On Possibility of Automatic Generation of Data Files and their Use in

Tasks of Descriptive Statistics

Mikul

s Gangur

and V

aclav Sova Martinovsk

Department of Economics and Quantitative Methods, Faculty of Economics, University of West Bohemia,

Univerzitn

ı 8, Plze

n, Czech Republic

Department of Business Administration and Management, Faculty of Economics, University of West Bohemia,

Univerzitnı 8, Plzen, Czech Republic

Keywords:

Quiz, Question with Data File, Descriptive Statistics, Automatic Generation, Moodle XML, LMS Moodle.

Abstract:

The contribution deals with the use of automatic generation of parameterized tasks and the inclusion of au-

tomatically generated data ﬁles as useful feature of generator. This functionality of the generator is used,

for example, in tasks of statistical data analysis. The contribution shows usage when generating a descrip-

tive statistics tasks. The basic principles of preparation of statistical data in both sample and population ﬁles

are described. Methods of integrating these data into the generated task for different output formats are also

explained. Selected group of tasks illustrating the outputs of the application of the described methods are

presented. The use of a generator for building a Question bank in LMS Moodle is shown as well as the prepa-

ration of writing tests. At the same time, various data ﬁles storage options and subsequent use with regard to

their lifetime are discussed. In the following, a new solution for the implementation of the automatic generator

of parameterized tasks in cloud is introduced. This approach will allow the involvement of more users of the

automatic generator.

1 INTRODUCTION

Automatic generation of parameterized task can gen-

erate a unique problem for each student. Students can

practise one task with different input parameters. It

helps them to understand the problem from different

points of view. In the article (Gangur, 2011a) an auto-

matic generator of questions and answers is described

including the architecture of the generator. The uni-

versal principle of the automatic parameterized ques-

tions generation was explained.

The generator generates variable parameters in the

task text. The values of these parameters are gener-

ated as a random value according to speciﬁed con-

straints speciﬁc to each task. The output of this gen-

erator module is a complete deﬁnition of the task in

universal XML format. Subsequent XSL transforms

the task into different output formats, depending on

the XSL template used. The most commonly used

formats are LaTeX and Moodle XML. The ﬁrst one

allows you to create a PDF ﬁle with the generated task

texts in one version for students and another version

for teachers with tasks answers for correction. The

second format is used to import the generated tasks

into the Question Bank in LMS Moodle. In this way,

a large number of variants can be generated from each

task.

Based on this principle, it is possible in LMS

Moodle to create a unique task for each student and

to assemble a unique quiz from multiple tasks. This

approach allows you to practice the problem on differ-

ent input data. This method helps students understand

the principles of problem solving instead of ineffec-

tive memorizing the solution process.

A speciﬁc approach is applied when generating

tasks from the ﬁeld of statistics and statistical pro-

cessing as well as an analysis of the data. In this case

just the statistical data are the key object. Creating the

data according to the required parameters, storing the

data in the ﬁle and inserting the information about the

ﬁle must be an integrated part of the generation pro-

cess, as well as the information about the data them-

selves and then storing them in the output description

of the generated task.

This contribution introduces the using of a new

feature of the question that can be processed by the

generator, i.e. automatically generated parameterized

data, i.e. data ﬁle. This feature and usage is described

and explained on an example of descriptive statistics

tasks in which sample and population data ﬁles are

446

Gangur, M. and Martinovský, V.

On Possibility of Automatic Generation of Data Files and their Use in Tasks of Descriptive Statistics.

DOI: 10.5220/0007742504460453

In Proceedings of the 11th International Conference on Computer Supported Education (CSEDU 2019), pages 446-453

ISBN: 978-989-758-367-4

used. The generation of sample data ﬁles is described

in this article as well as implementing an automatic

generator in cloud is explained. In the same described

way, it is possible to generate sample data ﬁles for

other statistical analysis tasks (statistical inferences).

The reminder is structured as following. The sec-

tion 2 provides an overview of the literature on the

problem of automatic generation of tasks. Section 3

describes methods for generating statistical data and

their integrating into the generated tasks. In this part

the subsection 3.2 explains the process of sample data

generation. Section 4 presents the results of the gen-

erating process in the form of examples of generated

tasks in different output formats. A brief description

of the generator implementation in cloud is also part

of this section. In section 5 some disadvantages and

limitations of proposed solution are discussed. Fi-

nally in section 6 we evaluate used methods and we

state conclusion.

