Project based Learning to Support Enterprise Business Analytics
Education
The Role of Cross Functional Groups to Enhance Cognitive Outcomes
Biswadip Ghosh
Dept. of Computer Information Systems, Metropolitan State University of Denver, Colorado, U.S.A.
Keywords: Business Analytics, Project based Learning, Cognitive Outcomes, Motivation Theory.
Abstract: Enterprise business analytics (BA) tools have gained significant attention as a viable option for
manipulating large data sets during complex business decision making. However, the cross-functional,
boundary spanning nature of these applications make them particularly difficult to learn for users, who
predominantly work in functional silos. A typical enterprise BA project involves aggregating large datasets
from multiple functional areas, discovering relationships in the data and building models to help visualize
and evaluate the selected key performance indicators (KPI). However, most BA learning programs
emphasize tool procedural or skill based knowledge, which does not allow end users to understand the
broader scope of enterprise analytics project implementations. Cross functional group project based learning
programs are needed to provide real world experiences, increasing the end user’s motivation to learn and
enhancing their cognitive outcomes. There is also a need to create validated models to assess the outcomes
of these learning programs. This research study develops and conducts an innovative project based learning
program among the users of a leading ERP vendor’s analytics tool and collects survey data to confirm the
benefits of such group project based learning programs in enhancing the participant’s motivation to learn
and improving their cognitive outcomes that emphasize cross functional concepts.
1 INTRODUCTION
Organizations generate enormous amounts of
operational data that contain valuable patterns,
relationships and business information. In seeking
improvements in their decision-making processes,
more and more organizations are turning to data-
driven decision making (Gartner, 2013). Business
analytics (BA) applications are specialized tools for
data analysis, query, and reporting that support
organizational decision-making (Chaudhuri, Dayal
and Narasayya, 2011). These tools enable
interactive access and manipulation of data in order
to gain valuable insights and can support
management decision making processes across a
broad range of business functions.
The organizational benefits of BA is mainly
gained by transcending the immediate focus for
achieving functional optimizations, that are “silo”-ed
and localized. This is accomplished by utilizing BA
applications that can aggregate cross functional
datasets extracted from other enterprise systems
such as Enterprise Resource Planning (ERP) or
external big data sources, to create new organization
wide capabilities. Examples include supply chain
management (SCM) integrated with customer
relationship management (CRM) for KPI’s (Key
Performance Indicators) that support “360 degree
views” (IBM, 2014). Successful BA
implementations require: (i) a holistic approach that
span multiple functional areas of the business, (ii)
identification and modelling of suitable KPI’s in the
chosen BA tool, (iii) adoption of data lifecycle
management practices for collection, cleaning,
aggregation and refresh activities, (iv) learning
programs to help users leverage BA technology to
manipulate large data sets and operationalize
analytics algorithms and (v) effective end-user
support. Organizational benefits can vary
significantly depending on the level of training and
insight of the business leaders, who are the ultimate
stakeholders of the BA applications (Bose, 2009).
There are three targeted goals of most end-user
learning programs (Gupta, Bostrom, Huber, 2010):
(1) skill-based goals (tool procedural) that target the
user’s ability to use the system, (2) cognitive goals
5
Ghosh B..
Project based Learning to Support Enterprise Business Analytics Education - The Role of Cross Functional Groups to Enhance Cognitive Outcomes.
DOI: 10.5220/0005341600050013
In Proceedings of the 7th International Conference on Computer Supported Education (CSEDU-2015), pages 5-13
ISBN: 978-989-758-108-3
Copyright
c
2015 SCITEPRESS (Science and Technology Publications, Lda.)
(tool conceptual or business procedural) that focus
on the use of the system to solve business problems
and (3) meta-cognitive goals that focus on building
the individual’s beliefs regarding their own abilities
with the system. The typical BA end-user learning
programs are focused on skill-based procedural
outcomes. Such programs do not allow the users to
grasp the cross functional knowledge needed to
identify the relationships in the data and select
“holistic” KPI’s, which transcend the domains of
their functional work area (Gupta, Bostrom and
Huber, 2010). Instead focusing the learning
program on cognitive outcomes (rather than skill-
based outcomes) could build business-conceptual
“big picture” knowledge that allows the users to
apply the BA tool to solve enterprise wide business
problems (Macris, 2011).
