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Computer Assisted Audit Tools and Techniques in Real World: CAATT's Applications and Approaches in Context

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  • ISEG – Lisboa School of Economics and Management. University of Lisbon

Abstract and Figures

Nowadays, Computer Aided Audit Tools (and Techniques’) support almost all audit processes concerning data extraction and analysis. These tools were firstly aimed to support financial auditing processes. However, their scope is beyond this, therefore, we present case studies and good practices in an academic context. Although in large auditing companies Audit Tools to do data extraction and analysis are very common and applied in several contexts, we realized that is not easy to find practical examples to teach this subject. Computer Science students’ and teachers usually use application manuals’, which are adequate to explore the tools itself, but they are not enough to do real data analysis: a “hands-on” approach is essential. Despite that, our students didn’t belong to any Computer Science graduation. CAATTs are good examples of software aimed to help auditors to analyze data, to explore and study distinct data sets in auditing processes. CAATTs represent an innovative approach to do data analysis, mainly because they allow the manipulation of very large data records with no significant added costs. Frequently, users have another difficulty: they are not able to adequate their data sets or to prepare their data in order to create useful input data, to do their analysis. In this paper, we present a proposal of extraction and data analysis, based on data sets obtained from a LMS platform (Learning Management System), a real data set that can be used to support students learning about CAATTs topic. Fundamental procedures to reach specific answers using a CAATT as a main tool to do data extraction and analysis are also referred. The context for this case study was an Information Technologies applied to Auditing course in a Master Degree graduation.
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Dynamic Publishers, Inc., USA
Isabel Pedrosa
1
, Carlos J. Costa
2
1
Instituto Superior de Contabilidade e Administração de Coimbra, ISCAC-IPC
Quinta Agrícola, Bencanta, 3040-361, Coimbra, Portugal
ipedrosa@iscac.pt
2
Information Science and Technology Department, ADETTI-IUL, ISCTE-IUL
Av.ª das Forças Armadas, 1649-026 Lisboa, Portugal
carlos.costa@iscte.pt
Abstract: Nowadays, Computer Aided Audit Tools (and
Techniques’) support almost all audit processes concerning data
extraction and analysis. These tools were firstly aimed to
support financial auditing processes. However, their scope is
beyond this, therefore, we present case studies and good
practices in an academic context. Although in large auditing
companies Audit Tools to do data extraction and analysis are
very common and applied in several contexts, we realized that is
not easy to find practical examples to teach this subject.
Computer Science students’ and teachers usually use application
manuals’, which are adequate to explore the tools itself, but they
are not enough to do real data analysis: a “hands-on” approach
is essential. Despite that, our students didn’t belong to any
Computer Science graduation. CAATTs are good examples of
software aimed to help auditors to analyze data, to explore and
study distinct data sets in auditing processes. CAATTs represent
an innovative approach to do data analysis, mainly because they
allow the manipulation of very large data records with no
significant added costs. Frequently, users have another
difficulty: they are not able to adequate their data sets or to
prepare their data in order to create useful input data, to do
their analysis. In this paper, we present a proposal of extraction
and data analysis, based on data sets obtained from a LMS
platform (Learning Management System), a real data set that
can be used to support students learning about CAATTs topic.
Fundamental procedures to reach specific answers using a
CAATT as a main tool to do data extraction and analysis are
also referred. The context for this case study was an Information
Technologies applied to Auditing course in a Master Degree
graduation..
Keywords: CAATTs, Auditing, Information Systems, Fraud
detection, E-Learning, Education, Knowledge discovery
I. Introduction
Nowadays, audit processes are supported by several
Computer Aided Audit Tools and Techniques, CAATTs.
CAATTs can be defined as “any use of technology to assist in
the completion of an audit. This (broad) definition would
include automated working papers and traditional word
processing applications” [2] or as “the use of certain software
that can be used by the auditor to perform audits and to
achieve the goals of auditing” [3]. CAATTs can be classified
“in four board categories: Data analysis software; Network
security evaluation software/utilities; Operating Systems and
Data Base Management System security evaluation
software/utilities; Software and code testing tools”, according
[3].
