The Data

This article describes data available, for internal Charles Sturt University use, in the following packages: data.csu.retention, data.csu.activity and data.csu.exit.

The retention dataset : `data.csu.retention`

This includes data on student demographics, enrolments, academic results, as well as data on the Retention Teams interventions.

The activity dataset : `data.csu.activity`

This includes LMS activity data on students. It is in a separate dataset due to its large size.

The exit survey dataset : `data.csu.exit`

This includes data on the exit survey results. It is not commonly used with the other data and is sometimes sensitive so is in a separate dataset.

Common design features of the data set

There are essentially four families of tables in the data:

Learning structure tables, containing data on the subjects, sessions and other details. These tables include data on the teaching sessions, campus_codes, and what offerings and subjects are available.
Student enrolment tables. These detail details about the student and their enrolment at the university. Student characteristics are contained in student_ids and student_demographics, details on their course and subject enrolments are contained in student_course and enrolments, respectively.
Student learning tables. These record what the student does once they are enrolled. Some detail the students activity on the subject sites through the LMS trace data, and their performance via assessment_marks and overall subject [grades]. Additionally there is some data on if the students decide to exit the university as to why they have done so.
Retention team campaign tables. These tables record data pertinent to the activities and interventions of the Retention Team. It includes lists of which students utilised the embedded_tutors, which raised flags for being at-risk, what triggers were used to identify at-risk students, and what kind of contact was made with students. The key table here is the interventions table, which attempts to summarise all the other tables into one, hopefully practical, place.

Common variables

A key part of the cleaning and processing of the data is consistent names for variables throughout the tables. These will be commonly used for as keys for making joins.

id : Unique student ID (character). Sometimes a student will have multiple ID’s, if they have re-enrolled. In this case the numeric part of the ID is the same, but there is an A or a B appended at the end.
session : Unique ID for the teaching session (numeric). The first four digits are the year, the last two correspond to when in the year the session runs. The main three teaching sessions end in 30, 60, 90. E.g. 202230 is the first main teaching session in 2022.
year : The calendar year.
subject : The six character subject code (for a unit of study), in the form of ABC123.
offering : The offering code. An offering is a subject (unit of study) taught in a particular session at a particular campus delivered in a particular mode (Internal or Distinace). The code is in the form ABC123_202230_PT_I, denoting the subject, session, campus and mode, separated by _.
campaign : The particular intervention ‘campaign’ that this data relates to. Details of these values are outlined in the notes on the flags table.

Data freshness

In addition to the tables, there are a collection of timestamps beginning with the prefix last_updated_*. These indicate when the data was last updated.

data.csu.retention::last_updated_student_opa
#> [1] "2023-01-16 09:34:13 AEDT"

Retention data package tables

The tables in the

`academic`

This table gives academic results by student (id), subject and session.

data.csu.retention::academic |> 
    slice(1:10) |> 
    mutate(  # anonymising 
        id = "student_id", subject = "ABC123", 
        offering = "ABC123_202230_W_D") |>
    glimpse()
#> Rows: 10
#> Columns: 8
#> $ id             <chr> "student_id", "student_id", "student_id", "student_id",…
#> $ session        <dbl> 202230, 202260, 202230, 202230, 202230, 202260, 202230,…
#> $ subject        <chr> "ABC123", "ABC123", "ABC123", "ABC123", "ABC123", "ABC1…
#> $ offering       <chr> "ABC123_202230_W_D", "ABC123_202230_W_D", "ABC123_20223…
#> $ grade          <fct> DI, LV, CR, CR, CR, CR, CR, DI, TA, DI
#> $ mark           <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
#> $ mark_bb        <dbl> 0.8000, NA, 0.7370, 0.7100, 0.7100, NA, 0.7382, 0.7620,…
#> $ grade_original <fct> DI, LV, CR, CR, CR, CR, CR, DI, TA, DI

grade is the final grade the student received for the subject.
mark is the percentage score the student achieved. Lots of this data is missing (97%)
mark_bb is an attempt to get an estimate the student’s mark from the LMS data. A lot of this data is also missing (42%). If you are looking for a best guess of the mark it is worth coalescing mark and mark_bb, but so far this data is very incomplete
grade_original indicates the first grade the student achieved in the subject. Sometimes there is an ‘admin override’ grade, so if grade_original is different to grade this indicates that an override has occurred.

