” it stated.

In March 2017 and July 2019, a notification issued by the HEC had called upon its affiliated universities to immediately discontinue their two-year academic programs. It had been observed by the HEC that despite its notification, the aforementioned programs are still being offered at institutions.

Local institutions will be held responsible for not acknowledging the HEC’s revised policies if the cancelled degrees are still active.

What is HEC’s New Policy?

The HEC will not recognize two-year Bachelor’s degrees including Bachelor’s of Arts (BA) and Bachelor’s of Science (B.Sc.), undertaken after the calendar year 2018. It also issued notifications to its affiliated schools and Degree Awarding Institutions (DAIs ) that it will not recognize these programs in the event of their certifications being awarded to their candidates.

The notification highlighted that students who had enrolled in the discontinued programs at higher education institutes before 31 December 2018 will be allowed to complete them until December 2020. Students who fail in these programs will be awarded the new Associate Degree (AD) upon their completion.

For the avoidance of doubt, students admitted to two-year post-higher secondary or equivalent programs after 31 December 2018 shall have been and shall continue to be admitted to Associate Degree programs.

Through the AD program, the HEC intends for its affiliated institutions to provide general education that has a broader spectrum of application in society. The enrolled students will be trained in the metrics of marketing, financial literacy, and ethics.

Will the Degrees of B.Com. and BA Students be Considered Now, or Will They Have to Reapply?

According to the HEC, these degrees will be accepted. Additionally, the degrees that had commenced prior to December 2019 will also be accepted.

Will Institutions be Fined if They Continue to Offer the Cancelled Programs?

Currently, this option is not under consideration primarily due to the on-going transition from the outdated degree programs to the international standard.

Will the HEC Cancel All the Programs Starting After December 2020?

All the HEC-affiliated institutions are liable to address this query. According to the HEC’s refined plans for the AD programs, the BA/B.Sc. degrees can be converted into ADs with minimum changes in the courses during the first year transition period. To facilitate this change, institutions offering AD programs will be allowed to convert their offered pre-existing program into the new one.

The HEC has also directed institutions to proceed with transient changes in their BA/B.Sc. curricula for the AD program. Although conditionally, the HEC-affiliates should continue transitioning to the AD programs with additions that are in accordance with the policies and guidelines that are periodically provided by the HEC.

What are AD Programs?

AD programs are two-year programs that extend two-year BA or B.Sc. degrees to four years to enable them to be internationally recognized. The goal of an AD program is to provide broad-based education to students along with experiential learning via skill-based courses.

As per the National Qualifications Framework developed by the HEC, AD programs are equivalent to 14 years of education. Prior to their completion, students can enroll themselves in the fifth semester of Bachelor’s programs of their choice after an evaluation of their transcripts by the concerned universities.

Do the Affected Students Have to Apply to Other Schools From Scratch? What About the One Year That They Have Passed?

The HEC’s semester guidelines only allow the exemption of some course credits according to its credits transfer policy.

Students can look for enrollment options in either four-year BS degree programs or BS AD programs. They can take the courses that they have studied in their transcripts. All individual cases will be considered by universities on the basis of the courses that have already been studied, and the universities will decide if the credit hours can be transferred or not.

Do the Affected Students Have to Apply to Other Schools From Scratch? What About the One Year That They Have Passed?

The HEC’s semester guidelines only allow the exemption of some course credits according to its credits transfer policy.

Students can look for enrollment options in either four-year BS degree programs or BS AD programs. They can take the courses that they have studied in their transcripts. All individual cases will be considered by universities on the basis of the courses that have already been studied, and the universities will decide if the credit hours can be transferred or not.

Are Master’s Degree Programs Being Terminated as Well?

According to the HEC, Master’s degree programs are not being terminated. However, candidates can apply for them only after completing a four-year Bachelor’s degree. For students who will have completed their B.Com/BSc programs by 2020, the decision will be taken by their universities.

