A Science Course Evaluation and Enhancement Programme. Performance of Students in a school-based Implementation


Academic Paper, 2017

18 Pages


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Abstract

This study is to evaluate the performance of the student to provide an enhancement that is best in the learning process in a school-based implementation of the science subject based in the result of the student performance in the achievement test. The study uses the second-hand data from the coordinator listed performance from grade 8 to 11 to describe and seeks the significant relationship, difference and predictors of the variables involve. The data used is heterogeneous in nature that the normality and symmetric characteristics is not normal. All the components of science are significantly related except the systems of science because of lacking information. The student grade level and the level of science are the predictors of the student achievement test. The student performance has different effect when it is categorized by class. The properties and structure of systems are the components of science that accepts the assumption and the properties, structure of system and designing solutions as the components of science which all of their means are equal when categorized by grade level. The implication are to consider the grade level before administering test and plan out the category per grade level to have a successful result in the implementation.

Introduction & Importance

Students of the 21st century as citizen are the important role player in understanding and making reasonable decisions about the developments in science and technology but taking and failing in subject matter still is significantly weak because of many students are under-challenged to see the relevance in studying science1. Science as a subject matter used different teaching methodology to make it interesting as taught in colleges and universities as simple experimental learning. Though, there are good data showing that traditional approaches to teaching science are not successful for a large proportion of students and few research-based approaches that achieve much better learning in the classroom rather than doing experimental projects2. Therefore the major objective of this study is to evaluate the performance of the student to provide an enhancement that is best in the learning process in a school-based implementation.

There are many ways to address these gaps between the primary purpose of science and the understanding of the students in learning science. One of which is by means of research that has been the major tool in providing solutions to an institutional change. Change is a great challenge in an institution especially in the educational advancement because of several issues to deal with. Some of these issues are lack of authentic assessment of what students actually learn that impossible to broadly measure that learning and likewise impossible to connect it to resources, absence of resources effective research-based teaching in science courses by developing and testing pedagogically effective materials, supporting technology, and providing for faculty development and political will on campus to bring about cultural change along these lines is absent3.

The main challenge is to find and educate sufficient teachers in the process, as well as the content, of science, its curricular approach and appropriate didactics and teaching approaches4. This made the researcher to make this evaluation to provide an enhancement to be used as an aid in teaching science. The evaluation is to check when the existing strategy used in teaching science is still useful. Then if it is not, a new strategy must be introduced to make the subject matter learnable and to fill-in the gap that this paper identified.

This paper seeks to evaluate the course offered in the institution then provide an enhancement program based on the results that would be extracted from the following of the queries:

1. What are the data to be used to ensure the representativeness of the target population?
2. What are the descriptive characteristics of students in the science achievement test administered?
3. Is there a significant relationship between the science achievement tests administered to the grade level of the students?
4. Is there a significant relationship that predicts the science achievement test administered to the grade level of the students?
5. Is there a significant difference between the science achievement tests administered to the grade level of the students?
6. How to improve the science program of the institution in the achievement test based on the gathered data?

This is significant to the institution, teachers, students and community of science to give emphasis on the improvements in the outcomes based on the implementation of this learning which can help the society. Then the science course can be appreciated not only by few but also by all enthusiasts with all aspect that this subject matter concerns. It is only by embracing the new challenges in teaching science can make students and teachers in continuous learning more productive5. Therefore, this could make a new change in the delivery of science course as a subject matter.

Methods

This research paper is following the quantitative approach that the researcher would try to test the objective theories of the data by examining the relationship among variables. The variables are in turn, be measured, typically on instruments to become numbered data that can be analyzed using statistical procedures6. The researcher is using a second-hand data that the coordinator of the science course collated after the achievement test is administered to the students. The data includes the current grade level, science achievement test, scale score and raw score with properties of systems, structure of systems, changes in systems, inquiry in science, designing solutions and systems of science as components.

The information is listed in groups according to their specific period. The total population of the respondents based on the list are ninety-six (N=96) and distributed in five periods which in the first period is sixteen (P1=16), second period is twenty-five (P2=25), third period is thirty-four (P3=34), fourth period is eleven (P4=11) and fifth period is ten (P5=10). The population is composed of fifty-six (G9=56) Grade 9 and forty (G11=40) Grade 11 at present.

