Home > Free Essays > Education > Academic Performance > Middle School: Students’ Exam Performance
Cite this

Middle School: Students’ Exam Performance Research Paper


Abstract

Theory and practice are integral and inseparable aspects of the curriculum in K-12 education, for they determine the learning outcomes among students at various academic levels. Balancing between theory and practice is essential in improving academic performance among students. Numerous studies have indicated that practice plays a critical role in the teaching and learning processes of science subjects, for they determine the overall performance.

As Robert Frost Middle School received grants to renovate its laboratory and there is a need to advance the performance of science courses, this study sought to establish the role of graded laboratory activities in the exam performance of eighth-grade students. Simple random sampling was employed in selecting 42 eighth-grade students at Robert Frost Middle School. The data of their laboratory performance and the exam performance was collected and entered into SPSS.

Correlation, one-way ANOVA, and post hoc analyses were performed to ascertain the role of graded laboratory activities in the exam performance of the 8th-grade students. Correlation findings showed that the laboratory performance and the exam performance have a very positive strong relationship (r = 0.964, p = 0.000). One-way ANOVA findings illustrated that there is a statistically significant difference in the exam performance among the three categories of the laboratory performance, F(2,39) = 98.224, p = 0.000. In support of the correlation and one-way ANOVA findings, post hoc analysis indicated that the exam performances between students with poor, average, and high laboratory performances had statistically significant differences.

Therefore, the findings point out that graded laboratory activities improve exam performance, and thus, Robert Frost Middle School should renovate its laboratories and integrate practice in its teaching and learning activities.

Introduction

Background of the Study

Across the United States, classroom educators in the areas of Science, Technology, Engineering, and Mathematics (STEM) at the middle school level (6th – 8th Grade) face the instructional conundrum of balancing between theory and practice in their strategies and activities (Kelly, 2014). According to McDaniel, Thomas, Agarwal, McDermott, and Roediger (2013), educators must advance student knowledge and performance by efficiently integrating laboratory work and practical activities into the standardized science curriculum to address the learning needs of a diverse student population.

Smart and Marshall (2013) support this idea, stating that practical tasks performed through laboratory activities contribute immensely to students better understanding theoretical concepts related to the various scientific fields of biology, chemistry, and physics. Fundamentally, graded laboratory activities determine the exam performance of students in different grades.

In a bid to improve educational performance among students, K-12 school district required empirical justification of the essence of laboratory work as an integral part of the curriculum at the middle school level. Essentially, there is a trade-off between the use of theory and practice in providing instruction for different schools of thought emphasize on either of them (Mandler, Mamlok-Naaman, Blonder, Yayon, & Hofstein, 2012).

Theoretical teaching and learning is an integral basis of knowledge, while practice is central in providing empirical experience, which allows students to apply their knowledge. However, the problem is that there are limited findings that support the use of laboratory work as opposed to theoretical learning activities such as drama, band, and chorus, in improving exam performance among students in science subjects. The motivation behind this study is to provide an alternative mode of teaching and learning advanced science courses. Moreover, as Robert Frost Middle School received grants to renovate its laboratories and revise its curriculum, it requires empirical findings to support and guide these changes.

Literature Review

Laboratories have become efficient learning tools for students in their middle-school science stages. More often, instructors dealing with the eighth-grade students in different American K-12 schools have cited laboratory experiments and student performances in various laboratory tests as necessary learning strategies that improve exam performance of learners. Laboratory activities form an integral part of science subjects and courses for they improve learning outcomes (Talanquer, 2013).

In an analysis of the American K-12 laboratory studies, Labov (2006) conducted a systematic review of three recent reports that elaborated on the improving science education. These reports supported the relationship between laboratory studies and high exam performance. In one of the intuitive reports, Labov (2006) discovered that laboratory studies have four principles of instructional design that enhance practical learning experiences and impart knowledge that is essential for the acquisition, retention, and transfer of learning concepts that are best for exams.

According to Labov (2006), laboratory lessons are designed with clear learning goals that are important in converting theory to practice, they are designed with clear learning outcomes, they are solicitously sequenced in consideration of the classroom science instructions, and they often incorporate student reflection. In a study that complimented these assumptions, Clymer and William (2007) conducted a review of how instructors are improving the way science should be graded.

