Introduction of Record Keeping for Continuous Assessment
In counselling, facts are basic. Counselling takes off only after the facts are poured out. Thus the comprehensive system of records has to be kept and maintained if such information is to be handy. Records should include the collected information on pupils, such as their scheme of studies, their wide range of interest and aptitudes, their health condition, their home environment and hobbies, and many more.
The information may include typical problems about some aspects of the pupils that have come to the limelight. For instance, holding perfect positions, outstanding accomplishments, exhibited personal traits; this information should be logically recorded and interpreted and would form the factual basis for most vital counselling. Thus, this type of content is to be preserved as an anecdotal record. A special file is earmarked for each pupil and teachers and other student counsellors who observe pupils closely are urged to note in the file short summary of behaviour they consider crucial to take into account.
A good record system will also include reports of all the personal meetings or interactions held with pupils. Records adequate to the placement services of the counselling program should also be kept. Besides, records relating to employment and to educational opportunities must be matched skillfully against records relating to the specific individual. One of the notable developments during the last few decades on counselling is the emergence of cumulative records. This record carries the reports on a pupil for a number of years, covering one level to another. Continuous assessment of a student covers his performance throughout the duration of a course rather than relying on an examination at the end of the course. Thus, records of the student’s scores are being kept from the 1st continuous assessment, 2nd continuous assessment and then the final examination. The total of the scores makes up the course or subject.
Thus, without a well-maintained system of record-keeping for pupils’ examination or test performance, there will be nothing to build on. Records provide a long term profile of achievement for each pupil. It is noteworthy that every effort must be made to use the results of examinations and tests as a feedback mechanism for the further development of public learning and teaching strategies and another curriculum process.
Finally, in keeping records, one should adopt the following:
- Make explicit what students know, understand and are able to do in relation to others and what has been taught in the curriculum.
- Record the achievement of all the students.
- The record should be manageable and can be easily understood by all who use them.
- Provide information about achievement especially the part of the curriculum taught.
- The record should be consistent throughout the school and are passed on from one teacher to the next.
- The information recorded should be in a form that will allow schools to monitor students’ achievement and progress.
- It is very important to have a clear well managed system of storing and retrieving pupils’ records.
Note, there is a need to check and maintain the right type of record books and documents on which to record information about each pupil. You may need to design and produce suitable material yourself.
The Report Cards
The transcript is a report usually given to a student who wants to transfer to another school. It is also given when a pupil has finished his primary six and is looking for a job.
The Transcript Contains:
- The cumulated performance of a child.
- Scores of the child in the area of academics.
- Character and skills.
- The relative position of the child in his class.
- The two termly summaries.
- The end of examination scores.
- Scores on the physical and social development
Class Records of Teacher
This record is kept in all facets of the school system including primary, secondary, and university/higher institutions. It contains;
- The number on the roll call in class.
- Daily attendance.
- Personal data.
- Detailed scheme of work for the week, semester, term and year.
- Already completed scheme of work.
The format for keeping this record varies from one school to the other and it has numerous benefits.
The measure of Central Tendency
The central tendency can be referred to as a measure of the central nature of data and it is used to summarize the data set. It is the tendency of the values of a random variable to cluster around the mean, median and mode. The mean and the median are summary measures used to describe the most typical value in a set of values. The difference between the mean and median can be illustrated with an example. Suppose we draw a sample of five women and measure their weight. They weighed 60 pounds, 60 pounds 90 pounds, 100 pounds and 130 pounds.
To find the median, the observations are arranged in order from the smallest to the largest value. If there is an odd number the middle value becomes the median but if there is an even number of observations, the average of the two middle values becomes the median. Thus in the above example, the median will be 90 pounds since 90 pounds is the middleweight.
Note that, the median may be a better indicator of the most typical value if a set of scores have an extreme value that differs greatly from other values referred to as outlier. On the other hand, the mean score usually promotes a better measure of central tendency when the sample size is large and does not include outliers. For instance, if a sample of an estimate of the income of 10 families is taken. If nine of the families have incomes between N50,000 – N130,000 but the tenth family has an income of 9,000,000,000 the tenth family is an outlier. If one now wants to choose a measure to estimate the income of a typical family, the median will do it better because there is an outlier while the mean will greatly overestimate the income of a typical family.
However, the three important measures of central tendency are the mean, the median and the mode. Having explained mean and median, the question now, is what is the mode? Mode is the most frequently occurring value in the data set. Furthermore, what are the characteristics of each of them?