In this paper we are focused on Data Analysis software
tools which, among other main uses, can be helpful to detect
fraud evidences’: “evidence are gathered by interviews,
document reviewing, CAAT use” [6].
Several studies before state that mainly big companies have
tools to do data analysis in data sets with large amount of data
records [2], [7], [10]. Regardless of this usage and the fact that
this tools are common in organizational context, students are
not very used to work with it in a learning context [16]:
software full packages and licenses are expensive and is not
easy to use, create and prepared data sets. Sometimes, to take
advantage of the empowerment of these tools, there is the
need to do a complex data “makeover” process to allow the
use of a data analysis tool, so the most time consuming
operation can be data understanding and data preparation,
most similar to SPSS, a tool that Master students were already
familiar with. Other reason is software licenses: an education
version could be enough to explore some data sets, however
some vendors don’t have it or the process to achieve it is
complicated and too much bureaucratic. However, the Return
on that Investment worth it: mainly, those efforts had good
results among students.
II. ISCAC Contect about CAATTs
ISCAC, Instituto Superior de Contabilidade e Administração
de Coimbra, a Polytechnic Portuguese Accounting,
Administration and Management School, has a Master
Computer Assisted Audit Tools and Techniques in
Real World: CAATT's Applications and Approaches
in Context
International Journal of Computer Information Systems and Industrial Management Applications.
ISSN 2150-7988 Volume 4 (2012) pp. 161-168
© MIR Labs, www.mirlabs.net/ijcisim/index.html
Pedrosa and Costa
162
Graduation Degree on Audit for Public and Organizational
Enterprises that includes a subject focused on Information
Technologies Applied to Auditing (ICT on Auditing). The
first year was 2008/2009 and fifty-nine students attended the
first edition; thirty-six, during the second and twenty during
the third. Mostly, these students had their first contact with
CAATTs in this subject. The assessment was based on
students’ labor experiences, which included education and
training, city council, audit organizations and accounting
offices. Students made use of that experience concerning the
applications they chose to do data analysis using CAATTs
software. However, to introduce and to practice the CAATTs’
contents, we realized we need data sets to test the concepts, all
the students at the same time, conducing to real learning and
good practices that they can use, later, in their specific data
sets. A demo educational version was available for students
and teachers: classrooms and students’ laptops were included.
That educational version was limited to databases with less
than 10.000 records. The full CAATT version “is adequate
when data amount does not allow assuring audit process
without a technological aid: paper amount will be impossible
to manage” [3]. The course was taught using a Learning
Management System, MOODLE, Modular Object-Oriented
Dynamic Learning Environment, supported by several
forums, documents, students’ contributions and submissions.
As in [11] and [12], “the power of Internet in learning
management, trough an E-Learning platform, MOODLE, was
explored as a tool to motivate students and to improve their
final skills”. In order to promote CAATTs’ usage, this project
was also inspired in [13], where is stated that Audit Command
Language, ACL, an auditing software classified as Data
Analysis was used “to help students become more
“technologically adept””.
In many situations, original data sets are “prepared” just
before being imported on CAATTs. A spreadsheet can be
used on data “preparation”. However, using the CAATT, new
data fields can be added and calculated, taking other native or
non native fields this is also an important moment on the
process: it was considered a core operation to learn how to
program using functions specific to this CAATT. The
CAATT functions’ syntax was not similar to any other
language they learned before. Indeed, only few students that
had worked with DBase IV mentioned that the syntax was
easy to learn and to use and similar to Dbase. However, in
order to simplify this step, we’ve done the analogy with
similar situations using a spreadsheet, a tool that all the
students knew and felt comfortable with. Although formulas
syntaxes’ are different, the arguments and the main aim were
similar.
