Journal of Applied Computing and Information Technology

Vol. 25, Issue 1, 2021-22

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Application of the global computing curriculum guidelines and skills frameworks for competency discovery and analysis: a case study of data analytics

Emre Erturk

Corresponding Author

Eastern Institute of Technology

New Zealand

Abstract— The current global computing curriculum guidelines including MISI2016, IT2017 and IS2020 are built to promote and facilitate competency-based higher education programs development and to enhance graduate employability. Their applications however are facing challenges in understanding, interpretation and operationalization. Taking data analytics and data engineering, this study shows how these guidelines are used to discover and analyze competencies, the boundaries between typical IT and IS programs and between IS undergraduate and postgraduate programs and further, the gaps for these programs to fill to incorporate professional practice competencies. The global skills frameworks are invoked and SFIA 7 is used to assist analysis.

Keywords- Data analytics: data engineering; curriculum guideline; competency-based curriculum; data skills

I.INTRODUCTION AND THE GENERAL APPROACH

The current global computing curriculum guidelines including MSIS2016, IT2017 and IS2020 are developed under a great influence by the concept of competency and the efficacy of competency-based approach. The aim is to close the gaps between curricular competencies (developed from fulfilling a curriculum) and professional practice competencies (needed in the industry) to enhance the worth of the computing programs and graduate employability. Their applications however are facing challenges in understanding, interpretation and operationalization. Great insights have been provided in previous studies from different perspectives such as understanding of IT (Information Technology) in the modern age, unpacking dispositions and visualizing competencies [13, 18, 24]. This study is an addition to the efforts.

A domain case is chosen to make the study scope manageable and the output information explicit to inform possible curriculum designs, reviews and revisions. Data analytics is chosen because it is a skillset that is increasingly demanded in the IT job market and an enlarging domain in the computing curricula [18, 23]. Moreover, it is a domain that is applied by tertiary education institutions variedly and the

Guozhen Huang

First Author

CIC Higher Education

Australia

curricular boundaries between different IT and IS (Information Systems) programs are not clear [15, 25].

This study answers three questions:

1)What are the competencies that are suggested from the global computing curriculum guidelines?

2)How IT and IS programs, undergraduate and post- graduate programs differ in competencies?

3)What are the gaps that need to be filled by the computing programs to incorporate professional practice competencies?

The answers to the first two questions will enable a clearer understanding of the competencies about data analytics, what is included in each of the typical IT and IS curriculum programs, and where their boundaries are. The answer to the third question will clarify the professional practice competencies that are possibly lacked in IT and IS programs, the gaps.

For answering the third question, the global IT skills frameworks including SFIA 7, e-CF 3.0 and SF for ICT are examined. These frameworks prescribe professional practice competencies that are needed in the industry; however, ambiguities, intricacies and variances in these frameworks may make their comparisons and cross-referencing impossible [7, 8]. This issue is resolved by a choice-making.

It is worth reiterating that competencies developed from fulfilling a curriculum are different from competencies that are needed in the industry, the former refers to curricular competencies and the latter, the professional practice competencies [2]. A clear understanding of this difference will help distinguish between what can be achieved in a university setting and what can be acquired through experience in workplace [14, 19]. This acknowledged, reconciling two streams of competencies will reveal the gaps where IT and IS programs should focus on.

The study starts with reviewing of the concept of data analytics and the concept of competency to prepare the lens for examining the curriculum guidelines. The overall structures,

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rationales, key concepts and competency specifications are then carefully reviewed, and all the relevant competencies relating to data analytics are captured. The understanding and interpretation that are involved in this process are aided by recent (since 2010) studies retrieved from four research databases including Google Scholar, ProQuest, IEEE Xplore and ScienceDirect. The key search words and their combinations that are used include “data analytics”, “data analyst”, “data analysis”, “business analytics” and “data scientist” as one category, and “competency”, “competence”, “skills”, “ability” and “capability” as another.

The curricular competencies for IS and IT programs, and IS undergraduate and postgraduate programs are then compared with each other. Care is taken to ensure competencies that are brought in comparisons are at the same level of abstraction (categorization). Bogging down too much to details may fall the study into the traps of endless exhaustivity, a tendency that is warned by the global educational associations [2, 9]. This process reveals the core competencies that are shared by all the programs, and different competencies (the boundaries) between them.

To discover the gaps between curricular competencies and professional practice competencies, three universal skills frameworks including SFIA 7, e-CF 3.0 and SF for ICT are examined. The same approach, processes and care that are taken when examining the curriculum guidelines are applied.

The examination turns out a challenging scenario that the skills frameworks vary significantly, which makes their comparisons not sense-making or even impossible. A choice of a framework is made to proceed the study. Then the professional practice competencies specified in this framework (SFIA 7) are compared with the curricular competencies, exposing the gaps [8].