The data is being evaluated according to the performance of the students in the science achievement test in Grade 8 and Grade 10. The data is a secondary all information is being adopted based on the result collated by the coordinator. The material used in the evaluation is the science achievement test that is administered in the respondents earlier year.

To generate the results of this paper various statistical techniques and methods used. These data analysis tools are used and adopted to provide solutions. As to the descriptive tool which relationship between variables are the concern, a Spearman Rho analysis is used because of the procedures a data and the exactness of the data presented. In finding the predictors of this study is the process of supporting the process of Rho. Lastly is the comparing the variables that can be useful.

Results

The following results are carefully analyzed by the researcher to meet the necessary evaluation based on the data gathered. The results are classified in six parts to address the queries of this paper.

Information Used to Represent the Target Population

The total population of the data is ninety-six (N=96), thus four items in the data is irrelevant for no data to be analyzed because of null entry therefore the sample size of this study is ninety-two (n=92). This is a best sample considering that the response rate is ninety-six percent (96%) therefore the margin of error that should be used in this research is one percent (α=1%) to get the generalization of the entire population.

In this paper five groups are to be tested statistically to seek for the science achievement test that has administered if there is the same pattern that has reflected in each group. The null hypothesis of this study is that there is no difference in means (H0: µ1 = µ2 = µ3 = µ4 = µ5) and the alternative hypothesis is that the means are not all equal (H1: The means are not all equal) in comparing the five groups, respectively. The hypothesis is tested with a margin of error that is 0.01 (α=1%).

Descriptive Characteristics of Students in Science Achievement Test Administered

The data gathered illustrates to be not normal in distribution because as seen in Figure 1 that the histogram of student scale score in an over-all view falls not into the center and it is likely being pulled going to the right. Thus, when it is analyzed according to its period an irregular behavior in the histogram of each is being observed.

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Figure 1: Histogram of the Data Student Scale Score

Now because of making sure about the normality of the data set another method is used which is called the QQ Plot which is literary means quantile-quantile plot to compare the plot with the standardized trend. A trend that also used by the data set using the mean, standard deviation and the sample values in generating standard data as a point of comparison. Thus, as observed the data represented by bubbles are not in line with the standard trend which means that clearly the data are not normally distributed.

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Figure 2: QQ Plot of the Student Score Scale

Though the data have this behavior that describe to be a characteristics of a symmetric data set but then there are some data that can be considered to make an authenticate assumption. This must be clearly identified because the recommendation that this paper provides cannot only provide aid but also it can make or break the future of the stakeholders when this can provide damage in the future. It is not that simple to decide on this very important decision because it deals with human being that provide information that is false.

To concretize the assumption it should not only provide information that is not sure. Therefore it should be supported and had been analyzed not only once but many times. Another assessment in the data set are being tried to make several test. One of it is to use some theory of well-renounced and being tested in various process of checking.

So as a support in Table 1 shows the real values that can truly make the characteristics of the data is not normally distributed. It is also being narrated in the table that options to challenge the result. Table 1 shows the descriptive characteristics of the data set like median and mode the gives clear explanation about the irregularities if the data. There are some researchers believe that when a mean is less than the mode it is called which means that the value of the data is directly pulled towards the median that affects the value of the curve towards the left which is called as the negatively skewed situation. Still the median can be used as the central tendency of the data sets.

Table 1: Quantitative Description of the Information

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Table 1 provides the information of the Students Science Scale Score (SSS) with the components of science namely Properties of Systems Raw Score (PSRS), Structure of Systems Raw Score (SSRS), Changes in Systems Raw Score (CSRS), Designing Solutions Raw Score (DSRS) and Systems of Science Raw Score (SSRS). In terms of an over-all result, its observable that behavior of the data is a negatively skewed that situation has the characteristics of pulling the data towards the left because of its skewness (-.800) value. Thus also supported with the values of the mean which is less than the mode that supported the characteristics that the data shows? The data is typically a distribution of data set that has a heterogeneous performance in nature thus the 10% of the mean is less than the standard deviation. This is also true to the components of the subject.