Mean is computed using all the values in the data set. When compared to the median or mode the mean varies less for samples taken from the same population. The mean is unique for a data set. It may not be one of the data values in the distribution. It is used in the computation of other statistics like the variance.
Not to be used for data sets containing outliers as the presence of outliers in a data set affects it.
There is the computation of mean for grouped and ungrouped data; the computation applies to both, but for the grouped data the midpoint of the class is used as x. Take, for instance, the following data set on the scores of 13 students after the first continuous assessment test, to find the mean of the following scores:
11, 12, 13, 16, 16, 17, 17, 18, 18, 19, 19,19, 20
The mean of the data set will be
- The median represents the 50% mark in a distribution.
- It is the balancing point in an ordered data set.
- It is a measure of position as well.
- It is used if the analysis requires the middle value of the distribution.
- Used to determine whether the given data value falls in the upper or lower half of the distribution.
- Median can be used even if the classes in frequency distribution are open-ended.
- Generally used when the data contains outliers.
For instance, in the example earlier given the median will be the middle number after having arranged the scores from the smallest to the largest which is 17. 11, 12, 13, 16, 16, 17, 17, 18, 18, 19, 19, 19, 20
As the number of the data is 13 which is an odd number, there is only one middle value in the data array which is the 7th =17.
The value or category that occurs most in a data set, but where all the elements in the data set have the same frequency of occurrence then distribution does not have a mode. If unimodal distribution, one value occurs most frequently in comparison to other values and if bimodal distribution, it means two elements have the highest frequency of occurrence.
However, the model has the following characteristics:
- The easiest average to determine and it is used when the most typical value is required as the central value.
- Is not unique, a distribution can have one mode, no mode at all, or more than one mode.
For instance, in the scores used
= 11, 12, 13, 16, 16, 17, 17, 18, 18, 19, 19, 19, 20
scores the mode is 19 as it occurred 3 times.
In summary, a measure of central tendency can be referred to as the term which defines the centre of data.
Measures of Dispersion
Peoples’ heights, ages, scores, etc. do vary. There is a need to measure the extent to which scores in a data set differ from each other. This type of measure is referred to as a measure of the dispersion of a distribution. It describes how scores within the distribution differ from the distributions of mean and median:
It is the simplest measure of dispersion – it could be looked at in two ways:
- As a quantity- that is the difference between the highest and lowest scores in a distribution example, the range of scores on a test is 24.
- As an interval- that is the lowest and highest scores may be taken as the range. For example, the range is 52 to 83.
The range is of limited use as a measure of dispersion because it reflects information about extreme values but not necessarily about typical values. However, the most commonly used measure of dispersion is variance and standard deviation.
Variance is the average squared difference of scores from the mean score of a distribution. In calculating the variance of data points, the difference between each point and the mean is squared because if the differences are summed directly, the result will always be zero, for instance, suppose a test of 10 marks is given to three students and each scored 5.5, 7.5 and 8 marks respectively.
The mean of this value is (5.5+7.5+8) ÷ 3 = 7,
if we summed the differences of the mean from each score we would get
(5.5-7) + (7.5-7) + (8-7) = -1.5 + 15+1-0 instead, we square the terms to obtain a variance equal to 2.25 + 0.25 +1 = 3.5.
This figure is a measure of dispersion in the set of scores. Variance is designated S2.
Standard diversion is a measure of dispersion describing the spread of scores around the mean. Simply, the square root of the variance is referred to as the standard deviation. Taking the square root of the variance ‘undoes’ the squaring of the differences that was done when variance was calculated. Standard deviation is designated S.
The standard deviation has this formula for the population mean.
S = while for sample score S = while for variance = S2 =
The standard deviation and variance are the most commonly used measures of dispersion, especially in social sciences. This is because both take into account the precise difference between each score and the mean. These measures are based on a maximum amount of information.
The standard deviation is the baseline for defining the concept of the standardized score, while the variance in a set of scores on some dependent variable is a baseline for measuring the correlation between two or more variables.
Note that actual scores from distribution are commonly known as raw scores.
When we are given an absolute deviation from the mean, expressed in terms of empirical units. It is difficult to tell if the difference is large or small compared to other members of the data set. But we will get more information about deviation from the mean when the standard deviation is used. The scores can be converted to S-scores (standards scores). The S-score being a measure of how many units of standard deviation the raw score is from the mean. Thus the S-score is a relative measure instead of an absolute measure. Raw scores are converted to standardized S-scores using the following equations
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