Stevens Institute of Technology
Howe School of Technology Management
AACSB
ASSURANCE OF LEARNING
Master of Science in
Business Intelligence
and Analytics
(BI&A)
LEARNING GOAL # 3
Students understand and can apply a broad range of business
analytic techniques including optimization, conceptual data
modeling, data warehousing and data mining. [Asakiewicz]
Responsibility: Chris Asakiewicz
January 2016
1
Table of Contents
1. INTRODUCTION: LEARNING GOAL BI&A #3 ............................................ 4
2. LEARNING OBJECTIVES AND TRAITS ..................................................... 5
3. RUBRICS .......................................................................................................... 6
4. ASSESSMENT PROCESS............................................................................... 7
5. RESULTS OF LEARNING GOAL ASSESSMENT - INTRODUCTION ..... 8
6a. RESULTS OF ASSESSMENT: Spring 2013 ................................................. 9
6b. RESULTS OF ASSESSMENT: Fall 2013 ................................................... 11
6c. RESULTS OF ASSESSMENT: Spring 2014 ............................................... 13
6d. RESULTS OF ASSESSMENT: Fall 2014 ................................................... 15
6e. RESULTS OF ASSESSMENT: 2015 ........................................................... 16
6f. RESULTS OF ASSESSMENT: 2016 ........................................................... 18
7a. SPECIFIC STEPS TAKEN IN Spring 2013 ................................................. 20
7b. SPECIFIC STEPS TAKEN IN Fall 2013 ..................................................... 20
7c. SPECIFIC STEPS TAKEN IN Spring 2014 ................................................. 20
7d. SPECIFIC STEPS TAKEN IN Fall 2014 ..................................................... 20
7e. SPECIFIC STEPS TAKEN IN 2015 ............................................................ 20
7f. SPECIFIC STEPS TAKEN IN 2016 ............................................................. 20
8. OUTCOMES: BIA LEARNING GOAL # 3 AFTER ROUNDS OF
ASSESSMENT ..................................................................................................... 20
9. CLOSE LOOP PROCESS – CONTINUOUS IMPROVEMENT RECORD .. 24
APPENDIX A ....................................................................................................... 26
APPENDIX B ....................................................................................................... 26
APPENDIX C ....................................................................................................... 27
APPENDIX D ....................................................................................................... 28
APPENDIX E ....................................................................................................... 29
APPENDIX F........................................................................................................ 30
2
3
1. INTRODUCTION: LEARNING GOAL BI&A #3
Students understand and can apply a broad range of business analytic techniques
including optimization, conceptual data modeling, data warehousing and data mining.
This goal is assessed in the “practicum” course BIA 686 Applied Analytics (or
one of the industry-specific practicum course) which is a required course in the
BI&A curriculum. This learning goal requires students to think analytically and
to synthesize material from other courses in the curriculum.
The assessment takes place in the form of a comprehensive exam administered in
the first week of the practicum course. The exam is composed of short questions
developed by faculty members who teach earlier courses in the BI&A curriculum.
The comprehensive is worth 5% of the student’s grade in the practicum.
4
2. LEARNING OBJECTIVES AND TRAITS
BI&A Learning Goal - 3: Objectives and Traits
Students understand and can apply a broad range of business analytic techniques
including optimization, conceptual data modeling, data warehousing and data mining.
BIA 3
Learning Objectives
Students demonstrate disciplinary understanding of the key business analytic techniques and
methods used in data management and data mining, process analytics and optimization, as well
as multivariate analysis.
Objective 1:
Traits
Trait 1:
Trait 2:
Trait 3:
Trait 4:
Trait 5:
The student demonstrates an understanding of Web Analytics (BIA660) or Social Network
Analytics (BIA658)
The student demonstrates an understanding of Data Warehousing and Business Intelligence
(MIS636)
The student demonstrates an understanding of Process Analytics and Optimization (BIA650)
The student demonstrates an understanding of Knowledge Discovery in Databases (MIS637)
The student demonstrates an understanding of Multivariate Data Analysis (BIA652)
5
3. RUBRICS
Objective 1: Students demonstrate disciplinary understanding of the key business analytic techniques and methods used in data management and data mining,
process analytics and optimization, as well as multivariate analysis.