In a cross functional project based learning
approach, the users are placed into small groups and
asked to define and address a “real-life” business use
case or scenario for the BA tools. The individuals
learn from the knowledge of group members, who
come from different functional areas to define a set
of cross functional KPI’s and build a logic driven
model that can be used to measure the KPI’s. They
share and combine their individual learning to
support building a “big picture of the organization”
and the collective group discourse (Wang and
Ramiller, 2009). They proceed to identify diverse
sources of data from across the organization. Such a
learning approach holds promise to address the
difficulties of applying BA tools to improve business
outcomes in individual processes without adversely
impacting broader organizational performance
(Chang and Chou, 2011).
The focus of the research on the assessment of
technology mediated learning programs has been on
how various factors such as types of tools,
instructional methods, the target system and
individual differences influences individual learning
outcomes (Bostrom, et.al, 1990). Compeau, et.al.
(1995) proposed a framework of key factors in the
management of end-user training that highlights
different phases of training such as initiation, formal
and informal and post training and addressed the
issue of transfer of learning to the workplace.
Project based training, which emphasizes the casual
transfer of knowledge among group members,
blends the formal and informal phases. PBL
participants learn from each other as well as from
the program content (Marcris, 2011; Leidner and
Jarvenpaa, 1995) and execute the learning program
in a genuine setting. It has been difficult to assess
cognitive outcomes of BA training during the
learning period as benefits need to be measured over
time, post learning, once the users are back on their
jobs (Gupta, Bostrom and Huber, 2010). Published
end user learning research does not report any
suitable measurement models that can be used
during the learning period to make cognitive
outcome assessments, thus creating a gap in the
research literature. The authenticity of the
environment posed by project based learning, which
demands participants execute genuine workplace
tasks, supports the assessment of cognitive outcomes
(Santhanam and Sein, 1995). Developing a
framework to allow end users to self-assess the
outcomes at the end of the training program holds
promise.
2 RESEARCH GOALS
The goals of this research are to develop an
innovative project based BA learning program that
can enhance the participant’s motivation to learn
along with a measurement model (extended from the
Technology Mediated Learning (TML) framework
in Gupta, Bostrom and Huber, 2010) to explore the
factors that contribute to higher cognitive outcomes.
Using a field study among participants, who took
part in an innovative project-based learning
program, this study has the following research goals:
1. Ascertain the benefits of project based learning
for BA tools on individual motivation and
cognitive outcomes.
2. Build and validate a measurement model of end
user BA learning that extends the TML
framework by adding a construct for group
interactions.
3. Validate that the above model can successfully
predict the level of cognitive outcomes of the
participants.
4. Understand the relationships between model
constructs - individual motivation, group
interactions and project based learning program
characteristics on cognitive outcomes.
3 BACKGROUND THEORY
In the Technology Mediated Learning (TML)
framework, the learning structures (or scaffolds)
support the delivery of the learning content (Gupta,
Bostrom and Huber, 2010). The learning structures
together with the content impact the learning
outcomes of the participants. Individual differences
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such as motivation, also play a role in how the
learning program can impact each end-users’
learning process and outcome (Gupta, Bostrom and
Huber, 2010). Enterprise BA application users must
grasp and integrate cross-functional knowledge so
they can communicate and work cooperatively with
users in other business functions (Wang and
Ramiller, 2009). Based on situated learning theory,
effective group learning programs must require that
group members reflect upon their learning and
contribute their experiences, observations and
insights back into the group’s collective discourse in
a team-based collaborative setting (Wang and
Ramiller, 2009). Such learning content also fosters
joint work, the need for business problem solving
and reflection and sharing of insights among the
team members (Ryan and Deci, 2000).
Shared cognition theory focuses on individual
learning within a social situation, allowing for social
interactions that support the individual’s cognitive
development with help from more capable team
members and peers. Each participant brings their
own experience and expertise to share their
knowledge with the team. There is a constant
interaction and collaboration among participants that
allows each individual to develop more improved
skills in solving problems, than if they worked alone
(Sharda, et.al., 2004). The joint experience allows
each participant to explore the scenarios from other
user’s perspectives and helps them to create new
meanings and explanations through shared
understanding and practical use to perform specific
tasks (Chang and Chou, 2011).
