Case Study for Analytics in Education

1. Analytics in Action: Topic- Higher Education
Case Study
February 2018
V.VATS (16056)
S.NAYAK(16107)
K.KUNJEKAR(16139)

2. Introduction
©2016 L. SCHLENKER
Structure
Introduction
Role Of Data Analysis In Education Sector
Business Model Of Education Institutes
Importance Of Data And Usage
Methodology & Statistical Model Used
Decision Making Approach
Conclusion & Lessons Learned

3. Learning Analytics in Education Sector:
 It is the measurement, collection, analysis and reporting of data about
learners and their contexts, for purposes of understanding and
optimizing learning and the environments in which it occurs.
 Critical Dimensions of Learning Analytics:
Data
Environment
Context (what?)
Stakeholders (who?)
Objectives (why?)
Methods (how?)
Introduction
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4. Video
Technology

5.  The broad availability of educational data has led to an interest in analysing
useful knowledge to inform policy and practice with regard to education.
 A data science research methodology is becoming even more important in an
educational context.
 This field urgently requires more studies, especially related to outcome
measurement and prediction and linking these to specific interventions.
 The purpose is to incorporate an appropriate data-analytic thinking framework
for pursuing such goals.
 The well-defined model can help ensure the quality of results, contribute to a
better understanding of the techniques behind the model, and lead to faster,
more reliable, and more manageable knowledge discovery.
Introduction
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6. Business Model of an Educational Institute
Technology

7. Video
Technology

8.  Understanding the impact of case study on students learning
engagement, process and experience
 Survey students attitudes (Negatives or Positives) toward university
services quality.
 Identify the problems facing conducting student feedback in the
university.
 Identify strategies and good practice for university services quality
assurance.
Problem Statement
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Case Study : Student’s attitude toward Ajman University of Science &
Technology (AUST) UAE-Fujairah university services quality assurance,
methodology.

10. Data Collection
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Gender Diversity Frequency
Male 127
Female 435
More than 60%
seats are
allocated to
women's in the
university

11. Data Flow Chart
Stakeholders
determined
questions
Questionnaire
distribution
Analysis of
results
Report noting
areas for action
Action plan
Implementation
and monitoring
Feedback to
stakeholders
01 02 03 04
07 06 05
Data Flow

12. Student Course Engagement Questionnaire
(SCEQ)
E
S
P
P
Skill Engagement
Emotional Engagement
Participation
Performance
The SCEQ measures 4 data scales

13. Research design
Survey questionnaires used a five-point Likert scale, ranging from strongly agree,
agree, neutral, disagree, to strongly disagree to determine the response of the
participant.
The first part of the questionnaire covers information,
includes questions about student gender, college, and
semester.
The Second part includes 29 questions regarding the opinion
of
the participant on the University Service Quality Assurance.

14. Preparation of the data
Pre-processing tasks:
1. Data gathering, bringing together all the available data into a set of
instances.
2. Data aggregation/integration, grouping together all the data from
different sources.
3. Data cleaning, detecting erroneous or irrelevant data and discarding it.
4. User and session identification; identifying individual users.
5. Attribute/variable selection, choosing a subset of relevant attributes from
all the available attributes.
6. Data filtering, selecting a subset of representative data to convert large
data sets into smaller data sets.
7. Data transformation, deriving new attributes from the already available
ones.

15. Analysis Process

16. Data Tools
Technology
• The statistical program SPSS, was used to analyse the data
collected.
• Seven Hundred questionnaires were distributed and 562
have been returned with 80% response rate.
• Cronbach’s alpha is used as a statistical tool to assess the
reliability of the data.
• If the Cronbach's alpha is high, then evidence explains that
the items measure the same construct.
• On the other hand, if the value of alpha is low, then the items
have little in common and are not good measures of the
single construct.

17. Introduction
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18. Data Tools
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Method Goal/description Example tasks Reference
Classification To define a set of classes, which are usually
mutually exclusive. To predict which classes an
individual belongs to.
To automatically detect
affective states, like confusion,
frustration, and boredom.
Ghergulescu and Muntean
(2014)
Value
estimation
To estimate the numerical value of some
variables for an individual.
To estimate learning outcomes
regarding student affect and
behavioral engagement.
Pardos et al. (2014)
Clustering To measure the similarity of individuals
described by data. To group similar individuals
together by their similarity, but not driven by
any specific purpose
To create groups of students
according to their personal
characteristics.
He (2013)
Frequent
pattern
mining
To find associations among variables based on
their appearing together in transactions and to
encode rules.
Identifying relationships in
learner behavioral patterns and
diagnosing student difficulties.
Kinnebrew et al. (2013)
Text mining To extract high-quality information from text. Recognize the emotion of
interactive text.
Tian et al. (2014)
Structural
analysis
To predict a link that should exist between
individuals, and possibly also estimate the
strength of the link.
To dynamically recommend the
tutorial dialog in a manner that
is responsive to the sensed
states.
D’mello and Graesser (2013)
Behavior
profiling
To characterize the typical or most noticeable
behavior of a subgroup or an entire population.
To profile anomalous behaviors. Hoque and Picard (2014)
Common methods for mining education data