BI&A LEARNING GOAL - 3: RUBRIC 1
BIA 3
Objective 1
Students understand and can apply a broad range of business analytic techniques including optimization, conceptual data
modeling, data warehousing and data mining.
Students demonstrate disciplinary understanding of the key business analytic techniques and methods used in data management and data
mining, process analytics and optimization, as well as multivariate analysis.
Trait
Trait 1:
Trait 2:
Trait 3:
Trait 4:
Trait 5:
Poor
0
Limited command of
business analytic techniques
used in this area
Good
5
Good command of business
analytic techniques used in
this area
Excellent
10
Strong command of
business analytic
techniques used in this area
The student demonstrates an
understanding of Data
Warehousing and Business
Intelligence (MIS636)
The student demonstrates an
understanding of Process
Analytics and Optimization
(BIA650)
The student demonstrates an
understanding of Knowledge
Discovery in Databases (MIS637)
Limited command of
business analytic techniques
used in this area
Good command of business
analytic techniques used in
this area
Strong command of
business analytic
techniques used in this area
Limited command of
business analytic techniques
used in this area
Good command of business
analytic techniques used in
this area
Strong command of
business analytic
techniques used in this area
Limited command of
business analytic techniques
used in this area
Good command of business
analytic techniques used in
this area
Strong command of
business analytic
techniques used in this area
The student demonstrates an
understanding of Multivariate Data
Analysis (BIA652)
Limited command of
business analytic techniques
used in this area
Good command of business
analytic techniques used in
this area
Strong command of
business analytic
techniques used in this area
Value
The student demonstrates an
understanding of Web Analytics
(BIA660) or Social Network
Analytics (BIA658)
Score
Criterion: Score below 25 is “below expectations”; between 25 and 40 is “meets expectations”; and greater than 40 is “exceeds
expectations”.
6
4. ASSESSMENT PROCESS
Where and when measured?
How measured?
Criterion
A web-based comprehensive exam
comprising short questions from
prior BI&A courses is administered
at the beginning of each practicum
course.
Questions submitted by each course
owner are graded and results
tallied.
Sampling: All students in the
BI&A program are assessed.
85% of students get a grade
of GOOD or better on the
final project as measured by
the rubric for this learning
goal.
7
5. RESULTS OF LEARNING GOAL ASSESSMENT INTRODUCTION
The results of the initial learning goal assessments carried out to date are included below.
Explanation
Each learning goal has a number of learning objectives and performance on each
objective is measured using a rubric that in turn contains a number of desired “traits”.
Students are scored individually on each trait.
The grading sheets for each student are used to develop a Summary Results Sheet for
each learning goal objective. A selection of these Summaries is included below.
The first table in the Summary Results Sheet for a learning objective and trait gives the
counts of students falling in each of the three categories:
- Does not meet expectations
- Meets expectations
- Exceeds expectations
The right-hand column in the table is used to record the average score of the students on
each trait. This table provides an indication of the relative performance of students on
each trait.
The second table on each sheet provides the counts of students who fall in each of the
above three categories for the overall learning objective.
The person doing the assessment provides explanatory comments and recommendations
on the bottom of the Results Summary Sheet. The recommendations improve content or
pedagogy changes for the next time the course is given.
8
6a. RESULTS OF ASSESSMENT: Spring 2013
LEARNING GOAL # 3: Students understand and can apply a broad range of business
analytic techniques including optimization, conceptual data modeling, data warehousing
and data mining.
LEARNING OBJECTIVE # 1: Students demonstrate disciplinary understanding of the
key business analytic techniques and methods used in web or social network analytics, data
management and data mining, process analytics and optimization, as well as multivariate
analysis.
