A Collaborative Game for Learning Algorithms

Foteini Grivokostopoulou, Isidoros Perikos and Ioannis Hatzilygeroudis

Department of Computer Engineering and Informatics, University of Patras, Patras, Greece

Keywords: Serious Games, Game-based Learning, Learning Efficiency, Collaborative Learning, Constraint Satisfaction

Algorithms.

Abstract: Educational games constitute an important mean of delivering effective learning procedures to students and

they can offer various learning opportunities in all levels of education. In this work, we present a

collaborative game that was developed to assist students in learning algorithms and we explore its learning

capabilities. The game aims to assist students in learning constraint satisfaction algorithms and it is based on

the map coloring game. In the context of the game, students can experiment and apply the algorithms in

various learning activities and training scenarios. The game has been integrated into the curriculum of the

artificial intelligence course in our university. An evaluation study was conducted in real classroom

conditions and revealed quite promising results which indicate that the game is an effective way to enhance

students’ motivation, engagement and interest and also it helps students to deeper understand the

functionality of constraint satisfaction algorithms.

1 INTRODUCTION

The rapid development of educational technology

has brought new tools that can be used in the

educational process. Indicative examples are the

educational games which are a rapidly growing field

and offer various possibilities for innovative

educational activities. One of the main features of

educational games is that they can offer pleasant and

attractive educational activities to users and can

attract their interest and also can cultivate and

strengthen their motivation for learning (Burguillo,

2010; Dickey, 2011; Ebner and Holzinger, 2007)

The educational games are a rich learning

environment and offer diverse and effective ways of

acquiring knowledge. In an effort to attract and

engage students and enhance the overall efficiency of

learning procedures, digital technologies and

computer educational games are examined to add fun

factors and make teaching more attractive, appealing

and most of all, efficient (Mihail et al., 2013). In our

days, a great part of the students, like most

individuals, spend a large part of their free time

playing computer games and in this line the

integration of games into curriculum of a course

could increase students’ interest and stimulation and

provide opportunities for learning in an entertaining

way.

Educational computer games can be used to teach

the area of computer science and researchers point

out that they could constitute an effective way to

provide more interesting learning environments for

knowledge acquisition and construction (Sung and

Hwang, 2013). Educational games have been used

successfully in both introductory computer science

courses and general artificial intelligence classes to

scaffold learning and bring excitement and

enthusiasm among students (Bayliss 2007; Parberry

et al., 2005; Sosnowski et al., 2013; Taylor, 2011;

Wong et al., 2010).

Collaborative learning is a term for a variety of

educational approaches which involve more than one

player in the playing of the game with the

pedagogical intention to promote cooperative learning

between those engaged in the game (Dickey, 2007).

Studies point out that students learn more effectively

when they collaborate with each other (Abid et al.,

2016) and in this context, collaborative games have

an important role as highly efficient and engaging

learning environments (Prensky, 2007). Collaborative

educational game-based learning can have a

substantial benefit on students learning mainly by

promoting positive learning attitudes and high

learning motivation (Romero et al., 2012; Sung and

Hwang, 2013). In addition, collaborative learning in

the context of educational games can also improve

Grivokostopoulou, F., Perikos, I. and Hatzilygeroudis, I.

A Collaborative Game for Learning Algorithms.

DOI: 10.5220/0006377405430549

In Proceedings of the 9th International Conference on Computer Supported Education (CSEDU 2017) - Volume 1, pages 543-549

ISBN: 978-989-758-239-4

543

students’ learning achievement, their self-efficacy

owing to the provision of knowledge organizing and

knowledge sharing embedded in the activities of the

collaborative gaming environment (Sung and Hwang,

2013).

In higher education, the educational games are

pointed out to be an effective approach to assist

students' learning. The integration of educational

games into the curricula of university courses can

provide the means of attracting students interest,

better and deeper engage them in learning activities

and improve their knowledge construction (Ebner and

Holzinger 2007; Hatzilygeroudis et al., 2012).

Algorithms constitute one of the most fundamental

domains in computer science discipline and it is

important for students to get a deep understanding of

their functionality. In artificial intelligence

curriculum, search algorithms and constraint

satisfaction algorithms are two important topics that

students come across and learn. It is considered

necessary that students learn efficiently and in depth

the way that algorithms function and also be able to

accurately apply them in various problems and

situations. However, the domain of search algorithms

is acknowledged by many tutors to be a difficult

domain to teach and also a very hard and complex

domain for students to deeply understand and

correctly implement (Grivokostopoulou et al., 2016;

Naser, 2008). Indeed, the domain consists of complex

cognitive and error prone processes that traditional

education fails to cover and efficiently teach.