II.KEY CONCEPTS: DATA ANALYTICS AND COMPETENCY

Data analytics as an IT skillset becomes an asset in the forms of infrastructure, human resources and the associated intangibles such as tacit knowledge and culture. It becomes an asset to the extent that it is employed and utilized by an organization [6].

The value of data analytics comes primarily from its capability and usefulness to make out from Big Data. “Big data is a term that is used to describe data that is high volume, high velocity, and/or high variety; requires new technologies and techniques to capture, store, and analyze it; and is used to enhance decision making, provide insight and discovery, and support and optimize processes.” [25].

Data analytics is seen to have gone through three genealogical phases: decision support systems (DSS) in 1970’s, business intelligence (BI) in 1990’s, and data analytics in 2010’s [5, 25]. It is a persisting skillset involving “getting data in” to a data mart or warehouse and “getting data out” from the data that is stored. It is important to train future data scientists with the corresponding programming and analytical skills [11]. With Big Data there are three general types of analysis: descriptive, predictive and prescriptive. The descriptive

summarizes what happened in the past, the predictive suggests what will happen in the future, and the prescriptive tells what to do. Different algorithms, mainly statistical, are used, with data mining, machine learning and neutral networks at the high end.

The three V’s (volume, velocity and variety) to identify Big Data was later extended to seven by [21]. They added variability, veracity, visualization and value as new dimensions. This conception has not been adopted in MSIS 2016, IT2017 and IS2020 so far. A newer perspective is to see data analytics in a lifecycle involving data management, data preprocessing and integration through data modelling and business intelligence to insight management [16]. The spectrum of big data analytics should be further identified to include data mining, machine learning, data science and systems, and its relations to artificial intelligence, distributed computing and systems, and cloud computing, taking into account technical aspects [22]. Again, this perspective is yet to be assessed for adoption to the guidelines.

Competency, in IT2008 and IS2010, is conceived as a body of knowledge, content and learning outcomes [10, 23]. This conception facilitates curriculum design, but is seen less reflecting the demand of the IT job market. It is therefore replaced by the triadic model which is expressed as “Competency = Knowledge + Skills + Dispositions” in IT2017 and IS2020.

A newer conception of competency is proposed by [17], namely, a holistic model expressed as “Competency = functional competence + cognitive competence + social competence + meta competence”. The functional refers to the ability to perform a range of activities, achieve specific outcomes and demonstrate industry standards. The cognitive is the ability to think and act in an insightful way to solve problems, including using tacit, practical and contextualized knowledge. The social is the ability to cooperate with others and the meta-competence, the ability to cope with uncertainty, self-learning, reflection and adaptation [11].

Although framed differently, the holistic model covers the same scope and content of IT competencies as does the triadic model. It emphasizes on ability which corresponds largely to the skills in the triadic model and for this reason, this study prioritizes ability and skills as working concepts to understand and identify technical competencies.

III.THREE CURRICULUM GUIDELINES

MSIS2016, IT2017 and IS2020 are three well-distributed IT and IS curriculum guidelines that are published by ACM, AIS and IEEE CS (two of them for each publication). They provide a broad landscaping and taxonomic mapping of the computing domains. MSIS2016, IT2017 and IS2020 stand for Global Competency Model for Graduate Degree Programs in Information Systems, Information Technology Curricula 2017, and A Competency Model for Undergraduate Programs in Information Systems respectively.

MSIS2016 [1] is related to postgraduate programs. It identifies nine IS competency areas, one of which is Data, Information and Content Management. Under each area are

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competency categories and under each category are actual competencies. An actual competency is then assigned with one of the four attainment levels including Awareness, Novice, Supporting (role), and Independent (contributor). Awareness refers to knowledge and understanding at general level. Novice indicates the ability to communicate effectively and perform essential activities under supervision. Supporting refers to the ability to collaborate with others to achieve desired outcomes, and Independent demonstrates the ability to perform complex tasks without supervision.

Data analytics as a skillset is integrated into the competency are of Data, Information and Content Management. In this area there are five relevant data analytics competencies predominately at Novice level, as shown Table I.

TABLE I. DATA ANALYTICS ATTAINMENT LEVELS UNDER MSIS2016

Competencies

Identifier

Levels of

 

Words

Attainment

Selecting appropriate data

Unstructured

Supporting

management technologies based on

data

(role)

the needs of the domain

 

 

Designing and implementing a data

Implement a

Novice

warehouse using a contemporary

data warehouse

 

architectural solution

 

 

Integrating and preparing data

Multiple data

Novice

captured from various sources for

types

 

analytical use

 

 

Selecting and using appropriate

Analytics

Novice

analytics methods

methods

 

Analyzing data using advanced

Identify

Novice

contemporary methods

patterns

 

IT2017 [3] addresses competencies in three IT domains: essential, supplemental and intermediate (overlapping essential and supplemental). The essential identifies the minimal competencies that must be obtained for an IT degree. The supplemental indicates competencies for more specialized work such as cloud computing and IoTs. Each competency is assigned with one of the three levels of learning engagement, namely, L1, L2 and L3. L1 indicates the minimal degree of engagement associating with fundamentals learning, L2 denotes a large degree of engagement associating with applications in complex problems and situations, and L3 refers to more time-intensive evaluations that require in-depth and personalized feedback and possibly employers’ input.

