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Video Games: A Potential Vehicle for Teaching Computational Thinking

  • Sue Inn Ch’ngEmail author
  • Yeh Ching Low
  • Yun Li Lee
  • Wai Chong Chia
  • Lee Seng Yeong
Open Access
Chapter

Abstract

Previous studies in computer science education show that game playing is negatively correlated with success in introductory programming classes in (Wilson, Shrock ACM SIGCSE Bulletin, vol 33, pp 184–188, 2001). However, informally, we observed that students who have previous gaming experience take to programming tasks easier than those without gaming experience. There have also been recent studies that show that playing strategic video games can improve problem-solving skills which is an essential skill in program design. This chapter presents the findings of our study to identify if a correlation between previous gaming experience (game playing) and individual computational thinking (CT) skills exists. To achieve this, a survey was administered on undergraduate students undertaking an introductory computing course to collect data on their gaming history and an individual assignment on Scratch. Each project was subsequently analysed to determine the level of mastery of core CT skills. Cochran–Armitage test of trend was then executed on each CT skill category with respect to the coded gaming experience. The results obtained during our analysis shows a correlation between gaming experience and specific categories of the CT skills domain particularly in the area of abstraction and problem-solving and user interactivity. The outcome of our study should be beneficial as ways to leverage on students’ gaming experience in the classroom will also be discussed.

Keywords

Computational thinking Game-based learning Games and learning Scratch assignment Introductory programming 

14.1 Introduction

Computational thinking has been touted as a twenty-first century skill that is as important as reading, writing and arithmetic (Wing, 2006). The term computational thinking was initially coined by Seymour Papert in his book Mindstorms (p. 182) (Papert, 1980) and further elaborated in (Papert, 1996; Barba, 2016). It was only in the communication by Wing (2006) did the term became popularized. In this communication, Wing described computational thinking as ‘solving problems, designing systems, and understanding human behaviour, by drawing on the concepts fundamental to computer science’. Since then, different researchers and technological groups have suggested that computational thinking involves a number of subskills; with each entity adding their own interpretation as to the key skills that encompass computational thinking. Many educational programmes are then devised around these skill definitions to introduce computational thinking to children. However, many of these programs use programming tools and environments to expose students to computational thinking bringing about the misconception that computational thinking is equated to computer science and subsequently equivalent to ‘programming’. As highlighted in (Fletcher & Lu, 2009), teaching computational thinking is not as simple as repackaging CS1, or CS0, and teaching it at an earlier stage. It is also acknowledged that without problem analysis skills, students who are proficient at programming languages would fail to create their own solutions (Koulouri, Lauria, & Macredie, 2015). However, problem-solving skills is not something that can be developed by every student over a short period of time particularly a semester long class. It requires students to experience and reflect on the consequences of their actions. This takes time, practice and effort that sometimes a semester-long course does not permit.

Digital games have been around since the creation of computers and long before computational thinking was popularized and labelled as an important skill. The advent of mobile computing platforms allows digital games to be easily obtained via the internet and gaming to be done anywhere and anytime. Video games which were once confiscated in classrooms are now being adopted by educators as a key teaching tool (Shreve, 2005). This comes as no surprise because digital games contain interactive, engaging and immersive elements that have educational affordances (Frazer, Argles, & Wills, 2008; Gee, 2005). According to (Klopfer, Osterweil, & Salen, 2009), the use of games in formal education can take the form of two approaches: (a) adoption of commercial, off-the-shelf (COTS) games or (b) the application of games in the traditional classroom setting. For the field of CT education, works reported for the former approach are rare whereas the latter approach can take the form of game design assignments or use of serious games. Game design assignments are assignments whereby students are given the task to design and create games to demonstrate the application of learnt technical concepts (Basawapatna, Koh, & Repenning, 2010; Leutenegger & Edgington, 2007; Monroy-Hernández & Resnick, 2008). Computer science (CS) educators have also explored the use of specially created games, also known as serious games, incorporated into traditional lesson plans so that students learn technical concepts through gameplay (Kazimoglu, Kiernan, Bacon, & MacKinnon, 2012; Liu, Cheng, & Huang, 2011; Muratet, Torguet, Jessel, & Viallet, 2009). Despite studies (Becker, 2001; Kazimoglu, Kiernan, Bacon, & MacKinnon, 2012; Liu, Cheng, & Huang, 2011) reporting an improvement in student engagement and motivation towards the CS content, these studies do not investigate the adoption rate of these games as leisure activities at the end of the course or the effects of extended usage of the designed serious games on students’ problem-solving and/or programming skills over time.