In this paper, we present a collaborative game that

assists students in learning algorithms and explore its

learning capabilities. The collaborative game aims to

assist students cooperate in learning algorithms and

apply them in various learning activities and training

scenarios. The game assists students in learning

constraint satisfaction algorithms and has been

integrated and used into the curriculum of artificial

intelligence course in our university. The evaluation

studies conducted revealed very promising results and

indicate the games to be an effective approach and to

assist students in deeper and more complete learning

algorithms and also to enhance students’ motivation

and engagement with educational procedures.

The reminder of the paper is organized as follows:

Section 2 presents related work on the utilization of

educational games into course curriculum while

Section 3 presents the collaborative games developed

and used to assists students in learning algorithms.

Section 4 presents the results from the experimental

evaluation and discusses the learning efficiency of the

game and the students’ experiences with them.

Finally, Section 5 concludes the paper and provides

various directions that future work can focus on.

2 RELATED WORK

The design, development and integration of

educational games in courses’ curriculum have

attracted over the last years the attention of the

researcher community. Various works point out the

important role of educational games in assisting

students’ learning and improving the learning

efficiency of educational procedures. A detailed and

complete overview of approaches can be found in

(Michael and Chen, 2005, Connolly et al., 2012, De

Gloria et al., 2014, Gibson and Bell, 2013). In

literature, there is great research interest and many

works study the design of educational procedures

and the development of games for teaching the

domain of computer science.

In (Markov et al., 2006) the N-Puzzle game is

used for teaching state-space search and machine

learning. The popular Pacman game has been used

in order to formulate educational versions of it assist

students in learning search algorithms functionality

(Grivokostopoulou et al., 2016). Indeed, Pacman

game is utilizing in the introductory artificial

intelligence course at Berkeley University for

teaching AI concepts such as state-space search,

adversarial search, Markov decision process and

probabilistic tracking (DeNero and Klein, 2010).

Furthermore, the Rook Jumping Maze has been used

for teaching uninformed search, stochastic local

search and machine learning (Neller, 2011). A

common characteristic of the above efforts is that

games are mainly involved in AI programming

projects. A project is designed based on a game that

asks for implementation of algorithms concerning

state search, reinforcement learning, etc. The game

acts as a motive for the students to make the

implementations. Another effort is presented in

(Chang et al., 2008) where a game-based learning

approach is used to help students learn graph theory

topics and more specifically Kruskal’s, Prim’s and

Dijkstra’s algorithms. The game is called Ticket to

Ride and the students through the missions that they

choose about connecting one city to another, come

across the implementation of the above algorithms.

In additional, COPS (Collaborative Online Problem

Solving) (Bachu and Bernard, 2014) is a web based

strategy game that assist the students improve their

problem solving ability by building program

flowcharts as a jigsaw puzzle. In the game, students

through the visual representations of the problem’s

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544

solutions and can follow more easily the logic in

their solution.

3 COLLABORATIVE

CONSTRAINT SATISFACTION

EDUCATIONAL GAME

Constraint satisfaction (CS) is a general approach for

solving problems, which can be expressed as

constraint satisfaction problems (CSPs). A CSP is

defined as: given a finite set of variables, together

with a finite set of possible values that can be

assigned to each variable and a list of constraints,

find the values of the variables that satisfy every

constraint. The map coloring problem is a famous

problem that can be modelled as a Constraint

satisfaction Problem. In this problem, a map

consisted of a number of regions is given and we

need to color each region of the map with one of a

given set of colors in such a way that no two

adjacent regions have the same color. A simple map

coloring problem is illustrated in Figure 1. The map

consists of four regions that are to be colored with

three colors: red, green or blue.

Figure 1: A simple four-region map-coloring problem.

The problem in Figure 1 can be represented as a

Constraint satisfaction Problem as follows:

Initially, we define a set of variables V where

each variable represents a region in the map. In the

example case of Figure 1, is define as:

V={V1,V2,V3,V4}.

Then, we define a set of value domains D where

each domain includes the values that corresponding

variable can take. In our case: D={D1,D2,D3,D4}

and D1=D2=D3=D4={red, green, blue}.

Finally, we define a set of constraints C where

each constraint represents a relation between

variables of the problem. More specifically, in the

example case of four map coloring problem C define

as , C={C12,C14,C23,C24,C34}, where Cij ≡ Vi≠Vj

(that is each region represented by its corresponding

variable has a different color/value from its adjacent

region)

Associated with every CSP is a constraint graph.

The constraint graph contains a node for each

variable and an edge between each pair of nodes for

which there is a constraint between the

corresponding two variables. Figure 2 represent the

constraint graph for the four region map coloring

problem where each edge represents two adjacent

regions in the map.