Data analytics, together with scalability, is identified as a supplemental domain. Five competencies are further identified and they are predominately at Level 2 as shown in following table.

TABLE II. DATA ANALYTICS LEVELS OF

LEARNING ENGAGEMENT UNDER IT2017

Competencies

Levels of

 

learning

 

engagement

Using appropriate data analysis methods to solve real-

2

world problems

 

Performing data preprocessing techniques—data

2

integration, data cleansing, data transformation, and data

 

reduction to clean and prepare data sets for analysis

 

Using big data platforms including but not limited to

2

Hadoop, Spark, and tools including but not limited to R

 

and RStudio, MapReduce and SAS to analyze data in

 

different application domains

 

Use data-intensive computations and streaming

2

analytics on cluster and cloud infrastructures to drive

 

better organization decisions

 

Examine the impact of large-scale data analytics on

2

organization performance using case studies

 

Using appropriate data analysis methods to solve real-

1

world problems

 

IS2020 [2] uses a matrix of six realms and two layers to identify competencies. The realms are used to identify the general IS domains and the layers, distinguish required from elective competencies. The required are the core for IS programs and the elective, the optional that are built upon the core. Each competency is identified as a knowledge-skill pair and each pair is assigned with one of the six Bloom cognitive levels. Data analytics is in the realm of Data and Information Management as an elective. Table III shows its seven competencies and where they are at the Bloom cognitive levels.

TABLE III. DATA ANALYTICS COMPETENCIES AND THEIR BLOOM COGNITIVE LEVELS UNDER IS2020

 

 

Competencies

 

Bloo

Bloom

 

 

 

 

 

 

m

keywords

 

 

 

 

 

 

cognitive

 

 

 

 

 

 

 

levels

 

Applying the principles of computational

2,4

Understand,

thinking (CT) to learning data science

 

 

analyse

Analyzing data science problems with a

2,3

Understand,

CT framework

 

 

 

 

 

apply

Expressing a business problem as a data

2,5

Understand,

problem

 

 

 

 

 

 

evaluate

Performing

exploratory

data analysis

3,6

Apply, create

from inception to the value proposition

 

 

 

Explaining the core principles behind

4

Analyse

various

analytics

tasks

such

as

 

 

classification,

clustering,

optimization,

 

 

recommendation

 

 

 

 

 

Articulating the nature and potential of

2

Understand

Big Data

 

 

 

 

 

 

 

Demonstrating the use of big data tools

5

Evaluate

on real world case-studies

 

 

 

 

As shown in Table IV on the next page, a comparison between IS undergraduate and postgraduate programs reveals that:

1)The undergraduate programs have a foundational part which is focused on principles, conceptions and understanding (data problems).

2)The postgraduate programs have an extension that covers a supporting (role) in selecting data management technologies, designing and implementing data warehouses.

3)In the processes to solve data analytics problems, the two programs share the ability to apply essential skills and principles, however the graduate programs have more engagement with using wider data sources and contemporary methods.

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TABLE IV. COMPARISON BETWEEN UNDERGRADUATE AND POSTGRADUATE IS

PROGRAMS FOR DATA ANALYTICS COMPETENCIES

IS2020

MISI2016

Comparison

• Applying the

 

Foundational:

principles of

 

o Principles and

computational

 

conceptions

thinking (CT) to

 

o Understanding of

learning data science

 

data problems

• Analyzing data

 

 

science problems

 

 

with a CT framework

 

 

• Expressing a business

 

 

problem as a data

 

 

problem

 

 

• Performing

• Integrating and

Common:

exploratory data

preparing data

o Application of

analysis from

captured from

principles and

inception to the value

various sources

essential skills in

proposition

for analytical

problem-solving

• Explaining the core

use

Different:

principles behind

• Selecting and

o Narrower vs wider

various analytics

using

data sources

tasks such as

appropriate

o Essential vs

classification,

analytics

contemporary

clustering,

methods

methods

optimization,

• Analyzing data

 

recommendation

using advanced

 

• Articulating the

contemporary

 

nature and potential

methods

 

of Big Data

 

 

• Demonstrating the

 

 

use of big data tools

 

 

on real world case-

 

 

studies

 

 

 

• Selecting

Extensional:

 

appropriate data

o Selection of data

 

management

management

 

technologies

technologies

 

based on the

o Design and

 

needs of the

implementation of

 

domain

data warehouse

 

• Designing and

 

 

implementing a

 

 

data warehouse

 

 

using a

 

 

contemporary

 

 

architectural

 

 

solution

 

Table V shows the results of a comparison between IS2020 and IT2017, suggesting:

1)The IT and IS undergraduate programs share the same scope in the data analytics domain.