Motivation theory has also been often used to
understand the individuals’ IT adoption and learning
behaviour (Ryan and Deci, 2000). Motivation
theory suggests that individual behaviour is
determined by two fundamental types of motivation:
extrinsic (utilitarian) motivation and intrinsic
(hedonic) motivation (Ryan and Deci, 2000).
Extrinsic motivation refers to performing an activity
because it is perceived to be instrumental in
achieving valued outcomes that are distinct from the
activity itself, such as improving job performance,
pay, or promotion (Ryan and Deci, 2000). Extrinsic
motivation has been found as significant predictors
of BA tool adoption (Igbaria, Parasuraman and
Baroudi, 1996). On the other hand, intrinsic
motivation emphasizes the importance of having an
enjoyable and playful learning experience (Sallam,
et, al., 2011). Intrinsic motivation refers to
performing an activity for no apparent reinforcement
other than the process of performing the activity per
se, such as participation in learning (Ryan and Deci,
2000). In the context of learning new technologies,
extrinsic motivation emphasizes an individual’s
personal gain associated with the technology (Ryan
and Deci, 2000).
4 RESEARCH MODEL
The research model is displayed in Figure 1. The
research constructs along with research hypotheses
are defined in the following subsections.
Figure 1: Research Model.
4.1 Cognitive Outcomes (CO)
Cognitive outcomes (CO) focus on the mental
awareness and judgments of the participants. If
cognitive outcomes are emphasized in the learning
program, then the participants build the capability to
apply their learning in real world scenarios (Gupta,
Bostrom and Huber, 2010). They grasp the path to
apply the acquired knowledge of BA tools and
methods towards effective modelling and analysis of
businesses scenarios so that appropriate KPI’s can
be selected and calculated from organizational data.
Cognitive outcomes also include the growth of self
confidence to allow the transfer of the learning to
new situation that require understanding the
ProjectbasedLearningtoSupportEnterpriseBusinessAnalyticsEducation-TheRoleofCrossFunctionalGroupsto
EnhanceCognitiveOutcomes
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interactions of multiple parts of a complex
organization.
4.2 Project based Learning (PBL)
Impacts CO
Project based learning (PBL) content refers to
instructional methods that encourage users to work
together to accomplish shared goals, beneficial to all
(Marcris, 2011; Alavi, Wheeler and Valacich, 1995;
Leidner and Jarvenpaa, 1995). PBL is a
constructivist approach that engages participants in
solving real world problems. Learning structures
refer to the scaffolds that support the delivery of the
content. Learning from peers is an important
component in project based learning aspeers
contribute to task orientation, persistence and
motivation to achieve” (Leidner and Jarvenpaa,
1995). To mimic real-world problems, which are
typically ill-structured, the assigned projects should
be loosely defined initially to require the groups to
collaborate extensively in order to characterize the
project scope. When the project groups are given a
great deal of autonomy to design and build their BA
project, it forces the participants to grasp integrative
knowledge so that they can communicate and
cooperate closely with other members (Chang and
Chou, 2011). Collaborative project based learning
(PBL) content refers to the presence of these
characteristics of collaboration – joint work, the
need for business problem solving and reflection and
sharing of insights among the team members (Alavi,
Wheeler and Valacich, 1995). The learning program
must also put forward and emphasize a rigorous and
proven methodology to launch the group efforts on a
strong foundation. This way, the groups understand
the process to navigate the typical pitfalls of a BA
project - conflicting goals, contexts, obstacles and
unknowns. The content and structure of the PBL
program influences group work, therefore, we state:
Hypothesis #1: Project Based Learning (PBL) has a
Significant Positive effect on the Level of Group
Interaction (GI).
