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DOI 10.1007/s10846-015-0202-6

Experiences Incorporating Lego Mindstorms Robots

in the Basic Programming Syllabus: Lessons LearnedAinhoa Alvarez Mikel Larranaga

Received: 26 March 2014 / Accepted: 20 January 2015

Springer Science+Business Media Dordrecht 2015

Abstract Basic Programming is a first year manda-

tory course of the Computer Engineering degree.

Both students and teachers face difficulties in this

course, which has high failure and drop-out rates.

Several authors have proposed the use of visual pro-

gramming environments and robots to overcome the

difficulties of this course, some of which have been

successful. This paper presents the two-year experi-

ment using Lego Robots carried out at the University

of the Basque Country (UPV/EHU) with around 100

students, along with the results. Satisfactory resultshave been obtained regarding both motivation and the

perception of the students of their learning process;

moreover the drop-out rate decreased even though

no statistical significance was obtained regarding the

final marks of the course. From those results and the

analysis of the data it was derived that robot sessions

should be more integrated in the curriculum, giving

them greater relevance in the final marks. In addition,

it is indispensable to classify course students and adapt

learning sessions to each student type due to the high

student heterogeneity.

A. Alvarez () M. Larranaga

Department of Languages and Computer Systems,

University of the Basque Country, UPV/EHU,

Vitoria-Gasteiz, Basque Country, Spain

e-mail:ainhoa.alvarez@ehu.eus

M. Larranaga

e-mail:mikel.larranaga@ehu.eus

Keywords Basic Programming Lego Mindstorms

Robots in Computer Engineering Education

1 Introduction

Basic Programming is a mandatory first year course

in the Computer Engineering degree that covers the

fundamentals of programming. Student heterogene-

ity in this course is so high that teachers experience

great difficulties to teach the course. More and morestudents have some prior programming knowledge.

Those students might have taken some programming

course (mainly focused on the syntax and semantics of

a certain programming language) before, or might be

retaking the Basic Programming Course. In our expe-

rience, most of those students are highly confident

with their programming mastery level but they lack

algorithm design skills. On the other hand, for many

students this course is their first contact with program-

ming issues. Therefore, it is a difficult course both for

students and teachers, with high failure and drop-outrates [8,12,21]. As a consequence of the high failure

and drop-out rates, there is a remarkable percentage

of retakers in the course and a general belief that the

course is extremely difficult.

Taking this into account, Basic Programming

course teachers at the UPV/EHU (with more than

10 years experience) have tried different teaching

strategies in the framework of a Blended Learning

environment. Students attend face to face lectures

mailto:ainhoa.alvarez@ehu.eusmailto:mikel.larranaga@ehu.eusmailto:mikel.larranaga@ehu.eusmailto:ainhoa.alvarez@ehu.eus

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(master lectures and laboratory sessions) and use

Learning Management Systems, such as Moodle, for

out-of-school activities. During the last three aca-

demic years (20112012, 20122013 and 20132014)

teachers have introduced Lego Mindstorms Robots to

support the course teaching with the aim of providing

a more effective and efficient education [1].This paper presents this experience and the lessons

learned from it. First, a detailed description of the

problems in Basic Programming Course and the pro-

posed solutions in the literature are described. Then,

the conducted experiment is presented. Next, the

results of the experiment are depicted. To sum up,

some conclusions and final remarks are provided.

2 Dealing with Basic Programming Syllabus

Difficulties

Programming is a complex task that requires both

declarative and procedural knowledge [21]. The for-

mer is related to the syntactic and semantic aspects

of a certain programming language, whereas the

later refers to problem solving and program design

tasks. Programming is a course with a high cognitive

requirement; mastering programming entails achiev-

ing the six kinds of instructional objectives defined in

Blooms taxonomy [3,4]and students face difficulties

in every one of them[21]. Blooms taxonomy classi-fies the instructional objectives that educators might

set for students into six levels related to the skills in the

cognitive domain: Knowledge, Comprehension,Appli-

cation, Analysis, Synthesis, and Evaluation. These

categories are ordered from the simplest to the most

complex and from concrete to abstract. Moreover, they

are classified as cumulative, i.e., to become proficient

in a skill such as Application, the student must master

a simpler one (e.g., Knowledge) first.

In addition, students in programming courses are

very heterogeneous [8, 12] which makes it difficult

for the teachers to design appropriate instructional

methods for the course [21].

