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The nus/contact dataset (v. 2006-08-01)  >  the sessions traceset

There is 1 trace in this traceset
last modified
short description

Traceset of contact patterns among students, collected during the Spring semester of 2006 in National University of Singapore.


The authors obtained the contact patterns among 22341 students, which were inferred from the information on class schedules and class rosters for the Spring semester of 2006 in National University of Singapore.

reason for most recent change
the initial version
release date
date/time of measurement start
date/time of measurement end

For each class, we obtained the sessions associated with the class, and the students enrolled in the class. A session can be of a certain type, for instance, a lecture session, a recitation session or a laboratory session. A class can have multiple sessions of each type. Sessions of the same type can be grouped into a session group. For instance, a class may hold two lecture sessions (delivering different content) in a week for the same set of students. Both these lecture sessions are said to belong to the same session group. On the other hand, a class with large number of students, may hold two lecture sessions (delivering the same content) in a week for different batches of students. These lecture sessions are considered to be in different session groups. A student signs up for a session group for each type of session in a class he is enrolled in, and is expected to attend all sessions within that session group.

Our Intranet portal does not provide detailed information about which session group a student has signed up for. To fill in these details, we randomly assign a student to a session group. To be more specific, given a student s, for each class c that s has enrolled in, for each session type t of c, s randomly and independently signs up for a session group of type t, and attends all sessions of that session group.

Our random assignment of students to session groups might result in conflicts - that is, a student might have signed up for two sessions which are held at the same time. We adopt a simple approach to deal with such conflicts. If a session group assigned to a student leads to a conflict, the student is randomly assigned to another session group of the same type. If it is impossible to resolve a conflict, the student will not be attending any session group of that type. In our trace, only 3% of all assignments resulted in unresolved conflicts.

After both screen scraping 2 and session assignment, we have a view of which student is attending which session at what time. This data provides us with in-class activity of a student for a week. We further simplify the model in several ways. Firstly, most sessions start on the hour and end on the hour. For the few sessions which are not, we round up the starting time and ending time of the sessions to the nearest hour. This simplification allows us to use one hour as one unit time. Secondly, we "compress" the time by removing any idle time slots without any active sessions. For example, suppose the last session of Monday ends at 9pm, and the first session of Tuesday starts at 8am. If Monday 8pm to 9pm corresponds to the 10th hour, then Tuesday 8am to 9am is the 11th hour in our model. This concept is similar to business days, which counts the number of days excluding weekends and public holidays. We refer to our compressed time unit as a business hour. By compressing the time, we can remove any effects introduced by idle hours during the night and during weekends. For the rest of this paper, when we use the unit hours, we are referring to business hours. Finally, class activities which are held every fortnight are assumed to be held weekly for simplicity.

disruptions to data collection
For a few classes, there are inconsistencies in the way data is stored on the class web sites. For example the schedule information is not available. Large classes (e.g., > 500 students) have different lecture sessions and we do not have information on which lecture sessions these students have signed up for. Also, for a given class, we do not have information on which students have signed up for which recitation and laboratory. We dealt with these issues by defining "session type" and "session group" and applying "random assignment" when the information is not sufficient (see the methodology description above for details).

The data we obtained from the Intranet portal gives us the session schedule of students, from which we can infer the contact patterns of students inside the classrooms. Students, however, are likely to come into contact with each other outside of class as well. For instance, at dining halls or libraries. The class schedules and rosters do not provide us with such information.

 the nus/contact/sessions/spring06 trace
 how to cite this traceset