On the other hand, children and adults learn best by playing. The work by (Ch’ng, Lee, Chia, & Yeong, 2017) delineates the gameplay elements possessed by popular COTS games for different game genres that support key skills in computational thinking. However, the study did not determine if there is indeed a correlation between playing video games and the mastery of key computational thinking skills. In this chapter, we therefore present our research design and findings to answer the research question on whether past gaming experience does influence specific computational thinking skills. If so, video games can be used as a vehicle to train students to think logically in a fun environment as an alternative to the forceful use of serious games or soldiering through the learning of a programming languages to teach students computational thinking.

14.2 Computational Thinking Skills

The main idea behind the computational thinking movement is that knowledge and skills derived from the field of computer science has far-reaching applications that can be beneficial to other fields too. However, since its conception, there have been different definitions about the skills that encompass computational thinking skills. Some of these definitions can be tightly coupled to programming while others are more loosely defined and general. For example, the CT skills listed by Moreno-León, Robles, & Román-González (2015) is more closely related to programming while the definitions provided by (Lee, Mauriello, Ahn, & Bederson, 2014) is general in nature with little reference made to programming. Barr and Stephenson (Barr & Stephenson, 2011) provided examples on how the nine core1 CT concepts and capabilities may be embedded in different discipline activities. Table 14.1 lists the different definitions of CT skills by different parties. A look at these definitions shows a repetition and overlap in some of the skills defined such as abstraction, algorithm design and problem decomposition.
Table 14.1

Table of comparison listing the difference skills encompassing CT defined by different parties

Organization/Researchers

CT skills

Definitions

Google for education (Google, 2018)

Abstraction

Identifying and extracting relevant information to define main idea(s)

Algorithm design

Creating an ordered series of instructions for solving similar problems or for doing a task

Automation

Having computers or machines do repetitive tasks

Data collection

Gathering information

Data analysis

Making sense of data by finding patterns or developing insights

Data representation

Depicting and organizing data in appropriate graphs, charts, words or images

Decomposition

Breaking down data, processes or problems into smaller, manageable chunks

Parallelization

Simultaneously processing or smaller tasks from a larger task to more efficiently reach a common goal

Pattern generalization

Creating models, rules, principles, or theories of observed patterns to test predicted outcomes

Pattern recognition

Observing patterns, trends and regularities in data

Simulation

Developing a model to imitate real-world processes

BBC Bitesize (BBC, 2018)

Decomposition

Breaking down a complex problem or system into smaller, more manageable parts

Pattern recognition

Looking for similarities among and within problems

Abstraction

Focusing on the important information only, ignoring irrelevant detail

Algorithms

Developing a step-by-step solution to the problem, or the rules to follow to solve the problem

Barr and Stephenson (2011)

Data collection

The process of gathering appropriate information

Data analysis

Making sense of data, finding patterns and drawing conclusions

Data representation

Depicting and organizing data in appropriate graphs, charts, words or images

Problem decomposition

Breaking down tasks into smaller manageable parts

Abstraction

Reducing complexity to define main idea

Algorithms and procedures

Series of ordered steps taken to solve a problem or achieve some end

Automation

Having computers or machines do repetitive or tedious tasks

Parallelization

Organize resources to simultaneously carry out tasks to reach a common goal

Simulation

Representation or model of a process. Simulation also involves running experiments using models

Berland and Lee (2012)

Conditional logic

Use of ‘if-then-else’ construct

Algorithm building

Set of instructions

Debugging

Act of determining problems in order to fix rules that are malfunctioning

Simulation

Modelling or testing of algorithms or logic

Distributed cognition

Rule-based actions

Lee, Mauriello, Ahn, and Bederson (2014)

