2016 CRLA Conference Session Old Dominion University-Peer Educator Program Jenn Grimm & Taia Reid

Tutoring Data – Helpful Functions, Formulas, & Tutoring Data Calculations Using Microsoft Excel

The HOME TAB

FORMATTING TOOLS…………………………………………………………………………………………………… Page 1

NUMBER TOOLS………………………………………………………………………………………………………….. Page 2

CONDITIONAL FORMATTING TOOLS……………………………………………………………………………. Page 3

SORT AND FILTER TOOLS…………………………………………………………………………………………….. Page 4

Other TOOLS

PIVOT TABLES……………………………………………………………………………………………………………… Pages 5-6

Using Pivot Tables to Calculate Sessions Visits & Hours by Course

REMOVE DUPLICATES………………………………………………………………………………………………….. Page 7

Using Remove Duplicates to Calculate Number of Unique Students Served

Additional DATA CALCULATIONS

DFWI RATE COMPARISIONS…………………………………………………………………………………………. Pages 8-11

UTILIZATION RATE CALCULATIONS……………………………………………………………………………… Page 12

CALCULATING % OF AGREEMENT FROM SURVEYS……………………………………………………… Page 13

ALLOCATING TUTORING HOURS………………………………………………………………………………….. Pages 14-15

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FORMATTING TOOLS

Many people make the mistake of believing that, if it’s in an Excel sheet, it’s going to look boring – but it doesn’t have to be boring! Make use of

common formatting tools from your Home tab to take your Excel sheet from drab to fab!

- Play around with font size, type, color, and other effects like bold, italics, and underline

- The paint bucket tool can also be your friend when attempting to distinguish headers, different columns, and key data

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NUMBER TOOLS

Make sure you are using the proper number format to present your data. Are you displaying…

A general number?

Visits 95

Currency? A date?

A percentage?

Utilization Rate

82%

Total $ 123,290.80 10/10/2016 10-Oct

Make use of the decimal buttons to

clean up your decimals and percentages.

Utilization Rate

81.5513%

Utilization Rate

82%

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CONDITIONAL FORMATTING TOOLS

Conditional Formatting allows you to quickly and easily search for data within your spreadsheet.

The ‘Equal To’ function allows you to custom highlight

cells with certain properties.

(Accomplish similar views with the ‘Greater Than,’ ‘Less Than,’

‘Between’ and ‘Text that Contains’ functions.)

Name

Adams, Joseph

Smith, Molly

Brown, Carol Murphy, Zachary

Cook, Jennifer

Nicoles, Timothy

Banks, Elizabeth

Cook, Jennifer Johnson, Andrew

Adams, Joseph

The ‘Duplicate Values’ function allows for

a quick-check if any responses appear

more than once in a given set of data.

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SORT AND FILTER TOOLS

Easily sift through large amounts of data by using the ‘Custom Sort’ feature.

Multiple levels of sorting can be done by selecting the ‘Add Level’ button in ‘Custom Sort.’

You don’t have to just sort by ‘Values.’ If, for example, you have a certain cell or font

color that is meaningful, you could sort data using that as a field.

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PIVOT TABLES

Pivot tables will quickly sort, organize, and calculate large data sets. Below is an example of how we use pivot tables to keep record of the

number of visits and amount of time our tutors spend with students for each course. The tutor scheduling software that we use always

gives us an output of each individual student visit record, and we use a pivot table to compile all of this data.

Step 1 – To make our lives easier, we first delete all of the info we don’t need to work with from the Excel sheet. This is one option, or you

could leave everything there and simply select the data you need when creating the table.

Step 2 – Highlight the columns/cells you would like included in the table and select ‘Pivot Table’ from the Insert Tab. Opt to either create

the table in the current sheet or open another sheet within the Excel workbook, and select ‘OK.’

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PIVOT TABLES Continued…

Step 3 – Next, drag the required categories into the

appropriate fields labeled ‘Filters,’ ‘Columns,’ ‘Rows,’ and

‘Values.’ For our purposes, we do the following to calculate

the number of session visits and tutoring hours for each

course:

Move ‘Course’ to the ‘Rows’ field.