`academic_details`

This is historic data, only available for the three main sessions in 2019, and only for HEPPP subjects in those sessions.

data.csu.retention::academic_details |> 
    slice(1:10) |> 
    mutate( # anonymising
        id = "student_id", subject = "ABC123") |> 
    glimpse()
#> Rows: 10
#> Columns: 7
#> $ id           <chr> "student_id", "student_id", "student_id", "student_id", "…
#> $ subject      <chr> "ABC123", "ABC123", "ABC123", "ABC123", "ABC123", "ABC123…
#> $ eai          <dbl> 0.570, 0.570, 0.825, 0.300, 0.600, 0.600, 0.800, 0.800, 1…
#> $ cm           <dbl> 0.63800, 0.63800, 0.51425, 0.03000, 0.54600, 0.54600, 0.3…
#> $ mean_mark    <dbl> 0.5922222, 0.5922222, 0.7287500, 0.3000000, 0.4475000, 0.…
#> $ submit_ratio <dbl> 1.0000000, 1.0000000, 0.8000000, 0.3333333, 1.0000000, 1.…
#> $ session      <dbl> 201960, 201990, 201990, 201990, 201930, 201990, 201930, 2…

eai indicates the mark the student received for their Early Assessment Item.
cm indicates the students’ course mark.
mean_mark indicates the average mark the student received in all their assessments (unweighted).
submit_ratio indicates the ratio of assessments submitted to assessments required in the subject.

`assessment_marks`

This data collects together assessments, grades and cumulative marks for selected subjects. Each row is a result for a particular assessment item (title), for a student id, in a given subject, in a given session. Some marks are numeric and some are categorical (grades, or pass / fail items).

data.csu.retention::assessment_marks |> 
    slice(1:10) |> 
    mutate( # anonumising
        subject = "ABC123", id = "student_id", bb_pk1 = "bb_pk") |> 
    glimpse()
#> Rows: 10
#> Columns: 15
#> $ subject        <chr> "ABC123", "ABC123", "ABC123", "ABC123", "ABC123", "ABC1…
#> $ session        <dbl> 202030, 202030, 202030, 202030, 202030, 202030, 202030,…
#> $ id             <chr> "student_id", "student_id", "student_id", "student_id",…
#> $ title          <chr> "Calculated Grade", "Calculated Grade", "Calculated Gra…
#> $ week_due       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
#> $ value          <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
#> $ eai            <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
#> $ threshold_mark <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
#> $ metric         <chr> "Total Pts: up to 100 Letter", "Total Pts: up to 100 Le…
#> $ grade          <chr> "FL", "DI", "DI", "DI", "CR", "DI", "DI", "PS", "HD", "…
#> $ bb_pk1         <chr> "bb_pk", "bb_pk", "bb_pk", "bb_pk", "bb_pk", "bb_pk", "…
#> $ score          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
#> $ out_of         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
#> $ score_p        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
#> $ sy_us          <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA

title is the name of the assessment. There are three special items, Calculated Grade, Administrative Override and Cumulative Mark. Calculated Grade is not an assessment mark, but rather the students’ final grade, as it appears in the LMS (this may not always match the official grade). If the grade was overwritten, this is stored as an Administrative Override. Cumulative Mark is the numeric mark that the student received, from their weighted assessment marks, used to calculate their grade.
week_due is the week the assessment was due.
value is the value of the assessment (its contribution to a total of 100 marks for the Cumulative Mark). Some assessments are required to pass, but have no weighting, and will have a value of 0.
eai indicated if the assessment item was used as an Early Assessment Item as part of a pre census campaign to identify disengaged students.
threshold_mark includes a value if the particular assessment item requires a minimum mark in order for the student to pass the subject. A threshold_mark of 0 indicates that the item must be submitted, but their is no required numeric value to achieve.
mertric indicates how the assessment item is assessed; for instance if it is a numeric score and if so what is it out of. This is taken directly from the assessment item header in the LMS.
grade is the assessments grade (if categorical, blank otherwise)
bb_pk1 is the primary key to the LMS data base (Blackboard) that matches the assessment item. It is possible that this will not always match if an academic deletes the item and recreates it.
score is the numeric score for the assessment item (if numeric, blank otherwise).
out_of is the maximum score that could be achieved for the assessment item (if numeric, blank otherwise).
score_p is the % score of the assessment item (if numeric, blank otherwise).
sy_us indicates a Satisfactory (SY) or Unsatisfactory (US) grade, if this is how the assessment was marked.