Algorithmic thinking

Logical steps required for constructing a solution to a given problem

Decomposition

Process of breaking down a large problem into smaller sub-problems or details

Pattern recognition

Identification of similar phenomenon

Pattern generalization and abstraction

Solving problems of a similar type because of past experience solving this type of problem

Unarticulated instances

Performing an action as a result of thought process that could not be articulated

Kazimoglu, Kiernan, Bacon, and MacKinnon (2012)

Problem-solving

Decomposing the problem to generate alternative representations and to determine if the problem can be solved computationally

Building algorithms

Construction of step-by-step procedures for solving a particular problem

Debugging

Analysing problems and errors in logic or in activities

Simulation

Demonstration of solution to proof that it works

Socializing

Brainstorming and assessment of incidents/strategies among multiple parties

Moreno-León, Robles, and Román-González (2015)

Abstraction and problem decomposition

The ability to break a problem into smaller parts that are easier to understand, program and debug

Logical thinking

Use of instructions to perform different actions depending on the situation

Synchronization

Use of instructions to enable the characters to perform in the desired order

Parallelism

Use of instructions to make several events can occur simultaneously

Flow control

Use of sequence and repetition blocks to control the behaviour of characters

User interactivity

Allows players to perform actions that can provoke new situations in the project through the use of input peripherals such as keyboard, mouse or webcam

Data representation

Ability to set information for all characters in order to execute the program properly

It is a common practice for researchers (Berland & Lee, 2012; Kazimoglu, Kiernan, Bacon, & MacKinnon, 2012) in the field of CT education to usually formulate their own definition of CT skills by rationalizing from literature and existing definitions. For our work in this chapter, we will use the CT skills defined by (Moreno-León, Robles, & Román-González, 2015).

14.3 Methodology

Data was collected from 736 first-year undergraduate students taking an ‘Introduction to Computers’ course at a private university in Malaysia. This course teaches students the basic concepts of what computers are, how computers store and process information, communicate with each other and applications of computers in daily life. The course also covers a brief introduction to programming, more specifically software design lifecycle, basic programming constructs and the different types of tools that can be used to develop software applications. Students were taught how to use Microsoft© Office tools to solve problems and basic game design using MIT Scratch (MIT, 2016) during the practical sessions of the course. Scratch was chosen as the development platform so that students can focus on the design of the solution instead of the syntax of a particular programming language. The students were from two different schools—School of Computing and School of Business. Table 14.2 shows the composition of students from each school.
Table 14.2

Gender distribution and composition of students from each school

Gender

Computing

Business

Total

Male

114

213

327

Female

41

368

409

Total

155

581

736

For the individual assignment, students were tasked to design a Catching Name Game—a game where the objective of the game is to collect characters that appear on the screen to spell out words. The students were given the freedom to determine the type of gameplay that they wish to submit for the assignment but the game must include components of their own name inside the game to reduce the possibility of students passing off someone else’s work as their own. An online questionnaire was administered on the students after they have submitted their Scratch Assignment to collect information on their gaming habits. Through the questionnaire students were asked to self-report their gaming habits (now and when they were young) through multiple choice questions (starting age and frequency of play) and open-ended questions (name of the favourite video game), refer to Appendix 1 for the full questionnaire. Based on the premise that gaming is a memorable experience during the students’ childhood or adolescence, students who truly played games and for those who have spent a sizeable amount of their time doing this would at the very least remember the name of the game that they have played and/or be able to describe the gameplay of that particular game.