Move ‘Course’ to the ‘Values’ field. This field defaults to

‘Count of Course,’ which counts for us the number of

times each particular course shows up in the data set. In

other words, this gives us a count of the number of session

visits for each course for which we have tutored.

Move ‘Duration’ to the ‘Values’ field. We then select the

drop-down menu and choose ‘Value Field Settings.’ From

there, we opt for ‘Sum’ instead of ‘Count.’ This way, the

duration of each session for each course is added up to

provide us with the total time our tutors have spent with

students for a particular course.

Step 4 – From there, it is simply a matter of cleaning-up and

organizing the data:

Edit headers

Adjust decimal points for session hours

Please note: When creating a pivot table in this manner, Column A

defaults to being the ‘Sort’ field. If we want to instead sort the

data by the number of visits (largest to smallest), we simply copy

the data and paste it into a blank Excel sheet, choosing ‘Values’

under ‘Paste Options.’ From there, we can use the ‘Sort & Filter’

field to organize the data by a field other than the Course

number.

Course No. No. of Visits

Sum of Session Hours

ACCT-201 27 24.5

ACCT-202 6 5.6

ACCT-301 6 3.2

ACCT-302 8 7.0

BIOL-121N 2 1.7

BNAL-206 5 4.9

BNAL-306 12 12.7

CET-200 9 6.9

CS-150 5 7.4

CS-250 2 0.9

CS-252 4 4.1

CS-333 1 2.2

ECON-200S 4 3.7

ECON-201S 1 0.6

ECON-202S 4 2.4

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REMOVE DUPLICATES

Another common function we use is ‘Remove Duplicates’ to get rid of duplicate values that may occur within a series of data.

This is especially useful when we are trying to find the unique number of students served from an Excel sheet that lists out

each individual session visit. In this instance, some students’ names will be listed multiple times – once for each visit. While it

is helpful to know how many tutoring sessions have occurred, administration also often wants to know the number of

individual students served by our tutoring center.

To find the unique number of students served by tutoring, we do

the following:

1) Copy over our visits data into a new sheet within the Excel

workbook.

2) Delete all columns, with the exception of the student name

column.

3) Select the entire student name column, then select ‘Remove

Duplicates’ from the ‘Data’ tab and ‘OK.’

4) Remove the header row, and the number of rows left with

student names equals the number of unique students.

Name

Adams, Joseph

Smith, Molly

Brown, Carol

Murphy, Zachary

Cook, Jennifer

Nicoles, Timothy

Banks, Elizabeth

Cook, Jennifer

Johnson, Andrew

Adams, Joseph

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DFWI RATE COMPARISONS

At the conclusion of each semester, we examine the potential impact of regular tutoring attendance on students’ grades. We do this by comparing

the rates of DFWI (students who earned a final letter grade of a D or F or who Withdrew from the course or received an Incomplete in the class).

We compare DFWI rates of students who regularly attending tutoring (3+ times) with their peers from their class sections who did not (0-2 times).

Here’s how we do this:

1) First, we collect all of the relevant data, including: students’ ID numbers (or names with middle initials), the number of tutoring visits for

each student, and the final course grade for each student (Note: You may need to work with your university’s Registrar, Assessment Office,

and/or individual course instructors to obtain this information). For the sake of time, we focus on our Top 10 courses based on those classes

that saw the most student visitors for tutoring.

2) Then, we clean up our Excel sheets for each course to make sure they include only the data needed: Student ID/Name, Number of tutoring

visits for the student (If needed, see instructions for creating a pivot table to compile the number of tutoring session visits for each student

from a visits report), Course Section, and Final Grade.

3) Once the Excel sheet is clean and ready to go, we sort and filter by ‘Session Visits’ from Largest to Smallest. We then highlight all of the

students who have attended 3+ sessions to assist with the next step of the process.