19. Data Tools
TechnologyQuality indicators for learning analytics. Source: summarized from
Scheffel et al. (2014)
Topic Criterion Representative statements
Objectives Teacher awareness Teachers change their behavior in some respects.
Teachers react in a more personalized way to how their students are dealing with
learning material.
Student awareness Students become more self-regulated in their learning processes.
Students are more aware of their learning progress.
Learning
Support
Learning support An early detection of students at risk.The
ability to explain what could help them to improve further.
Students regularly utilize the tools provided.
Learning
Measures and
Output
Learning outcome If teachers can gain new insights using the given methods.
Results are compared with other (traditional) measures.
Learning
performance
Change in workplace learning is measurable.
The extent to which the achievement of learning objectives can be demonstrated.
Data Aspects Open access Data are open access.Port
ability of the collected data.
Privacy Privacy is ensured.
Learners can influence which data are provided.
Organizational
Aspects
Acceptance &
uptake
Administrators invest in scaling successful tools across their programming.

20. Outcomes of Research
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63.10%
23.20%
13.70%
Students attitude toward the
questionnaire design
Positive Negative Neutral

21. Outcomes of Research
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54.40%31.06%
14.54%
Attitudes about the importance of
service quality assurance
Positive Negative Neutral

22. Outcomes of Research
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45.54%
38.02%
16.44%
Attitudes toward the use of feedback
Positive Negative Neutral

23. Outcomes of Research
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64.57%
23.03%
12.04%
Regarding the time and location of
the quality assurance assessment
currently used
Positive Negative Neutral

24. • Universities does not have their
independent data science team.
• They hire on the basis of
requirements.
• Some times research scholers act
as a data scientist to solve the
problem.
• Ref:
https://www.forbes.com/sites/gilpress/2012/09/12/fixing-
education-with-big-data-turning-teachers-into-data-
scientists/#2e0858453c6d
Data Scientist Team
Technology

25. Based on finding following recommendations
were made by data scientist.
• Introduce the SERVQUAL model that
grouped the measurement of different
services provided to students in one model
(Parasuraman et al., 1990) which include
assurance, empathy, reliability,
responsiveness and tangibility.
• Awareness campaign to students who are
yet unaware of services assurance
methodology used by AUST-Fujairah
Campus since there cannot be enough of
such a campaign.
Recommendation
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26. Reference
Conti….
• case study method encourages students’ skill
engagement and emotional engagement.
• case perceptions direct students to apply
surface approach to learning.
• case knowledge intensified learning
experience
Recommendation
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27. Significant findings also suggest that,
• Instructors might design an appropriate case
and instruct students to analyse the situation
of the case as well as to discuss an action
plan.
• In addition, the instructor should work on
students’ listening and communication skills
in group discussions to foster participation
engagement.
• judge students’ performance for enhancing
performance engagement and learning
process.
• Implement case based learning approach.
Recommendation
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28. • Administrators at the New York Institute of
Technology use data to make real-estate decisions.
• The Public Schools of Northborough and
Southborough leans on data to maximize its
technology investments.
• Georgia public schools leverage data to give
teachers a 360-degree view of students’ progress.
• Tristan Denley is using data to ensure students can
maximize the number of courses they take.
• In higher education, data scientists are tapping
metrics to improve student outcomes, but the push
goes far beyond that. Schools are using data to
better manage their facilities, fine-tune online
courses and allocate their course offerings.
Lesson Learned
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29. • http://www.centerdigitaled.com/k-12/Will-Data-
Scientists-Have-a-Big-Impact-on-Education.html
• https://search.proquest.com/business/docview/1786778
297/fulltextPDF/64A524E116D94939PQ/1?accountid=18
9594
• https://search.proquest.com/business/docview/1515061
536/fulltextPDF/22909B3EB49C429DPQ/1?accountid=18
9594
• http://www.centerdigitaled.com/k-12/Will-Data-
Scientists-Have-a-Big-Impact-on-Education.html
• https://link.springer.com/article/10.1186/s40561-016-
0040-4
References:
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