 

Read More

What is Variable And Types of Variables

 

Variables: Definition, Types of Variable in Research

Variable Definition in Research

A variable is any property, a characteristic, a number, or a quantity that increases or decreases over time or can take on different values (as opposed to constants, such as n, that do not vary) in different situations.

When conducting research, experiments often manipulate variables. For example, an experimenter might compare the effectiveness of four types of fertilizers.

In this case, the variable is the ‘type of fertilizers’. A social scientist may examine the possible effect of early marriage on divorce.

Here early marriage is the variable. A business researcher may find it useful to include the dividend in determining the share prices. Here dividend is the variable.

Effectiveness, divorce and share prices are also variables because they also vary as a result of manipulating fertilizers, early marriage, and dividends.

Types of Variable

1. Qualitative Variables.
2. Quantitative Variables.
3. Discrete Variable.
4. Continuous Variable.
5. Dependent Variables.
6. Independent Variables.
7. Background Variable.
8. Moderating Variable.
9. Extraneous Variable.
10. Intervening Variable.
11. Suppressor Variable.

Qualitative Variables

An important distinction between variables is between the qualitative variable and the quantitative variable.

Qualitative variables are those that express a qualitative attribute such as hair color, religion, race, gender, social status, method of payment, and so on. The values of a qualitative variable do not imply a meaningful numerical ordering.

The value of the variable ‘religion’ (Muslim, Hindu, ..,etc.) differs qualitatively; no ordering of religion is implied. Qualitative variables are sometimes referred to as categorical variables.

For example, the variable sex has two distinct categories: ‘male’ and ‘female.’ Since the values of this variable are expressed in categories, we refer to this as a categorical variable.

 

Similarly, place of residence may be categorized as being urban and rural and thus is a categorical variable.

Categorical variables may again be described as nominal and ordinal.

Ordinal variables are those which can be logically ordered or ranked higher or lower than another but do not necessarily establish a numeric difference between each category, such as examination grades (A+, A, B+, etc., clothing size (Extra large, large, medium, small).

Nominal variables are those who can neither be ranked nor logically ordered, such as religion, sex, etc.

A qualitative variable is a characteristic that is not capable of being measured but can be categorized to possess or not to possess some characteristics.

Quantitative Variables

Quantitative variables, also called numeric variables, are those variables that are measured in terms of numbers. A simple example of a quantitative variable is a person’s age.

The age can take on different values because a person can be 20 years old, 35 years old, and so on. Likewise, family size is a quantitative variable, because a family might be comprised of one, two, three members, and so on.

That is, each of these properties or characteristics referred to above varies or differs from one individual to another. Note that these variables are expressed in numbers, for which we call them quantitative or sometimes numeric variables.

A quantitative variable is one for which the resulting observations are numeric and thus possesses a natural ordering or ranking.

Discrete and Continuous Variables

Quantitative variables are again of two types: discrete and continuous.

Variables such as some children in a household or number of defective items in a box are discrete variables since the possible scores are discrete on the scale.

For example, a household could have three or five children, but not 4.52 children.

 

Other variables, such as ‘time required to complete an MCQ test’ and ‘waiting time in a queue in front of a bank counter,’ are examples of a continuous variable.

The time required in the above examples is a continuous variable, which could be, for example, 1.65 minutes, or it could be 1.6584795214 minutes.

Of course, the practicalities of measurement preclude most measured variables from being continuous.

Discrete Variable

Definition 2.6: A discrete variable, restricted to certain values, usually (but not necessarily) consists of whole numbers, such as the family size, number of defective items in a box. They are often the results of enumeration or counting.

A few more examples are;

• The number of accidents in the twelve months.
• The number of mobile cards sold in a store within seven days.
• The number of patients admitted to a hospital over a specified period.
• The number of new branches of a bank opened annually during 2001- 2007.
• The number of weekly visits made by health personnel in the last 12 months.

