Session Blocks

Summary

A session refers to period of time in a day where a performer engages in a set number of timings. Also, a session may apply to instances where a manager, adviser, or supervisor observes a performer for a set number of timings in a given day. A session block helps keep track of the sessions conducted with the performer. Using session blocks provide a more complete picture of exactly what occurred within selected sessions.

Note: Session Blocks only are displayed while in timings mode. Learn more about timings charts by clicking here!

An Example Session Block A 10 Minute Session, "Math Facts #1"

An Example Session Block’ appears above.A session 'Math Facts #1' was held on the date 06/22/2017, between 2:00pm and 2:10pm. The information is used to populate the 'four blank spaces at the top of a session block.

A session block will span to fit all of that data which falls between the start time, and start date, for the given date. In the above example for 'Math Facts #1', we have 9 accel data points which fall into the session block.

Why Use Session Blocks?

Session blocks help represent real time sessions, but what purpose do they serve? An even better question is, what can session blocks do for you?

a) More Accurate Analysis

Sessions blocks allow the chart reader to see what happened during a particular period of time. The more accurate representation allows for a more well-informed analysis of events. Therefore, with more information, chart readers have a greater likelihood to make better decisions.

Being able to view the data on a detailed, 'session' level, with clear boundaries on when and where sessions stop, can have a notable impact on data interpretation.

Below is a classic example of scenario playing out:

Without vs. With Session-Blocks                               ex. A - Without Session Blocks                            ex. B - With Session Blocks

On the left (exhibit A) multiple sessions of data are displayed without corresponding session blocks. On the right (exhibit B), the same data is presented but with two session blocks.
First, look at example A: 

• The data show a sharp decrease in the accel around data point 10

◦ Did the accel decrease because it's the start of a new session? Or was it mid session?

• A sharp increase in the decel occurs at data point 10

◦ How long into the session did this increase occur?

◦ Did this change occur mid-session or between sessions?

Now, look at example B:

• A sharp decrease in the accel takes place around data point 10

◦ It appears that for both sessions, accels started low and quickly increased throughout the session.

• A sharp increase arises with the decels around data point 10

◦ Occurs at the end of the session, same pattern doesn't occur at the end of the second session.

The above example demonstrates some of the insights gathered from simply using session blocks to represent orderly sessions.

b) Ability to Create Session Aims

Session blocks also have the feature of creating session aims, which can serve as an extremely useful tool for tracking day-to-day aims, and offers the ability to do a retrospective analysis on past aims.

Learn more about session aims by clicking here!

Example Session Aim An accel session aim of 12 / min.

c) Analytics (in the future)

The additional information provided in a session block opens up the possibility for much richer analytics in the future. While not currently developed, we plan on utilizing this extra information to provide users with useful aggregated information on statistics such as

• Total Sessions Count
• Overall Session Time
• Aims achieved
• Average measurements per session
• Average session length
• etc..

 

3. How to add a session block

a) When viewing a chart, click on the blue "Block" button located under "Add Resource"

b) Fill out the session block form

Add 'Session Block' Form

In the above form, the following fields appear:

• Title - Title of the session, displayed at the top of the session block.
• Description (optional) - Description of the session, displayed in the timeline.
• Time Range - The time range of the session. All measurements that fall between the two times will be considered to be part of the session.
• Session Aims (optional) - Session aims, displayed on the timings chart (read more about session aims here)

c) Hit 'Create'

As with all of the resources, session blocks can be edited in the timeline. 

 4) Nitty Gritty Details

The down and dirty, nitty gritty details aren't going to be used by every charter. If you would like to master all the tools, this section is just for you!

 a) Data Groups

You will have noticed that for every session block created, there also is a set of labels (date, title, start time, end time) displayed on the timings chart, as well as a left and right boundary (dark blue). We call that object a data group. Simply put, a data group is a group of consecutive resources.

Example of Three Data Groups Two 'new date' data groups, and one 'session block' data group.

There are two types of data groups,

• New Date - A resource that falls on a day which is different from the day of the resourced which was immediately before it. Then, create a new data group. In short, a data group will never contain resources from multiple days (and session blocks can't span multiple days either because of this). 'New Date' data groups only have one label filled in, the 'date' label at the very top. An example is shown in the 'Example of Three Data Groups' image. The first two data groups are 'New Date' data groups and have their date labels filled in with the date which the measurement falls on.
• Session Block - Every session block will create one data group, with all the labels filled in.

b) What Happens with lots of data in one 'data group'?

What happens if the amount of resources in one data group is 10 or more?

A Large Data Group A data group with more than 9 data points

Above is an example of a data group with more than 9 data points, in these scenarios, the data group width extends to contain a number of points that is equal to the smallest multiple of 10 which contains the number of points. In the above scenario, the data group extends across 20 points.