2 RELATED WORK

The problem of automatic generation of questions

has been dealt with in some speciﬁc domains. In

(Cristea and Tuduce, 2005) the questions were gener-

ated in the area of electrical circuit analysis; another

study focuses on question generation in the domain of

the object-oriented programming (Hsiao et al., 2008).

The problem of math/science tasks generation is ad-

dressed in (Ugurdag et al., 2009). The authors gen-

erate multiple choice questions in the process of im-

age modiﬁcation by means of a developed graphic

tool. In this case new values of parameterized ques-

tions are not generated automatically. Authors (Zhang

et al., 2017) solve the problem of automatically gen-

erated questions with images. They generate visually

grounded questions with various types for the same

visual input.

Test questions generated automatically directly

from chosen text can be seen as a special issue in this

direction (Zeng et al., 2013). Contributions dealing

with this topic concentrate mainly on generating ques-

tions from the English texts (Sung et al., 2007), (Afzal

and Mitkov, 2014), (Shah et al., 2017), from knowl-

edge database (Rocha and Zucker, 2018) or from web

(Cubric and Tosic, 2011) by semantic web technolo-

gies using domain ontologies. Automated creation of

adaptive tests with regard to the level of the knowl-

edge of individual students is an independent ﬁeld in

which intensive research is being carried out (Kapusta

et al., 2010).

Except for (Zhang et al., 2017), the above men-

tioned systems generate only text questions in a ba-

sic question type with numerical, short or multichoice

answers. In the same way, the data types of the in-

put variable parameters are of numeric types or text

types. None of these systems provide the possibility

of attached data ﬁles that are connected with the dy-

namically generated content of questions. The major-

ity of the above stated tools as well as other examined

instruments generate only online web tests.

In this contribution such features of the question

generator are introduced. The process of generating

dynamical sample data dependent on other generated

content of questions is explained as well as the im-

plementation of automatic generator in the cloud en-

vironment.

3 METHODOLOGY

The basic tool for generating a large number of tasks

is the automatic parameterized task generator devel-

oped by the authors of this contribution (Gangur,

2011a). This system allows you to generate tasks with

different variables in the input task text, and is able

to generate and draw the mathematical text (Gangur,

2011b). In addition to the automatically generated

input values, the generator is able to insert images

dynamically generated by the parameters in the in-

put text (Plevn

y and Gangur, 2016) eventually data

ﬁles that match the task entry text (Gangur, 2018).

The generator is used to generate tasks across a range

of areas, Financial Mathematics, Statistics, Statisti-

cal Data Processing, but also in Management Science

(Plevn

y and Gladavsk

a, 2014).

The process of transforming the generated ques-

tions into the demanded output format consists of two

basic steps:

1. The questions and correct answers are generated

in the selected software (Matlab, Mathematica,

etc.). The description of the generated questions

and all their parameters including the answers in

the universal XML structure are thus obtained.

2. The XML description of questions is used as the

input to the XSLT process. In this phase the

questions are transformed into the required ﬁnal

format according to the XSL template (Holzner,

2002).

Below we will describe the use of the dynamically

generated data ﬁles that are integrated as part of the

generated task.

On Possibility of Automatic Generation of Data Files and their Use in Tasks of Descriptive Statistics

447

3.1 Statistical Data Files

When generating statistical tasks, the generator’s fea-

tures are to create dynamic images and, in particular,

statistical data ﬁles unique for each student. Next, we

distinguish two types of data ﬁles:

• a temporary sample statistical ﬁle, which is part

of each variation of the generated task,

• stable population statistical ﬁle that is stored on

the available storage in only one version for all

the generated tasks.

The difference between the two ﬁle types can be

identiﬁed from two views. From the statistical point

of view, this is the difference between the sample data

ﬁle and the population ﬁle, as a key point in the pro-

cessing of statistical data and the use of statistical

inference and hypothesis testing the population pa-

rameters. The various generated tasks then show this

difference, and the more generated variants with the

sample ﬁle allow you to calculate the characteristics

of the various sample ﬁles derived from only one pop-

ulation data ﬁle.