Using qualitative and quantitative facts obtained from other studies, laboratories support learners in exam performances for they are tools of transforming theories into practice. Moreover, they are the best methods for assessing student learning outcomes and building creative skills that are essential in approaching examinations. According to Clymer and William (2007), laboratories give learners opportunities to develop practical approaches to learning, as they measure academic progress that learners have achieved.

In one intuitive study, Adair and Swinton (2012) conducted research about lab attendance and academic performance to determine whether the two performances associate or often differ. The two researchers reviewed the learning connection between the class performances and the laboratory performances. They did so by analyzing 50 minutes weekly sessions to examine how learners worked with the learning material covered during regular lectures.

In their assessment method, Adair and Swinton (2012) assumed that knowledge as an output of an education production function that entails a competition of two important learning factors: learners input from the regular classes and lab attendance. In this study, Adair and Swinton (2012) discovered that the higher the student lab attendance, the higher the educational outcomes in the regular exams given at the end of the learning terms.

However, some studies have provided contrasting views in instances where laboratory performance and exam performance have appeared to differ significantly. To examine this relationship, McDaniel et al. (2013) investigated quizzing approaches used during the regular tests and the final examinations. McDaniel et al. (2013) assessed quizzing in the middle-school science classes through three multiple-choice exams on different learning units.

This technique was to determine how learners responded to various laboratory exercises and regular subject questions while in regular learning sessions. Based on the study of McDaniel et al. (2013), it is evident that acquisition, retention, and transfer of knowledge seem to differ in the laboratory tests and the final examination tests. As examiners and instructors keep on using different exam items between the two learning procedures, there is a high possibility that the performances of laboratory exams and the final exams differ significantly.

In a bid to improve academic performance among students, researchers have performed diverse studies across different academic levels. Of note is the academic performance of K-12 schools, which form the basis of learning and career development among students. A study performed among 360 participants comprising students (155), teachers (25), laboratory technicians (2), and principals (6) revealed that science teachers, equipped laboratories, and competent technicians have considerable positive impact on academic outcomes among the tenth-grade students (Beyessa, 2014).

The findings support the assertion that laboratory activities provide hands-on experience for students to apply their knowledge and skills. As hands-on activities vary from one educational setting to another, their impact on academic performance also varies. Hussain and Akhtar (2013) investigated the impact of hands-on activities the science performance among the eighth-grade students and found out that students who undertook hands-on activities performed better than students who did not.

These findings reveal that hands-on activities suffice in educational settings with limited laboratory facilities and resources Moreover, Hussain and Akhtar (2013) observe that hands-on activities stimulate observation and reasoning; hence, making students infer, predict, and relate scientific knowledge with laboratory activities that they perform. From these studies, it is evident that graded laboratory activities have a significant impact on the performance the eighth-grade students.

Laboratory facilities and the extent of their utilization also play a central role in the academic performance among students. Ngozi and Halima (2015) conducted a study among 400 12th grade students and found out that absence of laboratory facilities have a negative impact on the academic performance of students. The availability of the laboratory facilities provides opportunities for students to get the first-hand experience and apply their theoretical knowledge. Ngozi and Halima (2015) also found out that the extent of utilization of available laboratory facilities is a major factor that determines the role of laboratory activities in improving academic performance among the eighth-grade students.

In essence, the presence of enough teachers, laboratory technicians, and customized curriculum in a school enhances optimal utilization of the available laboratory facilities and resources. According to Hussain and Akhtar (2013), laboratory facilities and resources allow students to apply scientific knowledge and skills in studying diverse phenomena related to their courses. Fundamentally, laboratory facilities and resources usher students into the realm of science and expand their empirical experience, resulting in improved academic performance, as evidenced in the overall exam.

Research Questions

  1. What is the correlation between the laboratory performance and the exam performance among the eighth-grade students doing science at Robert Frost Middle School?
  2. Do the laboratory performance and the exam performance differ significantly among the eighth-grade students doing science at Robert Frost Middle School?