Programming is usually taught using general-

purpose languages which are complex for students[8,

10]. Some programming languages require the stu-

dents to learn many concepts before beginning to do

any programming tasks, whilst other languages imply

typing in a large amount of programming code that

students hardly understand. Therefore, students have

to deal with algorithm construction and syntactic rules

at the same time. However, the biggest problem novice

programmers face is their lack of program solving

skills [8, 12]. This produces high drop-out and fail-

ure rates in programming courses [8]. To overcome

these problems, students need tools that help them to

acquire the required problem solving skills. Practicallearning situations are the most useful for learning

programming [12], i.e., the quality of teaching pro-

gramming improves using constructivist approaches

where students actively build knowledge rather than

being passive receivers of the knowledge [13,18,25].

Therefore, the more specific and practical the didactic

material, the better the result in the learning process.

Some authors have advocated the use of visual pro-

gramming environments as they reduce the cognitive

requirement to start working on programming tasks.

Although they do not solve the problems with the syn-tax, they might postpone the problem as they allow the

students to firstly focus on the task withdrawing the

syntactic rules [15, 26]. Once they have understood

the basic concepts, they can move to a non-visual

language and tackle the syntax problem. In addition,

many teachers have used games, microworlds, robots

and the like in programming courses [57,9,22,23].

A study was made [27] to compare the effects

of using either physical robots or robot simulators

for teaching programming concepts. They found that

there was not any significant difference between bothgroups regarding performance. However, they found

out that the group working with physical robots

was more motivated towards learning. Using physi-

cal robots allows students to better understand pro-

gram behaviour as it allows learners to bridge the

gap between concept and practice[10]. It also allows

students to create more interesting outcomes [10].

Moreover, students find them attractive. Therefore,

they promote learning [22].

No single programming environment is adequate

for all situations [10]. Therefore, beginning to workwith robots that later allow different program-

ming environments such as Lego Robots should be

regarded. They can be programmed using simple

visual environments at the beginning and then increase

the difficulty by programming them using a particular

programming language and specialized libraries such

as Lejos.1

1http://www.lejos.org

http://www.lejos.org/http://www.lejos.org/

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Another interesting aspect of Lego robots is that

many students feel familiar with them. Some have

used them in their childhood, others use them in

computer games2,3 and they even see them in films.4

Including robots into the course syllabus does not

automatically mean a better understanding or better

results of concepts by students[22]. In order to havea teaching value, their use must be carefully designed,

which requires a higher dedication of teachers[6].

All in all, the authors of this paper considered that

using Lego robots might help students to better under-

stand basic programming concepts and also acquire

the procedural knowledge and problem solving skills

required to succeed in programming courses. Given

this working hypothesis, the experiment described in

Section3has been carried out.

3 Experiment

To test the hypothesis, during the last three academic

courses teachers have used Lego Robots to aid the

first part of the course related to algorithm design

construction. The use of the robots was restricted to

algorithm design construction because the main goal

was to improve the problem solving skills of the stu-

dents on the one hand and introduce design concepts

on the other. Moreover, some authors[7] reported neg-

ative experiences when using the robots for the wholecourse, as the student could not access the robots

out of schooltime and, therefore, had less time to

practice.

In the 20112012 academic year, a pilot study was

carried out in which 19 students were involved. The

students involved in the pilot test used Lego robots

during two lab sessions. Feedback was collected by

means of logbooks, in which students and teachers

described the problems encountered and their opin-

ions regarding the experience. Due to the positive

results, the authors continued using Lego robots thefollowing two academic years. The process described

below has been put in practice during the last two

academic years and its results are described in this

paper.

2http://www.lego.com/en-us/city/games/3http://www.lego.com/en-gb/mindstorms/funzone/4The Lego Movie

3.1 Objectives and Process

To test whether or not Lego robots contribute to

improve the learning of design and problem solv-

ing skills, i.e., the working hypothesis, three different

objectives were established:

O-Motivation: Study the effects of using robots

in the motivation of students.

O-Improvement: Test whether the use of Lego

Mindstorms robots produce improvements in the

learning process of students: students marks and

drop-out rates.

O-Perception: Analysis of teachers and students

perceptions regarding how the use of the robots

influences the learning processes of students.

Each academic year, the experiment followed the

four-phase process depicted in Fig.1and described inthe following sections: Preparation, application, data

collection and data analysis. Each academic year the

process was adjusted taking into account the lessons

learned from previous years.

