Designing Physics Game to Support Inquiry Learning and to Promote

Retrieval Practice

Danu Pranantha

1,2

, Wei Chen

, Francesco Bellotti

, Erik Van Der Spek

, Alessandro DeGloria

and Matthias Rauterberg

Industrial Design Department, Eindhoven University of Technology, Eindhoven, The Netherlands

DITEN, University of Genoa, Genoa, Italy

Keywords:

Physics Games, Game Design, Simulation, Pseudo-tutor, Easy Authoring.

Abstract:

Instruction in physics aims at achieving two goals: the acquisition of body of knowledge and problem solving

skills in physics. This requires students to connect physical phenomena, physics principles, and physics sym-

bols. Computer simulation provides students with graphical model that unites phenomenon and principles in

physics. However, such minimally guided approach may harm learning since it overburdens the working mem-

ory. Also, simulation is inadequate in promoting problem solving skills since students need to exercise with

a variety of physics problems. Intelligent tutoring systems (ITS), in contrast, train students in solving physics

problems. In this paper, we designed an online puzzle game in physics that combines simulation and pseudo-

tutor (namely QTut). We addressed three challenges: extensibility, scalability, and reusability in designing our

game. We conducted usability tests with 10 participants on the game prototype to study the user performances

and perceptions for improvement. The results indicate the game as educative and moderately entertaining. The

use of scaffolding in the game positively contributed to the game learning experience. Moreover, the game

GUI expressed information well that made the game understandable, even with little instructions.

1 INTRODUCTION

Instruction in physics aims at achieving two goals:

the acquisition of body of knowledge and the ability

to solve quantitative problems in physics. To achieve

the learning goals, physics instructions should exam-

ine the knowledge structure of physics. Physics or-

ganize the body of knowledge into three levels: the

macroscopic level corresponds to physical objects,

their properties and behaviour; the microscopic level

explains the macroscopic level using concepts, theo-

ries and principles of physics; and the symbolic level

represents the concepts of physics as mathematical

formulae (Johnstone, 1991). Consequently, physics

instructions need to advocate the connection of those

levels to the students.

Lack of knowledge and/or misconceptions at the

microscopic level leads students to difﬁculties in

solving physics problems (Heyworth, 1999). The

use of concrete models, analogies and graphics may

help students to overcome those difﬁculties. In in-

quiry learning, computer simulation graphically mod-

els physical objects and unites the macroscopic, the

microscopic, and the symbolic levels together. This

approach urges students to actively seek questions,

explore the simulation environment, and discover

knowledge based on their observations.

However, such minimally guided approach may

harm learning since it does not align with work-

ing memory limitations (Kirschner and Clark, 2006).

This, to an extent, necessitates the use of scaffold-

ing, which is essential particularly for inquiry learn-

ing (Jong, 2006). The use of scaffolding reduces cog-

nitive load of the students when using computer sim-

ulation. A meta analysis also supported the use of

explicit instructions in learning (Alﬁeri et al., 2011).

Moreover, guided inquiry learning also helps students

to plan their simulation experiments (Bonestroo and

de Jong, 2012).

Nevertheless, using simulation alone is inadequate

in promoting problem solving skills. It is also impor-

tant for students to exercise with a variety of physics

problems and to perform retrieval practices at mi-

croscopic and symbolic levels (Karpicke and Blunt,

2011). Intelligent tutoring systems (ITS) train stu-

dents in problem solving skills using apprenticeship

and problem solving models (Woolf, 2009).

Given the advantages of simulation and tutoring

330

Pranantha D., Chen W., Bellotti F., Van Der Spek E., DeGloria A. and Rauterberg M..

Designing Physics Game to Support Inquiry Learning and to Promote Retrieval Practice.

DOI: 10.5220/0004849103300337

In Proceedings of the 6th International Conference on Computer Supported Education (CSEDU-2014), pages 330-337

ISBN: 978-989-758-020-8

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

system, we combined simulation and tutoring sys-

tem into a serious game for learning physics. Seri-

ous games have potentials in instruction particularly

for engaging the audience in challenging and con-

textualized activities; and students are encouraged

to query information, apply knowledge and practice

skills (Wouters et al., 2013; Connolly et al., 2012).