ASSESSMENT DATE: Spring 2013
NO. OF STUDENTS TESTED:
Learning Goal Traits
ASSESSOR: Chris Asakiewicz
6
COURSE: BIA 680
Number of Students
Not Meet Meet
Exceed
Expectat- Expectat- Expectat
ions
ions
-ions
The student demonstrates an
understanding of Web Analytics
(BIA660) or Social Network Analytics
(BIA658)
The student demonstrates an
understanding of Data Warehousing
and Business Intelligence (MIS636)
The student demonstrates an
understanding of Process Analytics
and Optimization (BIA650)
The student demonstrates an
understanding of Knowledge
Discovery in Databases (MIS637)
The student demonstrates an
understanding of Multivariate Data
Analysis (BIA652)
0
3
2
Avg.
Grade
on
Trait
7
3
0
0
0
0
0
6
10
1
2
2
6
0
2
2
7.5
Average Grade (Maximum 10)
Does not meet expectations 0; meets 5-25; exceeds 10-50
Not meet
Meets
Exceeds
Expectations Expectations Expectations
Total Students by Category
(Based on Average score across all traits)
0
Students meeting or exceeding expectations:
4
2
100%
COMMENTS:
Spring of 2013 is the first time this assessment was done in the program for a class
of 6 students (See Appendix). Students were given the option of answering 5 out of 6
questions on the evaluation.
9
REMEDIAL ACTIONS:
Any student who scored poorly on a specific question/area was guided as to how
best to improve their knowledge of the area.
10
6b. RESULTS OF ASSESSMENT: Fall 2013
LEARNING GOAL # 3: Students understand and can apply a broad range of business
analytic techniques including optimization, conceptual data modeling, data warehousing
and data mining.
LEARNING OBJECTIVE # 1: Students demonstrate disciplinary understanding of the
key business analytic techniques and methods used in web or social network analytics, data
management and data mining, process analytics and optimization, as well as multivariate
analysis.
ASSESSMENT DATE: Fall 2013
NO. OF STUDENTS TESTED:
Learning Goal Traits
ASSESSOR: Chris Asakiewicz
7
COURSE: BIA 686
Number of Students
Not Meet Meet
Exceed
Expectat- Expectat- Expectat
ions
ions
-ions
The student demonstrates an
understanding of Web Analytics
(BIA660) or Social Network Analytics
(BIA658)
The student demonstrates an
understanding of Data Warehousing
and Business Intelligence (MIS636)
The student demonstrates an
understanding of Process Analytics
and Optimization (BIA650)
The student demonstrates an
understanding of Knowledge
Discovery in Databases (MIS637)
The student demonstrates an
understanding of Multivariate Data
Analysis (BIA652)
0
0
7
Avg.
Grade
on
Trait
5.2
5
1
0
3.0
0
4
2
6.0
0
5
2
7.1
0
3
3
7.5
Average Grade (Maximum 10)
Does not meet expectations 0; meets 5-25; exceeds 26-50
Not meet
Meets
Exceeds
Expectations Expectations Expectations
Total Students by Category
(Based on Average score across all traits)
0
Students meeting or exceeding expectations:
COMMENTS:
11
2
5
100%
Spring of 2013 is the first time this assessment was done in the program for a class
of 6 students (See Appendix). Students were given the option of answering 5 out of 6
questions on the evaluation.
REMEDIAL ACTIONS:
Any student who scored poorly on a specific question/area was guided as to how
best to improve their knowledge of the area.
12
6c. RESULTS OF ASSESSMENT: Spring 2014
LEARNING GOAL # 3: Students understand and can apply a broad range of business
analytic techniques including optimization, conceptual data modeling, data warehousing
and data mining.
LEARNING OBJECTIVE # 1: Students demonstrate disciplinary understanding of the
key business analytic techniques and methods used in web or social network analytics, data
management and data mining, process analytics and optimization, as well as multivariate
analysis.
ASSESSMENT DATE: Spring 2014
NO. OF STUDENTS TESTED:
Learning Goal Traits
The student demonstrates an
understanding of Web Analytics
(BIA660) or Social Network Analytics
(BIA658)
The student demonstrates an
understanding of Data Warehousing
and Business Intelligence (MIS636)
The student demonstrates an
understanding of Process Analytics
and Optimization (BIA650)
The student demonstrates an
understanding of Knowledge
Discovery in Databases (MIS637)
The student demonstrates an
understanding of Multivariate Data
Analysis (BIA652)
ASSESSOR: Chris Asakiewicz
9
COURSE: BIA 686
Number of Students
Not Meet Meet
Exceed
Expectat- Expectat- Expectat
ions
ions
-ions
1 (Web)
7 (Web)
Avg.