2)IS programs have more engagement with knowledge and understanding applications whilst IT programs, processing and skills applications.

3)Systems and software such as Hadoop, R and SAS are specified for IT programs to ensure coverage and complexity in applying technologies.

TABLE V. COMPARISON BETWEEN IS AND IT UNDERGRADUATE PROGRAMS FOR DATA

ANALYTICS COMPETENCIES

IS2020

IT2017

Comparison

• Applying the

• Using appropriate

Common:

principles of

data analysis

o Scope of domain

computational

methods to solve

Different:

thinking (CT) to

real-world

o Knowledge vs

learning data

problems

skills applications

science

• Performing data

o Understanding vs

• Analyzing data

preprocessing

processing

science problems

techniques—data

o Unspecified vs

with a CT

integration, data

specified software

framework

cleansing, data

engagement

• Expressing a

transformation,

 

business problem as

and data reduction

 

a data problem

to clean and

 

• Performing

prepare data sets

 

exploratory data

for analysis

 

analysis from

• Using big data

 

inception to the

platforms such as

 

value proposition

Hadoop, Spark,

 

• Explaining the core

and tools including

 

principles behind

R, RStudio,

 

various analytics

MapReduce and

 

tasks such as

SAS to analyze

 

classification,

data in different

 

clustering,

application

 

optimization,

domains

 

recommendation

• Use data-intensive

 

• Articulating the

computations and

 

nature and potential

streaming analytics

 

of Big Data

on cluster and

 

• Demonstrating the

cloud

 

use of big data tools

infrastructures to

 

on real world case-

drive better

 

studies

organization

 

 

decisions

 

 

• Examine the

 

 

impact of large-

 

 

scale data analytics

 

 

on organization

 

 

performance using

 

 

case studies

 

 

• Using appropriate

 

 

data analysis

 

 

methods to solve

 

 

real-world

 

 

problems

 

IV. THREE SKILLS FRAMEWORKS

SFIA 7, e-CF 3.0 and SF for ICT are three industry skills frameworks that are used by IT practitioners, employers and IT professional bodies such as UKAS (the U.K.), ACS (Australia) and IT Professionals (New Zealand) for IT accreditations and certifications. They are used in this study to check and identify gaps in curricular competencies. SFIA 7, e-CF 3.0 and SF for ICT stand for Skills Framework for the Information Age (version 7), European e-Competence Framework (version 3.0), and Skills Frameworks for ICT respectively.

SFIA is a UK-based framework [20]. It identifies competencies in a matrix of 102 professional skills and seven levels of responsibilities. This matrix is highly differentiating to enhance pertinence and accuracy for competency identification, composition and application.

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The seven levels of responsibility, namely, Follow, Assist, Apply, Enable, Ensure and Advise, Initiate and Influence, Set Strategies, Inspire and Mobilize, are used to further identify

each competency. Data analytics is identified as a cluster of skills, as shown in Table VI.

TABLE VI. DATA ANALYTICS COMPETENCIES, LEVELS AND FOCUSES OF RESPONSIBILITY UNDER SFIA 7

Competencies

Levels of

Focuses of Responsibility

Detailed Descriptions

 

Responsibility

(In typical tasks)

 

• Applying mathematics, statistics,

3

Apply

• Undertaking analytical activities and delivers analysis

predictive modeling and machine-learning

 

(Perform a range of work

outputs, in accordance with customer needs and

techniques to discover meaningful patterns

 

under specific direction)

conforming to agreed standards

and knowledge in recorded data

 

 

 

• Analyzing data with high volumes,

4

Enable

• Applying a range of mathematical, statistical,

velocities and variety (numbers, symbols,

 

(Perform a range of

predictive modelling or machine-learning techniques

text, sound and image)

 

complex work under general

in consultation with experts if appropriate, and with

 

 

direction)

sensitivity to the limitations of the techniques

 

 

 

• Selecting, acquiring and integrating data for analysis

 

 

 

• Developing data hypotheses and methods, training

 

 

 

and evaluating analytics models, sharing insights and

 

 

 

findings and continuing to iterate with additional data

• Developing forward-looking, predictive,

5

Ensure and advise

• Evaluating the need for analytics, assesses the

real-time, model-based insights to create

 

(Perform an extensive

problems to be solved and what internal or external

value and drive effective decision-making

 

range of complex and self-

data sources to use or acquire

 