We designed the game as an online physics game

accompanied by a pseudo tutor to support learning.

The game graphically simulates the macroscopic level

whereas the example-tracing tutor (pseudo tutor) ex-

plains the physical phenomenon at both microscopic

and symbolic levels. We conducted usability tests on

the game prototype to collect user performance and

perception of the game as a learning media.

The remainder of the paper is organized as fol-

lows. Section 2 relates our work with the current state

of the arts, whereas Section 3 explains our game in

details. Section 4 discusses about the usability tests,

and Section 5 concludes our paper.

2 RELATED WORKS

Constructivist teaching has the greatest potential to

enhance learning where learners actively construct

knowledge through inquiry, apprenticeship, and col-

laboration (Woolf, 2009). In physics, construc-

tivist teaching uses computer simulation to challenge

learners constructing their own questions, exploring

the simulation environment, and inferring knowledge

similar to scientists.

Numerous computer simulations for learning

physics are available in the market. For instance,

PhET project provides a variety of interactive physics

simulations (Perkins et al., 2006). The PhET project

investigated several design factors of engaging and ef-

fective simulation (Adams et al., 2008a; Adams et al.,

2008b). The ﬁnding suggests that providing driving

questions encourage students to explore the simula-

tion (Adams et al., 2008c). Furthermore, balanced

challenges in form of affordance, constraints, analo-

gies, and the use of multiple representations maintain

students engagement (Podolefsky et al., 2010).

Several studies showed that the use of scaffold-

ing improves the effectiveness of simulation. For in-

stance, traditional instructions enhanced learners un-

derstanding of the simulation (Kolloffel and de Jong,

2013) and helped them to plan their experiments

(Bonestroo and de Jong, 2012). The use of concept

mapping with simulation enhanced deep learning (Gi-

jlers and de Jong, 2013).

However, computer simulation overlooked prob-

lem solving in physics that most students ﬁnd it dif-

ﬁcult. In contrast, Intelligent Tutoring Systems (ITS)

and Fading Worked Example (FWE) can be used to

nurture problem solving skills. For instance, Andes

tutor trained students on solving physics problems

(Vanlehn et al., 2005) improved the average exam

score of the students. Likewise, FWE supports ef-

fective learning but combining ITS and FWE did not

contribute to better learning (McLaren et al., 2008).

Combining a physics simulation with a tutoring

system may provide students with a graphical tool

for exploration (the macroscopic level) and a train-

ing tool for problem solving (the microscopic and

the symbolic levels). A practicable approach is us-

ing serious games to combine both simulation and tu-

toring systems. Serious games have strengths to ap-

peal and to motivate students (Connolly et al., 2012).

Meta analysis showed that games can be more effec-

tive than traditional instructions, but only when con-

sidering working memory limitations (Wouters et al.,

2013).

In this paper, we created an online puzzle game

in physics that uses simulation to represent physical

objects at the macroscopic level and a pseudo tutor

(namely QTut) to explain physical phenomenon at the

microscopic and the symbolic levels. QTut provides

students with hints and feedback; and it responds to

student queries. As a prototype, we created a physics

game for bachelor degree students. We conducted us-

ability tests to study the user performances and per-

ceptions of the game for improvement.

3 GAME SYSTEM DESIGN AND

DEVELOPMENT

3.1 Design Considerations

The game system was developed using bottom up ap-

proach by identifying challenges, devising solutions,

designing each solution as a module, and integrating

the modules into complete system. The graphical user

interface (GUI) and the gameplay emphasize the use

of scaffoldings in form of driving questions, informa-

tive feedbacks, user queries, and QTut responses.

The game was implemented using HTML5 and

JavaScript for rich web experiences and JSON

(JavaScript Object Notation) for lightweight data stor-

age. We followed rapid prototyping to iteratively cre-

ate prototypes over short period.

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3.2 Identifying and Addressing

Challenges

We considered three challenges in developing the

game system: extensibility refers to the ease to pro-

duce a variety of games for different topics, scalabil-

ity means the ease to attach new modules to the sys-

tem, and reusability corresponds to the use of some

modules for other purposes.

