=Paper=
{{Paper
|id=Vol-3029/paper03
|storemode=property
|title=Lempel: Developing the pattern recognition skill in computational thinking through an online educational game
|pdfUrl=https://ceur-ws.org/Vol-3029/paper03.pdf
|volume=Vol-3029
|authors=Ekaitz Polledo,Pablo Garaizar,Mariluz Guenaga
|dblpUrl=https://dblp.org/rec/conf/lasi-spain/PolledoGG21
}}
==Lempel: Developing the pattern recognition skill in computational thinking through an online educational game==
https://ceur-ws.org/Vol-3029/paper03.pdf
Lempel: Developing the pattern recognition skill in
computational thinking through an online educational
game
Ekaitz Polledo1[0000-0002-9928-7667] and Pablo Garaizar1[0000-0001-8160-9130] and
Mariluz Guenaga1[0000-0002-0311-2150]
1
Faculty of Engineering, University of Deusto, 48007 Bilbao, Spain
[epolledo, garaizar, mlguenaga]@deusto.com
Abstract. Computational thinking is a key set of skills for the 21st century's dig-
ital literacy. Taking advantage of computers to solve complex problems automat-
ically will be helpful in most future jobs. Among the skills that comprise Com-
putational Thinking, pattern recognition plays an important role in managing and
compressing information. To foster the development of this skill among primary
and secondary school students, we have developed Lempel. In this game, we pro-
pose a set of challenges of increasing complexity in which players have to pro-
vide a compressed version of the information presented. Lempel's fine-grained
interaction data logging system allows us to use Learning Analytics techniques
to better understand how the learning of this skill takes place.
Keywords: Computational Thinking, Pattern Recognition, Educational Games,
Text Compression.
1 Introduction
Due to business needs and the importance of technology in our society, the concept of
Computational Thinking has emerged in recent years, especially focused on compul-
sory education. STEM (Science, Technology, Engineering and Mathematics) are prior-
ity areas in education in Europe and basic skills in arithmetic, mathematics and science
are considered fundamental foundations for further learning [1]. This goes beyond pro-
gramming by enabling problem solving, system design and understanding of human
behavior by making use of the fundamental concepts of computer science [2]. Everyone
can benefit from applying these concepts to their daily lives, based on a spiral that in-
cludes society, science and technology in which all affect and enrich each other [3].
Computational Thinking main skills are decomposition, pattern recognition, algorithm
solving and abstraction.
Computational Thinking has become one of the topics of global attention as part of
the efforts to bring computer science to all K-12 schools [7]. In addition, initiatives
such as Hour of Code or CodeWeek have boosted the development of this competence,
making it accessible to millions of students through free digital platforms.
The increased use of digital tools for learning has resulted in the use of Learning
Analytics to be able to make decisions on a large amount of user interaction data. The
Society for Learning Analytics and Research (SoLAR) defined learning analytics as
"the measurement, collection, analysis and reporting of data about learners and their
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Learning Analytics in times of COVID-19: Opportunity from crisis 29
contexts in order to understand and optimize learning and the environments in which it
occurs." [10]. Numerous studies support it and demonstrate its potential to improve
engagement and motivation, to support teachers and even to predict results [11][12]. In
addition, in combination with other models [13] it can categorize learners according to
their way of processing information and can help to personalize their learning path.
In section 2 we present the fundamentals of computational thinking and some tools
used for its development. Section 3 describes the "Lempel" game itself, its design and
development phases. Section 4 describes the experimental approach used and prelimi-
nary results. Finally, conclusions and future lines of work are presented in section 4.
2 Computational Thinking
Many applications can be found for the development of Computational Thinking [8].
Code.org is a non-profit organization that has several games for learning programming
and creating new challenges. Scratch is a tool for creating games, animations and inter-
active resources using a visual programming language. Blockly is a game of successive
challenges for learning programming based on a visual programming library. Finally,
MakeWorld is a platform that provides a methodology and innovative educational re-
sources for learning STEM while developing Computational Thinking.