In case of the second, rather a technical problem,

the population ﬁle is one permanent ﬁle stored in a

dedicated open storage. This one is permanently ac-

cessible to all users and is exploited by various gener-

ated tasks, and a URL link to the ﬁle in the storage is

placed in the input task text. In case of a sample ﬁle,

we generate a temporary data ﬁle as part of the task

deﬁnition. It is necessary to treat access to these data

for different output formats of the generated task.

• In case of the MoodleXML (MoodleXML, 2014)

output format, a generated task is saved in the

Question Bank of LMS Moodle (Moodle, 2010)

and the sample data ﬁle that is part of the task def-

inition is stored in the internal repository of LMS

Moodle. A URL link to this ﬁle is generated as

part of task text (Gangur, 2018).

• If the output of the generated task is in the for-

mat LaTeX (LaTeX, 2013) and then a PDF ﬁle,

the link to the data ﬁle is again a part of the task

text, but in this case the URL is a reference to ded-

icated accessible storage for the data ﬁles. These

ﬁles are again divided into two types with respect

to their lifetime. The ﬁrst ones are the data ﬁles

that are part of the assignment of, for example,

exam tests. The lifetime of such data is only un-

til the test is entered. The second type of the ﬁle

is the data referenced from the input, for exam-

ple, sample tests that are part of various syllables,

e-courses, etc. The lifetime of such ﬁles is much

longer, theoretically unlimited.

3.2 Population and Sample Statistical

Data File Generation

The process of generating data ﬁles is shown on a

group of tasks that are based on one selected popu-

lation ﬁle. The source data of the example are taken

from a population ﬁle with the data from the last pop-

ulation census in the Czech Republic in 2011. The

data ﬁle contains 5894 records about municipalities

in 14 counties. Each county is made up of districts.

In the data the information are stored in variables ac-

cording Table 1. Each municipality is identiﬁed by its

name, district name and county name. Then the in-

formation about population of various groups in mu-

nicipality is stored. The monitored groups are total

population, marital status groups and age groups. The

population of each group is stored as total and sepa-

rately for male and for female.

The automatic generator works with the data

structure of the ﬁle according to a particular generated

task. In case of tasks that work with other population

the task solver of the automatic generator will con-

form to the structure of the used population ﬁles due

to the implemented task algorithm.

The population data ﬁle is too large and it is not

attached to every generated task. This particular ﬁle

is stored in cloud and the generating process creates

URL reference to this ﬁle in task text. In the generated

tasks the calculation of the population parameters is

demanded. The average and standard deviation of dif-

ferent population groups (see Table 1) are calculated

per municipality or per district in each county. For

example the average and standard deviation of male

per municipality are calculated, or the average age of

single female per district is calculated. These tasks

and their solutions are the same for every student.

The calculation of sample data characteristics is

more useful for practicing students. In these tasks

the same characteristics are calculated as the param-

eters of the population ﬁle (see previous paragraph).

The sample ﬁle creation is one of the most important

things. A unique sample ﬁle is generated for every

task and each student calculates the demanded statis-

tics for different sample ﬁle of one source population

ﬁle. Many variants of one task allow to recalculate

statistics (averages and deviations) for different sam-

ple data. This possibility is very useful when tracking

iteration of statistics to population parameter values.

The key procedure select

data() creates sample

data from the population data according to the list of

counties, demanded variables and demanded number

of records of each county. The procedure randomly

selects municipality records using generator of uni-

formly distributed pseudorandom integers in scale of

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448

Table 1: Variables in population census.

Municipality name District name County name

Population

total male female

marital status single marital status married marital status divorced

marital status widowed

aged 0-14 aged 15-19 aged 20-29

aged 30-39 aged 40-49 aged 50-59

aged 60-64 aged 65-69 aged 70-79

aged 80+

municipality number for every county. After this ran-

dom selection we need to run the post-process that

modiﬁes the sample data ﬁle according to the follow-

ing requirements:

• Each district in each county is represented by at

least two municipalities in the sample data.

• The sample data ﬁle contains the municipality

with the largest and least populous population.