`campus_codes`

In the offering codes (formatted like ABC123_202230_W_I) the third component (the W in the example here) indicates the campus that the offering is taught from. This table matches these one or two character codes to the campus name.

data.csu.retention::campus_codes |> glimpse()
#> Rows: 25
#> Columns: 2
#> $ campus      <chr> "Bathurst", "Albury-Wodonga", "Orange", "Wagga Wagga", "Du…
#> $ campus_code <chr> "B", "A", "OA", "W", "D", "UC", "WT", "C", "PT", "G", "HS"…

`embedded_tutors`

Part of the Retention Teams interventions is the embedded tutors program. This table is a record of students (id) who have attended a tutorial session, for a particular subject, in a particular session. It does not take into account multiple tutorial sessions for the same subject in the same session.

data.csu.retention::embedded_tutors |> 
    mutate( # anonymising
        id = "student_id", subject = "ABC123"
    ) |> 
    glimpse()
#> Rows: 1,637
#> Columns: 3
#> $ id      <chr> "student_id", "student_id", "student_id", "student_id", "stude…
#> $ session <dbl> 202130, 202130, 202130, 202130, 202130, 202130, 202130, 202130…
#> $ subject <chr> "ABC123", "ABC123", "ABC123", "ABC123", "ABC123", "ABC123", "A…

`contact`

A component of the various intervention campaigns run by the Retention Team involve communication with students. This table attempts to draw together records of the different types of contact that have happened, when, with who, and how they went. It is one row per contact attempt, for a particular student id, in a session, for a campaign, at a particular time (contact_timestamp).

data.csu.retention::contact |> 
    mutate(id = "student_id") |> 
    glimpse()
#> Rows: 24,756
#> Columns: 9
#> $ id                <chr> "student_id", "student_id", "student_id", "student_i…
#> $ session           <dbl> 202060, 202060, 202060, 202060, 202060, 202060, 2020…
#> $ campaign          <chr> "pre census", "pre census", "pre census", "pre censu…
#> $ dialogue          <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FAL…
#> $ contact_type      <chr> "call", "call", "call", "call", "call", "call", "cal…
#> $ contact_date      <date> 2020-07-27, 2020-07-27, 2020-07-27, 2020-07-27, 202…
#> $ contact_timestamp <dttm> 2020-07-27 10:48:39, 2020-07-27 14:35:41, 2020-07-2…
#> $ earliest_flag     <dttm> 2020-07-27, 2020-07-27, 2020-07-27, 2020-07-27, 202…
#> $ details           <chr> "outreach_campaign: HEPPP - Previous fails * * * cal…

dialogue indicates if the outreach team had a meaningful conversation with the student.
contact_type is the method of contact, usually call, sms or email.
contact_date the day the contact was made.
contact_timestamp is the timestamp of when the contact was recorded (the data is filled out at the end of the conversation, so their might be a little delay).
earliest_flag indicates the earliest flag that the student was contacted for. Sometimes students raise several flags in a given campaign, however the contact is usually initiated for the first flag only.
details eleborate why the contact was made, and any other notes that were made by the outreach team.

`enrolments`

A table of the subject enrolment records for each student. It includes when they enrolled in the subject and when they withdrew. This data generally takes about a week to align itself with what actually happened - so beware enrolment movements that are recent. One row per student id, per session, per offering, per subject - although it is technically possible that students could be moving in and out of the same subject on the same day.

data.csu.retention::enrolments |> 
    slice(1:10) |> 
    mutate(
        id = "student_id", subject = "ABC123", 
        offering = str_c("ABC123", session, "W", "D", sep = "_")
    ) |> 
    glimpse()
#> Rows: 10
#> Columns: 6
#> $ id            <chr> "student_id", "student_id", "student_id", "student_id", …
#> $ session       <dbl> 202360, 202360, 202334, 202344, 202374, 202330, 202330, …
#> $ subject       <chr> "ABC123", "ABC123", "ABC123", "ABC123", "ABC123", "ABC12…
#> $ offering      <chr> "ABC123_202360_W_D", "ABC123_202360_W_D", "ABC123_202334…
#> $ enrol_date    <date> 2022-09-04, 2022-09-04, 2022-09-04, 2022-09-04, 2022-09…
#> $ withdraw_date <date> 2022-09-06, NA, 2022-09-06, 2022-09-06, 2022-09-06, NA,…

`flags`

As part of some of the intervention campaigns students may be flagged as ‘at-risk’ for some reason. This is recorded in the flags table. Each row is for a particular concern, by student id, in a subject / offering, in a particular session for a particular campaign. The is also identified by when it occured, however this data has been inconsistently recorded over the years and may be in any of week, flag_timestamp or trigger_date.