Currently, several models have been defined to understand how students develop
Computational Thinking. Werner et al. have followed an analysis to describe how mid-
dle school students program in Alice [4]. Piech and collaborators have used a Markov
model to describe how students reach solutions [5]. Seiter and Foreman developed a
progression model that was used to relate good programming practices and the age of
the authors [6]. All the mentioned studies are based on the analysis of algorithm solving
and there are not numerous models based on the other 3 skills of Computational Think-
ing. This trend has led to consider coding as the core of Computational Thinking [7].
In this case, we are focusing on pattern recognition. Some authors highlight the im-
portance of the analysis of this competence and the lack of studies on it [14]. Previous
studies have examined some aspects of pattern recognition such as the identification
and completion of patterns with kindergarten students [15][16]. Moreover, this is one
of the most complete CT competencies associated with other competencies such as ab-
straction [17].
Therefore, we present Lempel, a tool for the development of computational thinking
based on pattern recognition. Our objective with this tool is, through the application of
Learning Analytics, to analyze the development of this computational thinking compe-
tence in learners taking into account their personal characteristics and the faced chal-
lenges. To perform this analysis, we anonymously collect user interactions following
the best practices of other authors in similar experiments using Learning Analytics and
Computational Thinking [18][19][20].
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30 Learning Analytics in times of COVID-19: Opportunity from crisis
3 Lempel
Lempel is an online educational game developed by the Deusto LearningLab group at
the University of Deusto. It has been created to be an educational resource to help the
development of Computational Thinking in a classroom or stand-alone environment.
Participants must compress a text string displayed on the screen composed of different
characters. To achieve this, they must recognize the pattern or patterns in the string and
insert them into containers called "registers". The game consists of a series of blocks
that represent the different characters of the strings or calls to the different registers. As
the blocks are inserted into the different registers and the different patterns are com-
posed, they will be replaced in the initial character chain, giving a visual response to
the participant's activity (Fig. 1).
Fig. 1. Lempel game main interface
Therefore, this game focuses on the development of Pattern Recognition, which is one
of the main skills of Computational Thinking and thus, it goes beyond programming
and algorithm-oriented applications by putting the focus on a data-oriented format and
its analysis and processing. This game also works on skills such as abstraction to be
developed while the user focuses on the patterns to be compressed and forgets about
the characters around him. It is designed to suitable for everyone, whether they have
previous knowledge or not. So, it is not necessary to have previously used Computa-
tional Thinking tools.
3.1 Game design.
Lempel is based on a space game theme. In this one, a ship going to the moon runs out
of space for the processing of all its data, therefore, its crew members must compress
them to be able to make space for the new ones and be able to arrive successfully.
Through a series of incremental difficulty levels, participants encounter a series of text
strings in which they have to recognize the available patterns and thus reduce their size.
3.1.1 Game mechanics & GUI
The interface of Lempel consists of three parts: the chain string to be compressed, the
registers to introduce the patterns and the progress indicators. Before starting every
block of levels, participants are introduced with tutorials about how to use the game.
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Learning Analytics in times of COVID-19: Opportunity from crisis 31
In the upper area of the game, you will find the string to be compressed. Each of the
characters in the string is represented by a letter and a color to make the game visually
more friendly, so the patterns will be easier to recognize. These blocks will be replaced
by circles representing the registers as you advance in the level and enter the patterns.
In the middle of the screen, you will find the registers. These are represented by a
number and the player will have the possibility to add up to 4 depending on the patterns
detected in the proposed chain. The player will have to drag to each of these the differ-
ent characters available forming a chain that represents a pattern.
Finally, in the lower area you will find the progress indicators. These represent the
degree of compression reached in the level by means of the size of the string and the
compression percentage, in text format, and the efficiency of the solution through a 5-
star scale.
When the player believes that the level is complete and his solution is correct, he
must confirm by pressing the "Send Code" button and the game will show them whether
it is correct, partially correct (it can still be compressed further) or incorrect.
The different gamification elements such as the stars or the compression limit to pass
the level are parameterizable and can be activated or deactivated depending on the de-
sired game mode.