Described in this way, a large number of sample

ﬁles can be derived from one population ﬁle by ran-

dom selection. As a result, each student solves his/her

own assignments, learns the principles of the solu-

tion and practices the basic algorithms of descriptive

statistics.

The principle used to integrate a data ﬁle into a

task deﬁnition allows you to create, by using built-in

functions of the used software, the sample data of the

desired statistical distribution and other (for example

extreme values and outliers) properties to practice ex-

ploration data analysis.

4 RESULTS

The described features of the automatic generator of

parameterized tasks extend the use of the generator in

the ﬁeld of statistical tasks and, in particular, statisti-

cal data analysis tasks. For the course Statistical Data

Analysis, 19 tasks in the ﬁeld of descriptive statis-

tics were created and 100 variants were generated for

each of these tasks, which were inserted into the LMS

Moodle Question Bank. In total, 1900 unique tasks

were generated for the 150 students enrolled in the

course. Other 19 tasks from the total of 211 tasks in

the course were created for the other areas, two sam-

ple tests, one sample tests, ANOVA, exploration data

analyses, normality and homoscedasticity testing.

The Figure 1 show the generated tasks in which

the parameters of population data are calculated. A

link to a population census ﬁle can be found at the be-

ginning of the task text. In ﬁgure 1a, the task is gen-

erated in the Moodle XML format and inserted into

the Question bank of LMS Moodle. Blank ﬁelds al-

low you to enter the required parameters values. The

Figure 1b depicts PDF format of the task generated

from the same pattern as the above described task in

Moodle. Such a task can be printed as a written test.

The Figure 2 depicts the different output formats

of the generated task in which characteristics of the

sample require to be calculated. The sample data are

randomly generated from the population data ﬁle ac-

cording to the procedure described in subsection 3.2.

In both outputs in LMS Moodle or in the PDF ﬁle, you

can ﬁnd at the bottom of the task the link to the sam-

ple ﬁle stored in the Moodle LMS repository or in the

reserved temporary storage facility. This differs from

the type of tasks in Figure 1. While the population

ﬁle is located in a single location and the data ﬁle is

common to all variants generated (the same URL is

given in the text of all variants of the given task type),

in case of sample ﬁle in the task in Figure 2, the gen-

erated data ﬁles are unique for each task variant, and

therefore, a hyperlink that is embedded in the input

text is another URL in each task variant.

Links to data ﬁles can be used when working on-

line in LMS Moodle to download a ﬁle and open it in

MS Excel. In case of a PDF ﬁle, the data can be ob-

tained directly only when the PDF ﬁle is opened on-

line. When writing a workload variation, it is neces-

sary to insert the displayed URL into the web browser.

For this reason, in the PDF variant of the URL, the

links to ﬁles are referenced throughout the full URL

speciﬁcation.

A major innovation of the generator is its imple-

mentation in cloud. This solution extends the capabil-

ity of using it for a larger number of users without the

need for a desktop installation of the generator and

support software Matlab (MathWorks, 2013).

On Possibility of Automatic Generation of Data Files and their Use in Tasks of Descriptive Statistics

449

(a) Output as question in Moodle (b) Output as question in PDF

Figure 1: Generator outputs of question with population data ﬁle.

(a) Output as question in Moodle (b) Output as question in PDF

Figure 2: Generator outputs of question with sample data ﬁle.

4.1 Automatic Generator in Cloud

One of the drawbacks of the existing solution is the

dependency on a particular computer where Matlab

is installed. That also means that besides the source

code of the generator and the source code of all tasks,

every user needs a valid Matlab license. If we con-

sider that, this solution is not cost-effective and also

very hard to conﬁgure and run. Wider usage is there-

fore very limited.

The proposed cloud solution removes all above

mentioned barriers. Since it is available in a software-

as-a-service (SaaS) principle, the end user needs only

a computer with a regular web browser (Martinovsk

and Plevn

y, 2018). No additional software or license

is needed. The usage is not limited by hardware (it

works on mobile or desktop), software (no special

programs needed), user skills (intuitive web GUI) or

physical location.