data.csu.retention::flags |>
    filter(session == 202230) |> 
    slice(1:10) |> 
    mutate(id = "student_id", subject = "ABC123", 
           offering = str_c("ABC123", session, "W", "D", sep = "_"),
           concern_detail = "detailed comments") |> 
    glimpse()
#> Rows: 10
#> Columns: 11
#> $ campaign             <chr> "pre census", "pre census", "pre census", "pre ce…
#> $ session              <dbl> 202230, 202230, 202230, 202230, 202230, 202230, 2…
#> $ id                   <chr> "student_id", "student_id", "student_id", "studen…
#> $ subject              <chr> "ABC123", "ABC123", "ABC123", "ABC123", "ABC123",…
#> $ offering             <chr> "ABC123_202230_W_D", "ABC123_202230_W_D", "ABC123…
#> $ trigger_date         <dttm> 2022-03-07, 2022-03-07, 2022-03-07, 2022-03-07, …
#> $ concern              <chr> "non submission", "non submission", "non submissi…
#> $ flag_timestamp       <dttm> 2022-03-10, 2022-03-10, 2022-03-10, 2022-03-10, …
#> $ week                 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
#> $ concern_detail       <chr> "detailed comments", "detailed comments", "detail…
#> $ reason_for_no_upload <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA

campaign indicates which of the Retention Team campaigns this flag belongs to. The early low engagement campaign is run in week two of session and identifies students with no subject site access since beginning of session, and a friendly email is sent. The pre census campaign is run in weeks 3 and 4, before census date, and is identifying students at higher risk of failure in a select number of subjects. Students are then contacted by the outreach team. A similar campaign was run in 2020 after census date, and was called the post census campaign. The persistent low engagement campaign is run just before census date, and is a more strongly worded contact for students with still no subject site access in the days before census date. The non genuine students campaign is run after census, and is for very disengaged students who did not withdraw from subjects prior to census. The former fail campaign is no longer run by the retention team, but was an early contact of students who had performed poorly in a prior session.
trigger_date indicates when the flag was intended to be triggered, such as the due date for an early assessment item.
concern indicates why the flag was raised. This can depend on the campaign. For the pre census campaign it is usually low activity for a period of inactivity on the subject site, or non submission for not submitting an early assessment item. For the former fail campaign (no longer running) it is always prior performance. For the non genuine students campaign there are variety of concerns, hopefully explaining clearly why the student was put on the list.
flag_timestamp indicates when the flag happened, where data available (78.8% complete)
week indicates the week of the session that the flag was raised. Where flag_timestamp is missing this is often there instead.
concern_detail is any other information pertinent to the flag.
reason_for_no_upload indicates a reason why this particular flag was not forwarded onto the outreach team. This is usually because the student has already been flagged in this campaign as at risk.

`flags_unticked`

When students are deemed at risk due to missing an early assessment item, this is checked with the academic. Some students are removed from the flag list (perhaps they had already organised an extension). These students do not appear on the flag list, but do appear on the flags_unticked table. This is a work in progress and currently only has data for the sessions: 202230.

data.csu.retention::flags_unticked |> 
    mutate(id = "student_id", offering = "ABC123_202230_W_D") |> 
    glimpse()
#> Rows: 599
#> Columns: 4
#> $ campaign <chr> "pre census", "pre census", "pre census", "pre census", "pre …
#> $ session  <dbl> 202230, 202230, 202230, 202230, 202230, 202230, 202230, 20223…
#> $ offering <chr> "ABC123_202230_W_D", "ABC123_202230_W_D", "ABC123_202230_W_D"…
#> $ id       <chr> "student_id", "student_id", "student_id", "student_id", "stud…

`interventions`

This table aggregates data from flags, contact, embedded_tutors and other sources to summarise what interventions have been made by the Retention Team and how they went. It is organised as one row per student id, per session, per campaign.

data.csu.retention::interventions |> 
    mutate(id = "student_id", intervention_target = "ABC123_202090_W_D") |> 
    glimpse()
#> Rows: 27,690
#> Columns: 9
#> $ id                     <chr> "student_id", "student_id", "student_id", "stud…
#> $ campaign               <chr> "early low engagement", "early low engagement",…
#> $ session                <dbl> 202090, 202090, 202090, 202090, 202090, 202090,…
#> $ intervention_timestamp <dttm> 2020-11-24 22:39:30, 2020-11-24 22:39:30, 2020…
#> $ intervention_target    <chr> "ABC123_202090_W_D", "ABC123_202090_W_D", "ABC1…
#> $ concerns               <chr> "early low engagement", "early low engagement",…
#> $ intervention           <chr> "email", "email", "email", "email", "email", "e…
#> $ intervention_result    <chr> "one-way communication", "one-way communication…
#> $ intervention_details   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…

