co-operative education analytics · satisfaction: key finding 5 10 ! effect of work term length !...
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CO-OPERATIVE EDUCATION ANALYTICS: SATISFACTION & RELATIONSHIPS AMONG PROGRAMS
Presenter: Yuheng Helen Jiang Authors: Yuheng Helen Jiang, Sally Lee (Satisfaction)
Lukasz Golab (Supervisor)
Department of Management Sciences
September 22, 2015
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Who we are… 2
¨ Professor Lukasz Golab ¤ Assistant Professor in Management Sciences, cross appointed to Computer
Science ¤ BSc in Computer Science from the University of Toronto (2001) ¤ PhD in Computer Science from the University of Waterloo (2006)
¤ Research Interests: big data; applications of big data on energy and education
¨ Yuheng Helen Jiang ¤ BASc. In Management Engineering from the University of Waterloo
(2013) n Accomplished 6 co-op terms using the Jobmine system
¤ (almost) MASc in Management Sciences from the University of Waterloo (2015)
¤ Research Interests: applications of data mining on energy and education
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Agenda 3
¨ Satisfaction ¤ Jiang, Yuheng Helen, Lee, Sally Wai Yin, Golab, Lukasz.
"Analyzing student and employer satisfaction with cooperative education through multiple data sources." Asia-Pacific Journal of Cooperation Education, 16(4), 225-240.
¤ Objective: determine the factors affecting student and employer success and satisfaction with the co-op experience
¨ Relationships among academic programs ¤ Master thesis: On Competition for Undergraduate Co-op
Placements: A Graph Mining Approach ¤ Objective: improve the co-op process by characterizing the
relationships and extent of competition for co-op placements among students from various academic programs
¨ Future work
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SATISFACTION
Title: Analyzing student and employer satisfaction with cooperative education through multiple data sources
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Satisfaction: Data 5
¨ 3 years (Winter 2009 – Fall 2011) of ¤ employers’ evaluations of students (19 sub-categories 1-4/
not applicable & overall evaluation 1-5) ¤ students’ evaluations of employers (overall evaluation 1-10)
¨ Engineering students only ¨ Other factors:
¤ Work term number ¤ Length of co-op terms: 4 months or 8 months ¤ Timing of the first work term: after 1 or 2 terms ¤ Location: abroad, domestic ¤ How to find a co-op job: regular process, self-arranged,
return (work term status)
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Satisfaction: Key Finding 1 6
¨ Overall: students are generally willing to learn new skills, but may not have much leadership experience
3.45 3.30 3.13 3.313.59 3.42 3.40
3.013.40 3.23 3.21
3.53 3.54 3.51 3.653.22 3.26
2.92
3.52
0.70 0.76 0.66 0.63 0.61 0.66 0.67 0.68 0.58 0.62 0.67 0.65 0.61 0.54 0.57 0.63 0.62 0.70 0.63
0.000.501.001.502.002.503.003.504.00
Average Standard DeviationFigure 2: Average and standard deviations of the scores of the 19 sub-categories of employers’ evaluations of students
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Satisfaction: Key Finding 2 7
¨ Overtime: students with more work experience receive higher scores
Figure 3: Percentage of co-op students in each evaluation category from outstanding to unsatisfactory and average of employer’s evaluation over first to sixth work terms
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Satisfaction: Key Finding 3 8
¨ 50% of evaluations are not applicable: conflict management and leadership
¨ Over 6 terms: ¤ integration of prior learning, setting goals, and
leadership decreased significantly
¨ Returning students: ¤ conflict management, leadership and integration of
prior learning decreased significantly
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Satisfaction: Key Finding 4 9
¨ Keywords in job titles and employer names ¤ First year
Employer Name Keywords Job Title Keywords
University Engineering
Ontario Assistant
Toronto Developer
General Software
Research Junior
System Architectural
Engineering Web
Canadian Technical
City Research
Environment IT
TABLE 2: Top 10 keywords from employer names and job titles for first-year engineering students
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Satisfaction: Key Finding 5 10
¨ Effect of work term length ¤ Nearly 70% of students stayed 8-month work terms with the same
employer ¤ 4-month with two positions:
n Students were rated higher on their ability to learn, quality of work, quantity of work, creativity, problem solving and reliability
¤ 8-month with one position: n Students were rated higher in goal setting, judgment, conflict
management, initiative and leadership n N/A% decreased in goal setting and integration of prior
learning n Students rated their employer 10% lower
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Satisfaction: Key Finding 6 11
¨ Timing of first work term ¤ Students’ evaluation of employers
n After 1 term > After 2 terms
¤ Employers’ evaluation of students n No significant difference
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Satisfaction: Key Finding 7 12
¨ International vs. Domestic ¤ 10% positions were outside of North America ¤ Keywords in job titles
n International: trainee, intern n Domestic: co-op
¤ First term working abroad n More self-arranged positions n Students were rated worse, and less satisfied
¤ Upper years n Students were rated better, and more satisfied
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Satisfaction: Conclusion 13
¨ Students’ perspective ¤ Expect N/A ratings in some categories ¤ Stay with the same employer? ¤ Work abroad?
