scott kodai distributed learning california state university, chico scott kodai distributed learning...

Scott KodaiDistributed Learning

California State University, Chico

Scott KodaiDistributed Learning

California State University, Chico

Extracting Useful Information with the PowerSight Kit

Extracting Useful Information with the PowerSight Kit

Evolution

• First focus was stability (Vista 4.0)• Next we looked at basic usage information

– Number of concurrent users– VistaStats application

• Then more detailed statistics– Count(*) queries on rpt_tracking

• Even more detailed statistics– Processing each “hit”

• Pivot tables, cubes and, eventually, a data warehouse

• A set of views in the database that are denormalized to make them easier to query

• rpt_gradebook• rpt_learning_context• rpt_learning_context_size• rpt_member• rpt_person• rpt_template• rpt_tracking

What is the PowerSight Kit?

Chico Specifics

• In Spring 2009:– 17,810 students enrolled– 1,060 faculty enrolled– 4,067 sections with enrollments

• A Vista section created for every section offered

• Learning Context Hierarchy matches institution structure– A division for every college and a group for every department

• Crosslisting– In Spring 09: 302 crosslists created, combining 711 child

sections

Challenge of Crosslisted Sections

• Division and Group for crosslist parents is special• Sometimes sections from different departments, or even

colleges, are crosslisted– Tracking activity should be counted for the child section, but it’s

recorded with the parent section’s LCID in rpt_tracking

• This makes it necessary to do queries by enrollment, and not just by section

Basic Idea

• Get a list of sections for the given term• For each section:

– Get a list of enrolled students and faculty– For each enrolled person:

• Query rpt_tracking for all “hits” for that person in that section

• Process each record, adding to accumulators as you go

• I used Perl hashes (and hashes of hashes) extensively

• Output files for further analysis

• I am not a programmer nor a DBA– TMTOWTDI

SQL Examples

• Getting all sections in a given term– select tm.assigned_lcid, lc.source_id, lc.name, lc.description,

lc.delivery_unit_type from webct.lc_term_mapping tm, webct.lc_term t, webct.learning_context lc where tm.lc_term_id = t.id and tm.assigned_lcid = lc.id and t.source_id = ?;

• Why not use rpt_learning_context?

SQL Examples

• Finding the parent section for a crosslisted child– select master_lcid from webct.xlist_lc_mapping

where child_lcid = ?

SQL Examples

• Finding the Division for a given section– select lc.name

from webct.learning_context_index lci, webct.learning_context lc

where lc.id = lci.left_lc_id and lci.hierarchy_level = 4 and right_lc_id = ?";

• Finding the Group for a given section– select lc.name

from webct.learning_context_index lci, webct.learning_context lc

where lc.id = lci.left_lc_id and lci.hierarchy_level = 3 and right_lc_id = ?";

SQL Examples

• Getting a list of enrolled students for a given section– select distinct p.webct_id

from webct.member m, webct.role r, webct.role_definition rd, webct.person p where m.id = r.member_id and r.role_definition_id = rd.id and m.person_id = p.id and m.learning_context_id = ? and m.delete_status = 0 and rd.name = 'SSTU' and p.webct_id != ?";

• Demo student id = “webct_demo_<lcid of section>”

SQL Examples

• Getting a list of activity for a person in a section– select t.tracking_id, t.session_id, t.event_time_mil, p.webct_id,

t.tool_name, t.action_name, t.page_name, t.dwell_time from webct.rpt_tracking t, webct.person p where t.person_id = p.id and t.learning_context_id = ? and t.event_time_mil >= ? and t.event_time_mil <= ? and p.webct_id = ?;

• Event_time_mil = milliseconds since 1/1/1970 (stored in UTC)

Output Files

• Summary_stats.txt• Summary_stats.csv• Faculty_list.csv• Student_list.csv• Detailed_stats.csv• Section_activity_stats.csv• Person_activity_stats.csv• Pageview_stats.csv

Answers… Or More Questions?

• Certainly usage statistics provide answers to many questions

• On the other hand, they often lead to more questions• How do you determine whether a person (faculty or

student) is ‘active’?– Are hits or sessions a better indicator of activity?

• How active is active? What is the difference between an ‘active’ section and an ‘engaged’ section?– Counting hits? Unique sessions? Tools used?

Active UsersTotal enrolled faculty: 1060

Total faculty (logged in at least once): 775

Total faculty with at least 25 hits/week avg: 418

Total faculty with avg one login per week: 630

Total faculty with avg three or more logins per week: 454

Total faculty with avg one or more logins per day: 256

Total enrolled students: 17810

Total students (logged in at least once): 16032

Total students with at least 25 hits/week avg: 11273

Total students with avg one login per week: 15130

Total students with avg three or more logins per week: 11642

Total students with avg one or more logins per day: 4300

Total students with avg 10 or more logins per day: 1

Avg Weekly Hits by Faculty

Faculty Use

Section activity

Total Sections: 4966

Sections with > 0 enrollments: 4067 (includes 411 with only 1 student)

Sections with Content: 2253

Sections with any activity: 2175

Sections with avg at least one login per student per week: 1465

Sections with avg at least three login per student per week: 103

Sections with avg one login per student per day: 47

Sections using 5 or less tools: 432

Sections using more than 5 tools: 1725

Average tools used per section (all sections): 4.25

Average tools used per section (tools used > 0): 9.79

Active vs. Engaged Sections

Tools Used

Third Party Powerlinks

Pivot Tables

• Pivot tables in Excel are a powerful way to “explore” large amounts of data

• Unfortunately, we’re dealing with way more data than Excel and Access can handle

• Currently starting a project to move this activity data to our data warehouse– Overall goal is to maintain usage data across LMSs to enable

longitudinal assessment

Scott Kodaiwww.aimlessmusing.com

Questions?