The starting age at which students reported their first foray into games and the responses from the open-ended question was used as a reference point to check the validity of the responses. For example, if the respondents claim that they started playing Candy Crush at an age of less than 6 years old, this response would be deemed invalid because Candy Crush was only released in the year 2012. Responses that were incomplete or those who gave nonexistent/invalid games for either instance were ignored in the study. If the game title provided by the respondents at either point of times—young and current—is valid, the respondents were categorized as having ‘previous gaming experience’. The assessment of computational thinking skills of the students’ Scratch project was done using the free web-based tool Dr. Scratch. The tool analyses each Scratch project in seven CT dimension (Moreno-León, Robles, & Román-González, 2015). Each dimension was then given a score that ranges from 0 to 3 according to the criteria provided in Table 14.3. The addition of the partial score from each dimension yields a CT Score and, based on this score, different feedback was also provided by the website to provide the student’s information and suggestions on improvement.
Table 14.3

Description for each CT dimension assessed by Dr. Scratch (Moreno-León, Robles, & Román-González, 2015; Robles, Moreno-León, Aivaloglou, & Hermans, 2017)

CT dimension

Basic (1 point)

Developing (2 points)

Proficient (3 points)

Abstraction and problem decomposition

More than one script and more than one sprite

Definition of blocks (creation of custom blocks)

Use of clones (instances of sprites)

Parallelism

Two scripts on green flag

Two scripts on key pressed or on the same sprite clicked

Two scripts on when I receive message, or video or input audio or when backdrop changes to

Logical thinking

Use of ‘If’ blocks

Use of ‘If … else’ blocks

Logical operations

Synchronization

Wait

Message broadcast, stop all, stop program

Wait until, when backdrop change to, broadcast and wait

Flow control

Sequence of blocks

Repeat, forever

Repeat until

User interactivity

Green flag

Key pressed, sprite clicked, ask and wait, mouse blocks

Webcam, input sound

Data representation

Modifiers of sprites properties

Operations on variables

Operations on lists

Since each of the CT dimension constitutes an ordinal variable and ‘Gaming Experience’ is a nominal categorical variable, Cochran–Armitage test of trend was utilized to investigate the relationship between each CT dimension and ‘Gaming Experience’. All statistical analysis was conducted using SAS Enterprise Guide software.

14.4 Results and Discussion

Based on the results obtained in Table 14.4, it was observed that there is a strong evidence (p-value = 0.0100) of an association between the CT dimension of Abstraction and Problem Decomposition and gaming experience of students. A plausible explanation for this correlation is that all games, regardless of game genre, have goals/missions and a reward mechanism that entices players to continue playing—an attribute which makes games engaging. Players would then try to find ways to maximize these rewards while minimizing damage to their game characters during the gameplay (Ch’ng, Lee, Chia, & Yeong, 2017; Gee, 2008). This feature in all games requires players to determine the problem that they are currently encountering and to devise new solutions based on whatever information, which may differ greatly depending on the game, that they have at hand. It was noted in (Adachi & Willoughby, 2013) that these were also the exact features that promote problem-solving skills. We posit that, perhaps, this is the attribute of COTs games that provide informal training to its players in the CT dimension of Abstraction and Problem Decomposition.
Table 14.4

Cochran–Armitage trend test results

CT dimension

Cochran–Armitage z-statistic

p-value

Abstraction and problem decomposition

2.3274

0.0100

Parallelism

−0.3318

0.3700

Logical thinking

0.6346

0.2628

Synchronization

−0.2861

0.3874

Flow control

0.9793

0.1637

User interactivity

−1.6750

0.0470

Data representation

0.5875

0.2784

It was also observed that there is a weak evidence (p-value 0.0470) to support the hypothesis of an association between the CT dimension of User Interactivity and gaming experience. A possible explanation to this phenomenon is that when students are exposed to video games and through repeated play over the years, they will indirectly pick up the basic elements needed to interact with computer software such as use of keyboard to input text and mouse to make selections; compared to those who have minimal or no exposure. Further investigation needs to be conducted if the same observation applies to those who are exposed to repeated general software usage and not only video games to determine if this observation holds.

Since the p-values of the Cochran–Armitage z-statistic is greater than 0.05 for the other CT dimensions considered in our study, we conclude that there is insufficient evidence from our sample to support the hypothesis that there is an association between ‘Parallelism’, ‘Logical Thinking’, ‘Synchronization’, ‘Flow Control’, ‘Data Representation’ and gaming experience of the students, respectively.

