Remote Robotic Experimentation

An Evaluation of Intention to Use by High School Teachers in Cyprus

Pericles Cheng

, Christos Dimopoulos

and Steven Case

Department of Computer Science and Engineering, European University Cyprus,

6, Diogenes Str., Engomi, Nicosia, Cyprus

School of Information Systems and Technology, Walden University,

100 Washinghton Avenue South, Minneapolis, MN 55401, U.S.A.

Keywords: Remote Robotic Experimentation, UTAUT, Intention to Use, High School Teachers, Cyprus.

Abstract: The Digital Agenda for Europe (2015) states that there will be 825,000 unfilled vacancies for Information and

Communications Technology by 2020. This lack of IT professionals stems from the small number of students

graduating in computer science. To retain more students in the field, teachers can use various educational

technologies to explain difficult concepts. One of these educational technologies is remote robotic

experimentation. The correlational study described in this paper utilizes the unified theory of acceptance and

use of technology acceptance model to examine if performance expectancy, effort expectancy, social

influence, and facilitating conditions can predict the intention of high school computer science teachers in

Cyprus to use remote robotic experiments in their classes. Surveys, based on the UTAUT survey instrument,

were collected from 90 high school computer science teachers in Cyprus, and a multiple regression analysis

was applied to measure the correlations between the constructs and finally the model fit of the analysis. The

results of the study show that if certain conditions are provided to the teachers then there is a higher probability

that they will use remote robotic experimentation in their classes.

1 INTRODUCTION

In 2015, the Digital Agenda for Europe reported that

there would be an estimated 825,000 unfilled

vacancies for Information and Communications

Technology (ICT) by 2020 (Digital Agenda for

Europe, 2015). This lack of ICT professionals can be

attributed to the small interest of high school students

to follow a computer science degree, as evidenced by

the decline in the number of students taking

computing and ICT A-level examinations (The Royal

Society, 2012). In addition, the increased number of

students dropping out of computer science or

changing majors has led to a reduced amount of

graduates(Chen, 2015).

Even though considerable research efforts have

been made for identifying the reasons behind the

student’ dropout from STEM-related fields of study,

little research has been implemented on the

conditions which can encourage younger students to

pursue a STEM-related field of study (Wang, 2013).

Several methods have been introduced to help

students better understand difficult concepts in

computer science courses. Techniques such as

problem-based learning (PBL) help by allowing

students to self-learn using real life problems

(Sangestani and Khatiban, 2013; Tsai and Chiang,

2013). One of the biggest constraints in the use of

PBL is the cost of owning and maintaining

equipment. Remote experimentation allows students

to use equipment that is remotely accessed reducing

costs. The use of remote robotics experiments during

the implementation of the educational process is one

of the factors which can potentially increase retention

rates in STEM-related fields of study.

The aim of this paper is to contribute to this area

of research by presenting the results of a study

performed among high school computer science

teachers in the country of Cyprus. The methodology

applied in this study utilized questionnaires that

measured the intention of high school teachers to use

robotic experiments in their classes. The Unified

Theory of Acceptance and Use of Technology

(UTAUT) (Venkatesh, M. G. Morris, et al., 2003)

was employed as the basis of the study. The aim of

this research effort was to identify underlying

Cheng, P., Dimopoulos, C. and Case, S.

Remote Robotic Experimentation.

DOI: 10.5220/0006688802190225

In Proceedings of the 10th International Conference on Computer Supported Education (CSEDU 2018), pages 219-225

ISBN: 978-989-758-291-2

219

conditions which can predict the intention of high

school teachers to use novel teaching techniques in

their classes. The novel teaching technique

introduced to the teachers in this study was remote

robotic experimentation. RRE allows students to

program robotic devices remotely providing hands-

on experience. Robotic devices are deployed in a

controlled environment and monitored through

webcams. Students can access the robots and

download code to them through a web interface and

then observe the actions that the robot will perform.

Several research studies have been conducted

examining the development and use of remote robotic

experiements (Osentoski et al., 2012; Kulich et al.,

2013; Verbelen et al., 2013; Casini et al., 2014).

Teachers can provide students with a problem and

then give access to a remote robot that is build based

on the specifications of the problem. Students can

then use a scheduling platform to reserve a timeslot

to use the robot and then upload their code on the

robot and observe the actions performed. This allows

students to access the robot at any time of the day

increasing the number of students that can experiment

and relieves the teacher from having to sacrifice class

time for students to experiment.

The rest of this paper is organised as follows:

Section 2 will provide a brief description of various

educational technologies used to enhance learning in

computer science classes. Section 3 will then describe

the theoretical framework used in this study and

finally section 4 will present the results of the study.

Finally, the paper will conclude with a brief analysis

of the findings and future work.