intervention_timestamp is a timestamp of when the intervention occurred, where available.
intervention_target indicates the target of the intervention. This is different for different campaigns. For the pre census campaign, the target will be a list of subjects / offerings that the student was flagged in. For the non genuine students campaign the target will be that students course enrolment, for instance.
concerns details a list of the concerns that pertain to this intervention.
intervention details what action was taken by the retention team, such as email or call.
intervention_result details a short outline of what the result of the intervention was, such as whether or not the outreach team talked to the student.
intervention_details includes in other information, such as notes that the outreach team may have made.

`offerings`

This includes data on the individual subject offerings. It is one row per offering.

data.csu.retention::offerings |> 
    slice(1:5) |> 
    mutate(academic_name = "Bob Katter", academic_email = "bob@csu.edu.au", 
           academic_id = "bkatter27") |> 
    glimpse()
#> Rows: 5
#> Columns: 8
#> $ offering              <chr> "ACC100_201990_B_D", "ACC110_201990_B_D", "ECO13…
#> $ subject               <chr> "ACC100", "ACC110", "ECO130", "ITC105", "JST110"
#> $ offering_subject_name <chr> "Accounting 1", "Accounting 2", "Business Econom…
#> $ session               <dbl> 201990, 201990, 201990, 201990, 201990
#> $ pre_census_focus      <lgl> TRUE, TRUE, TRUE, TRUE, TRUE
#> $ academic_name         <chr> "Bob Katter", "Bob Katter", "Bob Katter", "Bob K…
#> $ academic_email        <chr> "bob@csu.edu.au", "bob@csu.edu.au", "bob@csu.edu…
#> $ academic_id           <chr> "bkatter27", "bkatter27", "bkatter27", "bkatter2…

offering_subject_name is the name of the subject.
pre_census_focus indicates if the offering was part of the pre census campaign.
academic_name is the name of the teaching academic.
academic_name is the email of the teaching academic.
academic_id is the email of the teaching academic. This is sometimes more reliable for matching than the name.

`student_ids`

Data on student identifying variables. One row per student id, which is the university id.

data.csu.retention::student_ids |>
    names() # this is all identifiable data
#> [1] "id"        "firstname" "lastname"  "user_id"   "email"     "phone"    
#> [7] "first_id"  "pidm"

firstname and lastname are the first and last names of the student.
user_id is the students LMS login id.
email is the students email.
phone their phone number. Format varies.
first_id is the first id that the student had at Charles Sturt University.
pidm is a unique id that should be unique for a given student. This is used to match where id variables are different for the same student (due to re-enrolling at a later point in their academic career).

`student_demographics`

This table includes the most recent student demographic data, one row per student id.

data.csu.retention::student_demographics |>
    slice(1:10) |> 
    mutate(id = "student_id", firstname = "firstname", lastname = "lastname") |> 
    glimpse()
#> Rows: 10
#> Columns: 13
#> $ id                        <chr> "student_id", "student_id", "student_id", "s…
#> $ firstname                 <chr> "firstname", "firstname", "firstname", "firs…
#> $ lastname                  <chr> "lastname", "lastname", "lastname", "lastnam…
#> $ age                       <dbl> 52, 54, 47, 55, 47, 51, 53, 45, 47, 36
#> $ gender                    <chr> "Male", "Female", "Male", "Male", "Male", "F…
#> $ domesticity               <chr> "Domestic", "Domestic", "Domestic", "Domesti…
#> $ atsi                      <chr> "Not Australian First Nations", "Not Austral…
#> $ nesb                      <chr> "Not NESB", "Not NESB", "Not NESB", "Not NES…
#> $ atar_group                <chr> "Not Recent School Leaver", "Not Recent Scho…
#> $ parental_education        <chr> "University Level", "Unknown highest parenta…
#> $ ses                       <chr> "Low SES", "High SES", "High SES", "Low SES"…
#> $ disability_support_status <chr> "no Disability", "no Disability", "no Disabi…
#> $ remoteness                <chr> "(2) Outer Regional Australia", "(0) Major C…

age is the students age at the last time they were an active student (well, a best guess at that). Age demographic data should really be done as a snapshot from active students with data from OPA.
gender - students gender.
domesticity indicates if the student is regestered as a domestic student.
atsi indicates the students First Nation status.
nesb indicates if the student comes from a Non English Speaking Background.
atar_group, where available, indicates which band of marks the student received for their ATAR in regards to university entrance. This is patchy due to a large proportion of Charles Sturt students coming into university from alternative pathways later in life.
parental_education indicates the highest level of education the students parents received. If it is Not University Level then the student is regarded as First in Family.
ses is the students Social Economic Status, based on their postcode.
disability_support_status indicates if the student has requested support for their disability.
remoteness indicates how close to an urban centre the student lived, prior to university. The levels are, from more urban to more remote: Major Cities, Inner Regional, Outer Regional, Remote, Very Remote.