BA applications can offer several benefits that
include improving timeliness and quality of the
decision making process, providing actionable
information delivered at the right time, enabling
better forecasting, helping streamline operations,
reducing wasted resources and labor/inventory costs,
and improving customer satisfaction (Chaudhuri,
Dayal, Narasayya, 2011; Yeoh and Koronios, 2010
and Negash, 2004). The repeated interaction
between participants in the project groups creates a
set of norms, trust and mutual understanding that
bind the participants together and facilitate better
interactions, both during and post learning (Chang
and Chou, 2011). These project interactions allow
the members to exchange practical knowledge and
fill in the gaps in their understanding of the BA
application and cross functional impacts. The
knowledge sharing and repeated group interactions
fostered by the PBL program during the
collaborative group project promotes greater
cooperation, bridges gaps in understanding and
increases cognitive learning outcomes (Chang and
Chou, 2011). The users learn the practical use of
BA tool and methods by participating in genuine
real world experiences.
Hypothesis #2: Project Based Learning (PBL) has a
Significant Positive Effect on the Level of Cognitive
Outcomes (CO).
Problem based learning that uses authentic,
complex scenarios created an impetus for learning in
order to apply that knowledge to solve the problem
assigned (Uribe, Klein and Sullivan, 2003). Group
projects require individuals to cooperate and work
together but have significant learning benefits of
efficiency and productivity (Baskin, Barker and
Woods, 2005). Such projects allow individuals to
learn to face authentic situations, to share multiple
perspectives and support each other to accomplish a
greater outcome by imposing control over individual
behaviours to meet the group’s expectations of
performing group assigned roles. Group projects
become agents of socialization and control and act
as a motivational tool (Baskin, Barker and Woods,
2005). Therefore, we expect that if individuals
perceive these benefits of project based learning,
they may become more motivated to learn and
effectively utilize BA applications.
Hypothesis #3: Project Based Learning (PBL) has a
Significant Positive Effect on the level of Individual
Motivation (MV).
4.3 Group Interactions (GI)
Group theories suggest that many factors can
influence the outcomes of group-based learning
(Sharda, et.al., 2004). This includes group
characteristics, such as composition (level of
homogeneity and heterogeneity), amount of group
cooperation and the nature of group
communications. Group influence has been found
to emanate from a variety of sources (Agarwal,
2000; Lewis, Agarwal and Sambamurthy, 2003),
including co-worker, supervisor, and friends. In
working organizations, co-workers and supervisors
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are influential in determining technology acceptance
behavior (Schmitz and Fulk, 1991).
For successful cognitive outcomes, group
interactions must be optimized along with training
content and delivery structures (Sharda, et.al, 2004).
In collaborative group learning, the team members
share goals and learn together by working jointly
and solving the problems posed by the project. The
group interactions play a critical role in the learning
environment through the size and heterogeneity of
the team. The more diversity in the team, there is
more likely to be integration of knowledge from
multiple functional areas. Research has shown that
when team members are from differing
backgrounds, the discussions and knowledge sharing
is more intense leading to create more group
decisions (Sharda, et.al., 2004). Group interactions
impact learning outcomes by developing diverse
knowledge and building broader perspectives that
span business functions (Seethamraju, 2008).
Hypothesis #4: The Level of Group Interaction (GI)
has a Significant Positive Effect on the level of
Cognitive Outcomes (CO).
Group interactions (GI) comprise factors such as
if team members shared diverse view points and if
such interactions were valued as well as the nature
of cooperation and the level of dialog achieved
within the team. Greater cooperation and dialog
among a diverse team allows them to build
identification giving them a broader vision to further
enhance cross functional learning (Chang and Chou,
2011). In group based training programs, team
members from different functional areas work
together and influence each other’s motivation by
voicing demands for contributions. The level of
interaction within the group also facilitates
individual engagement with the learning program.
Hypothesis #5: The level of Group Interaction (GI)
has a Significant Positive Effect on the level of
Individual Motivation (MV).
4.4 Individual Motivation (MV)
Individual differences influence the formation of
mental models, which represent the outcomes of the
learning process (Gupta, Bostrom and Huber, 2010).
“States” (such as motivation) are general influences
on performance that vary over time and include
temporal factors such as motivation level and
interest level while “traits” (such as preferred
learning style are static aspects of information
processing affecting a broad range of outcomes over
time (Bostrom, Olfman and Sein, 1990).