 

initiated work under broad

• Specifying and applying appropriate mathematical,

 

 

direction)

statistical, predictive modelling or machine-learning

 

 

 

techniques to analyze data, generate insights, create

 

 

 

value and support decision-making

 

 

 

• Managing reviews of the benefits and value of

 

 

 

analytics techniques and tools and recommends

 

 

 

improvements

 

 

 

• Contributing to the development of analytics policy,

 

 

 

standards and guidelines

• Identifying, validating and exploiting

6

Initiate and influence

• Developing analytics policy, standards and guidelines

internal and external data sets generated

 

(Perform highly complex

• Establishing and managing analytics methods,

from a diverse range of processes

 

work involving technical,

techniques and capabilities to enable the organization

 

 

financial and quality aspects)

to analyze data, to generate insights, create value and

 

 

 

drive decision-making

 

 

 

• Setting direction and leads the introduction and use of

 

 

 

analytics to meet overall business requirements,

 

 

 

ensuring consistency across all user groups

 

 

 

• Identifying and establishing the veracity of the

 

 

 

external sources of information which are relevant to

 

 

 

the operational needs of the enterprise

 

7

Set strategy, inspire and

• Directing the creation and review of a cross-

 

 

mobilise

functional, enterprise-wide approach and culture for

 

 

(Lead on formulating and

analytics

 

 

implementing strategy)

• Leading the provision of the organization’s analytics

 

 

 

capabilities.

 

 

 

• Leading the organization's commitment to efficient

 

 

 

and effective analysis of textual, numerical, visual or

 

 

 

audio information

e-CF 3.0 is an EU-based framework [12]. It identifies 40 competencies in four dimensions (D1 to D4). D1 identifies five competency areas (ICT processes) including Plan, Build, Run, Enable and Manage. D2 provides a set of competencies for each area. D3 assigns each competency with one of the five proficiency levels (mapping EQF levels). EQF stands for European Qualifications Authority. D4 clarifies each set of competencies with exemplar knowledge and skills.

Data analytics is not identified as a separate competency but a component integrated in the competency set of Information and Knowledge Management. It is under the Enable umbrella.

Three proficiency levels are assigned to the competency set rather than to data analytics, leaving an ambiguity. Table VII shows how the competency set is identified.

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TABLE VII. DATA ANALYTICS COMPETENCY SET, PROFICIENCY, EXEMPLAR KNOWLEDGE AND SKILLS UNDER E-CF 3.0

Competency set

 

Levels of Proficiency

 

Knowledge

Skills

(Incl. data analytics)

 

(In typical tasks)

 

(Exemplar)

(Exemplar)

 

 

 

 

 

 

• Analyzing business processes and

3

 

• Methods to analyze information

• Gathering internal and external knowledge

associated information requirements

 

(Consulting)

 

and business processes

and information needs

• Providing the most appropriate

 

 

• ICT devices and tools applicable

• Formalizing customer requirements

information structure

 

 

 

for the storage and retrieval of

• Translating or reflecting business behavior

• Integrating the appropriate

4

 

data

into structured information

information structure into the

 

(IS strategy/holistic

 

• Challenges related to the size of

• Making information available

corporate environment

 

 

data sets

• Ensuring that IPR and privacy issues are

 

 

solutions)

 

• Challenges related to

respected

 

 

 

 

unstructured data

• Capturing, storing, analyzing data sets, that

• Correlating information and

5

 

 

are complex and large, not structured and in

knowledge to create value for the

 

(IS strategy or

 

 

different formats

business.

 

 

 

• Applying data mining methods

• Applying innovative solutions based

 

program

 

 

 

on information retrieved

 

management)

 

 

 

 

 

 

 

 

 

SF for ICT is a Singapore-based framework [4]. IMDA and SSG stand for Infocomm Media Development Authority and Skills-Future Singapore respectively. SF covers 104 job roles that comprise 80 technical and 18 generic competencies. The technical competencies are categorized into seven tracks and 32 sub-tracks to acknowledge career pathways. Each sub-track is identified with a line of job roles, critical work functions, key tasks and associated specific skills and competencies. Levels of proficiency are indicated but their source is not found in the framework document. Data analytics is identified and it is unclear whether it is a skill, a competency, or their combination, as skills and competencies are presented in concatenations. The track is Business Intelligence which is shared by job roles involving both data analyst and data engineer at a lower level. Table VIII shows how data analytics is shared by different jobs.