There are things to consider in making that the data is completely useful to be part of an improvement to the institution. Clearly gave the idea for example that data are confusing because other parameter suggests that it is really symmetric in some ways. Example is the values in the Table 1 that keeps the techniques that are prepared to process are the not correct, still it comes to be the right and prerogative of the researcher to clearly make a stand and prove that the data set is not normal and not symmetric.

Table 2: Characteristics of the Data on the Students Science Achievement Test by Components

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The skewness and the kurtosis of the data describe also a general not normal distribution amongst the data set. The science scale score is clearly not normal in distribution because both the skewness and kurtosis are less than the standard error. Which also very in the components that even it’s higher than the expected still never meets the requirements on the assessment of the kurtosis and skewness. Trying to prove that this data are really not normal to proceed to the next step of analyzing, the researcher computed the right-tailed probability to really check sufficient evidence to rule out the data is coming from a normal population. The result is still failure to suffices a normal population on the components except for the last which the information is null but the over-all result provides a value that is less than with the 5% of error which cannot be accepted because this paper requires 1% of margin in error which the results is also the same.

The concrete and strong belief of the researcher sometime comes from product of something big in the future when a right decision comes. Though it is not completely a clear but still the data is to completely follow the process of computing the items that could best describe the subject. There books that really intends to support the necessary claims.

To finally illustrate a histogram technique is being used. The figures below are the basis of the researcher to categorized the methods and techniques to solve this problem in the science subject if the institution.

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Figure 3: Histogram Characteristics of Data by Components of Science

In Figure 3 the data was grouped by components as variable to describe also the characteristics of the data is not normal and it’s not symmetry because by looking at the histogram no formation of a bell curve or even approximately like because it is very clear that it is not distributed in a normal way. Thus, the box plot provides a possibilities of describing the data normal but there are some inconsistent like the number of items and the non-uniformity of the population per group which is called the period where the students are enrolled.

The performance of the student based on the table show in Table 3 that the achievement test in science of the institution as of last year is only level 1 to 2 as the data tends to have a high frequency in this two level of science. Though, the results placed in the area where the data can be deceive by the level of performance of the students still the when it is cross tabulated with the result wither the it meets the standard it ranks very disappointing that not even half of the students met the standard in the achievement test in science. The results as an over-all total or results make only 17.39% who meets the standard. When grouped by period whom to be performing class as to the performance in the Science Achievement Test only Period 5 gives a high ranking with regards to percentage result with 40.00% meets the standard while the others have the rating 12.50%, 24.00%, 11.76 and 0.00% who meets the standard with respective to their period of class.

Table 3: Characteristics of the Students Science Achievement Test for Entire Program

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Cross-tabulating the data available is not only helping but also finding the right variables that are tends to make the compatibility of various data field to assess are being observed. As to the data plotted in Table 3, an observable bulk of data comes in the science level class which has also a data that being placed there.

Table 4: Characteristics of Achievement Test in Science according to Components and Level

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Significant Relationship between the Science Achievement Tests

In this part of the report, the author seeks for the relationship best to describe the two variables thus the initial hypothesis of these two variables to be describes as there is a significance relationship. The percentage of error to be tested in this data is 5% therefore the confidence level of the result is 95%. As shown in the figure below that the scatter plot is not linear and it doesn’t really form a line it is clear enough that using the Pearson r correlation method will not be applied in the two variables to seek its relationship. The best way to do this is to use the Spearman Rho method of identifying its significant relationship.

Table 4: Descriptive Correlation Characteristics of the Students Science Achievement Tests

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Table 4 illustrates that the relationship between the Science Achievement Tests is significant to the results in their science performance based on the components of science. It appears that the sigma value which is the indicator that it has a relationship is less than to the alpha. This not anymore new but still it has to be evaluated again not because it has relation it give a direct impact to all. Sometimes it has more complicated to observe that it signify because of thin line possibilities of not getting the real problem.