Research Methods

Participants

The study sampled 42 students from three classes (grade 8) at the Robert Frost Middle School, in Rockville, Maryland. This particular middle school was selected in this K-12 school district as it has recently received a grant to renovate its science laboratory and to revise its curriculum aligned to those improvements. The study used simple random sampling, which is a probability sampling technique, in selecting 42 students to participate in the study.

Randomization is an integral aspect of sampling for it eliminates researchers’ biases and enhances representation of the population (Tipton, Hedges, Vaden-Kiernan, Borman, & Sullivan, 2014). Owing to random sampling, the students selected, the participants had different demographic characteristics such as gender, age, and ethnicity. However, the study did not consider these demographic characteristics in performing analysis of the data collected.

Procedures

The study measured independent variable and dependent variable among students. The independent variable in the laboratory performance as reflected by the assignments and activities while the dependent variable is the exam performance as indicated by the final exam of science. On a continuous scale, the independent variable was measured on percent scored (0-100), which is the typical grading of laboratory performance.

Hence, the data of the laboratory performance was collected from the academic records and converted into percent to meet the requirement of the independent variable. The dependent variable, the exam performance, was measured on a continuous scale of 0-100%. The data of the dependent variable were obtained from the final exam of science subjects that students performed in the second term. Thus, the data of the laboratory performance and the exam performance was collected from 42 students and then entered into the SPSS 20 for analysis.

Data Analysis

In exploratory data analysis, the study used descriptive statistics. According to Gelman (2004), exploratory data analysis forms the basis of data analysis for it illustrates patterns and trends of data; hence, promoting interpretation. The descriptive statistics provided measures of dispersion and central tendency, which summarized the laboratory performance and the exam performance of the eighth-grade students.

Moreover, the study used correlation analysis to determine the relationship between the laboratory performance and the exam performance. Samuel and Okey (2015) explain that correlation depicts strength and direction of relationships between two continuous variables. Given that the laboratory performance and the exam performance are continuous variables, correlation analysis indicated the magnitude and direction of relationships.

In determining if the laboratory performance and the exam performance differ significantly among the eighth-grade students doing science at Robert Frost Middle School, a categorical variable was computed first. Using the continuous scale, categorical scale variable with three categories of grades, namely, poor (below 40%), average (between 40% and 70%), and high (above 70%) was computed employing a function named recode into a different variable in SPSS.

The computation of the categorical variable allowed analysis of one-way ANOVA to determine whether the laboratory performance and the exam performance differ significantly among the eighth-grade students doing science at Robert Frost Middle School. The reason to use the one-way ANOVA is in the fact that the study’s independent variable is on a categorical scale, and thus, it has three factors that influence one continuous dependent variable.

One-way ANOVA is applicable when more than two independent variables influence a continuous variable (Hsieh, Lee, & Chu, 2013). The use of one-way ANOVA allows hypothesis testing to determine whether the difference between the exam scores among students is observable and statistically significant. The significance of the F value in the ANOVA table shows the significance of the difference observed (Ostertagova & Ostertag, 2013). In this view, the p-value indicated the significance of the differences in the exam scores among students based on the laboratory performance levels.

The study also performed post hoc analysis to determine if the significance tested exists between each group. Ostertagova and Ostertag (2013) explain that one-way involved determination of the difference between group means. Given that the independent variables are three, it meets the requirement of post hoc analysis as stated by McDaniel et al. (2013). Therefore, post hoc analysis was performed to determine if the statistically significant difference observed among students is statistically significant between all the groups.

Results

Sampled Data

The following table (Table 1) presents sampled data from 42 students showing the laboratory performance, the exam performance, and laboratory performance categories.

Table 1.