Grade on
Trait
9.4 (Web)
7 (SNA)
1 (SNA)
5.6 (SNA)
0
5
1
6.3
0
3
4
7.9
0
8
0
5.0
0
1
7
9.4
Average Grade (Maximum 10)
Does not meet expectations 0; meets 5-25; exceeds 26-50
Not meet
Meets
Exceeds
Expectations Expectations Expectations
Total Students by Category
(Based on Average score across all traits)
0
Students meeting or exceeding expectations:
7
2
100%
COMMENTS:
Students were given the option of answering 5 out of 6 questions on the evaluation.
13
REMEDIAL ACTIONS:
Any student who scored poorly on a specific question/area were guided as to how
best to improve their knowledge of the area.
14
6d. RESULTS OF ASSESSMENT: Fall 2014
LEARNING GOAL # 3: Students understand and can apply a broad range of business
analytic techniques including optimization, conceptual data modeling, data warehousing
and data mining.
LEARNING OBJECTIVE # 1: Students demonstrate disciplinary understanding of the
key business analytic techniques and methods used in web or social network analytics, data
management and data mining, process analytics and optimization, as well as multivariate
analysis.
ASSESSMENT DATE: Fall 2014
NO. OF STUDENTS TESTED:
Learning Goal Traits
The student demonstrates an
understanding of Web Analytics
(BIA660) or Social Network Analytics
(BIA658)
The student demonstrates an
understanding of Data Warehousing
and Business Intelligence (MIS636)
The student demonstrates an
understanding of Process Analytics
and Optimization (BIA650)
The student demonstrates an
understanding of Knowledge
Discovery in Databases (MIS637)
The student demonstrates an
understanding of Multivariate Data
Analysis (BIA652)
ASSESSOR: Chris Asakiewicz
9
COURSE: BIA 686
Number of Students
Not Meet Meet
Exceed
Expectat- Expectat- Expectat
ions
ions
-ions
9 (Web)
Avg. Grade
on Trait
10.0 (Web)
3 (SNA)
3 (SNA)
0 (SNA)
4.3 (SNA)
0
6
0
5.0
0
1
6
9.3
0
5
4
7.2
0
2
8
7.2
Average Grade (Maximum 10)
Does not meet expectations 0; meets 5-25; exceeds 26-50
Not meet
Meets
Exceeds
Expectations Expectations Expectations
Total Students by Category
(Based on Average score across all traits)
0
Students meeting or exceeding expectations:
5
4
100%
COMMENTS:
Students were given the option of answering 5 out of 6 questions on the evaluation.
15
6e. RESULTS OF ASSESSMENT: 2015
LEARNING GOAL # 3: Students understand and can apply a broad range of business
analytic techniques including optimization, conceptual data modeling, data warehousing
and data mining.
LEARNING OBJECTIVE # 1: Students demonstrate disciplinary understanding of the
key business analytic techniques and methods used in web or social network analytics, data
management and data mining, process analytics and optimization, as well as multivariate
analysis.
ASSESSMENT DATE: 2015
NO. OF STUDENTS TESTED:
Learning Goal Traits
The student demonstrates an
understanding of Web Analytics
(BIA660) or Social Network Analytics
(BIA658)
The student demonstrates an
understanding of Data Warehousing
and Business Intelligence (MIS636)
The student demonstrates an
understanding of Process Analytics
and Optimization (BIA650)
The student demonstrates an
understanding of Knowledge
Discovery in Databases (MIS637)
The student demonstrates an
understanding of Multivariate Data
Analysis (BIA652)
ASSESSOR: Chris Asakiewicz
42
COURSE: BIA 686
Number of Students
Not Meet Meet
Exceed
Expectat- Expectat- Expectat
ions
ions
-ions
9
-
32
Avg.