This solution is also cost-effective. It can use all

advantages of cloud computing pay-as-you-use model

and with ﬂexible scaling, the actual cost may vary ac-

cording to the anticipated trafﬁc. It also enables the

possibility of extended functionality like sharing tests

between users, collaborative work etc.

The backend of all the application is based on the

already proposed generator in Matlab. To achieve in-

dependence on a particular computer or licence the

existing solution was extended by a set of functions

for communication with external programmes and the

whole project was translated into an autonomously

executable programme by means of Matlab Compiler.

It is necessary to have a licence for only one computer

on which the translation of the source ﬁles is under-

way (not necessarily in cloud).

The frontend is formed by an application created

in the PHP script language extended with the Slim

framework. The interface is based on a fully respon-

sive template using Bootstrap framework and jQuery

library.

Thanks to these components it was possible to

create a user friendly interface which can be oper-

ated comfortably without any further adjustments on

a wide range of devices (see ﬁgure 3).

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450

Figure 3: Application interface on a desktop.

5 DISCUSSION

The described solution also has its disadvantages and

limitations. The disadvantage of statistical data anal-

ysis tasks may be seen in the different iterative al-

gorithms by which various statistical data processing

systems solve the given problems. The resulting set

values of a task solution that Moodle checks with the

speciﬁed accuracy may then vary if students use soft-

ware other than the automatic generator that the Mat-

lab system uses in the described solution. Most often

the results differ when calculating the p-value, which

is often an important part of solving in statistical data

analysis tasks. A partial solution to this problem is

the very accurate work with the set tolerance for the

entered solution values. Small tolerance leads to dif-

ferent results when the LMS does not accept the val-

ues that are calculated by the correct procedure. Big

tolerance, on the contrary, allows acceptance of val-

ues, that the student has reached in the wrong way,

and like this, the correctness of the wrong algorithm is

conﬁrmed. Additionally, using such imprecise meth-

ods for further calculations can lead to an increase in

errors and to the rejection of other task resolving re-

sults.

Another limitation when working with the popu-

lation and sample data ﬁle is the size of the source

data in the population ﬁle. The automatic generator is

built on the encapsulated module to generate a single

task, and then uses this module to generate various

tasks. In case of creating a Question bank in LMS,

Moodle the system generates a given number of vari-

ants of the same task, and a test of different tasks is

created when creating a PDF test. This solution, in

which the module always loads the entire population

ﬁle over the network, is very lengthy and the gener-

ation of bigger number of tasks is a time-consuming

procedure. For this reason, it is advisable to place the

data of the population ﬁle in memory once only and to

inform the basic module about their loading and loca-

tion. The system does not have to retrieve population

data over the network when it is reused.

6 CONCLUSIONS

The described methods allow practical application in

the courses of statistics and statistical data processing.

The advantage is the possibility of creating a large

number of different tasks that can be stored in the e-

learning system repositories along with the generated

training data. A unique assignment for each student

supports the student’s own work and the possibility

of different variants of principally the same task re-

quires understanding of the principles of solving the

assigned tasks instead of ineffective memorizing pro-

cedures.

In the contribution, only generator options in the

used area are shown. An example of generating data

for statistical analysis describes the use of this gener-

ator functionality. However, this functionality is also

suitable for tasks from a wide range of areas where

the problem is described by a larger amount of input

On Possibility of Automatic Generation of Data Files and their Use in Tasks of Descriptive Statistics

451

data that cannot be entered in the task input text and it

is useful to place these input data in the attached ﬁle.

The paper does not address the evaluation of the

described approach used in the teaching process and

its effect on the effectiveness of teaching. This is part

of the future work where the results and the course

of study of two groups of students are compared. In

one group, the generated tasks were not used and the

exercises were performed on tasks that are the same

for all students. In the second group, they were used

to practice the generated task unique for each student.

Similarly, any impact of the length and course of the

problem solution on the student’s results is analysed.

The proposed solution for the implementation of

the automatic generator in cloud enables to extend its

use among more users and it offers its functionality

via the web interface. In the future we will also focus

on implementing this service as APIs.

ACKNOWLEDGEMENTS

This paper is published thanks to the support of the

internal projects SGS-2018-042 of the University of

West Bohemia in Pilsen, Czech Republic.

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