Case Summaries
Laboratory Performance Exam Performance Laboratory Performance Category
1 80 75 High Laboratory Performance
2 69 75 Average Laboratory Performance
3 45 48 Average Laboratory Performance
4 35 38 Poor Laboratory Performance
5 76 72 High Laboratory Performance
6 64 61 Average Laboratory Performance
7 36 34 Poor Laboratory Performance
8 32 29 Poor Laboratory Performance
9 54 50 Average Laboratory Performance
10 78 81 High Laboratory Performance
11 60 65 Average Laboratory Performance
12 79 75 High Laboratory Performance
13 39 45 Poor Laboratory Performance
14 29 35 Poor Laboratory Performance
15 36 48 Poor Laboratory Performance
16 55 65 Average Laboratory Performance
17 76 85 High Laboratory Performance
18 44 48 Average Laboratory Performance
19 25 32 Poor Laboratory Performance
20 91 88 High Laboratory Performance
21 63 68 Average Laboratory Performance
22 72 69 High Laboratory Performance
23 38 49 Poor Laboratory Performance
24 22 25 Poor Laboratory Performance
25 42 49 Average Laboratory Performance
26 72 76 High Laboratory Performance
27 33 39 Poor Laboratory Performance
28 34 42 Poor Laboratory Performance
29 64 68 Average Laboratory Performance
30 75 87 High Laboratory Performance
31 23 37 Poor Laboratory Performance
32 48 57 Average Laboratory Performance
33 76 78 High Laboratory Performance
34 44 46 Average Laboratory Performance
35 29 35 Poor Laboratory Performance
36 80 83 High Laboratory Performance
37 44 56 Average Laboratory Performance
38 31 37 Poor Laboratory Performance
39 71 78 High Laboratory Performance
40 62 69 Average Laboratory Performance
41 75 72 High Laboratory Performance
42 45 58 Average Laboratory Performance
Total N 42 42 42

Exploratory Data Analysis

The exploratory data analysis performed provides descriptive statistics of the laboratory performance and the exam performance of the eighth-grade students (N = 42). The descriptive statistics reveal that the students have a lower mean of the laboratory performance (M = 53.48, SD = 19.662) than mean the exam performance (M = 57.79, SD = 18.171). The laboratory performance has a range of 69 with maximum and minimum scores of 91 and 22 respectively.

In comparison, the exam performance has a range of 63 with maximum and minimum scores of 88 and 22 correspondingly. The laboratory performance has a skewness of 0.079 and kurtosis of -1.371 while the exam performance has a skewness of -0.032 and kurtosis of -1.265. The following table (Table 2) provides descriptive statistics of the laboratory performance and the exam performance.

Table 2.

Descriptive Statistics
Laboratory Performance Exam Performance Valid N (listwise)
N Statistic 42 42 42
Range Statistic 69 63
Minimum Statistic 22 25
Maximum Statistic 91 88
Mean Statistic 53.48 57.79
Std. Error 3.034 2.804
Std. Deviation Statistic 19.662 18.171
Variance Statistic 386.597 330.172
Skewness Statistic .079 -.032
Std. Error .365 .365
Kurtosis Statistic -1.371 -1.265
Std. Error .717 .717

Correlation Analysis

The correlation analysis (Table 3) reveals that the laboratory performance and the exam performance among the eighth-grade students have a very strong positive correlation that is statistically significant(r = 0.964, p = 0.000).

Table 3.

Correlations
Laboratory Performance Exam Performance
Laboratory Performance Pearson Correlation 1 .964
Sig. (2-tailed) .000
N 42 42
Exam Performance Pearson Correlation .964 1
Sig. (2-tailed) .000
N 42 42

One-Way ANOVA

Table 4 presents descriptive statistics of the exam performance of students in three categories of the laboratory performance, namely, poor, average, and high levels. Students with poor laboratory performance had the lowest level of the exam performance (M = 37.5, SD = 6.825) with maximum and minimum scores of 25 and 49 respectively. In contrast, the students with high laboratory performance had the highest level of the exam performance (M = 78.38, SD = 6.035) with maximum and minimum scores of 69 and 88 respectively. Besides, students with average laboratory performance had medium scores of the exam performance (M = 58.87, SD = 9.257) with maximum and minimum scores of 46 and 75 correspondingly. The descriptive statistics reveal that there are apparent differences in the exam performance among the three categories of the laboratory performance.

Table 4.

Descriptive Statistics of Exam Performance
N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean Minimum Maximum
Lower Bound Upper Bound
Poor Laboratory Performance 14 37.50 6.825 1.824 33.56 41.44 25 49
Average Laboratory Performance 15 58.87 9.257 2.390 53.74 63.99 46 75
High Laboratory Performance 13 78.38 6.035 1.674 74.74 82.03 69 88
Total 42 57.79 18.171 2.804 52.12 63.45 25 88

The ANOVA table (Table 5) shows that there is statistically significant difference in the exam performance among the three categories of the laboratory performance, F(2,39) = 98.224, p = 0.000.