2 RELATED WORK

The use of innovative educational technologies to

enhance teaching and explain difficult concepts to

students is a subject that has attracted a lot of

research. This section presents three educational

techniques used in teaching computer science

concepts to students beginning with problem-based

learning, remote experimentation and finally remote

robotics experimentation.

2.1 Problem-based Learning

Problem-based learning (PBL) is an educational

approach that allows students to take responsibility of

their own learning process (O’Grady, 2012; Lykke et

al., 2015). Using PBL, teachers present students with

a complex problem and then assign them to groups

that are responsible for identifying the key issues and

forming a solution to the problem through self-

learning (Karantzas et al., 2013). The five major

characteristics of the PBL approach are: the statement

of the problem, the definition of the problem in such

a way so as to promote student learning, the process

of self-directed learning by the students to solve the

problem, the reflection of the students on their

solutions to the problem, and the small groups

assigned to the problem (Scott, 2014).

The most prominent application of PBL is

medical education but in the past years computing has

become the second most prominent application (Tsai

and Chiang, 2013). PBL has been used in computing

education extensively and has been shown to promote

problem-solving skills, teamwork and motivation

(Martinez et al., 2014).

2.2 Remote Experimentation

One of the reasons that PBL is not widely used in

schools can be attributed to the increased time

required by the students to solve a particular problem

(Kong Pak-Hin, 2014). Providing a problem to

students and expecting them to self-direct their

learning process while at the same time allowing time

for interaction and collaboration between the students

requires time. This time overhead has a negative

impact in the educational process. In addition, PBL

may require extra facilities such as equipment, space

and personnel needed to achieve the required

solutions to the problem (Ionescu et al., 2013; Saad et

al., 2013).

Due to the aforementioned constraints in PBL,

two new educational technologies have emerged

allowing students to practice their problem-based

activities outside of the classroom. These two

technologies are virtual/simulated laboratories and

remote experimentation laboratories (Abdulwahed

and Nagy, 2013). Virtual labs allow students to use

simulation software in order to complete educational

tasks, reducing in this way the need for expensive

equipment. Other benefits include the manipulation

of time, allowing experiments that would normally

take significant time to complete, to finish faster, and

the removal of confusing details that do not influence

the experimental process (de Jong, Linn and Zacharia,

2013; Merchant et al., 2014). On the other hand,

remote laboratories allow students to use real physical

equipment remotely. The benefits of using remote

labs include reductions in required equipment, and

maintenance time, as well as availability on a 24/7

basis (Zubía and Gustavo, 2012). In addition, using

real physical equipment allows students to receive

CSEDU 2018 - 10th International Conference on Computer Supported Education

220

realistic feedback during the implementation of the

experimental process.

2.3 Remote Robotics Experimentation

During the past years, robotics research has grown

exponentially and it is predicted that the robotics

industry will significantly increase as well (Kulich et

al., 2013; Padir and Chernova, 2013). Robotics is

used in introductory design and computer

programming education in order to attract more

students to study in STEM fields (Esposito, 2017).

The biggest problem of using robots in education is

the high cost of equipment, maintenance and

availability of the robots. The development of a

remote robotic laboratory could help in allowing

students to perform PBL tasks using robots, while at

the same time reducing equipment and maintenance

costs, and providing constant access to the platform.

Using remote robotics laboratories allows

students to follow the experimental process from any

geographic area in the world, and have access to state-

of-the-art equipment (Heradio et al., 2016).

3 THEORETICAL FRAMEWORK

This research study aims to examine the intention of

high school computer science teachers in Cyprus to

use remote robotic experimentation in their

curriculum. Examining user acceptance of a

technology has been researched for several years and

there are several frameworks available that can help

identify the factors affecting acceptance. The theory

of reasoned action (TRA), technology acceptance

model (TAM), motivational model (MM), theory of

planned behaviour (TPB) and the unified theory of

acceptance and use of technology (UTAUT) (Oye,

A.Iahad and Ab.Rahim, 2014). This study has

adopted the UTAUT theoretical model in order to

examine the intention of participants to use remote

robotic experimentation during the educational

process.

UTAUT utilizes four main constructs in

examining intention behaviour (Venkatesh, M. G.

Morris, et al., 2003): performance expectancy, effort

expectancy, social influence and facilitating

conditions. In a study comparing various technology

acceptance models, UTAUT has been found to

predict more accurately intention of use (Venkatesh,

M. G. Morris, et al., 2003).

The first construct used in UTAUT is

performance expectancy (PE). PE is a measure of the

performance increase that users of the technology can

expect if they adopt the technology. In this study PE

would show how teachers expect remote robotic

experimentation can increase their teaching

performance in their classes.

Effort expectancy (EE) represents the expected

effort that a user will expend to learn and use the new

technology. The model in this study examines the

thoughts of the users with relation to the effort that

the teachers expect to put into learning how to use

remote robotics experimentation and the effort that

they will expend when teaching using the technology.