3.2 Phase 1: Preparation

This phase consists of two steps: Experiment design

and prior knowledge evaluation.

3.2.1 Step 1.1: Experiment Design

During the experiment design step, participants were

selected together with the evaluation methods and

deployment methodology to be used. To evaluate the

experiments, both quantitative and qualitative meth-

ods [24] were used. The former, quantitative meth-

ods, rely on closed-answer questions that restrict the

answer categories for each question; this enables the

statistical analysis of the gathered data. The later,

qualitative methods, include inquiry techniques which

allow capturing this subjective opinion of the users.

Motivation and perception was measured using both

surveys and interviews. To evaluate improvement pre

and post-tests were used.

Regarding the the selection of participants, the

20122013 students were divided into two groups.

The first group (G Experimental) used the robots,

whereas the second group (G Control) did not use the

robots. Vintage were randomly assigned to each of

the groups. G Experimental consisted of 14 students

http://www.lego.com/en-us/city/games/http://www.lego.com/en-gb/mindstorms/funzone/http://www.lego.com/en-gb/mindstorms/funzone/http://www.lego.com/en-us/city/games/

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Fig. 1 Phase and step

sequence for the experiment

taking the course for the first time and 8 retaking the

course, whereas G Control had 22 students, 9 of them

retaking the course.

Due to the positive results, the following year all

students attending lab sessions had the opportunity to

use the robots. A total of 52 students used the robots,

11 of whom were students retaking the course.

3.2.2 Step 1.2: Prior Knowledge Evaluation

Increasingly more Basic Programming course stu-

dents have some prior knowledge and experience pro-

gramming. While a percentage of students are new-

comers, some have already taken previous courses.

Therefore, a preliminary evaluation was conducted to

determine prior knowledge. Each academic year this

task was carried out in a different way. 20122013

students were provided with a simple problem and

requested to indicate how they would solve it using

simple expressions. The aim of this exercise was to

test whether or not they were able to express condi-

tional or iterative statements. However, this did not

give much information, as many students were able

to correctly express such kinds of expressions but

could hardly do the same when working with formal

pseudocode or flow diagrams. Therefore, the follow-

ing year this test was replaced with a questionnaire.

This questionnaire asked students whether they had

any previous knowledge or not. 46 % of the stu-

dents showed some previous knowledge from diverse

sources, for example, due to personal interest or from

high school.

3.3 Phase 2: Application

The application phase concerns the presentation to

students of the material to be used (software and

robots) and its use.

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3.3.1 Step 2.1: Introduction to LEGO Mindstorms

The experiments were oriented towards improving

programming skills rather than to robotic aspects.

Therefore, students were provided with previously

constructed robots (see Fig. 2a). During this step,

robots were presented to students along with an intro-duction to the development environment to be used

(see Fig.2b).

The NXT-G visual development environment was

selected for this experiment. It provides a palette (c)

which contains the blocks, such as iterative (d) or con-

ditional (e) statements, that can be dragged/dropped

into the work area. It also allows to compile programs

and transfer them to the robot (f).

3.3.2 Step 2.2: Using the Robots

In 20122013 robots were used in two consecu-

tive 90-min lab sessions. The following year a third

lab session was introduced two weeks later. During

those sessions students were provided with the robots

and a set of problems of increasing difficulty. Stu-

dents had to design the programs with the provided

software and, then, test them loading the programs

into the robots. The exercises to be solved during

the first session were related to the use of sensors

and basic forward and backward movements. The

second session was mainly focused on the under-

standing of the conditional and iterative statements.

Finally, the third session was centred on more com-

plex robot movements such as turning a certain num-

ber of degrees, following a black line or followinga circuit.

3.4 Phase 3: Data Collection

Based on the experiment design, student and teacher

feedback was collected using both surveys and

inquiries. Student knowledge was evaluated by means

of post-tests.

3.4.1 Step 3.1: Collecting Student Feedback

Student feedback was collected using an anonymous

survey composed of 24 items: 2 open-ended questions,

2 Yes/No questions and 20 five-point likert items

[14]. The last included answer options ranging from

Strongly disagree to Strongly agree.

Table1shows an excerpt of this survey where items

have been organised according to the objective they

were oriented.

Fig. 2 Robots used (a) and development environment (b)

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Table 1 Excerpt of the survey used to collect feedback from students

1 2 3 4 5

Q1 Did you find it easy to use the software?