Table 5.

ANOVA of Exam Performance
Sum of Squares df Mean Square F Sig.
Between Groups 11294.761 2 5647.381 98.224 .000
Within Groups 2242.310 39 57.495
Total 13537.071 41

As the ANOVA table shows that there is statistically significant difference among the three categories, post hoc analysis was done to provide robust findings (Table 6). Post hoc analysis indicated that the exam performances between students with poor laboratory performance and average laboratory performance differ statistically significantly (p = 0.000). Moreover, post hoc analysis showed that the exam performances between students with poor laboratory performance and high laboratory performance vary statistically significant (p = 0.000).

In a similar manner, post hoc analysis revealed that the exam performances between students with average laboratory performance and high laboratory performance differ statistically significantly. Thus, post hoc analysis exposed that the statistically significant differences exist between the exam performance of students with poor, average, and high laboratory performances.

Table 6.

Post Hoc Analysis
Dependent Variable: Exam Performance
Tukey HSD
(I) Laboratory Performance Category (J) Laboratory Performance Category Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval
Lower Bound Upper Bound
Poor Laboratory Performance Average Laboratory Performance -21.367* 2.818 .000 -28.23 -14.50
High Laboratory Performance -40.885* 2.921 .000 -48.00 -33.77
Average Laboratory Performance Poor Laboratory Performance 21.367* 2.818 .000 14.50 28.23
High Laboratory Performance -19.518* 2.873 .000 -26.52 -12.52
High Laboratory Performance Poor Laboratory Performance 40.885* 2.921 .000 33.77 48.00
Average Laboratory Performance 19.518* 2.873 .000 12.52 26.52

Discussion

Sampled Data

The data was sampled successfully from 42 eighth-grade students who participated in the study. The sampled data from 42 students revealed that the eighth-grade students have different performances in both the laboratory and the exam scores. Table 1 depicts that the laboratory performances were highly variable for students had poor (below 40%), average (between 41 and 69%), and high (above 70%) scores.

Exploratory data analysis of the sampled data provides vital trends and patterns regarding the performance of the eighth-grade students. Cho and Kim (2012) assert that establishment of patterns and trends are essential in the interpretation of data. In the assumption of normality, the laboratory performance gave a skewness of 0.079 and kurtosis of -1.371 whereas the exam performance had s skewness of -0.032 and kurtosis of -1.265.

These values of skewness and kurtosis of the laboratory performance and the exam performance are close to zero, which means that the data follow the normal distribution, and thus, meet the assumption of correlation and one-way ANOVA. Healey (2013) states that data that follows the normal distribution provide robust and valid findings. Moreover, the data had no significant outliers, which means that the results generated had enhanced validity.

Exploratory Data Analysis

From the descriptive statistics, it is apparent that the laboratory performance (M = 53.48, SD = 19.662) is lower than the exam performance (M = 57.79, SD = 18.171). These findings imply that the eighth-grade students perform the laboratory activities fairly, and thus, there is considerable room for improve to reflect or surpass exam performance. Ngozi and Halima (2015) found out that unavailability or underutilization of laboratory facilities decrease academic performance among learners. In this view, the lower laboratory performance than the exam performance implies that the eighth-grade students are not performing optimally in their academics.

Correlation Analysis

The correlation analysis provided robust findings, which indicated the nature and the direction of the relationship between the laboratory performance and the exam performance. The findings show that the laboratory performance and the exam performance among the eighth-grade students have a very strong positive correlation that is statistically significant(r = 0.964, p = 0.000). According to Rafter, Abell, and Braselton (2002), a correlation coefficient that is greater than 0.8 implies a very strong relationship between two variables.

As the correlation coefficient is very strong and statistically significant, it suggests that an increase in the laboratory performance among students results in marked increase in the exam performance. Studies have established that there is a positive relationship between the laboratory performance and the overall exam performance among students in different academic levels (Ngozi & Halima, 2015; Adair & Swinton, 2012). Hence, the correlation analysis indicates that the laboratory performance has a very strong positive relationship that is statistically significant with the exam performance among the eighth-grade students.