Grade
on
Trait
8
18
-
23
6
0
-
22
10
2
-
26
9
15
-
27
6
Average Grade (Maximum 10)
Does not meet expectations 0-4; meets 5-7; exceeds 8-10
Total Students by Category
(Based on Average score across all traits)
Not meet
Meet
Exceed
expectations Expectations Expectations
5
13
24
COMMENTS:
Students were given the option of answering 5 out of 6 questions on the evaluation.
16
REMEDIAL ACTIONS:
Any student who scored poorly on a specific question/area were guided as to how
best to improve their knowledge of the area.
17
6f. RESULTS OF ASSESSMENT: 2016
LEARNING GOAL # 3: Students understand and can apply a broad range of business
analytic techniques including optimization, conceptual data modeling, data warehousing
and data mining.
LEARNING OBJECTIVE # 1: Students demonstrate disciplinary understanding of the
key business analytic techniques and methods used in web or social network analytics, data
management and data mining, process analytics and optimization, as well as multivariate
analysis.
ASSESSMENT DATE: 2016
NO. OF STUDENTS TESTED:
Learning Goal Traits
The student demonstrates an
understanding of Web Analytics
(BIA660) or Social Network Analytics
(BIA658)
The student demonstrates an
understanding of Data Warehousing
and Business Intelligence (MIS636)
The student demonstrates an
understanding of Process Analytics
and Optimization (BIA650)
The student demonstrates an
understanding of Knowledge
Discovery in Databases (MIS637)
The student demonstrates an
understanding of Multivariate Data
Analysis (BIA652)
ASSESSOR: Chris Asakiewicz
46
COURSE: BIA 686
Number of Students
Not Meet Meet
Exceed
Expectat- Expectat- Expectat
ions
ions
-ions
9
-
36
Avg.
Grade
on
Trait
10
18
-
23
7
0
-
22
10
1
-
30
9
15
-
31
7
Average Grade (Maximum 10)
Does not meet expectations 0-4; meets 5-7; exceeds 8-10
Total Students by Category
(Based on Average score across all traits)
Not meet
Meet
Exceed
expectations Expectations Expectations
5
13
28
COMMENTS:
Students were given the option of answering 5 out of 6 questions on the evaluation.
18
REMEDIAL ACTIONS:
Any student who scored poorly on a specific question/area were guided as to how
best to improve their knowledge of the area.
19
7a. SPECIFIC STEPS TAKEN IN Spring 2013
The questions for MIS 636 and BIA 650 will be revised for the next round to ensure that
they better reflect the level of knowledge expected of the student.
7b. SPECIFIC STEPS TAKEN IN Fall 2013
The questions for MIS 636 will be revised for the next round to ensure that they better
reflect the level of knowledge expected of the student.
7c. SPECIFIC STEPS TAKEN IN Spring 2014
The assessment was given mid-semester, allowing time for students to prepare.
7d. SPECIFIC STEPS TAKEN IN Fall 2014
The assessment was given mid-semester, allowing time for students to prepare.
7e. SPECIFIC STEPS TAKEN IN 2015
The assessment was given mid-semester, allowing time for students to prepare.
7f. SPECIFIC STEPS TAKEN IN 2016
The assessment was given mid-semester, allowing time for students to prepare.
8. OUTCOMES: BIA LEARNING GOAL # 3 AFTER ROUNDS OF
ASSESSMENT
After First Round Review Spring 2013
Observations:
None of the students were able to answer the question on Data Warehousing and
Business Intelligence (MIS 636) correctly, and all students were able to answer the
question on Process Analytics and Optimization (BIA 650) correctly. Indicating that the
questions may have been either too difficult (MIS 636) or too easy (BIA 650).
Remedial Actions
The questions for MIS 636 and BIA 650 will be revised for the next round to ensure that
they better reflect the level of knowledge expected of the student.
After Review Fall 2013
20
Observations:
Only one student was able to answer the questions for MIS 636, Data Warehousing and
Business Intelligence correctly even after it was revised based on last semester’s results.
