Big Data Analytics
for Institutional Effectiveness
Alex Rudniy, Ph.D.
Raymond Calluori, Ph.D.
Perry Deess, Ph. D.
Presented at NJAIR 20th Annual Conference, Institutional Effectiveness: IE and IR.
St. Peter’s University, Jersey City, NJ,4/4/2014.
Goals
 Simplify and automate IR activities
 Produce graphical and tabular reports in a user-friendly way
 For users with fewer technical skills within IR
 Self-serve report generation for other departments
 Simpler than Cognos
 The big picture for
senior management
 Audit operational
database data
 Overcome restrictions
 FERPA protects student data
 Many stakeholders don’t have access privileges
 Dynamic report generation overcomes complexity of large
static reports
Technology
 Backend Microsoft SQL Server database
 Frontend designed in Microsoft Visual Studio
 Hosted on Windows Server
 Accessible from:
 Any platform via an internet browser
 Standalone desktop application
NJIT IRP Factbook
Past Reports as Adobe PDF
Current Reports as Excel Pivot Tables
01 Enrollment by Year, Class , AttSt, Ethn, and Gender, 2001-2013
Sum of CountOfSID
Ethnicity
Gender
Am.Ind./ Al.Nat.
Asian
Black
Hispanic
Year
Level
Class LevelAttendance
F MStatus
n/a F
M n/a F
M n/a F
M n/a
2013 Ugrad 1 Freshman
FT
72 245
28 84
54 235
PT
5 29
10
9 28
1 Freshman Total
77 274
28 94
63 263
2 Sophomore
FT
56 203
23 88
52 177
PT
1
3 16
2 12
6 26
2 Sophomore Total1
59 219
25 100
58 203
3 Junior FT
1 1
74 233
38 115
65 230
PT
3 23
8 18
4 45
3 Junior Total
1 1
77 256
46 133
69 275
4 Senior FT
65 204
29 103
71 238
PT
1
13 46
5 31
14 61
4 Senior Total
1
78 250
34 134
85 299
5 Ugrad Unclassfied
FT
1
1
2
2
3
PT
39 66
23 24
21 45
5 Ugrad Unclassfied Total
40 67
23 26
23 48
Ugrad Total
2 2
331 1066
156 487
298 1088
Grad
5 Ugrad Unclassfied
PT
4
2
2
1
2
5 Ugrad Unclassfied Total
4
2
2
1
2
Grad Unclassfied
FT
2
PT
2
3
2 3
1
3
Grad Unclassfied Total
2
5
2 3
1
3
GraduateFT
38 71
13 48
23 42
PT
2 1
59 123
37 95
48 120
Graduate Total 2 1
97 194
50 143
71 162
Grad Total
2 1
103 201
52 148
73 167
2013 Total
4 3
434 1267
208 635
371 1255
International
Multirace
F
M
n/a F M F
18 43
12 36
1
2 1
18 44
14 37
17 49
13 17
1
5
3
18 54
13 20
30 35
12 23
3
7
3
33 42
12 26
21 55
10 17
2
7
1 2
23 62
11 19
4
3
1
5
9
1 4
9 12
1 5
101 214
51 107
4
6
1
4
6
1
3
7
3
1
7
6
1
358 756
4 6
31 52
5 7
389 808
9 13
400 820
9 15
501 1034
60 122
Nat.Haw./ Unknown
Pac.Isl.
M F
M n/a
2
13 69
1 3
2
14 72
15 71
4 5
19 76
1 2 39 146
6 11
1 2 45 157
1 66 172
4 25
1 70 197
6
217 186
217 192
3 3 365 694
2 3
2 3
3
12 32
12 35
23 47
47 131
70 178
84 216
3 3 449 910
White
Grand Total
F M
n/a
72 389
1372
9 31
129
81 420
1501
68 341
1190
4 30
118
72 371
1308
90 419
1554
9 50
190
99 469
1744
80 433
1565
18 103
333
98 536
1898
5
28
65 102
807
65 107
835
415 1903
7286
1
3
31
1
3
31
1
2
11
4 15
88
5 17
99
48 108
1585
84 287
1129
132 395
2714
138 415
2844
553 2318
10130
Big Data Analytics
 In high demand by the industry and academia
 Scale differs by industry, e.g. bioinformatics vs. academics
 Features:
 Large scale of data
 Powerful servers are required for processing
 Components of a dashboard:
 Backend database
 Tabular representation
 Graphic representation
ETL Complications
 ETL = Extract, Transform, Load process
 ETL is needed to build a backend database
 Historical data is spread among multiple databases
 Data specifications lost/unknown
 Data need to be unified
 Attributes missing
 Attributes coded differently
 Attributes spread among multiple tables within the same
database
The Dashboard
 More than 30 years of data
 Accurate: from 1982
 Partial: 1957-1981
 Multiple dimensions
 Tabular & graphical representation
 Overcomes FERPA restrictions by aggregating data
 Impossible to identify a person
 Privacy concerns
 Does not contain: names, SSNs,emails, etc.
 Access allowed for secured user accounts
Dashboard Main Screen
Dashboard Structure
 Consists of multiple tabs on the top
 Each tab contains a pivot table and a linked chart
 Pivot table has several areas: filter area, column headers, row
headers, and data area
 Attributes can be moved between areas
Enrollment Tab, 1983-2013
 This view of enrollment contains
 Student IDs in the data area
 Semester type and year in the row header area
Enrollment Tab, 1983-2013 (cont.)
 Added student level (U/G)
Enrollment Tab (continued)
 Past 5 years enrollment by level and attendance status
Bachelors Retention, 1988-2012 Cohorts
 Total retention by year
Bachelors Retention, 1988-2012 (cont.)
 Female vs. male retention
Bachelors Graduation, 1988-2007 cohorts
 Six-year full-time first-time undergraduates’ graduation rates
 By Ethnicity
Dashboard Screens
 Main dashboard (static)
Dynamic:
 Applications
 Enrollment
 Bachelors Retention
 Masters Retention
 Bachelors Graduation
 Masters Graduation
 Awarded Degrees
 Ph.D. Retention
 Ph.D. Graduation