¨ Employers’ perspective ¤ N/A option exists
¨ Institutions’ perspective ¤ When to start the first work term? ¤ International positions? ¤ Data collection
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RELATIONSHIPS AMONG ACADEMIC PROGRAMS
Title: On Competition for Undergraduate Co-op Placements: A Graph Mining Approach
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Relationship: Data Overview 15
¨ One term of interview data ¤ 16,855 student-job interview pairs ¤ 2,890 jobs ¤ 4,194 students from 93 academic programs
¨ Job ¤ Job title, advertised programs, advertised seniority
¨ Student ¤ Academic program, academic year
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Relationship: Graph Definition 16
Source: http://rubymediagroup.com/wp-content/uploads/2015/06/facebook-graph-search-job-search.jpg
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Relationship: Graph Definition (Cont’d) 17
Figure 4.1: Full program graph
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Relationship: Degrees of separation 18
Source: https://www.careeraddict.com/img/candidate-606031-2013-11-14-11-37-24.jpeg
¨ Six degrees of separation
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Relationship: Degrees of separation 19
A program
Random program
1.57
¨ Programs are REALLY connected!
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Relationship: Two graphs 20
¨ Full program graph vs. Senior program graph ¨ Finding:
¤ Senior students compete for jobs with students from fewer programs (less edges)
¤ Relationships that do exists are stronger (thicker edges)
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Relationship: Motivation 21
¨ Unclear differences among academic programs ¤ Similar programs: clusters
¨ Increasing need for multi-disciplinary and well-rounded education [9,10,18,71,119] ¤ Multi-disciplinary programs: outliers
¨ Example of jobs for promotion ¤ Competing programs: fan-out metric
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Relationship: Similar programs 22
Figure 4.17: Hierarchy of partition results of senior program graph
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Relationship: Multi-disciplinary programs 23
¨ Management Engineering (senior): 5th highest entropy
Junior students Senior students
Figure 4.22 & 4.23: Word cloud for job titles of jobs that interviewed junior/senior Management Engineering students
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Relationship: Multi-disciplinary programs 24
¨ Question: well-rounded students or sets of specialized students?
Figure 4.36: Cumulative percentage of students over number of clusters of direct competitors (7 clusters)
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Relationship: Competing programs 25
16 programs do not have any jobs that interviewed only their students
8 programs have more than 30 percent of the jobs only interviewed their students
Figure 4.24: Level of competition of programs in the senior program graph in descending order
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Relationship: Conclusion 26
¨ Similar programs ¤ Academic programs did not always align well with the
groups of closely connected programs ¤ Clusters can be used to create job categories and
academic specializations ¨ Multi-disciplinary programs
¤ Identification and verification ¨ Competing programs
¤ Attract more employers that offer jobs to programs that face high competition
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Future Work 27
¨ Develop recommender systems ¤ Recommend jobs to students ¤ Recommend students to employers
¨ Temporal analysis ¤ What has changed over the years? ¤ How does co-op behavior relate to key events or social
factors? ¨ Ranking
¤ How do employers and students play the ranking “game”? ¨ Collaboration!
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THANK YOU! QUESTIONS?
Contact: Helen Jiang: [email protected] [email protected] Lukasz Golab: [email protected]