3.2.1 Extensibility

A. Game Level and Game Format

We created game level and game format to allow ex-

tensibility (Pranantha et al., 2012). The game level

clusters learning topics into levels based on their com-

plexity. The game format sets each game level as se-

ries of tasks a puzzle set drawn from the database (a

JSON ﬁle). A task or a task item is either a closed

ended question about a simulated event or an action

request in the simulation area.

Figure 1 described a puzzle set that consists of

several task items. Each task item has two types of

data: the scaffolding data and the simulation data.

The scaffolding data (Program 1) has an id, a ques-

tion, a list of feedbacks, a sequence of possible an-

swers, and an index of correct answer. Subsequently,

the simulation data (Program 2) includes a collection

of objects and a list of available responses to the ac-

tion request.

Figure 1: A puzzle set and a task item.

Program 1 (Scaffolding data):

{

id : 1,

question : "What is the friction force?",

feedback : ["Friction force = Normal force

x cos(alpha), where alpha is the

angle of the friction force with

respect to horizontal ground",

"Well done"],

answerLst : ["120 kg.m/s2","115 N","117 N",

"114 kg.m/s2"],

idxCorrectAns: 2

}

Program 2 (Simulation data):

{

id : 1,

objects : [ {

"id": 1,

"name": "crate",

"class": "crateActor",

"position": {"x": 0, "y": 230},

"size": {"w": 640, "h": 20},

"image":"images/force/crate.png"

...

responseLst: {

"response": ["createJoint"],

"objects":[{

"to":["extension"],

"anchor":{"x":1,"y":1}

}]

}

Using the game format, a game consists of a se-

quence of inter-related tasks can be easily created

to learn problem solving skills. Some tasks can be

recalled several times to promote retrieval practice

which is essential for learning (Karpicke and Blunt,

2011).

B. Pseudo Tutor

Beside the scaffolding data in the task item, we cre-

ated QTut, a pseudo tutor. QTut allows students to

query some information in relation to the task at hand.

To support the extensibility of QTut, we created

knowledge triplet (Qs, R, DA), where Qs refers to a

list of query samples; R represents a response to a list

of query samples Qs; and DA denotes dialog act (Pro-

gram 3). The knowledge triplet (subsequently called

triplet) represents QTUt knowledge on learning top-

ics. Consequently, the number of triplets is contingent

to the coverage of the learning topics in the game.

Program 3 (Knowledge tripets):

{ "Qs": ["Define normal force","What is

normal force"],

"R": "Normal force (N) is the component

(perpendicular to the surface

of contact) of the contact force

exerted on an object by,

for instance, the surface of a

floor or wall, preventing the object

from penetrating the surface",

"DA": { "key": ["what", "define"],

"intention": "ASK_EXPLAIN" } }

We use the triplets to construct a N-gram term fre-

quency - inverse document frequency (TF-IDF) table

(Table 1) that measures how concentrated the occur-

rence of a given word in a collection of triplets. Words

with high TF-IDF numbers imply a strong relation-

ship with the triplet they appear in, suggesting that if

that word were to appear in a query, the triplet could

be of interest to the student.

Table 1: An example of N-gram TF-IDF table with 2

triplets.

N-gram words TF-IDF of TF-IDF of

triplet 1 triplet 2

Net force 0.40 0

Normal force 0 0.4

Force 0.10 0.10

TF-IDF is computed as follows. Suppose we have

a collection of N triplets. Deﬁne f

i j

to be the fre-

quency (number of occurrences) of term i in triplet

j. Then, deﬁne T F

i j

to be f

i j

normalized by dividing

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it with the maximum number of occurrences of any

term in the same triplet (1) (Rajaraman and Ullman,

2011).

T F

i j

max

( f

k j

)

(1)

Whereas the IDF for a term is dened as follows.

Suppose term i appears in n

of the N triplets in the

collection. Then,

IDF

= log

(2)

The TF-IDF score for term i in triplet j is then

computed as

T F − IDF

i j

= T F

i j

.IDFi (3)

We developed a Naive Bayes classiﬁer to deter-

mine the similarity between a query and the triplets

using TF-IDF information (Manning et al., 2008).

QTut subsequently ranks the similarity values in de-

scending order and removes triplets that have similar-

ity values below a certain threshold. QTut performs

intention matching on the DA of the remaining triplets

with the following rules: if it ﬁnds a match, then re-

turns the corresponding triplet; otherwise, returns the

triplet with the highest similarity value.