Descriptive Statistics

Comparative analysis of the exam performance among students with diverse levels of the laboratory performance gave more insights about the patterns and trends among the eighth-grade students. One-way ANOVA reveals that there are apparent differences in the exam performance among students with poor, average, and high laboratory performances. Descriptive statistics of the exam performance of students in each category of the laboratory performance exhibited varied mean scores. The descriptive statistics show that students with poor laboratory performance had the lowest level of the exam performance (M = 37.5, SD = 6.825).

In comparison, students with average laboratory performance had medium level of the exam performance (M = 58.87, SD = 9.257) while students with high laboratory performance had the highest level of the exam performance (M = 78.38, SD = 6.035). Hussain and Akhtar (2013) found out that hands-on activities improve the academic performance of science students. In this view, the descriptive statistics indicated that are noticeable differences in the exam performance among the three categories of the laboratory performance.

One-Way ANOVA

Hypothesis test using one-way ANOVA revealed that there is statistically significant difference in the exam performance among the three categories of the laboratory performance, namely, poor, average, and high levels, F(2,39) = 98.224, p = 0.000. This finding means that laboratory performance determines the exam performance among the eighth-grade students. Moreover, the finding supports the existence of a very strong positive correlation between the laboratory performance and the exam performance among the eighth-grade students. Beyessa (2014) highlights laboratory facilities and technicians as some of the major factors that influence academic performance of science students. Essentially, the statistical significance of the differences among the exam performances is due the laboratory performance among the eighth-grade students.

Post-Hoc Test

Post hoc analysis further indicated that there is statistically significant difference between the three categories of laboratory performance. Comparison of the exam performances between students with poor and average laboratory performances indicated that they have statistically significant differences (p = 0.000). Also, comparison of the exam performances between students with poor and high laboratory performances using post hoc analysis showed that they have statistically significant differences (p = 0.000). Likewise, post hoc analysis revealed that the exam performances between students with average and high laboratory performances have statistically significant differences (p = 0.000).

These findings are consistent with earlier findings, which indicated that laboratory activities improve academic performance among science students for they provide opportunities for applying acquired knowledge and skills while expanding cognitive capacities (Beyessa, 2014; Hussain & Akhtar, 2013; Adair & Swinton, 2012). Therefore, post hoc analysis supported the findings of correlation analysis and ANOVA test, which inferred that the laboratory performance influence the exam performance among the eighth-grade students at Robert Frost Middle School.

Conclusion

Although numerous factors influence the academic performance among students, laboratory activities comprise a chief factor that boosts academic performance among students doing science courses. K-12 schools face the challenge of maintaining an intricate balance between theoretical and practical aspects of teaching and learning for they determine overall academic outcomes among students. This study aimed to establish the role of graded laboratory activities in improving the overall exam performance among the eighth-grade students with the view of enabling Robert Frost Middle School to renovate its laboratories and revise its curriculum in relation to evidenced obtained.

The analysis of data obtained from 42 eighth-grade students reveals that laboratory performance reflects the exam performance of students. Correlation analysis indicated that there is a very strong positive relationship between the laboratory performance and the exam performance (r = 0.964, p = 0.000). One-way ANOVA and post hoc analyses collectively indicated that there are statistically significant differences among the three categories of the laboratory performance, F(2,39) = 98.224, p = 0.000. Since findings reveal that laboratory activities are critical determinants of the academic performance, this study recommends that Robert Frost Middle School should renovate its laboratories and integrate practice in its curriculum to improve teaching and learning experiences among students.

References

Adair, K., & Swinton, O. (2012). Lab attendance and academic performance. International Scholarly Research Network, 1(5), 1-5.

Beyessa, F. (2014). Major factors that affect grade 10 students’ academic achievement in science education at Ilu Ababora General Secondary of Oromia Regional State, Ethiopia. International Letters of Social and Humanistic Sciences, 32(1), 118-134.

Cho, G., & Kim, B. (2012). Identification of research patterns and trends through text mining. International Journal of Information and Educational Technology, 2(3), 233-235.

Clymer, J., & William, D. (2006). Improving the way we grade science. Educational Leadership: Science in the Spotlight, 64(4), 32-42.