Q2 Did you find the exercises to be solved using the robots easy?

O-Motivation

Q3 Using the robots has made the course more interesting

Q4 Using the robots has made the course more fun

Q5 I would like to have used the robots in more lessons

Q6 I wish the robots were available out of the school timetable

Q7 I would like to use the robots in other courses

Q8 Would you have preferred not to have used the LEGO robots? YES/NO

O-Perception

Q9 Using the robots has helped me to understand conditional statements

Q10 Using the robots has helped me to understand iterative statements

Q11 Using the robots has helped me appreciate my knowledge level on design concepts

Q12 Using the robots has helped me comprehend the usefulness of the course

Q13 I found the 3rd session with the robots easier after the lessons YES/NO

Q14 Additional comments or suggestions regarding the experience

In 20122013, information regarding the feel-

ings transmitted by the students in the Exper-

imental group (G Experimental) in relation with

the experience was also collected from the stu-

dents in the control group (G Control). To this

end, the two-question survey shown in Table 2 was

used.

3.4.2 Step 3.2: Testing Student Knowledge

The evaluation of the Basic Programming course

entails three exams along with a project. The first

exam is related to algorithm design skills, and there-

fore the results from this exam were used to evaluate

the students knowledge.

3.4.3 Step 3.3: Collecting Teacher Feedback

After each session, teachers participating in the exper-

iment indicated how it had been developed. They

were encouraged to describe the problems encoun-

tered along with their impressions and perceptions.

3.5 Phase 4: Data Analysis

Data from different years was integrated and it was

then analysed using both quantitative and qualitative

methods. Principal Component Analysis was used to

analyse the Likert-scale items, whilst Wilcoxon test

was used to determine the significance of the improve-

ments in the students marks. The conducted analysis

allows to derive a set of conclusions and facts related

to the experiment that are presented in the following

section.

4 Results

This section describes the results of the experiment

carried out, to which end the data collected on the

Table 2 Excerpt of the

survey used to collect

feedback from students in

the control group

QC1 Would you have preferred to use the robots? YES/NO

QC2 Have any comments from your classmates on

G Experimental motivate your answer to question QC1?

Which ones?

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last two academic years was used. As no signifi-

cant difference was found between the data of both

academic years, the collected data was integrated

and analysed together. First, each of the established

objectives are tackled. Then, some considerations

regarding the design and implementation of the robot

sessions are presented.

4.1 O-Motivation

Motivation is directly related to student satisfac-

tion. The provided survey (see Table 1) included

a set of questions regarding whether the students

were satisfied with the experience and were moti-

vated to use the robots in more lessons or in other

courses.

To evaluate student satisfaction and motivation, a

Principal Component Analysis was conducted on thecollected data. The Kaiser, Meyer, Olkin measure for

sampling adequacy (KMO index) was computed to

determine the appropriateness for such a kind of anal-

ysis. Given that the score of the KMO was 0.83, the

collected data is suitable for such a kind of analysis.

The Principal Componentshowed that one component

could mostly describe the answers, while the effect

of the other components was insignificant. Figure 3

graphically illustrates the component identified in the

Principal Component Analysis. The left side of the

figure shows the histogram of the values, whilst theright side shows a heat map representing the score per

student. As can be observed, most of the responses

regarding motivation are contained in the positive side

of the range.

Figure4presents the distribution of the answers of

the students by question. In particular, the 68 % of

the students who found that Using the robots made

the course more interesting also agreed when asked

whether Using the robots made the course more fun

(Questions Q3 and Q4).

62 % of the students would have liked to use the

robots in more lessons(Q5) or in other courses (Q7).

It is worth noting that 70 % of the students would have

liked to use them outside of the school timetable(Q6).

To evaluate this objective, the responses to the sur-

vey for G Control students were also evaluated. 68 %

of the G Control students would have liked to use the

robots (QC1). This rate even increases to 100 % if only

newcomers are considered. In addition, it is remark-

able that the main reason behind that answer was the

positive comments they heard from G Experimental

students.

In agreement with this answer, only 22 % of the

students indicated that they would have preferred not

using the robots (question Q8 of the survey). More-

over, the students giving this answer were mainly

retakers.The teachers reflected that they observed stronger

motivation in the students using the robots, which is

consonant with [6], who pointed out that students

want to play with them and thus are willing to invest a

lot of time and mental energy. Moreover, seeing their

students recording videos with cellphones to send to

their friends and also competing to see whose robots

could follow the black line faster or make the cir-

cuit shown in Fig. 5 was extremely gratifying for the

lecturers.