3.2.2 Scalability and Reusability

To facilitate scalability and reusability, the game sys-

tem is divided into functionality modules (Figure 2):

a) tutoring module delivers questions, provides hints

and feedbacks, and responds to queries; b) physics

simulation module handles all graphical events based

on laws in physics; c) delivery module draws a task

item from the puzzle set either in random, sequential,

or difﬁculty based order; and d) data module accesses,

organizes, and manipulates game database (i.e., game

contents, game conﬁguration, and user log).

Figure 2: A stack of modules as a complete system archi-

tecture.

QTut (in the tutoring module) has two modes:

text only and text with speech. We use a free text-

to-speech (TTS) web service

to convert texts into

speeches for the latter mode (Figure 3). QTut sends

the texts to the TTS web API using HTTP GET

and the TTS web API subsequently synthesizes the

speeches and sends them to QTut.

Figure 3: Converting Text to Speech.

To minimize the needs of user management and

to support the game distribution, the system is con-

nected to a social networking platform (Facebook) us-

ing Facebook Javascript API

. The system extracts

user information on Facebook to be stored into the

database.

3.3 Gameplay

Gameplay refers to formalized interaction that occurs

when players follow the rules of a game and experi-

ence its system through play (Salen and Zimmerman,

2003). Game rules are, therefore, the heart of any

game which distinguish game (e.g., a football match)

from play (e.g., two kids pass a ball to each other).

Furthermore, instruction process -presenting new in-

formation, integrating new knowledge, and connect-

ing new knowledge with prior knowledge- is essential

in learning (Ferguson-hessler and de Jong, 1991). In

serious games, game rules and instruction process are

entwined to deliver fun yet educative gameplay. The

rules should be easy to comprehend and the instruc-

tion process should consider working memory limita-

tions.

The game rules are as follows. All game levels

are initially locked except at the base level (level 1).

For simplicity, all task items in a level have the same

weight for scoring. However, each level has three

most difﬁcult task items which each is indicated by

a star. If a student answers a starred task item, he will

receive one star.

A level has a topic related to its preceding and suc-

ceeding levels. For instance, force and torque can be

VoiceRSS Text To Speech (http://voicerss.org/)

Facebook Developer API (https://developers.

facebook.com/)

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333

two successive levels. If a level is unrelated to its pre-

ceding, the QTut presents an introduction to denote

topic transition. A student may progress to a level

(i.e., unlock a level) if he has passed its preceding

level. A student completes a level if he earns at least

two stars and scores above a certain threshold. During

the game, a student may query QTut about concepts,

formulas, and terminologies. The game rules do not

impose timeout but we use the timer for logging pur-

pose.

3.4 Graphical User Interface (GUI)

The layout of the game GUI was designed using grid

systems to group all elements according to their func-

tionalities. This allows the game users to easily com-

prehend the interface (Elam, 2004). Figure 4 (left)

shows the wireframe of the game GUI: tutor area on

the top right consists of a tutor avatar and an input

text to enter query for the tutor, information area on

the middle presents feedback and task from the tu-

tor, and simulation area on the bottom plays physics

events. The ﬁnal GUI of the Physics game prototype

is shown in Figure 4 (right).

Figure 4: the wireframe of the game GUI (left) and the

game GUI (right)

The GUI elements (e.g., buttons and playable ob-

jects) use the feedforward and feedback concept to

allow intuitive interaction. Feedforward is the infor-

mation that occurs during or after user action, for

instance, on-screen messages indicating what to do.

Feedback is ’the return of information about the result

of a process or activity’ (Wensveen et al., 2004). For

instance, clicking on a button opens a new window.

Figure 5 shows the use of feedforward and feed-

back in a Logout button. Feedforward conveys an

implicit message that the logout button is click-able

by changing its color upon mouse-over event; and the

feedback responds to user action (a click) by chang-

ing the logout button into a login button. Feedforward

is also used to help students in problem solving. For

instance, a calculator button appears if a task item

Figure 5: An example of feedforward and feedback in a

Logout button.

asks student to calculate force. The physics simula-

tion shows a ruler if student needs to measure length

or distance.