III. Literature Review
CAATTs can be defined as “any use of technology to assist in
the completion of an audit. This (broad) definition would
include automated working papers and traditional word
processing applications” [2] or as “the use of certain software
that can be used by the auditor to perform audits and to
achieve the goals of auditing” [3]. CAATs’ use is, nowadays,
accepted in data analysis mainly because it is “useful when
large amounts of data are involved or complex relationships
of related data need to be reviewed programmatically to glean
appropriate evidence from the aggregated data” [4]. They also
“increase the efficiency of the conclusions about data
analysis” [5]. Despite all the arguments look like so recent, we
can conclude in [5], that CAATTs are not a new concept or
approach: this study “An Audit Approach to Computers: A
new practice manual” was published in 1978 and the
paradigm is still changing as in [1]: GAS, Generalized Audit
Software, running on Web 2.0, using a collaborative approach
and several different profiles to improve Audit Software
usage.
CAATTs can be classified “in four board categories: Data
analysis software; Network security evaluation
software/utilities; OS and DBMS security evaluation
software/utilities; Software and code testing tools”, according
[3]. We can include IDEA, Interactive Data Extraction and
Analysis, and ACL, Audit Command Language, in the first
category and, among other main uses, can be tools used as
fraud detection: “evidence are gathered by interviews,
document reviewing and CAAT use” [6].
According [7], auditors accepted the CAATs, but more recent
studies [8] and [9], suggest that CAATTs acceptance is very
low, it is variable according to the companies and it is
dependent on the company dimension. In [9], the Technology
Acceptance Model, TAM, was used to study Information
Technologies' acceptance among auditors and “the results
from an experiment with experienced auditors suggest that
firms have the ability to influence the implementation of new
technology by using longer-term budget and evaluation
periods and by communicating the approval of remote
superiors regarding the software. In the absence of such firm
interventions, the individual characteristics of the auditor
(risk-aversion and perceptions of budgetary pressure)
determine implementation decisions”.
In [17], Unified Theory on Users Acceptance and Use on
Technology, UTAUT, studied the “examination of contextual
factors and individual characteristics affecting technology
implementation decisions in auditing”. We realized that the
contact that auditors (present or in a near future) can have with
the CAATTs during a Master Degree, could be relevant to
mitigate the individual characteristics, difficulties and
resistances referred before. Consequently, we decided to
define specific supporting materials to teach Data Analyses
Tools and to help teachers and students in this learning
approach. Other factors in CAATTs acceptance can be
relevant, whoever, they will be future work.
IV. Proposing a Process for data Analysis and
Exploration using a CAATT
To do an efficient Data Analysis, we can use the same
methodology defined as the main techniques on Data Mining
and add new steps:
• Extraction
Exploration,
Transformation
Model building and validation
Deployment.
Reuse.
Extraction is the process to obtain the data set, usually from
data center or a data server. On exploration and
transformation, the process “usually starts with data
preparation which may involve cleaning data, data
Computer Assisted Audit Tools and Techniques in Real Word: CAATT’s Applications and Approaches in Context
transformations, selecting subsets of records and - in case of
data sets with large numbers of variables ("fields") -
performing some preliminary feature selection operations to
bring the number of variables to a manageable range” [18].
Indeed, in this sentence the statement “to explore data” is used
with the meaning of “data understanding as an elementary
step to be succeeded”. Databases, attributes and data types are
not easy to figure out and previous background knowledge on
the area can be a core preparation on accomplishing the right
result [19]. This paper focus on exploration, transformation
and on CAATTs’ usage to achieve efficient results.
CAATTs to do Data Analysis may also be referred as a tool to
deployment and to reuse: after we answered to core questions
on the data, we can create macros to set some parameters and
to repeat all the process later with similar but new data.