4.2 O-Improvement

Improvements in the learning process have been

studied analysing two different elements or factors:

Improvements of the student marks and reduction of

the drop-out rates. Regarding the effect of using the

robots on the drop-out rates, the evolution of the last

7 academic years was analysed (see Fig. 6). As can

be observed, the percentage of sitting students barely

reached 47 % before 20112012, the academic year in

which a pilot study was conducted. The percentage ofthe sitting students has significantly increased since,

reaching a peak 63.38 % in the 20122013 academic

year. Therefore, using the robots appears to have a

positive effect by reducing the drop-out rates.

As regards the improvements in the student marks,

a between-subject study[11] was carried out to com-

pare the marks of the students using the robots with

those not using them in the 20122013 academic year.

The average marks were compared and the Wilcoxon

test was applied to check the significance of the aver-

age differences. In agreement with the works of [7,

17,19], no statistical significance was found between

groups (p = 0.05907). However, some remarkable

issues could be observed (see Fig. 7). Although no

significant difference was found, G Control students

achieved, in general, lower scores, while the scores of

the G Experimental students are more sparse. Besides,

it can be observed that the distributions of the scores

do not follow a normal distribution, which suggests

that there might be different clusters in each group.

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Fig. 3 Student Motivation

4.3 O-Perception

The survey also included some questions regard-

ing the student perceptions of their learning pro-

cess. Again, the KMO index was computed to

assure that the data collected was suitable for Prin-

cipal Component Analysis. However, the score this

time was 0.61, which states a poor adequacy for

such a kind of analysis. Therefore, Principal Com-

ponent Analysis was discarded and the analysis

focused on the Likert-scale items and open questions

of the survey. Figure 8 shows the distribution of

the answers to the questions regarding perception

(see Table1).

65 % of the students answered thatUsing the robots

helped them to understand conditional statements and

64 % stated that it helped them to understand condi-

tional statements (Q9, Q10).

70 % of the students also found that Using the

robots has helped them to appreciate their knowledge

level(Question Q11).

It is also worth noting that when regarding whether

the robots helped them realizing the usefulness of the

course, only 26 % indicated this was not the case (Q12).

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Fig. 4 Answers to survey questions related to the O-Motivation objective

Most students also provided positive feedback

in the open-ended question (Q14). Those students

pointed out that It is a useful means to introduceprogramming concepts, It has allowed me to under-

stand programming structures in an easy and enter-

taining way, Labs have been interesting to get used

to basic programming concepts. However, some stu-

dents reflected that they considered that the lessons

with the robots were unconnected with the course.

4.4 Considerations Regarding the Design

and Implementation of the Lessons with the Robots

The design of the sessions must take into account that

adjusting the difficulty of the sessions is essential to

increase students knowledge and motivate them; they

must neither be too easy nor too difficult. During the

first year, the exercises were too easy (90 % of stu-

dents indicated that this was the case Q2). This issue

was corrected for the second year. The selected devel-

opment environment did not pose any difficulties to

the students either. 75 % found it easy to use, and only

6 % had some considerable difficulties (Q1).

Regarding the qualitative study, it showed that

some students felt the robot related tasks were not

integrated enough in the course. Therefore, they pre-ferred to focus on writing Java code as they felt this

was the main task to be evaluated. They indicated that

With one lab it is enough to see the usefulness of the

course in real life and that Labs should be used to

write Java programs. This can be motivated because

the use of robots was not evaluated but it was pre-

sented as complementary material. Therefore, these

results are consistent with the study of [16], which

claims that students mainly participate in the course

tasks that are evaluated regardless of their pedagogical

value. A solution to this can be to modify the courseevaluation system to make students feel the experience

integrated in the course. This solution was previously

used with satisfactory results in this course for the

algorithm design section (giving it higher weight in

the final mark).

It is also noticeable that even if students indicate

that using the robots makes the course more fun and

more interesting and they would like to use them out

of school timetable, they would not like to use them

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Fig. 5 Robot following the black line (left) and in the circuit (right)

Fig. 6 Percentage of sitting students per course

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Fig. 7 Violin plot comparing G Control and G Experimental scores

Fig. 8 Questions regarding O-Perception

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in more lessons (Questions Q4, Q3, Q6 and Q5 of the

survey).