The third construct in UTAUT is social influence

(SI). Social influence measures how susceptible users

are to outside influences such as colleagues,

managers or even close family and friends. The

survey provided to the high school teachers is mostly

based on the influence that the Ministry of Education

has on the decision of the teachers to use remote

robotic experimentation.

Finally, the last construct examined in UTAUT is

facilitating conditions (FC). This variable examines

the intention of someone to use a technology based on

the existence of the conditions required for the

technology to work. These facilities include the

hardware, software, training and support channels

that can help the adopter succeed in using the

technology. In remote robotic experimentation the

facilitating conditions include the robotic platforms,

the servers and software required for the platform to

work and the high availability of these resources. In

additions the teachers would need to be trained and

supported throughout the use of the platform. In the

survey described in this paper we examined how

important these facilitating conditions are for the high

school teachers in order to adopt the use of remote

robotic experimentation.

4 DATA COLLECTION &

ANALYSIS

A questionnaire was distributed to all high school

teachers in Cyprus, through the Cyprus High School

Computer Science Teachers Association. The

questionnaire used the UTAUT survey instrument

that has already been validated in several research

studies (Venkatesh, M. M. G. Morris, et al., 2003; Li

and Kishore, 2006; Thomas, Singh and Gaffar, 2013).

The survey instrument evaluated the four main

constructs identified in UTAUT, namely performance

expectancy, effort expectancy, social influence and

facilitating conditions. Out of approximately 400

registered high school computer science teachers we

Remote Robotic Experimentation

221

received 90 responses. Using G*Power analysis with

a medium effect size (f = 0.15), error probability (a =

0.05) and a power of 0.80, the minimum required

sample size is 85 participants.

The questionnaire was distributed to all high

school teachers in Cyprus through an email invitation

sent by the Cyprus High School Computer Science

Teachers Association. In the email the teachers found

a link to a Google forms questionnaire that explained

the research and that no identifying information will

be collected.

The research question was to check if

performance expectancy, effort expectancy, social

influence and facilitating conditions could

significantly predict the intention of high school

computer science teachers in Cyprus to use remote

robotic experimentation. This evaluation was done

using multiple regression analysis to determine if the

four independent variables had a significant

relationship to the dependent variable, namely

behavioural intention.

4.1 Data Analysis

The data gathered from the questionnaires was based

on a Likert-scale ranging from 1 (Strongly Disagree)

to 7 (Strongly Agree). The questions were grouped in

five main groups representing the four independent

variables, namely performance expectancy, effort

expectancy, social influence, facilitating conditions,

and the dependent variable which was behavioural

intention. A multiple regression analysis was

performed on the responses using the SPSS statistical

software.

An exploratory factor analysis was initially

performed on the data in order to validate the five

constructs that were examined in the theoretical

framework. The factor analysis correctly grouped

together four of the five factors with an eigenvalue of

more than 1.0. Performance expectancy, effort

expectancy, facilitating conditions and behavioural

intention were identified whereas social influence

was not clearly identified in the pattern matrix.

Due to the low number of responses in the survey

the study tested the validity of the sample using such

tests as multicollinearity, normality,

homoscedasticity and independence of residuals. All

tests were within permissible parameters validating

the survey data.

The regression analysis of the four independent

variables in relation to the dependent variable is

presented in table 1. The model as a whole was able

to significantly predict behavioural intention, F(4,85)

= 11.417, p = .000, R

= .34. The R

value indicates

that the model can predict the intention of high school

computer science teachers in Cyprus to use remote

robotic experimentation in their classes with a total

variability of 34.9%.

Table 1: Regression Analysis Summary for Predictor

Variables.

Variable B SE B β T p

PE .191 .130 .164 1.467 .146

EE .343 .131 .287 2.614 .011

SI -.085 .118 -.094 -.721 .473

FC .357 .131 .347 2.733 .008

Note. N = 90

4.1.1 Performance Expectancy

As previously mentioned, PE measures the expected

performance increase that the high school teachers

expect from using remote robotic experimentation.

The analysis of the data shows that even though there

is a positive correlation between PE and BI (.191) a p

value that is greater than 0.05 indicates that PE is not

a strong predictor of BI. Studies involving teachers

that also use UTAUT have also shown that PE is not

a significant predictor of behavioural intention (Chen

and Chen, 2015).