Continuous Variable

A continuous variable is one that may take on an infinite number of intermediate values along a specified interval. Examples are:

• The sugar level in the human body;
• Blood pressure reading;
• Temperature;
• Height or weight of the human body;
• Rate of bank interest;
• Internal rate of return (IRR),
• Earning ratio (ER);
• Current ratio (CR)

No matter how close two observations might be, if the instrument of measurement is precise enough, a third observation can be found, which will fall between the first two.

A continuous variable generally results from measurement and can assume countless values in the specified range.

Dependent and Independent Variables

In many research settings, there are two specific classes of variables that need to be distinguished from one another, independent variable and dependent variable.

Many research studies are aimed at unrevealing and understanding the causes of underlying phenomena or problems with the ultimate goal of establishing a causal relationship between them.

Look at the following statements:

• Low intake of food causes underweight.
• Smoking enhances the risk of lung cancer.
• Level of education influences job satisfaction.
• Advertisement helps in sales promotion.
• The drug causes the improvement of a health problem.
• Nursing intervention causes more rapid recovery.
• Previous job experiences determine the initial salary.
• Blueberries slow down aging.
• The dividend per share determines share prices.

In each of the above queries, we have two variables: one independent and one dependent. In the first example, ‘low intake of food’ is believed to have caused the ‘problem of underweight.’

It is thus the so-called independent variable. Underweight is the dependent variable because we believe that this ‘problem’ (the problem of underweight) has been caused by ‘the low intake of food’ (the factor).

Similarly, smoking, dividend, and advertisement all are independent variables, and lung cancer, job satisfaction, and sales are dependent variables.

In general, an independent variable is manipulated by the experimenter or researcher, and its effects on the dependent variable are measured.

Independent Variable

The variable that is used to describe or measure the factor that is assumed to cause or at least to influence the problem or outcome is called an independent variable.

The definition implies that the experimenter uses the independent variable to describe or explain the influence or effect of it on the dependent variable.

Variability in the dependent variable is presumed to depend on variability in the independent variable.

Depending on the context, an independent variable is sometimes called a predictor variable, regressor, controlled variable, manipulated variable, explanatory variable, exposure variable (as used in reliability theory), risk factor (as used in medical statistics), feature (as used in machine learning and pattern recognition) or input variable.

The explanatory variable is preferred by some authors over the independent variable when the quantities treated as independent variables may not be statistically independent or independently manipulable by the researcher.

If the independent variable is referred to as an explanatory variable, then the term response variable is preferred by some authors for the dependent variable.

Dependent Variable

The variable that is used to describe or measure the problem or outcome under study is called a dependent variable.

In a causal relationship, the cause is the independent variable, and the effect is the dependent variable. If we hypothesize that smoking causes lung cancer, ‘smoking’ is the independent variable and cancer the dependent variable.

A business researcher may find it useful to include the dividend in determining the share prices. Here dividend is the independent variable, while the share price is the dependent variable.

The dependent variable usually is the variable the researcher is interested in understanding, explaining, or predicting.

In lung cancer research, it is the carcinoma that is of real interest to the researcher, not smoking behavior per se. The independent variable is the presumed cause of, antecedent to, or influence on the dependent variable.

Depending on the context, a dependent variable is sometimes called a response variable, regressand, predicted variable, measured variable, explained variable, experimental variable, responding variable, outcome variable, output variable, or label.

An explained variable is preferred by some authors over the dependent variable when the quantities treated as dependent variables may not be statistically dependent.

If the dependent variable is referred to as an explained variable, then the term predictor variable is preferred by some authors for the independent variable.

Levels of an Independent Variable

If an experimenter compares an experimental treatment with a control treatment, then the independent variable (a type of treatment) has two levels: experimental and control.

If an experiment were to compare five types of diets, then the independent variables (types of diet) would have five levels.

In general, the number of levels of an independent variable is the number of experimental conditions.