V. Two Cases
Before we decided to create this Cases/Tutorials, we followed
other approaches, namely, in the first year of the course,
several IDEA data analyses applications were run during
classes and assessments: IDEA for Education in “MOODLE
data analysis in a Polytechnic School” and “Students grades
and absences control in a Secondary School”; IDEA for audit
organizations: “Inventory control”; and IDEA for Accounting
offices: “Studding patterns using SAFT-PT file”. As the
MOODLE data can become the most usable data (because of
the number of schools in Portugal that is currently using this
e-Learning platform, we choose to improve that case study
and to create a tutorial using data extraction from MOODLE
Server at ISCAC, data transformation using IDEA and data
analyses. The main aim was to study how could be possible to
improve MOODLE usage at ISCAC taking advantage of
students’ background as MOODLE users.
A. CASE 1: using CAAT Data Bases as a first approach to
use the tool
1) The very beginners: using IDEA Data Bases to a first
approach to CAATs
IDEA has an example Project, composed by eleven databases
IDEA Metadata files, IMD extension, with data sets for a first
approach to IDEA to beginners. Using that data set, our main
aims were:
present, gradually, IDEA tool with examples to guide the
students for real important data verification and
validation, sums, patterns and error/”missings” detection;
use native data sets (the ones distributed with the
software), easy to use, to learn basic functionalities on
IDEA;
to promote class/students interaction and knowledge
discovery based on students questions. The focus was on
new information and not on the tool.
2) Elementary aims about CAAT tool
To achieve successfully our plan we choose Sample-Bank
Transactions: it has only four attributes, it was simple, all the
students had background on this subject, and, as a
consequence on that, the focus was not on understanding
complex data but on the right questions and processes we
wanted the students to learn.
Sample-Bank Transactions has the attributes described in
Table 1, 1166 records and some of those records are in Figure
1.
Table 1. Bank Transactions Native attributes
Name Description Comments
TransID
Transaction
number
This must be unique (primary
key)
Type Cheque or
Deposit
It must be filled in
Date
Day for the
transaction
It must be filled in with a data
value
Amount Transaction
amount
Depending on transaction
Type, the amount must be
positive for Deposits (Credits)
and negative for Cheques
(Debits)
Figure 1. Bank Transactions: first records
Using Sample-Bank Transactions, students were focused on
the answers to the following questions:
Are there any null Deposits or null Cheques? If yes, in
which dates did it happened? Are there other important
transactions during the same reference days?
Are there any duplicated transactions?
Are there negative Deposits and/or positive Cheques?
What are the dates? Are there other transactions during
those days?
Which are the transactions with the highest values? Can
we find a justification about it?
Most of the previous questions were stated by students: after,
the focused on learning how to answer the questions with
IDEA.
3) IDEA functionalities
To answer last questions, we realized that a “hands on”
approach could the most effective. We just introduced briefly
the IDEA tool, explored the main aspects in the application
main window, as in Figure 2, and we started to answer the
questions stated just before.
They learned how to:
Use Field Statistics to find the null values and store it on a
new database named null transactions: they found 4
Deposits in 17-02-2008, 07-05-2008, 26-07-2008 and
163
Pedrosa and Costa
01-10-2008. Using the functionality Join Databases to
cross null transactions with Sample-Bank Transactions
they discovered all the transaction done concerning the
referred days.
Use Duplicate Key Detection to detect the duplicated
transactions: only one, the TransID 1505, referring to two
Cheques -11.359,36 and -9.758,84.
Use Summarization to group records by Type, create two
new databases one for Deposits (769 records) and the
other to Cheques (397 records). Criteria and Equation
Editor window were introduced to explain how to filter
the negative or positive amounts in each database. No
irregular records were found.
Use Top Records Extraction to check the top 5
extractions: a total of 7.826.192,90, mainly on 2
transactions 3.899.925,00 (TransID 125) and
3.536.451,99 (TransID 420).
After this exercise, planned for 2 hours, the students felt much
more confident with this tool and the aims that we established
just before. We proceeded, analyzing the databases
Sample-Detailed Sale and Sample-Sales Representatives. To
explore the formulas and to use IDEA as a tool to discover
information in a specific and real context, we decided to use
MOODLE data.