4.1.2 Effort Expectancy

Effort expectancy measures the effort that the high

school teachers expect to put into learning and

teaching remote robotic experiments in their

classrooms. EE was the second most significant

predictor of behavioural intention with a positive

slope (.343) and a p value of .011. This finding

suggests that high school teachers in Cyprus would

adopt remote robotic experimentation if the effort

required to learn to use the technology and then apply

it to their classroom is small. Further analysis of the

data showed that the squared semi-partial coefficient

(sr

) for effort expectancy was .229 indicating that

22.9% of the variance of BI is based on effort

expectancy. Several other studies also support the fact

that effort expectancy can be a strong predictor of

behavioural intention in teachers (Bhatiasevi, 2015;

Sharon et al., 2015; Tosuntas, Karadag and Orhan,

2015).

4.1.3 Social Influence

The third independent variable examined by the study

is social influence. This variable examines the

influence that people in the user environment have on

the behavioural intention to use a technology. SI had

a slightly negative slope (-.085) and a high p value

CSEDU 2018 - 10th International Conference on Computer Supported Education

222

(.473) indicating that SI is not a predictor of BI. This

trend regarding Information and Computing

Technologies (ICT) seems to be justified by other

literature (Escobar-Rodriguez, T., Carvajal-Trujillo,

E., & Monge-Lozano, 2014; Oye, A.Iahad and

Ab.Rahim, 2014). Social influence is considered to be

the least significant predictor of behavioural

intention.

4.1.4 Facilitating Conditions

The strongest predictor of behavioural intention in

this study was facilitating conditions. Facilitating

conditions are said to predict use behaviour (UB) in

UTAUT rather than BI. Nevertheless, research has

shown that even though FC is not directly associated

with BI there is a relationship that has a strong

positive correlation between FC and BI (Raman and

Don, 2013; Bhatiasevi, 2015). In this study FC has a

positive slope (.357) with a low p value (.008). The

squared semi-partial coefficient (sr

) was .239

indicating that 23.9% of the variation in behavioural

intention was based on FC.

4.2 Model Analysis

The results of the survey study showed that teachers

are more willing to adopt the remote robotics

experimentation technique if the facilities are

provided to them. This means that the teachers would

want to have the hardware and software relating to

remote robotic experiments available at all times as

well as training and support for the use of the

experiments.

The second strongest predictor of behavioural

intention was effort expectancy; this means that

teachers need to know that the use of remote robotic

experimentation will take little effort to learn and use

in the classroom. If the technology is hard to learn and

takes too much time from the teacher to setup and

deploy in the classroom then there is a high

probability that they will not want to use it.

The last two constructs, namely performance

expectancy and social influence, could not be

considered predictors of behavioural intention for the

use of remote robotic experiments.

Overall, the UTAUT model fit showed that it

could predict the behavioural intention of high school

computer science teachers in Cyprus to use remote

robotics experimentation. The results of the study can

be used to strengthen the conditions that teachers

would like to see met before adopting remote robotic

experiments. In this way, the curriculum makers can

focus on making technology as easy as possible to

learn and deploy, as well as on providing all

necessary infrastructure in order to use and support

remote robotic experimentation.

5 CONCLUSION AND FUTURE

WORK

This research study aimed in identifying the intention

of high school computer science teachers in Cyprus

to use remote robotic experimentation in their classes.

The results are very important for curriculum makers

because it can help them understand what the teachers

need in order to embrace this new technology. The

theoretical framework was based on UTAUT, which

uses four main constructs that can help predict

behavioural intention. These four constructs are

performance expectancy, effort expectancy, social

influence and facilitating conditions.

The study showed that there are constructs such

as facilitating conditions and effort expectancy which

can increase the probability that high school teachers

will use remote robotic experiments. The study also

showed that performance expectancy and social

influence had no effect in the intention to use the

technology.

The results allow curriculum policy makers to

identify appropriate ways in which to deliver a new

curriculum based on robotics education, one that will

have increased chances of being adopted by the

teachers. Since the study showed that facilitating

conditions is the biggest predictor of the intention of

high school teachers to use remote robotics

experimentation, policy makers will have to ensure

that required facilities are already in place before the

introduction of the technology to the teachers. This

means that the hardware and software should be in

place and operational, while a group of people will

provide support to the teachers through the training

and application stages of the educational process.

In addition to facilitating conditions, effort

expectancy was also found to be a strong predictor of

intention to use remote robotic experimentations.

This means that policy makers will have to take

measures in developing a curriculum that is easy to

adopt and make the use of the remote experimentation

platform as easy as possible for both teachers and

students. If teachers find that the system is hard to

learn or that the platform has several operational

problems, then they will be reluctant to adopt it.

Future work includes the development of a remote

robotic experimentation laboratory for high school

computer science teachers in Cyprus. This laboratory

Remote Robotic Experimentation

223

will allow the implementation of Training of Trainers

courses which will educate teachers in the use of

remote RobotC experimentation and will increase

their exposure to the educational technology.

Having a remote robotic laboratory available will

also allow researchers to perform studies that will

gauge the possible educational improvements of

remote robotic experimentation over other

educational approaches.

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