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4) We then sort and filter by ‘Course Section.’ We use the highlighted yellow data to help us identify which course sections had ‘regular tutees’

from those that did not. We delete all of the student data for the course sections that did not have any regular tutoring attendees, since we

don’t consider them to be in the same comparison group as our Tutee Group.

5) After this, we then simplify the grade data into two categories: ABC or DFWI. Any student who earned an A, A-, B+, B, B-, C+, C, or C- is in

the ABC group. Any student who earned a D+, D, D-, F, W, or I is in the DFWI group. We assume any students missing a final grade are a ‘W’

and add them to the DFWI group. Group assignments can easily be made by using sort and filter by ‘Course Grade’ and then quickly

assigning students to the ABC or DFWI group.

6) After this is complete, we sort and filter again by ‘Session Visits’ from Largest to Smallest. Then, we separate the data out into two groups –

regular attendees vs. non-regular attendees.

7) After this, we calculate the number of students who earned an ABC vs. DFWI for each group by using an Excel formula called ‘Count If.’ In

order to complete this calculation, we find it is easiest to setup a spot on the Excel sheet for calculating the Tutoring Group vs. Non-Tutoring

Group and the number of ABC’s vs. DFWI’s.

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8) To setup a ‘Count If’ formula, we select in the cell next to ‘ABC’ underneath the ‘Tutoring Group’ section and then type in “=COUNTIF(“.

After this, you’ll select the range of cells that has the ‘Course Grade’ data for the tutoring group, which is column I in my Excel sheet, so the

formula continues “=COUNTIF(I:I,”. After the comma, you will want to select the cell that says ‘ABC’ underneath the ‘Tutoring Group’

section, which is cell F17 on my Excel sheet. So, the complete formula is “=COUNTIF(I:I,F17)” or “=COUNTIF(cell range, count value)”. Then,

we hit ‘Enter,’ and the number of students who are in the ABC group for the Tutoring Group is automatically calculated.

9) We then repeat the above step for the ‘DFWI’ category for the ‘Tutoring Group’ and for the ‘ABC’ and ‘DFWI’ categories for the ‘Non-

Tutoring Group.’

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10) Once this step is complete, we calculate the DFWI Rate for each group by using a division formula. We divide the DFWI total from the total

of both the ABC and DFWI groups. So, in an instance where the ABC total for the ‘Tutoring Group’ is in cell G17 and the DFWI total is in cell

G18, the formula would look like this: “=G18/(SUM(G17:G18))” or “=DFWI total/(SUM(ABC total & DFWI total))”. We select ‘Enter,’ and we

have our DFWI rate for the Tutoring Group. We repeat the same process for the Non-Tutoring Group, and finally we convert both decimals

into percentages using the ‘%’ symbol from the Home tab.

11) We repeat this process for our Top 10 courses. Once we have done this, we use Excel to add-up the numbers (NOT the percentages) for

each course’s ABC Tutoring Group, DFWI Tutoring Group, ABC Non-Tutoring Group, and DFWI Non-Tutoring Group. From these larger

numbers, we calculate the DFWI rate for both the Tutoring and Non-Tutoring Groups following the above steps. We consider even a 0%

difference in the DFWI Rates between the Tutoring and Non-Tutoring Groups to be a good thing, since tutoring is often viewed as remedial,

which means ‘struggling students’ are more likely to seek out our services. During a good semester, our DFWI Rate for the Tutoring Group

may be lower. When reporting this data, we always clarify that results are NOT statistically significant, since the Tutoring Group so small.

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UTILIZATION RATE CALCULATIONS

We also like to keep track of our utilization rates, along with the overall costs of our tutoring services per visit and student served. This is to help us

ensure we are using our resources as efficiently as possible.