Figure 2. Main IDEA Window Application
B. Case 2: LMS data analysis in a Polytechnic Institute
ISCAC is a Portuguese Accounting, Administration and
Management School, focus on Accounting, Administration,
Management, Informatics and Law (Solicitors). MOODLE is
being used as an e-learning platform since September 2006.
Considering the period from the beginning until August 2007,
two scientific working papers were published about the usage
of MOODLE among students and teachers at ISCAC. In [14]
and [15] it is possible to find a previous data analysis, made
with Access and reporting to the first year of MOODLE usage
at ISCAC, 2006-2007considering all the interactions between
students and the platform, the contents available on courses as
fora, chats, tests as interactive tools with course community,
files and links. However, after this work no new studies about
MOODLE courses, users and users-interaction with this
platform were developed. The first time we use a similar data
set was on the Master Course 1st edition. After it, we realize
that this data set could join all the topics that we should focus
on a course like this and, afterwards, this approach could be
“exported” to other schools.
IDEA analysis about MOODLE main aims’ is to explore
several IDEA functionalities using real data and a familiar
application context for all the students, using IDEA as the
main tool for data extraction and analysis. As in a data mining
tool, we focused on data understand, to define the questions
considering the set we have, prepare and transform data
attributes to have a real answer. During all this steps we
reviewed some aspects from our 3.1 example and have the
opportunity to learn several new others. Adding macros to this
tutorial in order to “redo” all this analysis with new data
extracted from MOODLE, was our last conquer.
1) Data description
In this study, we defined the following topics to do MOODLE
usage analysis: courses created on MOODLE (courses table),
Categories (courses_cats table), forum usage (forum_discuss
table) and posts related to the forums created (forum_posts
table) and users registered on the platform (users table). The
Educational IDEA 8.04 had 10000 registers as limit for each
database and this was the main reason why we didn’t get any
other indicators. However, we will refer that on topic
“conclusions and future work”: indicators such as courses and
user activities and MOODLE platform logs are important data
to know in a future work. For instance, we realized that the
number of invalid logs on the system was very high and one
explanation can be the new users’ creation process: the can
create their own account and choose their password, even if
they don’t belong to ISCAC. Sometimes, if they forgot the
password, it is possible to create another user: the only
criterion is that the email address can’t exist already in
MOODLE users’ data base. However, this procedure can
represent a lack of security and a risk to the organization. In
Table 2, we have each database description with all the
attributes and, in Figure 3, a COURSES_CAT database view.
The data set was extracted from version 1.9+ .
Table 2. MOODLE Databases
Name Contents #reco
rds
Data
preparation
COURSES
All MOODLE platform
courses, each with this
fields: ID (N), Category
(N), Sortorder (N),
Password (C),
FullName(C),
Shortname(C),
Summary(C), Format
(social, topics) (C),
StartDate(N), Visible
(N), Timecreated (N),
Timemodified (N)0.24
395 Date and time in
Unix Timestamp
format: number
of seconds since
January, 1, 1970.
New fields
created to store
this data in IDEA
Date and Time
format
COUR
SES_C
AT
All MOODLE platform
categories: ID (N),
Name (C), Description
(C), Parent (N),
Sortorder (N),
Coursecount(N),
Visible(C),
Timemodified (C),
Depth (C), Path (C),
Theme (C).