The use of robots was oriented to the acquisition

of design concepts and algorithm design. However,

from the survey it becomes apparent that the last lab

session was easier. This session was composed of

more complex exercises but students had already hadsome theoretical design sessions. This brings to think

that maybe students need more theoretical background

before beginning with the robots and that the robots

might be an appropriate means to put into practice the

concepts learned in a real and motivating context.

During the study a very different attitude towards

the robots from retakers or non-retakers was detected.

The main neutral or negative opinions regarding the

use of robots came from retakers, which was also

detected towards visual tools[15]. Moreover, the vio-

lin plots in Fig. 7 show that the distributions of thescores do not follow normal distributions, which sug-

gests that there might be different clusters in each

group. This indicates that there is an important student

heterogeneity in the course. Therefore, the teaching

methodology should be better adapted to the hetero-

geneity of students, as not all the students have the

same learning requirements. So, the course should be

approached differently for different student types. For

example, maybe robots could be used with all the stu-

dents but the programming environment could differ

taking into account whether they are retakers or not.

5 Conclusions and Lessons Learned

This paper has presented a two-year experiment using

Lego Robots in a Computer Science Basic Program-

ming course. The experiment results regarding O-

Motivation and O-Perception objectives where satis-

factory. The analysis of the survey answers showed

that the interest and and motivation of the students

increased due to the use of robots. Regarding student

learning awareness, which is essential for the learn-

ing process, the experiments also proved that students

perceived that the robots helped them to better under-

stand course concepts. With respect to the perception

of the teachers, they detected an increase in student

motivation and improvement of class atmosphere. For

the teachers, it was also very stimulating to see how

some students even recorded the robot performing

some tasks. Moreover, some students (on their own

initiative) competed to determine whose robots went

faster through the provided circuits.

Regarding O-Improvement, no statistical evidence

of improvement on student marks due to the robot

use was observed. After a deeper analysis of the col-

lected data, this seems to be due to the great influence

of being repeaters. However, the drop-out rate wasreduced from an average of 60 % to 40 %.

In summary, the experience was very encourag-

ing but it should be improved taking into account the

results. The main adjustment to the used approach is to

better integrate robot sessions in the course syllabus.

Additionally, as mentioned above, the students in this

course are very heterogeneous. Therefore, some tasks

can be appropriate for a certain kind of student (e.g.

newcomers) whereas they might be discouraging for

others. Hence, teachers have to deal with such student

heterogeneity. In this aspect, we consider that studentsshould be better classified so that the different pro-

files can be used to adapt the proposed tasks to each

students specific needs [2,20].

Due to the positive results obtained, the course

teachers plan to continue with the experience through-

out the following academic years. However, the

approach must be adjusted taking into account the

results obtained. The main planned modification is

the use of different development environments accord-

ing to the category of students. For example, those

that have no previous experience programming willbe provided with the Enchanting (http://enchanting.

robotclub.ab.ca) environment, retakers will directly

work with leJOS and the remainder will continue

using NXT-G. A more exhaustive pretest will be

designed for the next academic year to properly clas-

sify the students.

Acknowledgments This work is supported by the Basque

Government (IT722-13), the University of Basque Country

(UFI11/45) and the Gipuzkoa Council (FA-208/2014-B).

References

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Dr. Ainhoa Alvarezreceived the PhD degree in Computer Sci-

ence from the University of the Basque Country (UPV/EHU)

in 2010. She is a Senior Lecturer at the UPV/EHU, where

she develops her research activities within the GaLan group

(http://galan.ehu.es/Galan/). Her research work has been mainly

focused on blended-learning systems and currently it is oriented

to the area of computer based engineering education.

Dr. Mikel Larranaga received his PhD in Computer Science

from the University of the Basque Country in 2012. He is

currently a Senior Lecturer at the Department of Computer Lan-

guages and Systems at the University of the Basque Country. He

has been working in the Computer Based Education area withinthe Galan research group (http://galan.ehu.es/Galan) for the last

ten years. His current interests include knowledge acquisition,

concept mapping, computer based engineering education and

intelligent tutoring systems.

http://galan.ehu.es/Galan/http://galan.ehu.es/Galan/http://galan.ehu.es/Galanhttp://galan.ehu.es/Galanhttp://galan.ehu.es/Galan/