# Tutor Hours Offered # Hours Utilized Utilization Rate # Visits Cost/Visit # Unique Students Cost/Student

648.5 565.36 87% 565.36 $ 11.47 258 $ 25.14

To calculate these numbers, we do the following:

1) First, we add-up how many hours our tutors have been available to provide tutoring by looking at when our tutors are scheduled/available

to assist students. = # Tutor Hours Offered

2) Then, we use our visit calculations (referenced in the Pivot Tables section on pages 5-6) to divide the # Hours Utilized with the # Tutor

Hours Offered for the Utilization Rate.

3) After this, we use our visit calculations (referenced in the Pivot Tables section on pages 5-6) to divide the # Visits by the # Tutor Hours

Offered multiplied by our average hourly tutor salary ($10/hour) to calculate the Cost/Visit. In our Excel sheet, the # Tutor Hours Offered is

in cell C61, while the # Visits is in cell F61, which means the formula is “=(C61*10)/F61” or “=(# Tutor Hours Offered*Salary)/# Visits”.

4) Finally, we use our # of unique students calculations (referenced in Remove Duplicates section on page 7) to divide the # Unique Students

by the # Tutor Hours Offered multiplied by our average hourly tutor salary ($10/hour) to calculate the Cost/Student. In our Excel sheet, the

# Tutor Hours Offered is in cell C61, while the # Unique Students is in cell H61, which means the formula is “=(C61*10)/H61” or “=(# Tutor

Hours Offered*Salary)/# Unique Students”.

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CALCULATING % OF AGREEMENT FROM SURVEYS

For many of our surveys, we use a 5-point Likert scale for students to rate their tutoring experiences. We use a reporting system called Qualtrics

(though, a free version like Survey Monkey could also be used) to capture and compile student responses to our satisfaction surveys. The one

challenge with Likert scales is that the data can be a little ‘messy’ to report out for each individual response item.

For this reason, we choose to report out the % of agreement students have to each response. This is fairly easy to do in Excel with the correct

formula. For each statement tutees’ respond to, we simply add up the number of agree and strongly agree responses and divide them by the total

number of responses. So, in an instance where the ‘agree’ responses are in cell E10, the ‘strongly agree’ responses are in cell F10, and the ‘Total

Responses’ are in cell H10, the formula looks like this: “=(SUM(E10:F10))/H10)” or “=SUM(Agrees & Strongly Agrees))/Total Responses”.

This makes our reports look much cleaner and easier to understand than if we were to report out each individual response or even the mean.

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ALLOCATING TUTORING HOURS

As a program on a tight budget that supports 70+ courses for the entire undergraduate student population each semester, we often have to make

our money stretch so that we use it in the most efficient way possible. For this reason, we are selective as to which courses we support each

semester, and we do our best to schedule tutoring as efficiently as possible.

When selecting the courses we support each semester, we use the following criteria: course DFWI rates, courses that have a high number of

students receiving D’s and F’s on mid-term grades reports, tutoring attendance numbers from previous semesters, student requests from each

semester, and courses that professional advisors recommend based on their experiences meeting with students.

Once we have our courses selected, we allocate tutoring hours for each course based on funding and past demand. First, based on the funding we

receive on an annual basis for tutoring, we are able to determine that we can pay all of our tutors to tutor for X number of hours per week, after

factoring their hourly salary and the number of weeks of tutoring each year. This year, we were able to offer a total of 136 hours of tutoring for all

of our courses per week. From there, we allocated hours to each of the courses we support. Most courses only received 1 hour of tutoring allotted

per week, while our courses that have been very popular in the past received up to 5 hours of support per week. All course allocations total up to

136 hours of tutoring per week.

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Then, as we hire tutors, we allocate each tutor a different number of weekly work hours based on the courses they are able to support. We track

this in a spreadsheet with the courses in Column A with each tutor’s name as headers at the top of the other columns.

Next to the course name, we also include the number of hours allotted to each course. Beside that number, we calculate the numbers that

have actually been allotted to each course by subtracting the number of hours allotted to all of the tutors from the total number of hours allotted

to each course.

In addition, we use SUM formulas to track how many hours we are able to offer to each tutor based on their courses they are able to

support.