3.1.2 Levels
The current version of LEMPEL is composed of a total of 61 levels, 5 of which are
tutorials distributed throughout the game. The levels have been organized in groups of
levels of the same category and ordered based on their difficulty calculated through a
heuristic formed by the following variables: length of the string to be compressed, dif-
ferent characters available, size of the solution, letters not belonging to patterns, pat-
terns that must call other patterns, number of registers to be used (patterns), size of the
registers and patterns composed by equal letters.
The different levels can be classified into the categories listed in Table 1. Each of
the initial character chains has an initial size and the solutions indicate the sum of the
resulting string and the size of the registers.
Table 1. Level types included in Lempel.
Level Type Description Level Example
ABCDABCDABCDABCD (16)
These levels are composed of patterns
Levels with 1 pattern Solution: 1111 (9)
with different letters.
1: ABCD
BBBBBBBBBBBB (12)
Levels with repetitive let- These levels include patterns formed by
Solution: 1111 (8)
ters the same letter repeatedly.
1: BBB
These levels contain some letters that
ABCDCDCDCDCDCDCD (16)
Levels with letters out of are not part of any pattern, therefore
Solution: AB1111111 (12)
pattern they should not be entered in the regis-
1: CD
ters.
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32 Learning Analytics in times of COVID-19: Opportunity from crisis
These levels include 2 different patterns CBCBCBCBADADADAD (16)
Levels with 2 patterns and can be combined with patterns Solution: 11112222 (14)
from the previous categories. 1: CB 2: AD
These levels use one of the registers to ABCABCABCABCABCABCABCABCAB
Levels with 2 registers, call another one. The first pattern multi- C (27)
using one as a multiplier plies its content the number of times it Solution: 222 (11)
is called from the second one. 1: ABC 2: 111
Levels with 2 patterns These levels contain 2 different pat-
ABCABCDDABCABCABCABCDD (22)
and registers with charac- terns. The first one is simple and the
Solution: 2112 (13)
ters and calls to other reg- 2nd one is made up of characters and
1: ABC 2: 11DD
isters calls to the first of the registers
AAABBBAAAAAABBBBBBAAAAAAB
These levels combine previous catego-
Levels with 3 and 4 reg- BBBBBAAAAAABBBBBBBBAAA (48)
ries using up to 4 registers increasing
isters Solution: 1233321 (11)
the complexity of the registers.
1: AAA 2: BBB 3: 1122
3.1.3 Logging System
One of the main keys for the analysis of this game for the development of Compu-
tational Thinking is its event fine-grained logging system. Each of the triggered events
contains the following information:
• User information obtained at the beginning of the activity through a brief
questionnaire. Fields included: user, username, age, gender, group ref.
• Event timestamp. Fields included: user timestamp, server timestamp, level
delta time, log order (incremental number).
• Level information. It is the information about the level and challenge of
which the event is being registered. Fields included: challenge code (game
version), level reference.
• Game information. Fields included: action container (orig./dest.), action
object, action position (orig./dest.), action, code, dictionaries (code on each
of them), size, size of solution, score.
All these events are logged into the following scenarios: level start, result check,
error, partial and success solutions, dictionary add/remove and drag and drop or click
actions over blocks.
3.2 Technological Implementation
Nowadays there are many technological alternatives for game development. One of the
most widespread options for this type of platform is web development including tech-
nologies such as TypeScript and HTML5.
In this case, as it is a text-processing oriented game and does not require very com-
plex graphic processing, web technology has been chosen for the development using
the Angular framework. Furthermore, as this technology is accessible from any
browser, it facilitates access from any computer available in educational centers.
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Learning Analytics in times of COVID-19: Opportunity from crisis 33
This web application communicates in real-time with an Apache server that imple-
ments a REST API through the Symfony framework, and the data is stored in a MySQL
database. Customized implementation of the logging-storage system has been chosen
due to the positive previous experiences on similar projects and the possibility of data
integration between Computational Thinking tools for the personalization of the learn-
ing process.