73 Beyond date and
time in Unix
timestamp
format,
understanding
the hierarchical
categories and
dead with paths
FORU All MOODLE platform 3113 New virtual date
164
M_DIS
CUSS
top discussions: ID (N),
Course (N), Forum (N),
Name (C), Firstpost (C),
UserID(N), GroupID(C),
Assessed (C),
Timemodified (C),
TimeStart (C), Timeend
(C), NewID (C)
type fields
created to store
date and time
converted from
Unix timestamp
format
FORU
M_POS
TS
All MOODLE platform
posts in every forum
discussions: ID (N),
Discussion (N), Parent
(N), UserID (N), Created
(C), Modified (C),
Mailed (C), Subject (C),
Message (C), Format
(C), Attachment (C),
TotalScore (C),
MailNow (C)
6038
New virtual date
type fields
created to store
date and time
converted from
Unix timestamp
format to IDEA
USERS
All registered MOODLE
platform users since the
beginning: ID (N), Auth
(C), Confimed (N),
Deleted (N), Username
(C), Firstname (C),
Lastname (C), Email
(C), Emailstop (N),
Firstaccess (N),
LastAccess (N),
LastLogin (N),
Currentlogin (N), LastIP
(C), Mailformat (N),
Maildigest (N),
Maildispay (N).
4101
Last IP was only
with 14
characters: all
the ones which
had 3 digits for
each network
were converted
in mumbers.
Also Date and
Time format as
before.
The attributes, considering the example of database COUSES
on Figure 3, were hard to understand for the students before
the first data transformation, mainly in what concerts to the
date/time values: records ID=2 and ID=3 have
STARTDATE=11498076000 and TIMECREATED
=1149792990, which are unusual values, but possible and
correct as we will see later. To understand the courses
hierarchical places, we need to join this table with
COURSES_CAT, to find the path attribute and the name of
the category.
Figure 3. COUSES database
2) Questions about MOODLE data
As in “Data Preparation” column in previous Table 2, the
need to do data transformation was detected in order to be
possible to answer some important questions:
number of registered (new) users each year (academic
year);
number of users with @iscac (teachers) and
(@alumni.iscac (students) emails to find a pattern on users
account and the connection to email servers at ISCAC.
Only teachers have that email server, so it can be a
distinction between teachers and students, however, a
teacher can register himself with another email address.
NOTE: at MOODLE, the same user can act as teacher in
some courses and as student in other context. With these
databases, there is no way to know if someone has a role as
teacher or as student in a specific course because there is
no way of join users and courses;
Non-confirmed and deleted users;
Users last logins: time last since last login, users’ last IP
access;
Top users, categories and courses: in forum discussion and
in forum posts.
Patterns for forum activities and forum discussions
(morning, afternoon, night, weekends);
After data analysis, several new data fields were add to store
important information and IDEA functions were applied to
create those new fields.
3) @Functions and IDEA: data transformation and some
brief examples
All these time calculus are based on the principle that the day
was stored in Unix timestamp as the number of seconds since
January, 1, 1970. All these concepts were new for these
students as they are present (or future auditors) but not
computer science experts. Also all the concepts about using
formulas on IDEA were novel: only one student which was
expert on Dbase IV felt comfortable in this new context. Most
students were also experts on Excel so, another approach
could be: to do all the data transformation on Excel and then to
import it at IDEA. However, that methodology was not
followed because to be expert using IDEA formulas was one
of our previous aims.
Some new fields were created using the menu Data>Field
Manipulation and the option Append to compute data in a
way that could be easiest to answer questions evolving dates
in last topic:
Date: all the attributes mentioned as date types (however,
on IDEA and until then, this attributes were numbers
type). New attributes (or fields) created were
FIRSTACCESS_DIA, LASTACCESS_DIA,
LASTLOGIN_DIA, CURRENTLOGIN_DIA. New field
LASTLOGIN_DIA (and all the other new fields above),
data type will be Virtual Date and the formula should do
the conversion between the number of seconds that we
have, as explained before, and we present it using IDEA.