Significant Relationship that Predicts the Science Achievement Test Administered

Table 5: Multiple Regression Model Predicting Students Science Scaled Scores

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Table 5 is the result of the estimated relationship among variables using regression modeling. The variables that are estimated in this analysis is the Science Achievement Test Scale Score as the dependent variable which varies its results based in the variables. This are called the predictor variables that has effect to the result of the dependent variables and it is also being named as independent variables. These variables are the Grade Level of the student during the period of the test (Student Grade Tested At) and the Level of the student in the science subject (Science Level).

Based on the analysis of the variables a model was then developed using the process done. The model tells to be the bases of the Student Scale Score value when student science level and the grade level of the student are identified. The model is the result of three hundred sixty-four fifty-three hundredth (364.53) added to the product of thirty-seven and sixty-four hundredth (37.64) and the student level in science subtracting the product of student grade level and seven and two hundredth. The Student Achievement is represented by. The independent variables are represented as and which is the Science Level and Student Grade Tested At respectively. The model is illustrated in Figure 4.

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Figure 4: Model of the Variables after Regression Analysis

The model can predict the value on the achievement test result of the student. The result is now the basis of the authorities to be aware that when the grade level of the student is identified with the specific grade level gave a result to the achievement test that can be assured that by using this all preparation can be set before administering the specific action with regard to the success of the institutions. This is very important because insuring good results may be used in this model.

Significant Difference between the Science Achievement Tests Administered

The following results are the comparison of the lens which will be the point of comparison of the study that is identified by various hypotheses which is being framed before discussion to clearly understand the things that are similar in nature between the two comparing variables.

The hypothesis below compares the statistical significant difference of the variables which is the student performance in science between the group of period where the student attended and the group of student performance in Science Achievement Test, the group of science components of subject matter and to the Grade level of the students.

H0: All mean values are equal

H1: The means are not all equal

The results of the analysis is tested with the 1% significant level which is being illustrated in this paper as the basis of margin error which is also the basis of all results

Table 6: Comparative Statistics on Science Test Performance by Class Period

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Table 6 which is the comparison of the student achievement test result in science to the period attended resulted to reject the null hypothesis because the observable probability (p-value=0.002) is less than the significant level which is 1%. This means that the variable that has been compared is significantly difference which accepts the alternative hypothesis which all means are not equal.

Table 7: Comparative Statistics on Science Subjects Performance by Class Period

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Table 7 is the comparison also between the performance of the student in the components of science and the period attended. The result is classified in two categories which are the accepted and not accepted components of science. The properties and structure of systems are the components of science that accepts the assumption of the comparison which means that all of the means are equal and it has a significant difference on the comparison. The change in systems, inquiry in science and the designing solutions are the components that rejects the assumption which means that all of their means are not equal and it has a significant difference on the comparison. The illustration of their box plot is being showed in Figure 5.

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Figure 5: Box Plot of Student Performance in Science Subject by Class Period

Table 8 is the comparison of the students’ performance in their grade level during the test administration which when all of their achievement are collated and compared it reject the assumption that all the grade levels mean are equal that it has significance difference which means that the two grade levels is not to be combined. The result when it is compared with the components of the examination it resulted to accept the assumption in the properties, structure of system and designing solutions as the components of science which all of their means are equal. The change in systems and inquiry of science rejects the assumption that their means are not equal.

Table 8: Comparative Statistics on Students Performance by Grade Level

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Improvement in the Science Program

The results of this paper provided a wide idea that science is perfectly administer in the time where the understanding of the students are clear in terms of usage and importance of science. This very clear in the study which illustrated that the overview and the usage of the subject matter can be clearly understands when the appreciation and the preparedness of the students to be tested are ready7. In this case the institution is very well-known on this matter that it clear that in the Grades 8 to 11 are the perfect timing of administering the achievement test. The results tells that it is significantly related all the components of science to be tested to the students that can be predicted according to their grade level and the level of science that has been acquired.

On the other hand it is being discussed that the methodology is a great effect to the things that a student can clearly understand the subject matter, it is also manifested in the result that when the performance is being classified to be in different period that clearly provides an information which has also a different approach, which is supported the result in the testing of comparison that significantly all the means of the performance in each class is not equal that resulted to different results. If the teaching approaches of the teachers and the belief of the person who delivers the learning has different nor the same delivery of strategy as to the point of Wieman which all of the teacher teaches science has one belief on the strategy teaching science8, then it is not any more effective patronize this type of notion that the institution should provide action for these matter.