14.5 Implications for Educators and Researchers

Our findings show that there is a correlation between previous gaming experience and the CT dimensions of Abstraction and User Interactivity. At this moment, we cannot tell which particular aspects of specific COTs video games that support the cultivation of these particular skills or why the correlation exists without further investigations. However, our findings does encourage the idea that COTs games possess the potential to cultivate skills as suspected by Klopfer, Osterweil, and Salen (2009), Shreve (2005). The question now is: How do we actually harvest it to make it work for CT education?

The report by Klopfer, Osterweil, and Salen (2009) has presented creative ways in which games can be incorporated into classrooms to support different aspects of learning. The most common approaches that are currently utilized for CT education is the use of games as programming and reflective systems via game development assignments and serious games respectively. However, the creation of serious games takes time and skills that most educators do not possess and pale in comparison to the expertise possessed by the games industry. An alternative is to use COTs games instead of serious games. The online content provided by (BBC, 2018) on thinking computationally utilizes a simple platform game to illustrate each aspect of CT skill without any programming. It should also be possible for educators to do the same in the classroom. For example, students can be tasked to pick and play their own choice of game from a pool of games (Blizzard, 2018) and subsequently asked to share about the challenges that they faced while trying to clear particular levels of the game (abstraction and problem decomposition) and how they overcome those challenges (algorithm). Another approach is to group students according to the choice of games that they have previously picked to have smaller discussions and knowledge exchange on how particular levels of the game are solved. A simple worksheet is included in Appendix 2 for students to work on before the face-to-face session on introduction of CT concept Abstraction and Problem Decomposition. The students’ work can also be used as examples for discussion on the CT component of Algorithmic Thinking.

Another issue that educators face with incorporating games into the classroom is the struggle to cover the mandated curriculum and have games within the same block of time. An unorthodox solution to this problem is to leave play outside the classroom, in its original place, so that students have the freedom to play (explore, experiment and fail without penalties). The limited face-to-face time within the classroom can be used to facilitate discussions on what students did and to guide them to reflect on their own observations or actions during the play. Class activities that can be done within the classroom may be something as simple as free discussions of their gaming experiences of popular game titles. When conversations are started by students and led by students, students are given the opportunity to identify their own strengths and build their confidence in communication and social skills. The study by (Berland & Lee, 2012) observed that players exhibit skills to identify problems and build solutions based on their own observation and actions during gameplay. The same attribute can probably be observed on online gaming forums where players virtually gather to discuss issues that they face while trying to complete levels of a video game. However, with this approach, it is difficult to standardize the learning outcome or control the actions of the students since it is not within the teachers’ power to control students’ actions outside the classroom as it is within the classroom. A solution to this is to assign students the task to create a piece of work (video/written piece/blog) at the end of the semester demonstrating how they identify and solve problems that they face while playing their favourite games. For those who are unenthusiastic about gaming, contrary to common beliefs that everyone plays video games, the act of gaming can also be replaced with their own hobbies—crafting, sports, collecting items.

The idea of incorporating video games into education is an idea that has been around for some time (Annetta, 2008). In fact, video games have been reportedly used to teach city planning (Terzano & Morckel, 2017) and English as a Second Language (Miller & Hegelheimer, 2006) with positive feedback. Video game players were also found to fare better at surgical skills in the study conducted by (Rosser et al., 2007) and were noted to have better graduate attributes than non-gamers in (Barr, 2017). For the field of CT education, we believe that video games have the same potential to be used as an effective tool for teaching and learning within and outside the classroom. The results reported in our study provide preliminary proof of this. In the future, we plan to obtain concrete evidence advocating the use of video games as a potential vehicle for learning and teaching computational thinking by implementing the ideas put forth in a real classroom environment.

Footnotes

  1. 1.

    Definition proposed by the American Computer Science Teacher Association (CSTA) and International Society for Technology in Education (ISTE) for use in K-12 education.

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Authors and Affiliations

  • Sue Inn Ch’ng
    • 1
    Email author
  • Yeh Ching Low
    • 1
  • Yun Li Lee
    • 1
  • Wai Chong Chia
    • 1
  • Lee Seng Yeong
    • 1
  1. 1.Sunway UniversitySubang JayaMalaysia

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