Figure 2: A simple four-region map-coloring problem.

The coloring game has both single player and

multiplayer modes. The objective of the game is

similar for the two modes. The player has to color a

map, using a set of available colors so that adjacent

regions of the map do not have the same color. The

complexity of the generated maps (number of

distinct areas) and the available colors set are

parameterized. The collaborative game aims to assist

learners to realize and better understand through the

game the concept of constraint satisfaction during

problem solving and the educational process.

Initially the students can determine the

complexity of the problem to be solved. They can

either decide by themselves on how many colors

they think are adequate for solving the problem or

can choose one of the three available difficulty

levels (easy, medium, difficult).The determination of

the level of difficulty of each map based on the

number of colors that the map needs to be resolved

and the maximum number of areas bordering each

region. A screenshot of the multiplayer coloring map

game on a simple case is illustrated in Figure 3.

A Collaborative Game for Learning Algorithms

545

Figure 3: Map-coloring problem Multiplayer game.

In the first level of the game, students use additional

colors beyond the minimum required. In the second

level, one additional color is used and in the third

level the minimum number of colors is used, thus

making the game more difficult and challenging to

solve. The latter is concerned with whether the user

chooses from the set of all available colors for each

area, or the system filters the colors that cannot be

used (i.e., propagates the constrains, in other words

makes an AC-3 step). Also, for each option of the

player, it creates the appropriate tree structure

representing the regions, the coloring steps taken

and how propagation of constraints as presented in

Figure 4. In this way, the student can get a useful

model of how the constraints are propagated, which

is an important and fundamental characteristic of

CSPs. Furthermore, a student can step back to

previous states (of the solution path), a process

known in search algorithms as backtracking.

Backtracking is in several cases a difficult to grasp

aspect of search algorithms. Using the game, the

student can see it in practice while playing. Also, the

students can solve the problem as many times as

they want and find different or better solutions, by

learning the constraint satisfaction algorithms.

Different color combinations can produce sets of

different solution paths. Additionally, the game

calculates the student’s score, which is determined

by the difficulty level of the problem, the students’

actions and the time taken to solve it.

Figure 4: Treestructure of Map-coloring problem.

Playing with the game, students unknowingly

become familiar with concepts like constraints

propagation, backtracking, multiple solution paths

and others by actually emulating steps used by a

search algorithm. This can greatly facilitate teaching

of the theory behind CSP solving and corresponding

algorithms, finally, they can use the game again to

test the obtained knowledge on algorithms and

experiment with them, working out their differences

and details. This can be easily done, given that really

the game can take on many different configurations

and make multiple trade-offs between complexity

and fun factors. Of course, the tutor could specify

different scenarios to make learning more directed

and avoid useless playing.

4 EVALUATION

We conducted an experimental evaluation study in

the context of the artificial intelligence course in our

university. The purpose of the experimental study

was to evaluate the learning effectiveness of the

collaborative game and identify students' attitudes,

perceptions, and usage-intentions toward learning

with them.

4.1 Method

In the context of the evaluation study, we compared

learning with the collaborative game versus the

learning with the single player game. The

participants in this evaluation study were 100

undergraduate students (both female and male) that

were enrolled in the artificial intelligence course.

They were in the 4th year of their study and their age

ranged from 21 to 24 years. In the context of the

study, a pre-test / post-test experimental procedure

was followed. Initially, the participants of the study

were randomly assigned to two groups (control vs

experimental) and so, we created two groups that

were named GroupA and GroupB respectively. The

first group, GroupA, consisted of 50 students, where

22 were female and 28 were male, and the second

group, GroupB consisted of 50 students where 23

were female and 27 were male.

We followed a pre-test, post-test study design

followed by a questionnaire survey to assess the

learning effectiveness, the learning attitudes and the

motivation of students and also for gathering

students’ attitude, opinion and experiences about the

collaborative game. The students of the GroupA,

which constitutes the control group, learned with the

educational game as a one-player game. Students of

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546

GroupB, which constitutes the experimental group,

were given access to use the collaborative game and

to study the Constraint Satisfaction Problem. The

experiment consisted of four main phases which are

the pre-test, the learning phase, the post-test and the

questionnaire phase used to collect students’

thoughts, opinions and experiences about the game.

The overall structure of the evaluation study is

illustrated in Figure 5.

Figure 5: The overall structure of the experimental study.

The two groups followed the same procedure;

both groups were given a pre-test and then GroupA

learned about CSP algorithm with the single-player

game, while the students of GroupB learned with the

assist of the collaborative game afterwards, both

groups were given a post-test. After the post-test, all

participants were given a questionnaire to fill in. The

pre-tests and post-tests were isomorphic and

incorporated structurally equivalent exercises on

CSPs and were conducted in the computer room of

our department. After the learning activities the

students were asked to fill in a questionnaire and

express their affect, opinions and experiences

towards the games and assess their learning

assistance.