Background Variable

In almost every study, we collect information such as age, sex, educational attainment, socioeconomic status, marital status, religion, place of birth, and the like. These variables are referred to as background variables.

These variables are often related to many independent variables so that they influence the problem indirectly. Hence they are called background variables.

If the background variables are important to the study, they should be measured. However, we should try to keep the number of background variables as few as possible in the interest of the economy.

Moderating Variable

In any statement of relationships of variables, it is normally hypothesized that in some way, the independent variable ’causes’ the dependent variable to occur. In simple relationships, all other variables are extraneous and are ignored. In actual study situations, such a simple one-to-one relationship needs to be revised to take other variables into account to better explain the relationship.

This emphasizes the need to consider a second independent variable that is expected to have a significant contributory or contingent effect on the originally stated dependent-independent relationship. Such a variable is termed a moderating variable.

Suppose you are studying the impact of field-based and classroom-based training on the work performance of the health and family planning workers, you consider the type of training as the independent variable.

If you are focusing on the relationship between the age of the trainees and work performance, you might use ‘type of training’ as a moderating variable.

Extraneous Variable

Most studies concern the identification of a single independent variable and the measurement of its effect on the dependent variable.

But still, several variables might conceivably affect our hypothesized independent-dependent variable relationship, thereby distorting the study. These variables are referred to as extraneous variables.

Extraneous variables are not necessarily part of the study. They exert a confounding effect on the dependent-independent relationship and thus need to be eliminated or controlled for.

An example may illustrate the concept of extraneous variables. Suppose we are interested in examining the relationship between the work-status of mothers and breastfeeding duration.

It is not unreasonable in this instance to presume that the level of education of mothers as it influences work-status might have an impact on breastfeeding duration too.

Education is treated here as an extraneous variable. In any attempt to eliminate or control the effect of this variable, we may consider this variable as a confounding variable.

An appropriate way of dealing with confounding variables is to follow the stratification procedure, which involves a separate analysis for the different levels of lies confounding variables.

For this purpose, one can construct two cross­tables: one for illiterate mothers and the other for literate mothers. If we find a similar association between work status and duration of breast­feeding in both the groups of mothers, then we conclude that the educational level of mothers is not a confounding variable.

Intervening Variable

Often an apparent relationship between two variables is caused by a third variable.

For example, variables X and Y may be highly correlated, but only because X causes the third variable, Z, which in turn causes Y. In this case, Z is the intervening variable.

An intervening variable theoretically affects the observed phenomena but cannot be seen, measured, or manipulated directly; its effects can only be inferred from the effects of the independent and moderating variables on the observed phenomena.

In the work-status and breastfeeding relationship, we might view motivation or counseling as the intervening variable.

Thus, motive, job satisfaction, responsibility, behavior, justice are some of the examples of intervening variables.

Suppressor Variable

In many cases, we have good reasons to believe that the variables of interest have a relationship within themselves, but our data fail to establish any such relationship. Some hidden factors may be suppressing the true relationship between the two original variables.

Such a factor is referred to as a suppressor variable because it suppresses the actual relationship between the other two variables.

The suppressor variable suppresses the relationship by being positively correlated with one of the variables in the relationship and negatively correlated with the other. The true relationship between the two variables will reappear when the suppressor variable is controlled for.

Thus, for example, low age may pull education up but income down. In contrast, a high age may pull income up but education down, effectively canceling out the relationship between education and income unless age is controlled for.

Concept

The concept is a name given to a category that organizes observations and ideas by their possession of common features. As Bulmer succinctly puts it, concepts are categories for the organization of ideas and observations (Bulmer, 1984:43).

If a concept is to be employed in quantitative research, it will have to be measured. Once they are measured, concepts can be in the form of independent or dependent variables.

In other words, concepts may explain (explanatory variable) of a certain aspect of the social world, or they may stand for things we want to explain (dependent variable).

 

Read More