`student_course`

This table includes data on the students enrolment in a particular course (program of study). It is one row per student id, per course, per admit_session.

data.csu.retention::student_course |>
    filter(admit_session == 202030) |> 
    slice(89:99) |> 
    mutate(
        id = "student_id", course = "Course Name", course_code = "10000AB"
    ) |> 
    glimpse()
#> Rows: 11
#> Columns: 16
#> $ id                      <chr> "student_id", "student_id", "student_id", "stu…
#> $ course                  <chr> "Course Name", "Course Name", "Course Name", "…
#> $ course_code             <chr> "10000AB", "10000AB", "10000AB", "10000AB", "1…
#> $ course_level            <chr> "Graduate Certificate", "Graduate Certificate"…
#> $ course_faculty          <chr> "FOBJBS", "FOBJBS", "FOBJBS", "FOBJBS", "FOBJB…
#> $ mode                    <chr> "D", "D", "D", "D", "D", "D", "D", "D", "D", "…
#> $ stud_fee_type           <chr> "D", "D", "P", "D", "P", "P", "D", "P", "P", "…
#> $ stud_rate_code          <chr> "HXCSS", "HXCSD", "PC44D", "HXCSD", "PC44D", "…
#> $ basis_of_admission      <chr> "Work and life experience", "A higher educatio…
#> $ admit_session           <dbl> 202030, 202030, 202030, 202030, 202030, 202030…
#> $ catalog_year            <dbl> 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020…
#> $ last_registered_session <dbl> 202090, 202060, NA, 202090, 202030, 202060, 20…
#> $ latest_leave_session    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 202160, 20…
#> $ gpa                     <dbl> 6.25, 4.75, 0.00, 5.75, 4.75, 4.00, 4.67, 4.75…
#> $ points_att              <dbl> 32, 32, 0, 32, 32, 48, 32, 32, 0, 32, 16
#> $ points_comp             <dbl> 32, 32, 0, 32, 32, 16, 32, 32, 0, 16, 8

course is the name of the course.
course_code is the (usually 6 character) course code. Sometimes this changes for the same course so course name is usually better for matching.
course_level is the level of the course, the most common being Bachelor Pass level.
course_faculty is the (abbriviated) faculty that the course is run by.
mode is the delivery mode for the course, D for distance, I for internal and M for mixed.
stud_fee_type
stud_rate_code
basis_of_admission indicates how the student was admitted to the course.
catalog_year relates to the commencing (check this) cohort year that the student belongs to.
last_registered_session is the last session the student was registered in. Should be blank for continuing students.
latest_leave_session records the last session the student took a leave of absence (I think).
gpa is the students Grade Point Average in this course.
points_att is the number of credit points the student has attempted.
points_comp is the number of credit points the student has successfully completed.

`sessions`

The sessions table details important dates for the teaching session. One row per session.

data.csu.retention::sessions |> 
    glimpse()
#> Rows: 120
#> Columns: 8
#> $ session              <dbl> 201815, 201830, 201845, 201860, 201875, 201890, 2…
#> $ teaching_period_name <chr> "Term 1", "Session 1", "Term 2", "Session 2", "Te…
#> $ tp                   <chr> "T1", "S1", "T2", "S2", "T3", "S3", "T1", "S1", "…
#> $ start_date           <date> 2018-01-08, 2018-02-26, 2018-05-21, 2018-07-09, …
#> $ census_date          <date> 2018-02-02, 2018-03-23, 2018-06-08, 2018-08-03, …
#> $ end_date             <date> 2018-04-27, 2018-06-15, 2018-08-10, 2018-10-26, …
#> $ results_release_date <date> NA, 2018-07-06, NA, 2018-11-09, NA, 2019-03-01, …
#> $ length_in_weeks      <chr> "16 weeks", "16 weeks", "12 weeks", "16 weeks", "…

teaching_period_name is the common name for the teaching session.
tp is the shortened version of the common name.
start_date is the date that teaching commences in the session.
census_date is the date that students must unenrol from a subject before accruing a debt.
end_date is the last teaching date of the session.
results_release_date is the date when results are released, where available.
length_in_weeks is the length of the session.