Motivation theory has been used often to
understand individuals’ IT use and learning
behaviour (Van Der Heijden, 2004; Tharenou,
2001). Motivation theory suggests that individual
behaviour is determined by two fundamental types
of motivation: extrinsic (utilitarian) motivation and
intrinsic (hedonistic) motivation (Alavi, Wheeler,
and Valacich, 1995). In the context of project based
learning programs, the individual characteristics
from the TML framework is measured using
individual motivation as states and individual
learning style as traits. (Note: individual learning
styles is used as a demographic variable and is not
part of the research model of this study
Hypothesis #6: The level of Individual Motivation
(MV) Moderates the Relationship between Project
based Learning (PBL) and Cognitive Outcomes.
(CO).
5 DATA COLLECTION
The study involved a 4 week face to face learning
program for a leading vendors BA tools. There were
74 participants in the 4 week program and they were
provided 2 hours/week of instruction about analytics
methods, principles and case studies as part of the
theoretical portion of the learning program. This was
coupled with a practicum that required the
participants to use the CRISP-DM (www.crisp-
dm.eu) methodology to define and implement a
business analytics project. As the methodology
requires the involvement of business users to help
define user scenarios, the participants were given
access to a BA consultant and clients in the energy
industry. A large data set was extracted from the
client company’s ERP system and provided to the
participants to work with. The data set contained
financial, production (OPEX), materials, exploration
project management (CAPEX), human resources,
and operational maintenance, training and safety
data.
The 74 participants were divided into small
groups (4-5 members) and assigned a BA project
scope, such as human resources, supply chain,
financial management and energy exploration. The
first objective of the participants was to thoroughly
understand, from a business perspective, what their
assigned business customer really wanted to
measure and accomplish with the BA project. The
participants documented the business use cases and
made decisions on how to utilize the data set to
support the KPI’s deemed necessary by the business
ProjectbasedLearningtoSupportEnterpriseBusinessAnalyticsEducation-TheRoleofCrossFunctionalGroupsto
EnhanceCognitiveOutcomes
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user. The groups then designed and built BA
dashboards that displayed the functional variables
and relationships (in the data). They designed
quantitative KPI models to add “what-if” scenarios
with the BA tools. Contacts in the client energy
company and the BA consultant were available
during the entire duration of the project to answer
questions and review project scope and designs.
Three formal face to face review meetings were
arranged to review dashboard projects at weekly
intervals with the BA consultant to establish a
realistic performance expectations.
The project work was supplemented with
lectures on various topics such as business analytics
models and case studies, requirement gathering and
documentation, dashboard design, data modelling
and management, and project management. The
sequence and content of the 4 week project based
BA learning program is listed in Table 1. A survey
was administered at the end of the 4
th
week after the
projects were submitted by the groups.
Table 1: Ba learning program schedule.
Wk Theoretical Topics Practical Group Work
1
1. BA Case Study (Ghosh
and Scott, 2011)
2. Business Use Cases &
Requirements
3. CRISP-DM
4. Data Visualization
1. Analyze Case Study and
Understand nuts and bolts
of a BA Project
2. Use Visualization Tool on
data sets to understand data
relationships.
2
5. Key Performance
Indicators (KPI)
6. Modelling approaches
7. Information Lifecycle
and Data Quality
3. BA Tool training
4. Identify suitable KPI for
the use cases
5. Build logic based model
3
8. Data management and
storage options (ETL)
10.Predictive Analytics and
data Relationships
6. Identify Input data and
sources
7. Data Storage Design
8. Build and test prelim project
& user reviews
4
11. Unstructured data, Text
Mining, Big Data, Real
Time Analytics
12. BA project Feasibility
9. Add “what-if analysis”
model to BA project
10.Build, test, final project
11. Project Retrospective
6 ANALYSIS AND RESULTS
The demographic data of the participants is listed in
Table 2. It shows that the preferred learning style of
the participants was “learning by doing”, followed
by “learning by thinking and watching”. All of
these learning styles are supported by the group
project based learning format used in this BA
program. Some of the participants commented on
the group interactions and the need to interact to
define the projects.
The survey data was analyzed with SPSS to
ascertain measurement validity of the multi item
survey constructs. The constructs – Group
Interactions (GI), Intrinsic Motivation (IM),
Extrinsic Motivation (EM) and Cognitive Outcomes
(CO) were modelled as reflective, while the Project
Based Learning (PBL) construct was modelled as
formative. The Individual Motivation (MV)
construct was modelled as a second order construct
using the IM and EM constructs. The measurement
model, the paths and relationships among the
constructs were tested with Smart-PL structured
equation modelling (SEM) software to test the
hypotheses.
