t way to do that would be to investigate her students’ test performance in a number of ways.
The first thing she did was separate her students’ test scores based on the time of day she held her lectures (morning vs evening). Next she recorded the type of test students were writing (multiple choice vs short answer). She selected a random sample of students from her morning (n = 6) and evening (n = 7) classes (total of 13) and recorded scores from two of their tests as shown below.
Morning
Evening
Multiple Choice
Short Answer
Multiple Choice
Short Answer
66
74
70
45
64
55
80
55
72
77
78
55
70
57
84
60
61
58
64
70
67
69
84
60
70
63
DATA Set 1:
Good morning sunshine. Is Time of Day important?
1. Prof. Maya recently read an article that concluded students retained more information when attending classes in the morning. Based on this finding she thought students in her morning class might have performed differently on their Short Answer test scores when compared to students in her evening class. Does the data support her hypothesis? [15 points]
Multiple Guess! Does Exam Type matter?
2. Prof. Maya also knew that students often did better on multiple-choice tests because they only have to recognize the information (rather than recall it). Given this, she thought students attending the morning class might perform differently on the Multiple-Choice test when compared to the Short Answer test. Does the data support her hypothesis? [15 points]
DATA Set 2:
We’ll try anything once. Does the new Tutorial Plan work?
3. Combining all of her students (and ignoring time of day), Prof. Maya asked her TAs to try a new – and very expensive - tutorial study plan. She then chose a random sample of 20 students to receive the new study plan and another sample of 30 to continue using the old study plan. Following an in-class quiz, she divided the students into 3 levels of achievement (below average, average, and above average), and then created the frequency table below. Does the new expensive tutorial study plan improve student performance? [15 points]
Below average
Average
Above Average
New plan
7
7
6
Old plan
6
15
9
DATA Set 3:
How are YOU doing?
4. Finally, Prof. Maya thinks that her 2018 class is doing better than her 2017 class did. She decided to collect a sample of test scores from the students in her course this year (combining all of the groups) and compare the average with her previous year’s class average. Does the data support her hypothesis? [15 points]
The 2017 class average = 63%
The 2018 sample size = 25
The 2018 sample standard deviation = 11
The 2018 sample average = use your actual midterm mark (yes, you the student reading this :)
Bonus: What does it all mean?
5. Bonus: IF Prof. Maya had complete control of how and when she ran her course in 2018, considering all the info you just found in the 3 data sets, write a brief statement of how you would recommend she set-up the course next year – and explain why. [5 points]
t way to do that would be to investigate her students’ test performance in a number of ways.
The first thing she did was separate her students’ test scores based on the time of day she held her lectures (morning vs evening). Next she recorded the type of test students were writing (multiple choice vs short answer). She selected a random sample of students from her morning (n = 6) and evening (n = 7) classes (total of 13) and recorded scores from two of their tests as shown below.
DATA Set 1:
Good morning sunshine. Is Time of Day important?
1. Prof. Maya recently read an article that concluded students retained more information when attending classes in the morning. Based on this finding she thought students in her morning class might have performed differently on their Short Answer test scores when compared to students in her evening class. Does the data support her hypothesis? [15 points]
Multiple Guess! Does Exam Type matter?
2. Prof. Maya also knew that students often did better on multiple-choice tests because they only have to recognize the information (rather than recall it). Given this, she thought students attending the morning class might perform differently on the Multiple-Choice test when compared to the Short Answer test. Does the data support her hypothesis? [15 points]
DATA Set 2:
We’ll try anything once. Does the new Tutorial Plan work?
3. Combining all of her students (and ignoring time of day), Prof. Maya asked her TAs to try a new – and very expensive - tutorial study plan. She then chose a random sample of 20 students to receive the new study plan and another sample of 30 to continue using the old study plan. Following an in-class quiz, she divided the students into 3 levels of achievement (below average, average, and above average), and then created the frequency table below. Does the new expensive tutorial study plan improve student performance? [15 points]
Below average
Average
Above Average
New plan
7
7
6
Old plan
6
15
9
DATA Set 3:
How are YOU doing?
4. Finally, Prof. Maya thinks that her 2018 class is doing better than her 2017 class did. She decided to collect a sample of test scores from the students in her course this year (combining all of the groups) and compare the average with her previous year’s class average. Does the data support her hypothesis? [15 points]
The 2017 class average = 63%
The 2018 sample size = 25
The 2018 sample standard deviation = 11
The 2018 sample average = use your actual midterm mark (yes, you the student reading this :)
Bonus: What does it all mean?
5. Bonus: IF Prof. Maya had complete control of how and when she ran her course in 2018, considering all the info you just found in the 3 data sets, write a brief statement of how you would recommend she set-up the course next year – and explain why. [5 points]