A possible formula to create a new field new virtual data
beginning with, for instance, LASTLOGIN Unix
Timestamp, 1246669569 seconds, should be converted to
Computer Assisted Audit Tools and Techniques in Real Word: CAATT’s Applications and Approaches in Context
165
Pedrosa and Costa 166
04 Jul 2009, using this formula on Equation Editor like
stated on figure 3:
@if(LASTLOGIN
<>0;@DaystoD(LASTLOGIN/(24*60*60)+DtoDays(@Ctod
(“01-01-1970”;”DD-MM-YYYY”))))
With this new fields will be possible to extract month, year
and to do a Pivot Table presenting new users for each
academic year. We suggest the students to check their formula
results on http://www.timestampconvert.net/.
Figure 4. IDEA Equation Editor and Data Field Manipulation
Time: like before, on date, several new time fields were
created, converting the timestamp value to
time.FIRSTACCESS_HORA, LASTACCESS_HORA,
LASTLOGIN_HORA, CURRENTLOGIN_HORA. With
this new attributes we can have a usage platform pattern
taking the time of the day. For instance, if
TIMECREATED =1149792990, this means 18:56:30.
Forum discussion and users: The process to calculate date
and time for several accesses is the same as defined
before, but, in this case, we want to obtain a graph with the
top ten users that posted more on forums’. On forum-post
database, we must summarize data by ID (user ID), using
the option Summarization, save the result in a new
Database, Total forum discussion by users and join it with
Users data to obtain all the data about users, in order to
present it, for instance, as a dynamic graph, as in Figure 5.
Figure 4. Users discussion top ten (all forum). Note that the
top user has almost 40% of all top ten-forum discussions
4) Answers on MOODLE data set
After the data transformation and some new attributes
creation, we can answer to the questions we stated before,
using several IDEA functionalities and, in some situations,
new attributes and calculus:
number of registered (new) users each year (academic
year): taking the USERS table, we created a new attribute,
year. After, we used Summarization to group all the
records (# 4101) by year of register. The result was 725
records in 2006, 1847 in 2007, 1203 in 2008 and, until
3-07-2009 (our last record on the USERS table), we have
304 for 2009 year.
• number of users with @iscac or @alumni.iscac emails.
A new data field was created only to store if the email had
the pattern @iscac.pt, with the formula
@Match(EMAIL;"@iscac.pt"), and another to the string
“@alumni.iscac”. The result was 134 records for the first
and 453 for the last (only 11%). Therefore, we can
conclude that most teachers use their ISCAC mail account
but few students are connected with their institutional
mail. This can mean that students don’t check often their
institutional mail –probably, they don’t receive any
Students Office messages or any other institutional ones.
A solution was to do a compulsory update of all the users,
with the institutional.
• Non-confirmed and deleted users: criteria confirmed=0
and create another database with that 25 records. All of
them were Erasmus Students. As MOODLE is only
available in Portuguese, this could be an important
disadvantage. Therefore, as the school intend to proceed
with its internationalization, to receive more students from
abroad to attend graduation and master courses, there must
be a MOODLE English version. Using the criteria
deleted=1, there are 237 deleted users. All of them have a
suffix with a timestamp in the email account
5) Discussion on MOODLE data evaluation
We could conclude from all MOODLE data:
number of registered (new) users each year (academic
year): this number has a significant grow in 2008. Many
new courses were created during that year. We can predict
that this number won’t be higher in the next years:
although there are new master and post-graduate courses,
many of the attendees were already students at ISCAC and
the MOODLE account was not erased;
number of users with @iscac emails: There are 134
registers and the number has no significant grow since
2007/2008. Since September 2008 there are only 16 new
registers. The main justification is the registration process
for almost all the teachers in the first years of the project
and there are almost no new teachers’ admissions recently.
Non-confirmed and deleted users: 237 deleted users and
25 non-confirmed users. Analyzing this last data, we
found that these are new records, with non Portuguese
names, so., probably, it could mean that, when we receive
foreign students, we must create other solutions for this
students.;
Users last logins: time last since last login: calculating the
number of days since last login until the data reference
day, and then doing a first stratification using IDEA, we
realized that 1986 users were on the platform at least on
the previous 50 days. Doing a shorter stratification, 1830
users were on MOODLE at least on 10 last days. We must
exclude also users that logged only once in MOODLE,
682: in this case, LASTLOGIN=0, 17% from total users.