Table 2: Survey Demographics (Total =74 responses).
Variable Mean Standard Deviation
Hrs/week
on Project
4.43 hours 1.74 hours
Preferred
Learning
Styles
Learn by Doing (48); Learn by Thinking (27),
Learn by watching (22); Learn by Feeling (7)
(Note: Some users Selected multiple learning
styles)
BA Project
Scope
Human Resources (19); Supply Chain (21);
Financial Controlling (22), Energy Exploration
Operations (12)
Participant
Comments
about
Project
Experience
1. Needs more definition; 2. Defining the
scope of what to accomplish made it difficult;
3. Interesting project. 4. Most difficult part was
dealing with the messy data. 5. Project was
frustrating and at first, then we figured it out;
6. Working with 3rd party was difficult; 7.
Smaller groups would allow faster design. 8.
Group work was very helpful.
In PLS, validation is done using the composite
reliabilities (CR) and average variance extracted
(AVE) from the measurement model. The CR
should be greater than 0.7. The AVE measures the
variance captured by the indicators relative to
measurement error and it should be greater than 0.5.
Moreover, the square root of each construct’s AVE
needs to be greater than the correlation of the
construct to the other latent variables to demonstrate
discriminant reliability. As seen from Table 3 and
4, the composite reliabilities for all measures were
high ranging from 0.7045 to 0.9088. Moreover, the
AVE values are above 0.5 and the square root of the
AVE of each construct is greater than the correlation
of that construct with other constructs, respectively
(Table 3). Consequently, evidence for internal
consistency and reliability of the measurement
model are supported by these results (Tables 3 & 4).
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Table 3: Latent Variable Correlations & Square Root of
AVE (in bold).
EM GI IM CO PBL
EM
0.8184
GI 0.4327
0.7824
IM 0.6217 0.4748
0.9088
OUT 0.5838 0.4488 0.6275
0.8054
PBL 0.6233 0.6068 0.6270 0.7019
0.7045
Smart-PLS, v2.0, was used to test the 6 research
hypothesis in the model. A bootstrap re-sampling
procedure was conducted using 200 samples and
path coefficients were re-estimated using each of
these samples (Chin, 1998). The results of the
hypotheses testing is shown in Table 5 and indicate
support for 5 hypothesis at the 99% confidence
level. The hypothesis H4 (Group Interactions with
Cognitive Outcomes) was only supported at the 95%
confidence level (not at 99% confidence).
Table 4: Measurement Model Reliability, R-square.
Construct AVE Composite
Reliability
R-Square Cronbach
Alpha
GI 0.6122 0.8873 0.3682 0.8439
IM 0.8260 0.9047 n/a 0.7895
EM 0.6698 0.8143 n/a 0.6907
CO 0.6488 0.8805 0.6344 0.8189
PBL n/a n/a 0.4964 n/a
MV n/a n/a 0.4931 n/a
Table 5: Hypothesis Testing (99% significance).
Hypothesis Path StdErr T-Stat Sig
H1: PBL -> GI 0.6254 0.0651 9.3268 YES
H2: PBL -> CO 0.4385 0.2777 1.9690 YES
H3: PBL -> MV 0.4419 0.1175 3.8873 YES
H4: GI -> CO 0.3447 0.2589 1.6956
N
O*
H5: GI -> MV 0.3215 0.1498 2.2727 YES
H6: PBL * MV-> CO 0.2516 0.0576 2.5270 YES
*- hypothesis H4 significant at 95% confidence
The results of the PLS analysis finds evidence to
support the notion that project based BA learning
programs promote strong group interactions that
drive to increase participant motivation. The
contents of the learning program, such as the use of
authentic real-world scenarios, the involvement of
external business people and the diversity in the
participant backgrounds supports building higher
cognitive outcomes of the participants. The level of
individual motivation is also found to moderate this
relationship between PBL and cognitive outcomes.
The level of group interactions also increases the
level of cognitive outcomes (at the 95% confidence
level).
