Week 6 Final Paper Assessment Design

Running head: DATA EVERYWHERE 1

DATA EVERYWHERE 2

Week 4: Data…Data Everywhere!

Pamela Weems-Baker

EDU 645: Learning & Assessment for the 21st Century (NMA1931A)

Dr. Julie-Ann Adkins

August 26, 2019

Data Analysis in Educational Assessment

I will discuss the different methods of interpreting statistical data. I will determine the mean, mode, and median. Moreover, I will interpret the data that is in the bar graph. The bar graph contains the performance of each student. I will also interpret data concerning the number of questions that were performed poorly. Statistics are important in the education profession since it ensures that quality is maintained. It also enables one to monitor the performance of the students.

Mean, Median, and Mode

Mean means the average of the data. Mode means the most common number in the data, while the median is the middle set of the data (Bakker & Van Eerde, 2015). To determine the mean of the test results, provided one has to add the scores of all students and then divide it by the number of students. The mean is 78.6. To determine the median, arrange the test results in alphabetic order, (Schabenberger & Gotway, 2017). The median is 97. The mode is the number that is repeated most frequently. The mode is 75 since it appears three times.

Educational Assessment

Bar graphs are used to determine the relationship between different statistical data. The bar graph shows the number of learners and the scores that they had. It can be seen that learners scored above average. Only one learner scored 43 out of 120. Most of the student scored above 63. The student score means that most of the students understood the questions.

Applying the Data

Most of the learners failed question two. The number that most students scored correctly was number four. All learners got question four correctly. Based on the incorrect answers, I would use a different concept to ensure that the students understood. I would design lessons based on student learning design. I would group students based on their interest, ability, and topic. Moreover, I would access the students using formative assessment. From time to time, I would adjust the lesson content and continuously assess the student.

Interpreting Mean, Median, and Mode

Mean, Mode, and median are similar because they are used to interpret statistical data. However, Mean is different because it is used to determine the average data. Median is the middle number when the data is arranged in ascending order. If there are two numbers in the middle one should divide both numbers by two. Mode, on the other hand, is the number that appears frequently. Mean is the best measure of central tendency when the data distribution is symmetrical and continuous (Alhamzawi, et al., 2019). For example, when the data is normally distributed. The mode is the best measure of central tendency when dealing with nominal data. Mean, and mode is preferred when dealing with other types of data. Median is preferred when the data is skewed. I would use this data when sharing results so that parents or learners can determine the areas to improve.  

Conclusion

Statistics are important in the education profession because it enables the teaches to know when teaching has effectively been done (Altbach, et al., 2019). It helps the teacher to understand whether learners understand the concept or if they need to cover more of it through homework and assignments. Statistics also ensures that the quality of education is kept high. Also, it monitors the progress of the student., (Crowder, 2017) Understanding the statistics will improve practice and support student learning because students will be able to understand their strengths and weakness. For example, by assessing the number of question that the students have performed poorly, one can look for new methods or techniques of teaching the concept.

References

Altbach, P. G., Reisberg, L., & Rumbley, L. E. (2019). Trends in global higher education: Tracking an academic revolution. BRILL.

Alhamzawi, R., Yu, K., & Mallick, H. (2019). Quantile Regression and Beyond in Statistical Analysis of Data. Journal of Probability and Statistics, 2019.

Bakker, A., & Van Eerde, D. (2015). An introduction to design-based research with an example from statistics education. In Approaches to qualitative research in mathematics education(pp. 429-466). Springer, Dordrecht.

Crowder, M. J. (2017). Statistical analysis of reliability data. Routledge.

Schabenberger, O., & Gotway, C. A. (2017). Statistical methods for spatial data analysis. Chapman and Hall/CRC.