3.3 Game evolution
The Lempel platform has gone through 3 different versions until it reached the currently
available one.
First version (40 levels, 2 tutorials). This version started with no real-time feedback
about the results of the game, participants had to validate their solutions for it. After the
first experimentations (136 participants), only 58% of the participants reached level 20.
Moreover, the ratio of correct solutions only reached 30% at that level. Therefore, it
was concluded that the levels were not well designed to follow an increasing difficulty
path and therefore, some concepts were not being correctly understood by the partici-
pants.
Second version (40 levels, 4 tutorials). This version of the game started with 2 new
tutorials from level 20 onwards. After piloting this version (114 participants), it was
observed that the results of players reaching level 20 had improved substantially, reach-
ing 93%. This was due to a better understanding of the higher levels through the tuto-
rials and the redesign of the predecessor levels. Even so, we detected that there was still
a gap in players reaching the higher levels (53% at level 26).
Third version (56 levels, 5 tutorials). This version introduces new levels between 20
and 40. In addition, levels are reordered complying with the heuristic of leveling pre-
viously mentioned. This version includes real-time scoring of game status and changes
in the initial chain to observe what is happening in each move. It includes also com-
pression efficiency limits, in which the user must compress a minimum of half of the
best solution, and scoring stars, in which the user can see how he is performing the level
in real-time and thus be able to rectify if his solution is not the most appropriate.
4 Experimentation methodology
The Learning Analytics process, as mentioned above, consists of the following phases:
Measurement, Collection, Analysis and Reporting of the data. Once the development
of the platform has been completed, the experimentation phase begins in which its us-
age data are collected and analyzed for subsequent decision making.
4.1 Materials & Tools
The aforementioned tool Lempel has been integrated into Kodetu platform (https://ko-
detu.org) - a platform that integrates several tools related to Computational Thinking.
It allows the management of groups for the different experimentations, employing
unique access codes for each group, and a common access/registry for different tools.
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34 Learning Analytics in times of COVID-19: Opportunity from crisis
The different game levels are organized as follows. Each group of levels is preceded
by a tutorial. Levels 1-10 are composed of simple patterns and introduce the user to the
game. Next, levels 11-18 combine the patterns of the previous levels with characters
that do not form a pattern. In levels 19-26 we find two patterns in each chain and as in
the previous ones, in levels 27-30 we find these combined with characters that are not
part of the pattern. Levels 31-36 introduce the registers that are used to call other ones
(recursion). Finally, levels 37-40 introduce 2 patterns and registers with a combination
of characters and calls to other registers. At this point, the player will have worked
through all the Pattern Recognition techniques and will find the more complex levels
41-56 with up to 3 and 4 patterns.
4.2 Participants
By June 2021, 16 experiments have been carried out by inviting secondary and high
school students to activities organized by the Faculty of Engineering of the University
of Deusto. After performing the cleaning of test data and erroneous users, a total of
337,231 interactions have been recorded from 393 participants between the ages of 13
to 16 years old (Mean: 14.46, SD: 1.16, Girls: 47.07%, Boys: 47.84%, Others: 5.09%,
Workshops: 16).
Participants have been divided into 4 different groups: A) Participants with neither
limit on compression nor stars during the game (65 participants, Mean: 14.32, SD: 1.19,
Workshops: 3), B) Participants with 50% limit on compression but no stars during the
game (107 participants, Mean: 14.65, SD: 1. 09, Workshops: 4), C) Participants with
no limit in compression but with stars during the game (106 participants, Mean: 14.58,
SD: 1.06, Workshops: 4), D) Participants with 50% limit in compression and stars dur-
ing the game (115 participants, Mean: 14.27, SD: 1.25, Workshops: 5).
4.3 Experimentation procedure
At the beginning of each session, each group of participants was assigned to an exper-
imental group and was informed about the objective and their voluntary participation
in the session. To ensure that all players correctly accessed their game session, they
were given a 5-letter group code that would show them only their game version. Before
showing the game, participants were asked about their demographics (age, gender, ed-
ucation level), whether they knew how to program before the workshop (yes or no),
whether they have played Kodetu before (yes or no), and their like for technology (1-
min to 10-max).