7 CONCLUSIONS
Many organizations find that data-driven decision
making is difficult to implement due to reasons such
as poor quality and incompatibilities of transactional
data, complicated algorithms to process the data and
the end-user time involvement necessary for
learning to use the analytics techniques and tools.
Business analytics (BA) applications have gained
significant attention as a viable option to incorporate
the use of large sets of data to address the challenges
of making complex business decisions. Market
leading BA tools have the potential to facilitate data
driven decision making by allowing easier data
manipulation, visualization and processing.
However, the complexity and diversity of BA tools
and their functional boundary spanning nature make
their learning difficult at the individual level. Recent
research has found that individual motivation and
group support as important determinants that
influence an individual’s decision to learn and use
BI tools.
This study aims to contribute to the body of
knowledge by developing an innovate project based
learning program for BA tools and building a model
to measure the effect of the PBL program on
individual motivation and cognitive outcomes of the
participants. The developed PBL program allows
users to learn the concepts of BA collectively and is
supported by a market leading BA tool. The unique
features of the program are (1) use of actual real
world client data and (2) availability of client
business users to allow the participants to collect
analytics business requirements, (3) the functional
diversity of group members and (4) the iterative
approach to the project development using periodic
reviews. A validated model of measuring BA
cognitive outcomes from PBL is a product of the
study.
7.1 Future Work
While this study was done in a face to face learning
setting, much of today’s end user learning occurs
online. The author plans to extend the study to an
online learning setting with the same BA learning
program to assess cognitive outcomes. The
generalization of the research model to the online
environment will allow its use for Massive Online
Courses (MOOC) and provide a vehicle for
measuring cognitive outcomes of the participants.
MOOCs represent a new educational delivery
opportunity, whose potential pedagogical impact
needs to be researched (Fox, 2013). According to a
ProjectbasedLearningtoSupportEnterpriseBusinessAnalyticsEducation-TheRoleofCrossFunctionalGroupsto
EnhanceCognitiveOutcomes
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Gartner survey (Gartner, 2013), business analytics
and analytics were a CIO’s top technology priority
in 2012 and 2013 and it is expected that the global
market for BI tools would reach $14 billion in 2013.
The term “Big Data” is used to refer to the emergent
field of analytics using data being created external to
the company. Currently 4 EB of data are created
each day and this number is doubling every 3 years.
Recent IBM estimates suggest that 4.4 million big
data analytics jobs related to collecting and
processing such data made available through the
internet will be created by 2015 (IBM, 2014). The
possibility to efficiently deploy effective BA
learning to a much wider community by
supplementing online content with real world project
experience presents unique prospects.
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APPENDIX
The Survey Instrument is below:
Average hours/week spent on Project_____
Business Function ___________________
PJ1-The project methodology was well defined from
the beginning
PJ2-I believe the project was a complex and
authentic business scenario
PJ3-The project gave me a lot of autonomy and
freedom
PJ4- The project required joint work and problem
solving
PJ5- The project required the sharing of diverse and
multi-functional insights
CO1-I mastered cross functional knowledge, which
transcends just knowing the technical skills
CO2- I was able to identify learning strategies that I
can use in future projects
CO3-I am confident that I can do another business
analytics project in the future
CO4- The project improved my appreciation of the
value of using business analytics tools
GI1-There was a lot of teamwork and cooperation in
my group
GI2- My contributions were valued by my group
members
GI3-I learnt from the knowledge shared by my
group members
GI4-My group engaged in a lot of dialog and
discussion
GI5-I learned from the different functional
perspectives shared by group members
IM1-I worked hard on the project as I wanted to
learn as much as I could
IM-2-I worked hard on the project as the project
game me a lot of personal satisfaction
IM3-I worked hard on the project as it was very
challenging
EM1-My team mates motivated me to work harder
on the project
EM2-I worked harder on the project as I did not
want to let my team mates down
EM3-I worked harder on the project only to get a
better evaluation.
When I learn …. (Rate the following from 1-4):
I like to deal with my feelings _____
I like to think about ideas ______
I like doing things ___
I like to listen and watch _____
ProjectbasedLearningtoSupportEnterpriseBusinessAnalyticsEducation-TheRoleofCrossFunctionalGroupsto
EnhanceCognitiveOutcomes
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