The users that left ISCAC were not erased from Server
databases, which create a larger number for the ones who
didn’t log on for more than 200 days (since the beginning
of this scholar year),
Users last IP access: considering last logins and knowing
which are the IP addresses belonging to ISCAC intranet, is
possible to state that only 104 users logged on from
ISCAC last time. However, to define if users use
MOODLE, mostly out of school or preferentially “inside”,
we must have other details: we can forget that we have
only each MOODLE user last access and to answer the
previous question we must have all the logs. Again, the
IDEA version creates the limit to our analysis, but we can
define that the study will concern only with users’
activities during last four months.
VI. CONCLUSION and Future Work
CAATTs are stated as important software in what concerns
to Auditing and Data Analysis However, there are some
problems when the focus is on CAATTs learning during a
master course: real and interesting data sets with no specific
additional background knowledge for the students are not
easy to find. However, firstly, the focus should be on the data
and not on the tool: students must understand the data and
define the opportune questions to the data analysis and that
process must be completely independent from the tool. A
simple data set was taken to do the first approach to this
method. After, we studied a real data set, to improve students
competences on CAATTs as a data Analysis Tool and to
understand the patterns concerning to MOODLE usage.
Several indicators were presented using IDEA as a data
extraction and analysis tool. The last focus was on using a
CAATT, IDEA, in Information Technologies on Auditing
course in a Master Degree graduation. It was relevant to use
data that students could easy understand and manipulate.
Additionally, it was important to define new approaches after
data analysis mainly to improve MOODLE usage. We
realized several new ideas to increase MOODLE users range
and, concerning the application, doing that data analysis and
report it with IDEA was easy to understand and with no
significant time spare.
In a near future we intend to systematize this study with
macros to reuse the analysis, to check the main results with
new data, with some parameters to define the filters. To spare
this practices that we followed, we can create partnerships
with other schools to explain the method and analyze other
new cases.
Many data on MOODLE can also be used as real data set to
explore other CAATTs functionalities: data sampling is a
good method to check the patterns on non succeeded logs
attempts.
Another topic could be to study SAFT-PT, Standard Audit
File for Taxes Purposes Portugal, using tests and macros
able to support data check, to cover all the topics not available
on commercial analyzers. In this case, we should focus mostly
on taxes issues; define a transversal study team, with
specialists on taxes, taxes inspectors and supervisors. As this
is a very important subject, in Portugal, probably the
analyzers will be improved very soon.
Acknowledgment
This work was partially supported by FCT MCTES. Ref.ª
SFRH/BD/49994/2009. We want to acknowledge all the
students that were involved on ICT for Auditing classes,
during this past three years, whom, with their feedback and
suggestions on our approaches, helped to create this case
study.
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Author Biographies
Isabel Pedrosa teaches in the Mathematics and Computer
Science Department of Instituto Superior de Contabilidade
e Administração de Coimbra, ISCAC, since 2000. She is
Master in Computer Science and has a Degree in
Computer Engineering by Coimbra University, 1994. She
teaches since 1993. Now, she is preparing her thesis about
the main topic CAATTs, in Doctoral Program in Science
and Information Technologies at ISCTE - IUL. She
coordinates courses on Information Technologies on
Auditing in Master Graduation Courses.
Carlos J. Costa teaches in the Computer Science
Department of ISCTE, since 1993. He also taught in the
Portuguese Catholic University, Portuguese Open
University, IPAM and ISCEM. He worked in a IT
consulting firm and in an Investment Bank. He has a Ph.D.
in Computer Science - Information Systems and Data
Bases, ISCTE, 2002, MsC in Corporate Strategy, ISCTE,
1995, and Degree in Business Administration from the
Faculty of Economics and Business of the Portuguese
Catholic University (Lisbon). 1990
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