As for BIA 650, Process Analytics and Optimization, all of the students answered
satisfactory after the questions revision.
Remedial Actions
The methodology for answering the question for MIS 636 will be re-emphasized as part
of the course.
The following table shows the average scores on each goal objective for the
last 5 years.
After Review Spring 2014
Observations:
By conducting the assessment in mid-semester, students had more time to prepare,
showing better command of the subject matter.
Remedial Actions
Students were told at the beginning of the semester, exactly which areas the assessment
would cover.
The following table shows the average scores on each goal objective for the
last 5 years.
Spring 2013
Fall 2013
Spring 2014
Objective 1
Understand the analytic techniques
6.1
6.7
7.3
After Review Fall 2014
Observations:
By conducting the assessment in mid-semester, students had more time to prepare,
showing better command of the subject matter.
Remedial Actions
21
Students were told at the beginning of the semester, exactly which areas the assessment
would cover.
The following table shows the average scores on each goal objective for the
last 5 years.
Spring 2013
Fall 2013
Spring 2014
Fall 2014
Objective 1
Understand the analytic techniques
6.1
6.7
7.3
7.6
After Review 2015
Observations:
Given the nature of questions in MIS 636 and MIS 637, many students spend time
emphasizing minor details and not fully answering the question asked. As a result, in
2015, the grading of all questions was simplified. Students either answer the question
correctly and completely or not.
Remedial Actions
In 2016, the comprehensive will be restructured as a set of multiple choice questions and
answers. This will make administering the test far more efficient. In addition by having
multiple sets of questions associated with each topic it will become much easier to
determine whether students actually know the material associated with the particular
topic.
The following table shows the average scores on each goal objective for the
last 5 years.
Spring 2013
Fall 2013
Spring 2014
Fall 2014
2015
Objective 1
Understand the analytic techniques
6.1
6.7
7.3
7.6
7.8
After Review 2016
22
Observations:
Given the nature of questions in MIS 636 and MIS 637, many students spend time
emphasizing minor details and not fully answering the question asked. As a result, in
2016, the grading of all questions was simplified. Students either answer the question
correctly and completely or not.
Remedial Actions
In 2017, the comprehensive will be restructured as a set of multiple choice questions and
answers. This will make administering the test far more efficient. In addition by having
multiple sets of questions associated with each topic it will become much easier to
determine whether students actually know the material associated with the particular
topic.
The following table shows the average scores on each goal objective for the
last 5 years.
Spring 2013
Fall 2013
Spring 2014
Fall 2014
2015
2016
Objective 1
Understand the analytic techniques
6.1
6.7
7.3
7.6
7.8
9.0
23
9. CLOSE LOOP PROCESS – CONTINUOUS IMPROVEMENT
RECORD
Assurance of Learning
Assessment/Outcome Analysis
Close Loop Process - Continuous Improvement Record
Program: Master of Science in Business Intelligence & Analytics
Goal 3: Students understand and can apply a broad range of business analytic techniques
including optimization, conceptual data modeling, data warehousing and data mining
Goal Owner: Chris Asakiewicz
Where Measured: A web-based comprehensive exam comprising short questions from prior BI&A
courses is administered at the beginning of each practicum course.
How Measured: Questions submitted by each course owner are graded and results tallied.
Sampling: All students in the BI&A program are assessed.
Closing the Loop: Actions taken on specific objectives
Objective 1
When
Assessed:
Remedial
Action
Outcome
from previous
assessment:
When
Assessed:
Remedial
Action
Outcome
from previous
assessment:
When
Assessed:
Students demonstrate disciplinary understanding of the key business
analytic techniques and methods used in web or social network analytics,
data management and data mining, process analytics and optimization,
as well as multivariate analysis.
2016
As a result of our Spring 2015 assessment, students were told at the
beginning of the semester, exactly which areas the assessment would
cover.
Students had more time to review key material from previous courses in
the curriculum, resulting in better performance on the most recent
assessment.
2015
As a result of our Spring 2014 assessment, students were told at the
beginning of the semester, exactly which areas the assessment would
cover.
