An educational information system to follow up on the perceived IT

skills of pre-service teachers

MONCEF BARI

Department of Didactics

University of Quebec at Montreal

405 Sainte-Catherine Est, Montréal, QC H2L 2C4

CANADA

Abstract: - We describe an educational information system for following up the perceived IT skills of pre-

service teachers. Such an information system will help to undertake the necessary actions to better prepare them

for mastering, and eventually for using computer applications during their service teaching.

The article introduces the IT skills of pre-service teachers intended to be tracked then it describes the approach

used to collect the data related to those skills (questionnaire and survey). The approach used to store and

compute the collected data is based on various computer applications and fields, namely Google forms, Google

spreadsheets, Structured Query Language (SQL), and Weka for the data mining part

Key-Words: - Educational information system, IT skills, perceived IT skills, pre-service teachers, following up,

information system architecture

Received: June 15, 2021. Revised: January 12, 2022. Accepted: February 20, 2022. Published: March 23, 2022.

1 Introduction

As societies become more and more digitally

oriented, it is essential to prepare people to master

the use of computers in most human activities. This

objective must be undertaken from primary school

or even from early childhood. Therefore, teachers

are among the first to have the responsibility of

developing the computer skills of their pupils.

Teachers, on the other hand, need to acquire their

own computer skills during their schooling. Having

a tool following up the evolution of their skills

during their college studies will help undertake the

necessary actions to better prepare them for such a

task.

Information and communication technologies (ICT)

skills of teachers, consequently those of pre-service

teachers (hereinafter the “participants” or

“students”), have been a subject of interest for a

long time since computer use started spreading in

most human activities. One of the main challenges

remains to find the best way to teach ICT to pre-

service teachers.

In order to have a better understanding of the

perceived IT skills of pre-service teachers, it is

interesting to (1) have a digital tool to collect the

data about these skills and (2) use different means

allowing various kinds of analysis including several

types of comparisons according to different criteria

and variables. These means will allow us to undergo

quantitative analysis, different kinds of queries, and,

eventually, data mining in order to better understand

current situations (i.e. snapshots at a particular point

of time) and the evolution over an interval of time of

several years.

This article is organized as follows: after the

introduction, section 2 explores the IT skills of pre-

service teachers, section 3 presents the survey used

to collect their perceived IT skills, section 4

describes the educational information system used

to store and process the collected data and, finally,

section 5 concludes this article.

2 IT skills of pre-service teachers

Research suggested that “computer skills instruction

increased students’ technology integration self-

efficacy only when instructors modeled teaching-

related examples and provided students with

multiple mastery experiences of technology

integration practices” [1]. As stated by Charles

Buabeng-Andoh [2], “it is recommended that

courses such as computer-supported learning, ICTs

and designing instructional materials should be

introduced in initial teacher training programs to

improve teachers’ level of confidence and

perceptions towards the use of ICT”. Many various

strategies have been used. For instance, a research

project using intelligent tutoring found that

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approach to be “effective in improving the learning

effectiveness of students with low-level prior

knowledge” [3]. Knowing that “use of technology

takes time and requires a paradigm change for

teachers to adopt it” [4] showed that “computer

ownership, the internet access and amount of daily

computer use do not correlate with the attitude

towards computer-assisted education”, what is more

important is the perceived usefulness and the

enjoyment since they have a positive effect on the

attitude of teachers towards the use of ICT for

teaching.

In our case, we have established a set covering a

wide range of computer applications and

technologies among which pre-service teachers

usually have variable levels of mastery. This range

of computer applications and technologies are the

following:

 Use of most current applications: texts,

spreadsheets, forms, presentations

 Creating, editing, and managing digital

objects (i.e. image, audio, video)

 Use of interactive boards

 Use of advanced internet search

 Creating and managing websites

 Creating online interactive activities

 Use of visual programming platforms

 Use of digital communication with teachers

and fellows

 Use of cloud technology to store data and/or

to use online applications

 Use of various computer applications for

learning

The next section presents the approach used to

collect the perceived ICT skills of the pre-service

teachers.

3 The approach used to collect the

data

The approach adopted consists of a survey based on

a questionnaire which will be sent to all of the

students of the teacher training programs of the

Faculty of Pedagogy of Dalat University.

The questionnaire comprises 30 questions, created

using Google Forms. The questions are written in

Vietnamese, followed by their English translation,

and are mainly related to the computer applications

and technologies listed in the previous section.

Other questions relate to the participants' program,

their progress in the program, their gender, the use

of a computer at home, and whether they have

already had an IT course at the university or/and at

the high school.

For most of the questions, the answers are based on

a multiple-choice approach. For instance, the

choices related to the questions about the perceived

IT skills are as follows: Very high, High, Average,

Poor, Very poor, I don’t know what it is.

Prior to sending the questionnaire to the

participants, it was sent to 12 randomly selected

participants, from different programs, in order to

validate their understanding of the questions.

Following this validation step, adjustments were

made to 6 questions in accordance with the feedback

obtained.

The survey will be conducted for four years starting

2021. The link to the questionnaire form is sent to

the participants by email with an explanation about

the purpose of the research project and an invitation

to complete it. For the first survey conducted in late

2021, 381 answers have been received.