TABLE VIII. DATA ANALYTICS TASKS IN JOBS

Data Analytics

Levels of

Job Roles

 

Proficiency

 

 

 

 

• Identifying underlying trends and

2

• Data analyst

patterns in business data using

 

• Associate

statistical and computational

 

data engineer

techniques and tools

 

 

• Developing, applying and evaluating

3

• Data analyst

algorithms, predictive data

 

• Associate

modelling and data visualization to

 

data engineer

identify trends and patterns in data

 

 

• Designing and conducting data

4

• Business

studies to drive organizational

 

intelligence

decisions and insights

 

manager

• Managing and enhancing

5

• Business

organizational data science

 

intelligence

capability by refining financial and

 

director

other business performance criteria

 

 

and design data studies

 

 

Table IX on the right shows key tasks of an exemplar role (the case of data analyst):

TABLE IX. KEY TASKS OF AN EXEMPLAR ROLE (FOR

THE DATA ANALYST)

Critical

Identify

Prepare and

Present

work

business needs

analyse data

insight

functions

 

 

 

Data

• Identify

• Gather data from

• Develop

Analyst

information

internal systems

automated and

 

needs of

and external

logical data

 

stakeholders

resources

models and

 

required for

• Perform data

data output

 

decision-

entry tasks in

models

 

making

data collection

• Translate

 

• Assist in the

systems

analysis into

 

transaction of

• Clean and update

common

 

business

databases to

business

 

needs into

remove

language to

 

analytics and

duplicated,

influence

 

reporting

outdated or

business

 

requirements

irrelevant

decisions or

 

• Recommend

information

actions

 

types of data

• Perform data

• Design data

 

and data

validation and

reports and

 

sources

quality control

visualization

 

needed to

checks

tools to

 

obtain the

• Perform basic

facilitate data

 

required

extract, transform

understanding

 

information

and load related

through

 

and insights

activities to

storytelling

 

• Assist in

prepare data for

 

 

identifying

analysis or

 

 

potential

transfer

 

 

business

• Analyze data to

 

 

intelligence

identify trends,

 

 

service

patterns and

 

 

offerings

correlations to

 

 

required by

support decision-

 

 

the business

making

 

 

 

• Propose solutions

 

 

 

and

 

 

 

recommendations

 

 

 

to address

 

 

 

information

 

 

 

needs

 

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SFIA 7, e-CF 3.0 and SF for ICT are built with different perspectives, focuses, intersections and dimensions. Data analytics is identified variedly and in cases ambiguous and intricate. It can be a cluster of skills, a competency or a component that is combined with others to serve larger competencies. Give the big differences, reducing analysis of them to comparisons may not be appropriate nor even possible. To proceed the analysis, a framework must be chosen.

SFIA 7 is chosen for it is the best fit-for-purpose choice. The purpose is ultimately to inform curriculum designs, reviews and revisions. As compared with the other two, SFIA 7 is seen to have provided a set of competencies that best match the competencies derived from IS2020, MISI2016 and IT2017 in scope, content and abstraction (categorization), which enables a comparative analysis. SFIA 7 also provides a clearer basis for assigning proficiency levels.

As shown Table X, a comparison between SFIA 7 and each of the curriculum guidelines yields the following outcomes.

1)Each of IS and IT undergraduate programs covers the same scope of domain as SFIA 7. IS postgraduate programs cover more, however the same core.

2)Both IS programs focus on understanding and analysis of problems rather than performing tasks to solutions. They also focus on knowledge rather than skills applications.

3)Undergraduate programs in IS appear short in engagement with AI (machine learning) and Big Data (in sources and formats).

4)IT undergraduate programs appear short in engagement with AI (machine learning), but not short with traditional systems and software (coverage and complexity).

5)Postgraduate programs in IS typify senior roles, covering a scope wider than the norm scope in professional practice.

In general, the professional practice competencies are largely or predominately satisfied by IS and IT programs. However, all the programs have more or less gaps to fill.

TABLE X. COMPARISONS BETWEEN SFIA 7 VS IS2020, MISI2016 AND IT2017 IN THE COMPETENCIES

 

SFIA7

IS2020

Comparison

 

 

 

 

 

• Applying mathematics, statistics, predictive

• Applying the principles of computational thinking (CT) to

Common:

 

modeling and machine-learning techniques to

learning data science

o Scope of domain

 

discover meaningful patterns and knowledge

• Analyzing data science problems with a CT framework

 

 

in recorded data

• Expressing a business problem as a data problem

 

 

• Analyzing data with high volumes, velocities

• Performing exploratory data analysis from inception to the

Different:

 

and variety (numbers, symbols, text, sound

value proposition

o Performing vs understanding and

 

and image)

• Explaining the core principles behind various analytics

analysis

 

• Developing forward-looking, predictive, real-

tasks such as classification, clustering, optimization,

o Skills vs knowledge applications

 

time, model-based insights to create value and

recommendation

o Specified vs unspecified AI (machine

 

drive effective decision-making

• Articulating the nature and potential of Big Data

learning) applications

 

• Identifying, validating and exploiting internal

Demonstrating the use of big data tools on real world

o Specified vs unspecified Big Data

 

and external data sets generated from a diverse

case-studies

involvements

 

range of processes

 