4.2 Results and Discussion

An independent t-test was used on the pre-test. The

means of pre-test for GroupA and GroupB were 4.62

and 4.71 respectively. The results showed no

significant difference among the students of the

groups (p=0.61> 0.05) so it was concluded the two

groups had equivalent prior knowledge on the

domain of Constraint Satisfaction before the

experiment.

After that, an ANOVA analysis was performed.

The ANOVA results indicate the differences in post-

test scores are statistically and significantly different

between the two groups (F=142.4 p=0.00<0.05).

Finally, the results showed that the performance of

the students of GroupB, who used the collaborative

game, was better than that of the control group.

Table 1 presents the post-test results, in which the

mean values of the post-test scores were 6.41 for

Group A, and 8.01 for Group B.

Table 1: Analysis of the Post Test Results.

Groups

Mean

GroupA

6.41

0.65

GroupB

8.01

0.87

Subsequently, the students of the GroupA and

GroupB were asked to fill in a questionnaire that

included questions for evaluating usability of the

one-player and collaborative game respectively,

stating their experience and their opinions about the

learning process. The questionnaire included 12

questions. The questions Q1-Q9 were based on the

Likert scale (1: not at all, 5: very much). Questions

10-12 were open type questions and concerned

strong and weak points of the educational games or

problems faced.

Table 2: Results of the Questionnaire.

Question

GroupA

Mean

GroupB

Mean

The games make the CSP

more understandable.

3.88

4.46

The games assisted me in

getting a deeper understanding

of the functionality of the CSP

after playing.

4.25

4.65

I enjoyed playing and learning

with the games.

4.05

4.52

The games made me more

active in the course.

3.95

4.16

The games can increase my

motivation.

3.55

4.42

The games can enhance my

engagement in the course.

3.45

4.43

The games can enhance my

learning interest.

4.01

4.45

The using of the games for

learning is more interesting

than other ways of learning.

3.88

4.60

I suggest the games to be

integrated into the course and

be used by the next year’s

students.

3.5

4.46

A Collaborative Game for Learning Algorithms

547

The results of the questionnaire are very

encouraging indicating the very positive attitude that

the students of GroupB have towards the integration

of games in the course curriculum. The

questionnaire results showed that students of

GroupB indicated that the games make the CSP

more understandable to them (4.46) and that they

assisted them in getting a deeper understating of the

way that each algorithm functions (4.65). More

specifically, students found the collaborative game

to increase their motivation (4.42) and also to

enhance their engagement in the course (4.43) and in

addition to greatly enhanced their interest for

learning (4.45). Also, students indicate that they

enjoyed the integration of the collaborative game in

the context of the course (4.52) something that made

them also more active in the course (4.16).

Furthermore, the results show that students found

the games and learning with collaborative game very

interesting and more interesting that other ways of

learning (4.60). Finally, students suggest the

collaborative game to be integrated into the course

curriculum so that to be used by the students of next

years (4.46).

The results of the experimental study shows that

the attitude of the students towards the collaborative

game was very positive and also that the game has

great learning capabilities. More specifically, an

interesting aspect of the study concerns the increase

of students’ engagement with the course and also the

increase of their efforts in a very positive and

enjoyable way.

5 CONCLUSIONS

With the great advancement of the technology, the

educational games constitute an important mean of

delivering effective learning procedures to students

and they can offer various learning opportunities in

all levels of education. Serious games can scaffold

students learning and bring excitement and

enthusiasm among students and most of all, assist

them in learning more efficiently, in an interactive

and entertaining way. In this work, we presented a

collaborative game that was developed with the aim

to assist students in learning constraint satisfaction

problems and algorithms. In the context of the

collaborative games, students can experiment and

apply constraint satisfaction algorithms in various

learning activities and training scenarios. The game

has been integrated and used in the curriculum of

artificial intelligence course in our university. An

evaluation study was conducted in real classroom

conditions and revealed quite promising results. The

results indicate that the collaborative game

constitutes an effective way to enhance students’

motivation, engagement and interest and also help

them to deeper and more completely understand

CSP algorithms and their functionality.

The findings of the study provide various

directions that future work can focus on. Initially, a

bigger scale evaluation will be designed and

conducted with the aim to provide a more complete

insight of the learning efficiency of the game and

also evaluate specific educational capabilities of

them such as the feedback and the assistance offered

to students. Another direction for future work

concerns the integration of learning analytics

mechanisms into the games that will record and

analyse students’ behaviour and actions during the

learning activities.

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