3.5 The Physics Game Prototype

The Physics game prototype, intended for bachelor

degree students, has two levels: force and torque. The

ﬁrst level consists of nine close ended questions. The

questions are either conceptual or quantitative prob-

lems. The second level has six action requests that de-

mands student to interact with objects in the simula-

tion area. Figure 6 (top-left) shows a list of game lev-

els where all levels are locked except level 1 (force).

Figure 6 (top-right) shows a task item in the ﬁrst level

that asks about stationary state. Figure 6 (bottom)

shows a task item in the second level that demands

student to balance the mobile toy. Each correct an-

swer is awarded with ten points and a starif the task

item is a starred task item. A student passes a level

if he earns two stars (three stars are available in each

level) and scores above 50% (i.e., 50 points for level

1 and 30 points for level 2).

Figure 6: locked level (top-left), level 1 (top-right), and

level 2 (bottom).

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4 USABILITY TESTS

We conducted usability tests to study the user perfor-

mances and perceptions of the game for improvement.

4.1 Methodology

To test the Physics game prototype, we assigned each

participant to complete two game levels. Conse-

quently, each participant had to earn two stars and

achieve 50% of points in each game level. The partic-

ipant may query QTut whenever he needs assistance

to solve a task item.

The usability test can be described as follows.

1. The participant ﬁlls out a pre-questionnaire about

his knowledge on physics and his exposure to

games.

2. The participant plays with the Physics game.

Meanwhile, the game system creates three types

of logs: game level log summarizes the progress

of the user at each game level, task log records

the user performance in each task, and tutor log

records the dialogs between the user and QTut.

3. The participant ﬁlls out a post-questionnaire about

his subjective perception of the game, including

QTut, the contents, the gameplay, and the enjoy-

ment.

4.2 Participants

We recruited 10 participants (graduate and undergrad-

uate students) for the tests (age mean = 26.6 y/o, age

SD = ±2.5, 3 participants were female) and each par-

ticipant was rewarded with 5 Euros.

According to the pre-questionnaire responses, all

participants have undergraduate levels of physics or

above, except one participant who has high school

level of physics; and classical mechanics (e.g., New-

tonian principles) is the most familiar concept.

The participants play games 1-5 times a week

(mean = 2, SD = ±1) and a playing session lasts

for 1 hour on average. Most participants play games

for fun and identify themselves as occasional gamers.

The pre-questionnaire also shows that notebook is the

most frequent device for gaming among participants.

This suits well to our proposed system.

4.3 Results

We divided the test results into two areas: the user

performance based on the game log, and user subjec-

tive perception based on the post-questionnaire.

4.3.1 User Performance

The participants spent approximately 14.5 to 29 min-

utes to complete the game (mean = 19.8, SD = ±4.7)

(Table 2). The mean score is 120 points with a min-

imum of 90 points and a maximum of 150 points.

The ﬁnal scores of the ﬁrst and the second partici-

pants are missing due to hardware problem during ex-

periment. There is no signiﬁcant difference on game

time (F(3,6) = 0.78, p = 0.55) and ﬁnal score (F(3,4)

= 4.81, p = 0.08) between participants with respect to

their prior knowledge. All participants retried level 1,

whereas 2 participants retried level 2. This suggests

that the participants were familiarizing themselves to

the games at the ﬁrst level.

Table 2: User Gaming Data.

User Time Score #Retry #Retry Prior

(mm:ss) (pts) level 1 level 2 knowledge

1 18:44 - 1 0 high

2 20:09 - 1 0 medium

3 25:24 90 1 0 low

4 14:56 120 1 1 medium

5 14:28 130 1 0 medium

6 15:33 110 1 0 high

7 14:46 130 1 0 high

8 17:54 130 1 0 very high

9 25:26 150 1 0 very high

10 28:25 120 1 1 medium

Mean 19:46 123 1 0.20 -

SD ±4:42 ±17.53 0 ±0.42 -

Table 3 shows all activities performed by the par-

ticipants. Only one participant used features on Face-

book (i.e., like/dislike and comment) due to privacy

concern. All participants kept the QTut audio on, 5

participants asked some questions to QTut, and 1 par-

ticipant changed QTut avatar. Scaffolding tools (cal-

culator, ruler, and trigonometry illustration) were fre-

quently accessed by all participants.