`subjects`

The subjects table contains the latest subject level data. Whilst the offering table is tied to a session, this table only grabs the latest data, one row per subject.

data.csu.retention::subjects |> 
    glimpse()
#> Rows: 2,803
#> Columns: 5
#> $ subject      <chr> "EMG201", "EMG208", "MGT579", "PHL402", "PHL502", "MGM505…
#> $ subject_name <chr> "Standing Operating Procedures: Training and Exercise Dev…
#> $ faculty      <chr> "FoBJaBS", "FoBJaBS", "FoBJaBS", "FoAaE", "FoAaE", "FoBJa…
#> $ school       <chr> "Australian Graduate School of Policing and Security", "A…
#> $ faculty_name <chr> "Faculty of Business, Justice and Behavioural Sciences", …

subject_name is the name of the subject.
faculty_name is the full name of the faculty that teaches the subject.
faculty is the abbreviated name of the faculty that teaches the subject.
school is the school that teaches the subject.

`triggers`

This table attempts to store data on the triggers used to flag ‘at-risk’ students in several of the campaigns. It is one row per offering per campaign per session.

data.csu.retention::triggers |> 
    mutate(offering = "ABC123_201930_W_D", subject = "ABC123") |> 
    glimpse()
#> Rows: 3,175
#> Columns: 11
#> $ offering           <chr> "ABC123_201930_W_D", "ABC123_201930_W_D", "ABC123_2…
#> $ campaign           <chr> "pre census", "pre census", "pre census", "pre cens…
#> $ trigger_date       <dttm> NA, NA, 2019-03-29, NA, 2019-03-20, NA, 2019-03-22…
#> $ subject            <chr> "ABC123", "ABC123", "ABC123", "ABC123", "ABC123", "…
#> $ session            <dbl> 201930, 201930, 201930, 201930, 201930, 201930, 201…
#> $ trigger            <chr> "course requirement", "non submission", "non submis…
#> $ trigger_detail     <chr> "did not attend res school", "did not submit, no i2…
#> $ trigger_title      <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
#> $ trigger_notes      <chr> NA, NA, NA, NA, "EASTS", "Online", "EASTS", "EASTS"…
#> $ value              <dbl> NA, NA, 0.20, NA, 0.30, 0.10, 0.10, 0.20, 0.15, 0.1…
#> $ imputed_from_flags <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FA…

trigger_date is the date that the trigger was instigated.
trigger is the type of condition used to see if a student was at risk. The most common types in the pre census campaign are low activity for inactive use of the LMS and non submission for the non submission of an early assessment item.
trigger_detail includes more detail on the trigger item, where available.
trigger_title is the title of the assessment if used.
trigger_notes outlines any additional information relevant to the trigger used.
value is the % weighting of the assessment item, if that is what was used as a trigger.
imputed_from_flags indicates if the trigger information was missing from the Retention Team’s campaign data, and was instead imputed from the flag list. This should not happen, but…we work with the data we have.

Activity data package tables

`activity`

This table is an aggregate of the trace data from the Learning Management System. It aggregates per student id, per day (date), per subject offering site.

data.csu.activity::activity |> 
    slice(1:10) |> 
    mutate(id = "student_id", offering = "ABC123_202290_W_D") |> 
    glimpse()
#> Rows: 10
#> Columns: 6
#> $ id       <chr> "student_id", "student_id", "student_id", "student_id", "stud…
#> $ offering <chr> "ABC123_202290_W_D", "ABC123_202290_W_D", "ABC123_202290_W_D"…
#> $ date     <date> 2022-11-11, 2022-11-15, 2022-11-16, 2022-11-27, 2022-11-11, …
#> $ logins   <dbl> 1, 1, 3, 1, 2, 1, 1, 1, 1, 1
#> $ clicks   <dbl> 1, 3, 24, 6, 18, 2, 5, 3, 10, 4
#> $ views    <dbl> 0, 2, 5, 0, 7, 2, 4, 1, 3, 4

date is the day the data was aggregated. The day changes at midnight, so a student working late one night into the early hours of the next morning would spread that activity of two adjacent days.
logins measures how many times the student logged into the subject site.
clicks indicates how many times the student clicked on anything in the subject site.
views counts how many different pages (items) the student viewed in the subject site.