Gelman, A. (2004). Exploratory data analysis for complex models. Journal of Computational and Graphical Statistics, 13(4), 755-779.

Hsieh, Y. C., Lee, C. I., & Chu, K. K. (2013). Effect of innovative ontology-based approach on learning performance of students with ANOVA method. International Journal of Applied Mathematics and Statistics, 38(8), 191-201.

Hussain, M., & Akhtar, M. (2013). Impact of hands-on activities on students’ achievement in science: An experimental evidence from Pakistan. Middle-East Journal of Scientific Research, 16(5), 626-632.

Kelly, N. (2014). Teaching science in elementary and middle school: A project-based approach. Interdisciplinary Journal of Problem-Based Learning, 8(1), 8-16.

Labov, J. (2006). Education at the national academies: Three recent reports on improving science education. Life Sciences Education, 5(2), 12-18.

Mandler, D., Mamlok-Naaman, R., Blonder, R., Yayon, M., & Hofstein, A. (2012). High-school chemistry teaching through environmentally oriented curricula. Chemistry Education Research and Practice, 13(2), 80-92.

McDaniel, M. A., Thomas, R. C., Agarwal, P. K., McDermott, K. B., & Roediger, H. L. (2013). Quizzing in middle‐school science: Successful transfer performance on classroom exams. Applied Cognitive Psychology, 27(3), 360-372.

Ngozi, D., & Halima, S. (2015). Inadequate laboratory facilities and utilization: Pedagogical hindrance to students’ academic performance in biology in senior secondary certificate examination in Zaria Metropolis, Kaduna State, Nigeria. International Business Research, 8(9), 124-134.

Ostertagova, E., & Ostertag, O. (2013). Methodology and application of one-way ANOVA. American Journal of Mechanical Engineering, 1(7), 256-261.

Rafter, A., Abell, L., & Braselton, P. (2002). Multiple comparison methods for means. SIAM Review, 44(2), 259-278.

Samuel, M., & Okey, L. (2015). The relevance and significance of correlation in social science research. International Journal of Sociology and Anthropology Research, 1(3), 22-28.

Smart, J. B., & Marshall, J. C. (2013). Interactions between classroom discourse, teacher questioning, and student cognitive engagement in middle school science. Journal of Science Teacher Education, 24(2), 249-267.

Talanquer, V. (2013). School chemistry: The need for transgression. Science & Education, 22(7), 1757-1773.

Tipton, E., Hedges, L., Vaden-Kiernan, M., Borman, G., & Sullivan, K. (2014). Sample selection in randomized experiments: A new method using propensity score stratified sampling. Journal of Research on Educational Effectiveness, 7(1), 114-135.

This research paper on Middle School: Students’ Exam Performance was written and submitted by your fellow student. You are free to use it for research and reference purposes in order to write your own paper; however, you must cite it accordingly.

Need a custom Research Paper sample written from scratch by
professional specifically for you?

Writer online avatar
Writer online avatar
Writer online avatar
Writer online avatar
Writer online avatar
Writer online avatar
Writer online avatar
Writer online avatar
Writer online avatar
Writer online avatar
Writer online avatar
Writer online avatar

301 certified writers online

GET WRITING HELP
Cite This paper

Select a referencing style:

Reference

IvyPanda. (2020, October 16). Middle School: Students' Exam Performance. Retrieved from https://ivypanda.com/essays/middle-school-students-exam-performance/

Work Cited

"Middle School: Students' Exam Performance." IvyPanda, 16 Oct. 2020, ivypanda.com/essays/middle-school-students-exam-performance/.

1. IvyPanda. "Middle School: Students' Exam Performance." October 16, 2020. https://ivypanda.com/essays/middle-school-students-exam-performance/.


Bibliography


IvyPanda. "Middle School: Students' Exam Performance." October 16, 2020. https://ivypanda.com/essays/middle-school-students-exam-performance/.

References

IvyPanda. 2020. "Middle School: Students' Exam Performance." October 16, 2020. https://ivypanda.com/essays/middle-school-students-exam-performance/.

References

IvyPanda. (2020) 'Middle School: Students' Exam Performance'. 16 October.

More related papers