It is very clear on the results that change should take place to the institution because the results has a identified facts that it is not anymore providing good quality of results in the administration of the subject matter. Clear enough to provide changes not only the teaching strategy but also to the administration of such situation that the change in teaching but also change in the entire institutions belief or culture9.

Therefore change is very important nearly the result provides solutions on this paper. It should be taken into consideration that main challenge is to find and educate sufficient teachers in the process, as well as the content, of science, its curricular approach and appropriate didactics and teaching approaches are clearly the main issue to points out10. This paper provides solution by providing an action plan that is highly recommended to be included in the planning of the institution. Planning the action is the first step to be accomplished then it should be deliberated upon the revision of the program. The teaching administration, program outline, period of administration and the change in the system is important to be included to provide clear path to the success of this actions. Monitoring and evaluation should be taken into priority to keep updating and improvements to the administration of this new action in the science program.

The framework that this paper suggests can help the institution to administer this action plan to achieve this new action. This could also be used as basis of other institutions when providing a science program enhancement program. In this new age the action plan should be a result of a situational incident that clearly manifested a result. So this framework is just a reference that best resulted to this action plan. This framework also can be modified to its best implementation based on the culture of the institution.

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Figure 5: Framework of Action Plan Implementation

References

Creswell, J. W. (2014). Research design: qualitative, quantitative, and mixed methods approaches (4th ed.). (V. Knight, Ed.) Thousand Oaks, California, USA: SAGE Publications, Inc.

Donaldson, G. (2005). Improving achievement in science in primary and secondary schools. Livingston, Almondvale Way: HM Inspectorate of Education.

Harris, G. (2006, October). Columbus: Teachers Leading Change in Their Schools. (A. Stern, Ed.) National Science Foundation’s Urban Systemic, 12-14.

Tang, Q. (2009). Current challenges in basic science education. Paris, France: UNESCO.

Wieman, C. (2008). Science Education in the 21ST century using the tools of science to teach science. Forum for the FUture of Higher Education, 61-64.

[...]


1 Donaldson, G. (2005). Improving achievement in science in primary and secondary schools. Livingston, Almondvale Way: HM Inspectorate of Education.

2 Wieman, C. (2008). Science Education in the 21ST century using the tools of science to teach science. Forum for the Future of Higher Education, 61-64.

3 Wieman, C. (2008). Science Education in the 21ST century using the tools of science to teach science. Forum for the Future of Higher Education, 61-64.

4 Tang, Q. (2009). Current challenges in basic science education. Paris, France: UNESCO.

5 Harris, G. (2006, October). Columbus: Teachers Leading Change in Their Schools. (A. Stern, Ed.) National Science Foundation’s Urban Systemic, 12-14.

6 Creswell, J. W. (2014). Research design: qualitative, quantitative, and mixed methods approaches (4th ed.). (V. Knight, Ed.) Thousand Oaks, California, USA: SAGE Publications, Inc.

7 Donaldson, G. (2005). Improving achievement in science in primary and secondary schools. Livingston, Almondvale Way: HM Inspectorate of Education.

8 Wieman, C. (2008). Science Education in the 21ST century using the tools of science to teach science. Forum for the Future of Higher Education, 61-64.

9 Wieman, C. (2008). Science Education in the 21ST century using the tools of science to teach science. Forum for the Future of Higher Education, 61-64.

10 Tang, Q. (2009). Current challenges in basic science education. Paris, France: UNESCO.

18 of 18 pages

Details

Title
A Science Course Evaluation and Enhancement Programme. Performance of Students in a school-based Implementation
Author
Year
2017
Pages
18
Catalog Number
V961532
ISBN (Book)
9783346310262
Language
English
Tags
Statistics, Evaluation, Assessment
Quote paper
Dr. Noel Sobejana (Author), 2017, A Science Course Evaluation and Enhancement Programme. Performance of Students in a school-based Implementation, Munich, GRIN Verlag, https://www.grin.com/document/961532

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