Once the initial questionnaire is completed, the player is introduced to the game and
the game procedure is explained. Upon completion of each level, if it is partially correct
the player has the opportunity to improve it or continue, and if it is perfect, the next
level is shown until the end of the game.
The experimentations lasted 60 minutes, of which 15 minutes were used for the gen-
eral explanation of the game and 45 minutes for playing on their own.
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Learning Analytics in times of COVID-19: Opportunity from crisis 35
4.4 Preliminary results
After obtaining the logs generated by the application, a preliminary comparison has
been made to observe the performance of the participants in the different versions.
First, we analyzed the
achievement level of partici-
pants. All participants
achieved level 18, which is the
last level with one register. In
the following levels, as diffi-
culty increases, participants
were dropping out accordingly
(Fig. 2).
Fig. 2 Permanence rate through levels
We analyzed the success percentage of participants (Fig. 3). A level is successful if
the participant achieves the best possible solution. They have the opportunity to im-
prove their solutions if they are not introducing the best one, so that result is the last
solution proposed. As it can be observed, the group with limits and stars (D) has the
best performance maintaining its success rate always upper than 85%.
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
92 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 93 19 20 21 22 23 24 25 26 27 28 29 30 94 31 32 33 34 35 36 95 37 38 39 40
A - NS / NL B - NS / SL C - SS / NL D - SS / SL
Fig. 3 Success rate on 1-40 levels Fig. 4 Quality of the solution (avg.)
Concerning this analysis, we have represented in Figure 4 the quality of the given
solutions by each user. The quality is the percentage of compression being 0% the worst
correct solution and 100% the best solution. We found that on levels 1-18 percentages
are maintained in 99% on groups with stars (C, D), group D continues on this trend till
level 44 (where the last participant arrived). On the contrary, group C continues a sim-
ilar approach to group B from level 19, where 2 register levels start. Group A has an
average quality on those levels of 89% and group B of 96%. Taking into account levels
1-40, group A, with no limits neither stars, is the worst one with an average of 79%,
continued by group B with 93%, group C with 95%, and on top group D with 99%.
In addition, we observed that there are levels (like 5 and 10) where there are qual-
ity decreases in the easiest levels. This matches with the levels at which new con-
cepts, such as patterns with repeated characters, characters that do not belong to pat-
terns, etc. are introduced.
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36 Learning Analytics in times of COVID-19: Opportunity from crisis
Fig. 5 Interactions per user (avg.) Fig. 6 Time to resolve (avg.)
We also analyzed the interactions per user (Fig. 5) and the time they need to resolve
each level (Fig. 6). As it can be observed, both indicators follow a similar correlation.
We found that all groups follow a similar average of interactions and time. The differ-
ence is remarkable on most difficult levels, or in those where new concepts are intro-
duced. On levels 1-18, the worst performing group is A with an interaction average of
14,72 and level completion average of 46 seconds, continued by group B with 12,72
interactions and 41 seconds, group C with 11,09 interactions and 36 seconds, and finally
group D with 10,97 and 37 seconds. Groups that include stars (C, D) have similar results
both on interactions and level time.
5 Conclusions
The present work provides an educational tool, LEMPEL, which allows us to under-
stand and analyze how learners acquire knowledge about certain computational think-
ing skills, such as pattern recognition. This platform integrates into a data compression
game a fine-grained logging system, which allows us to register each of the events that
students trigger on the platform. The information captured allows us to apply learning
analytics techniques to evaluate the development of the different competencies.
An exhaustive analysis of the interactions logged on the platform is currently under-
way. Preliminary results indicate that the stars and solution quality limit included in the
game and the improvements in the leveling are an aid to improve performance during
the learning path. In addition, as future work we will carry out statistical analysis to
analyze the performance in pattern recognition taking into account the user's character-
istics, level types, etc.
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