 

 

 

 

 

 

SFIA7

MISI2016

Comparison

 

 

 

 

 

• Applying mathematics, statistics, predictive

• Integrating and preparing data captured from various

Common:

 

modeling and machine-learning techniques to

sources for analytical use

o Core scope of domain

 

discover meaningful patterns and knowledge

• Selecting and using appropriate analytics methods

Different:

 

in recorded data

• Analyzing data using advanced contemporary methods

o Performing vs understanding and

 

• Analyzing data with high volumes, velocities

• Selecting appropriate data management technologies

analysis

 

and variety (numbers, symbols, text, sound

based on the needs of the domain

o Skills vs knowledge applications

 

and image)

• Designing and implementing a data warehouse using a

o Narrower vs wider scopes

 

• Developing forward-looking, predictive, real-

contemporary architectural solution

(involvement of data warehouse and

 

time, model-based insights to create value and

 

management technology)

 

drive effective decision-making

 

 

 

• Identifying, validating and exploiting internal

 

 

 

and external data sets generated from a diverse

 

 

 

range of processes

 

 

 

 

 

 

 

SFIA7

IT2017

Comparison

 

 

 

 

 

• Applying mathematics, statistics, predictive

• Using data analysis methods to solve real-world problems

Common

 

modeling and machine-learning techniques to

• Performing data preprocessing techniques—data

o Scope of domain

 

discover patterns and knowledge in data

integration, data cleansing, data transformation, and data

 

 

• Analyzing data with high volumes, velocities,

reduction to clean and prepare data sets for analysis

Different:

 

variety (numbers, symbols, text, sound, image)

• Using big data platforms including but not limited to

o Specified vs unspecified AI (machine

 

• Developing forward-looking, predictive, real-

Hadoop, Spark, and tools including but not limited to R

learning) applications

 

time, model-based insights to create value and

and RStudio, MapReduce and SAS to analyze data in

o Unspecified vs specified software

 

drive effective decision-making

different application domains

engagements

 

 

 

 

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SFIA7

 

IS2020

Comparison

 

 

 

 

 

 

• Identifying, validating and exploiting internal

• Use data-intensive computations and streaming analytics

o Narrower vs wider scopes (evaluation

 

and external data sets generated from a diverse

on cluster and cloud infrastructures to drive better

of impact on organizations,

 

range of processes

organization decisions

 

involvement with cloud

 

 

• Examine the impact of large-scale data analytics on

infrastructure)

 

 

organization performance using case studies

 

 

 

• Using appropriate data analysis methods to solve real-

 

 

 

world problems

 

 

 

V.RECOMMENDATIONS, LIMITATIONS AND FUTURE RESEARCH

Appreciating the great potential values and benefits of the global computing curriculum guidelines, this study joins the efforts to facilitate their applications. Through a series of examinations and analysis of MISI2016, IT2017 and IS2020, the study discovered the curricular competencies for typical IT and IS programs in a case domain of data analytics. Furthermore, through invoking SFIA 7, e-CF 3.0 and SF for ICT and using SFIA 7 for comparison, the study revealed the gaps for the programs to focus on for enhancing competency- based curriculum and graduate employability. Based on the outcomes of the study, it is recommended that:

1)Designs, reviews and revisions of IS programs (undergraduate and postgraduate) should have a focus on competencies for performing tasks and skills applications.

2)A focus for IS undergraduate programs is on engagement with AI (artificial intelligence and machine learning) techniques and Big Data in various sources and formats.

3)For IS postgraduate programs, it would be beneficial to check if their scope can be narrowed to make space for incorporating more skills applications.

4)It would be beneficial for IT undergraduate programs to check if AI techniques including machine learning can be engaged more to meet the requirements in the industry.

Particular systems and software for AI techniques learning are not named in SFIA 7. However, in SFIA Beta 8, the newest version, key competencies including evaluating trained models, selecting examination metrics and tracing machine learning outcomes are specified. Advanced analytic techniques in the data science spectrum, including data/text mining, pattern matching, semantics analysis, sentiment analysis, network and cluster analysis, multivariate statistics and simulation, are also mentioned [20]. Systems and software that accommodate these competencies and techniques better should be considered in curriculum designs, reviews and revisions.

This study is limited in that the data sources are primarily the reports from the global IT educational and professional associations. They satisfy IT and IS curriculum designs, reviews and revisions at program level. For analysis of specific learning outcomes, assessment and other curricular components at course, unit or module levels, more studies in depth, such as examining the IT job market, would be much beneficial.