Students had more time to review key material from previous courses in
the curriculum, resulting in better performance on the most recent
assessment.
Fall 2014
24
Remedial
Action
Outcome
from previous
assessment:
When
Assessed:
Remedial
Action
Outcome
from previous
assessment:
When
Assessed:
Remedial
Action
Outcome
from previous
assessment:
When
Assessed:
As a result of our Spring 2014 assessment, students were told at the
beginning of the semester, exactly which areas the assessment would
cover.
Students had more time to review key material from previous courses in
the curriculum, resulting in better performance on the most recent
assessment.
Spring 2014
Students were told at the beginning of the semester, exactly which areas
the assessment would cover.
Students had more time to review key material from previous courses in
the curriculum, resulting in better performance on the most recent
assessment.
Fall 2013
Only one student was able to answer the questions for MIS 636, Data
Warehousing and Business Intelligence correctly even after it was
revised based on last semester’s results. As for BIA 650, Process
Analytics and Optimization, all of the students answered satisfactory
after the questions revision.
The methodology for answering the question for MIS 636 will be reemphasized as part of the course.
Spring 2013
After First Round Review Spring 2013
Remedial
Action
Observations:
None of the students were able to answer the question on Data Warehousing
and Business Intelligence (MIS 636) correctly, and all students were able to
answer the question on Process Analytics and Optimization (BIA 650)
correctly. Indicating that the questions may have been either too difficult (MIS
636) or too easy (BIA 650).
Remedial Actions
1. The questions for MIS 636 and BIA 650 have been revised for the next
round to ensure that they better reflect the level of knowledge expected
of the student.
2. Any student who scored poorly on a specific question/area is guided as
to how best to improve their knowledge of the area.
25
APPENDIX A
Assessment Exercise
Spring 2013 Evaluation Results
APPENDIX B
Assessment Exercise
Fall 2013 Evaluation Results
Student
1
2
3
4
5
6
7
Total
Average
Trait
Question
1
2
6
2
3
2
15
3.0
Poor
Question
2
5
4
5
8
8
10
10
50
7.1
Good
Question
3
5
10
10
10
5
5
45
7.5
Good
Question
4
4
7
8
7
5
31
6.2
Good
Question
5
5
5
5
5
0
5
25
4.2
Good
Question
6
3
4
10
5
4
10
36
6.0
Good
MIS 636
Data
Warehousing
and Business
Intelligence
MIS 637
Knowledge
Discovery in
Data Bases
BIA 652
Multivariate
Data
Analysis
BIA 660 Web
Analytics
BIA 658
Social
Network
Analytics
BIA 650
Process
Analytics and
Optimization
26
Total
Average
21
29
14
42
37
27
32
202
4.2
5.8
4.7
8.4
6.2
4.5
6.4
40.1
6.7
APPENDIX C
Assessment Exercise
Spring 2014 Evaluation Results
Student
1
2
3
4
5
6
7
8
9
Total
Average
Trait
Q1
6
4
0
4
7
0
0
10
7
38
6.3
Good
Q2
5
5
5
0
5
5
5
5
5
40
5.0
Good
Q3
0
5
10
10
10
10
10
10
10
75
9.4