SQL for activity aggregation

This query is run on the Blackboard DDA public schema table.

/* aa by-day */
select id                                                           as id,
       replace(coalesce(child_course_id, course_id), 'S-', '')      as offering,
       date                                                         as date,
       count(distinct session_id)                                   as logins,
       count(session_id)                                            as clicks,
       count(distinct content_pk1)                                  as views

from (select u.student_id                       as id,
             cm.course_id                       as course_id,
             cmchild.course_id                  as child_course_id,
             aa.session_id                      as session_id,
             aa.timestamp::date                 as date,
             aa.content_pk1                     as content_pk1
      from activity_accumulator aa
               inner join users u on u.pk1 = aa.user_pk1
               inner join course_main cm on aa.course_pk1 = cm.pk1
               inner join course_users cu on cu.crsmain_pk1 = cm.pk1 and u.pk1 = cu.users_pk1
               left join course_main cmchild on cmchild.pk1 = cu.child_crsmain_pk1
      where u.student_id is not null
        and cu.role = 'S'
        and u.lastname not like '%PreviewUser'
        and aa.event_type != 'SESSION_TIMEOUT'
        and cm.course_id similar to 'S-%202290%' 
        and aa.timestamp::date >= make_date(2023, 2, 1) -- one month at a time
        and aa.timestamp::date < make_date(2023, 3,  1)
        ) as tab1
group by id, offering, date;

Exit survey data package tables

`exit`

data.csu.exit::exit |> 
    slice(1:10) |>
    mutate(id = "student_id", name = "name", other_info = "other info") |> 
    glimpse()
#> Rows: 10
#> Columns: 19
#> $ id                          <chr> "student_id", "student_id", "student_id", …
#> $ name                        <chr> "name", "name", "name", "name", "name", "n…
#> $ date                        <dttm> 2016-01-14 22:56:13, 2016-01-20 22:04:46,…
#> $ program                     <chr> "3409BC", "1416SW", "4311HN", "9001YW", "4…
#> $ faculty_program             <chr> "Faculty of Education", "Faculty of Arts",…
#> $ study_mode                  <chr> "I was a distance education student", "I w…
#> $ future_study                <chr> "I plan to return to the same course in th…
#> $ contact_request             <chr> "Yes", NA, NA, NA, NA, "No", NA, "No", NA,…
#> $ discuss_further             <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
#> $ discuss_csu                 <chr> "No", "No", "No", "No", "No", "No", "No", …
#> $ who_discuss_csu             <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
#> $ discuss_outside             <chr> "No", "Yes", "No", "Yes", "No", "Yes", "No…
#> $ who_discuss_outside         <chr> NA, "Family (including partner, parents, e…
#> $ withdraw_prior_commencement <chr> "No", "No", "Yes", "Yes", "Yes", "Yes", "Y…
#> $ factors_not_to_commence     <chr> NA, NA, "I was offered a position at anoth…
#> $ factors_leave               <chr> NA, "Time - My family life doesn't leave m…
#> $ factors_withdraw            <chr> "Time - Time required to attend Residentia…
#> $ factors                     <chr> "Time - Time required to attend Residentia…
#> $ other_info                  <chr> "other info", "other info", "other info", …

date is the day the form was submitted.
program is the course code (yes program of study and course are the same at Charles Sturt.)
faculty_program is the faculty that runs the course.
study_mode is the self reported mode of study.
contact_request - did the student want to be contacted regarding their decision?
discuss_further - only available in more recent surveys, answers the question Are you willing to be contacted to discuss your CSU experience?
discuss_csu - did the student discuss their decision with anyone at CSU?
who_discuss_csu - who did they discuss their decision with at CSU?
discuss_outside - did the student discuss their decision with anyone outside of CSU?
who_discuss_outside - who else did they discuss their decision with?
withdraw_prior_commencement - did the student withdraw prior to commencing study?
factors_not_to_commence - reasons for not commencing, selected from a drop down list.
factors_leave - reasons for leaving, selected from a drop down list.
factors_withdraw - reasons for withdrawing, selected from a drop down list.
factors - all of the factors above, collected together.
other_info contains free text comments.

The retention dataset : data.csu.retention

The activity dataset : data.csu.activity

The exit survey dataset : data.csu.exit

Common design features of the data set

Common variables

Data freshness

Retention data package tables

academic

academic_details

assessment_marks

campus_codes

embedded_tutors

contact

enrolments

flags

flags_unticked

interventions

offerings

student_ids

student_demographics

student_course

sessions

subjects

triggers