Table 3: User Activity.

Activities # Participants

Like/dislike on Facebook 1

Leave a comment on Facebook 1

Turn on/off QTuts audio 0

Query to QTut 5

Change QTut avatar 1

Access competition table 1

Use calculator 10

Use ruler 10

Open trigonometry illustration 8

4.3.2 User Subjective Perception

Based on the post-questionnaire responses, the par-

ticipants found QTut helpful. Nine participants pre-

ferred to have QTut synthesized speech since it helped

them to learn better and to retain their attention. Like-

wise, the participants found QTut responses informa-

tive and QTut avatar pleasant (Figure 7). Five partici-

pants (who made queries to QTut) perceived QTut to

be accurate (4.0/5 on a Likert scale).

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335

Figure 7: User perception of QTut’s responses and avatar.

All participants agreed that they learned and/or re-

called some concepts in physics: force, weight, fric-

tion, and torque. Eight participants claimed that they

might understand the game without given any instruc-

tions (or mission) due to the icons and the GUI. This

illustrates the expressive power of the GUI; and feed-

forward and feedback concepts were successful in de-

livering such GUI.

In term of the timer, 5 participants preferred to

have a timeout since timeout challenges and encour-

ages student to answer as many as possible. On the

other hand, 4 participants preferred to have a time

bonus since it constitutes positive feeling compared

to timeout.

Figure 8 compares the average user perception of

the game prototype to the game that they regularly

play (the control). The participants perceived the

Physics game prototype as signiﬁcantly educational

compared to the control (F(1,18) = 22.785, p <0.001).

Although the Physics game prototype was less enter-

taining compared to control, the difference is not sig-

niﬁcant (F(1,18) = 1.056, p = 0.318). In addition, the

Physics game prototype offers somewhat equal chal-

lenges to the control (F(1,18) = .051, p = 0.824).

Figure 8: User perception of the Physics game against the

control.

Positive feedbacks from the participants include

good GUI (color and animation), helpful scaffolding

tools (QTut, ruler, and calculator), interactive learn-

ing, hard but do-able problems, and enthralling game.

In addition, the participants preferred some scaffold-

ings (ruler and calculator) to be always displayed in-

stead of selectively displayed when necessary. Con-

versely, negative feedbacks mostly involve the learn-

ing materials, e.g., too many calculating problems.

5 CONCLUSIONS

In this paper, we have presented our work on design-

ing a physics game to supports inquiry learning and

retrieval practice using simulation and pseudo-tutor

(QTut). The game was implemented as an online

puzzle game that used driving questions to encour-

age students to explore the simulation. We addressed

three challenges in designing the system: extensibil-

ity, scalability, and reusability. Consequently, we de-

ﬁned game levels and game format to cope with ex-

tensibility. Also, knowledge triplets were designed to

represent QTut knowledge. The system was divided

into modules to allow scalability and reusability. The

game GUI was designed using feedforward and feed-

back concepts and grid system. Subsequently, usabil-

ity tests on the game prototype were conducted to

study the user performances and perceptions for im-

provement.

The results indicate the game as educative and

moderately entertaining. The use of scaffolding (vi-

sual, auditory, and QTut) positively contributed to the

game experience. Moreover, the game GUI expressed

information well that made the game understandable,

even with little instructions. However, our study can

be seen as encouraging preliminary results and not as

decisive proof of our concept due to the limited num-

ber of participants.

Future works will investigate the usefulness of

the game for learning. Several game features can be

further explored and optimized to better contribute

to learning. For instance, comparing the game with

other learning systems, e.g., computer simulation, can

be useful to determine the effectiveness of the scaf-

folding techniques in the game. Enjoyment factor,

which is equally important to learning, can be further

studied. The enjoyment can be measured and sub-

sequently correlated with the learning effects. Sev-

eral frameworks for measuring enjoyment based on

the ﬂow theory (Kiili and Lainema, 2008; Fu et al.,

2009) can be used for this purpose.

ACKNOWLEDGEMENTS

This work was supported in part by the Erasmus

Mundus Joint Doctorate in Interactive and Cogni-

tive Environments, which is funded by the EACEA

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336

Agency of the European Commission under EMJD

ICE FPA n 2010-0012.

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