Excellent
Q4
10
0
10
10
10
5
10
10
10
75
9.4
Excellent
Q5
5
5
5
5
5
5
5
10
0
45
5.6
Good
Q6
5
10
5
10
5
10
0
0
10
55
7.9
Good
MIS636
BI&DW
MIS637
KDD
BIA652
MDA
BIA660
Web
BIA658
SNA
BIA650
PA&O
Not
Answered
27
Total
31
29
35
39
42
35
30
45
42
7.3
Student
Average
6.2
5.8
7.0
7.8
7.0
7.0
6.0
9.0
8.4
APPENDIX D
Assessment Exercise
Fall 2014 Evaluation Results
28
APPENDIX E
Assessment Exercise
2015 Evaluation Results
29
APPENDIX F
Assessment Exercise
2016 Evaluation Results
Student
Points Possible
Chatterjee, Kanad
Cong, Nie
Cong, Nie
Cong, Nie
Cong, Nie
Bisne, Mandar
Boying, Xu
Cong, Nie
Ding, Ruoyang
Feng, Fei
Gao, Zhonghua
Hu, Xiaodai
Jain, Kanika
Jawla, Ankit
Jiaqi, Sun
Joshi, Sandeep
Lai, Zhidong
Lankireddy, Sruti
Lu, Gao
Lu, Ruihan
Manjiri, Sawant
Meng, Zihan
Miao, Jingyu
Moodithaya, Neetha
Nakita, Deshpande
Naman, Jain
Nguyen, Dat
Ojuola, Adebola
Prithvi, Raavi
Rahui, Bhatia
Rui, Xuei
Svenson Jr., Paul
Tengijao, Zhu
Thacker, Ankit
Vardhan, Harsh
Villa, Nicholas
Wang, Xiao
Wang, Yida
Wei, Qing
Xinyue, Xu
Yating, Ian
Yijng, Mao
Yilin, Wei
Yiuchen, Lin
Zhang, Lei
Zhang, Yu
Number Answered
Score
Average
Q1
10
Q2
10
Q3
10
Q4
10
Q5
10
Q6
10
0
1
1
1
1
0
0
1
0
1
0
1
1
1
1
0
1
1
0
0
1
1
1
1
0
0
0
0
1
1
1
0
0
0
0
0
1
1
1
1
1
1
1
1
0
0
1
1
1
1
1
0
0
1
0
0
1
0
1
1
1
0
1
1
1
0
1
0
1
1
0
0
1
1
1
1
1
1
0
0
0
1
1
1
0
1
1
1
1
1
0
0
0
1
1
1
1
0
0
1
0
1
1
1
0
1
1
0
1
1
0
0
1
1
1
1
0
0
1
0
1
1
1
1
0
0
1
1
1
1
1
1
1
1
1
1
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
1
0
0
0
1
1
1
0
1
1
1
0
1
1
0
1
0
1
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
1
1
1
1
0
0
1
1
0
1
0
1
0
1
1
0
0
1
1
1
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
1
1
0
1
1
1
1
1
0
0
1
1
0
0
1
0
1
1
0
1
1
1
0
0
0
0
1
1
1
0
0
1
0
1
0
0
0
0
0
1
1
27
30
31
37
36
22
270
7
Good
300
11
Excellent
310
7
Good
370
10
Excellent
360
9
Excellent
220
10
Excellent
MIS636
MIS637
BIA652
BIA660
BIA658
BIA650
BI&DW
KDD
Multivariate
Data
Analytics
Web
Analytics
Social
Network
Analytics
Process
Analytics &
Optimization
30
Answered
5
3
5
5
5
5
3
3
5
3
4
4
5
5
5
5
2
5
5
2
1
5
3
5
3
3
3
4
1
5
5
5
3
3
3
2
4
5
5
4
5
5
5
5
5
3
3
Score
50
Average
Rating
30
50
50
50
50
30
30
50
30
40
40
50
50
50
50
20
50
50
20
10
50
30
50
30
30
30
40
10
50
50
50
30
30
30
20
40
50
50
40
50
50
50
50
50
30
30
6
10
10
10
10
6
6
10
6
8
8
10
10
10
10
4
10
10
4
2
10
6
10
6
6
6
8
2
10
10
10
6
6
6
4
8
10
10
8
10
10
10
10
10
6
6
Good
Excellent
Excellent
Excellent
Excellent
Good
Good
Excellent
Good
Excellent
Excellent
Excellent
Excellent
Excellent
Excellent
Poor
Excellent
Excellent
Poor
Poor
Excellent
Good
Excellent
Good
Good
Good
Excellent
Poor
Excellent
Excellent
Excellent
Good
Good
Good
Poor
Excellent
Excellent
Excellent
Excellent
Excellent
Excellent
Excellent
Excellent
Excellent
Good
Good
50 Point
Scale
00 to 04
05 to 25
26 to 50
10 Point
Scale
0 to 4
5 to 7
8 to 10
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
46
Statistics
Poor
Good
Excellent
5
13
28