REFERENCES

[1]ACM & AIS. (2017). MSIS 2016 Global competency model for

graduate degree programs in information systems. https://www.acm.org/binaries/content/assets/education/msis2016.pdf

[2]ACM & AIS. (2021). A competency model for undergraduate programs in information systems. https://dl.acm.org/citation.cfm?id=3460863

[3]ACM & IEEE-CS. (2017). Information technology curricula 2017. Curriculum guidelines for baccalaureate degree programs in information technology. https://dl.acm.org/citation.cfm?id=3173161

[4]IMDA & SSG. (2017). Skills Framework for Infocomm Technology. Retrieved on 10 September 2021 from Skills Framework for ICT (imda.gov.sg).

[5]Batistič, S., & Van Der Laken, P. (2019). History, evolution and future of big data and analytics: a bibliometric analysis of its relationship to performance in organisations. British Journal of Management, 30(2), 229-251.

[6]Bharadwaj, A. S. (2000). ‘A resource-based perspective on information technology capability and firm performance: an empirical investigation’,

MIS Quarterly, 24, pp. 169–196.

[7]Brown, J. (2020). An examination of the Skills Framework for the Information Age (SFIA) version 7. International Journal of Information Management, 51, 102058.

[8]Brown, J., & Parr, A. (2018). ICT skill frameworks: do they achieve their goals and users’ expectations? Advanced Journal of Professional

Practice, 1(2), 38-47.

[9]DPBOKTM Foundation. (2019). Digital Practitioner Body of

KnowledgeTMStandard. https://www.opengroup.org/certifications/dpbok

[10]Ekstrom, J. J., & Lunt, B. M. IT2008: Information Technology Model Curriculum. In Seventh LACCEI Latin American and Caribbean Conference for Engineering and Technology (LACCEI’2009) (pp. 2-5).

[11]Erturk, E., & Jyoti, K. (2015). Perspectives on a Big Data application: What database engineers and IT students need to know. Engineering, Technology & Applied Science Research, 5(5), 850-853.

[12]European e-Competence Framework. (2013). e-CF 3.0. http://ecompetences.eu/wp-content/uploads/2014/02/European-e- Competence-Framework-3.0_CEN_CWA_16234-1_2014.pdf

[13]Frezza, S., Clear, T., & Clear, A. (2020, October). Unpacking Dispositions in the CC2020 Computing Curriculum Overview Report. In 2020 IEEE Frontiers in Education Conference (FIE) (pp. 1-8). IEEE.

[14]Ho, S. Y., & Frampton, K. (2010). A competency model for the information technology workforce: Implications for training and selection. Communications of the Association for Information Systems, 27(1), 5.

[15]Lawler, J., & Molluzzo, J. C. (2015). A proposed concentration curriculum design for big data analytics for information systems students. Information Systems Education Journal, 13(1), 45.

[16]Lu, J. (2020). Data Analytics Research-Informed Teaching in a Digital Technologies Curriculum. INFORMS Transactions on Education, 20(2), 57-72.

[17]Persaud, A. (2020). Key competencies for big data analytics professions: a multimethod study. Information Technology & People.

[18]Sabin, M., Impagliazzo, J., Alrumaih, H., Tang, C., & Zhang, M. (2018). IT2017 Report: Implementing A Competency-Based Information Technology Program. In Proceedings of the 49th ACM Technical Symposium on Computer Science Education (pp. 1045-1046).

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[19]Sánchez, F., Soler, A., López, D., Martín, C., Ageno, A., Belanche, L.,

... & Marés, P. (2014). Developing professional skills at tertiary level: A model to integrate competencies across the curriculum. In 2014 IEEE Frontiers in Education Conference (FIE) Proceedings (pp. 1-9). IEEE.

[20]SFIA Foundation. (2020). SFIA. https://sfia-online.org/en

[21]Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263-286.

[22]Sun, Z., & Huo, Y. (2021). The spectrum of big data analytics. Journal of Computer Information Systems, 61(2), 154-162.

[23]Topi, H. (2019). Reflections on the current state and future of information systems education. Journal of Information Systems Education, 30(1), 1.

[24]Waguespack, L., & Babb, J. (2019). Toward visualizing computing curricula: The challenge of competency. Information Systems Education Journal, 17(4), 51.

[25]Watson, H. J. (2014). Update tutorial: Big Data analytics: Concepts, technology, and applications. Communications of the Association for Information Systems, 44(1), 21.

AUTHORS’ BACKGROUND IN RELATION TO THIS TOPIC:

1.Dr Guozhen Huang is a senior academic with experience in course development, curriculum review, and external moderation across business and information systems. He is a member of the experts group within the Tertiary Education Quality and Standards Agency (TEQSA) in Australia.

2.Dr Emre Erturk is an experienced academic, Senior Member of the global Association for Computing Machinery (ACM), and a contributing reviewer for the 2017 ACM/IEEE Report, Information Technology Curricula 2017. He is also an experienced online course developer, researcher, and has worked in the past within NZQA teams on new IT qualifications development.