Once the responses are received, the answers based

on multiple-choice are transformed to numeric

values according to the correspondence shown in

Table 1:

Table 1. Correspondence between multiple choices

values and numeric values (weights)

Multiple choices values

Weight

Very high

High

Average

Poor

Very poor

I don’t know what it is

No answer

This transformation step is named “Weighting” (see

Figure 1, section 4). Thus, it becomes possible to

grant a weight based on numerical values allowing

for multiple kinds of comparisons: between

individuals, between programs, between years in the

programs, etc. Showing the changing over time is

also becoming easier to do.

The next section describes the educational

information system used to store and process the

collected data resulting from the survey.

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Volume 19, 2022

4 Description of the following up

educational information system

The approach to creating the educational

information system to follow up on the perceived IT

skills of pre-service teachers is described in Figure

1. This approach uses various computer applications

and fields, namely Google forms, Google

spreadsheets, Structured Query Language (SQL),

and Weka for the data mining part.

Figure 1. The architecture of the following up

educational information system

As mentioned in the previous section, the Google

form comprises 30 questions. This form allows, by

itself, to extract basic information in order to make

comparisons between the different programs,

between the years according to the progress in the

program of the students, between their gender or

whether they own a personal computer or not. These

initial analyses are based on the data such as the

absolute values, the means, and the standard

deviations related to the different answered

questions.

This kind of data suits the analysis of the answers

gathered for a given year: they allow us to have a

snapshot of the situation at a particular point in time.

It is also suited for making comparisons between

several years, i.e., comparing the snapshots taken at

different points of time several consecutive years.

For more complex analysis, especially if we handle

data related to several years, we need more

sophisticated tools. We have decided to use SQL

queries and data mining tools. Let's present them in

turn.

SQL is used to communicate with databases,

independently of their internal representation.

According to the American National Standards

Institute (ANSI), it is the standard language for

relational database management systems. Is it

possible to use this powerful language on

spreadsheets because they are more and more being

used to create and manage information systems that

rely on data repositories that can grow significantly.

Thus, spreadsheets may be seen as databases and [5]

proposed “an expressive and composable technique

where intuitive queries can be defined ... builds on a

model-driven spreadsheet development

environment, and queries … expressed referencing

entities in the model of a spreadsheet instead of in

its actual data”. Their proposal has been

implemented relying on Google's query function for

spreadsheets.

Since spreadsheets have become very popular

databases in use nowadays it is possible to “extend

the usage of spreadsheets in any direction as it

provides great flexibility in terms of data storage

and dependency of stored data” [6]. The same

authors “surveyed some of research which took

great attention over spreadsheets and its

applicability in different functional cases, such as

Data Visualization, SQL Engines and many more”.

Dealing with a great number of attributes and sheets

may result in difficulties to implement efficient

databases relying on spreadsheets only. Thus, [7]

suggested another approach based on “importing the

spreadsheet data into a database manager, joining

tables, and performing the analysis using database

querying”.

A similar approach may be found in [8] since they

built an exploration tool, namely DataSpread, “that

holistically unifies databases and spreadsheets”.

In our case, since we don't have too many sheets, we

use, for now, a more conventional solution based on

Google's query function for spreadsheets. This

function allows us to write simple as well as

complex queries based on SQL.

When there will be thousands of questionnaires,

their data can serve as a basis for more sophisticated

processing. At this point, it will be possible to use

data mining tools for performing different data

mining tasks such as data preprocessing,

classification, clustering, association rule mining,

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visualization, … These tasks are mainly based on

machine learning algorithms.

Simply stated, data mining is a part of artificial

intelligence techniques used to extract useful

knowledge from raw data. This extracted knowledge

may then be used to help make decisions or better

understand various phenomena. Data mining

techniques are used in many different domains,

including education. A new research area is

emerging and many authors, for instance [9] and

[10], are talking of educational data mining.

We have already chosen the Weka platform, a Java-

based software suite that implements a large number

of machine learning algorithms [11]. This choice

was easy to do not only because we have already

used Weka in previous data mining projects [12]

and also because it is an open-source platform

widely used in the scientific community. Moreover,

it is easy to convert spreadsheets files to Weka-

supported file formats.

5 Conclusion

We have presented an educational information

system to follow up on the perceived IT skills of

pre-service teachers. This information system allows

us to (1) have a digital tool to collect data about

these perceived skills and (2) use different means

allowing different kinds of analysis including

several types of comparisons according to different

criteria and variables. With these means, it will be

possible to undergo quantitative analysis, different

kinds of queries, and, eventually, data mining in

order to better understand current situations (i.e.

snapshots at a particular point of time) and the

evolution over an interval of time.

The analysis of the data resulting from the use of

this educational information system will give rise to

subsequent publications.

Acknowledgment

The author wishes to thank:

- Nguyễn Thị Ái Minh, School of Education,

University of Da Lat, Vietnam, for her support since

the very beginning of the project “A platform to

follow up on the perceived IT skills of pre-service

teachers” and for handling and managing the

questionnaire and the survey

- Messica Bari for her review of this article.

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Volume 19, 2022

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DOI: 10.37394/232010.2022.19.4

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E-ISSN: 2224-3410

Volume 19, 2022