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Investigation of a hybrid learning environment for teaching
Physics using the Olympia architecture
K.C. Muñoz Esquivel, B.Sc. (Hons.)
School of Computing and Intelligent Systems
Faculty of Computing and Engineering
University of Ulster, Magee Campus
E-mail: [email protected]
Abstract
Virtual learning environments (VLEs) have still challenges to overcome with the objective of improving a
student’s knowledge, understanding and motivation. Simultaneously, it is agreed that positive effects in the
student’s motivation for learning are obtained by enhancing the human computer interaction (HCI),
resulting in positive effects in the student’s acquisition of knowledge and understanding. The aim of this
research is to test this hypothesis by adding features specific to the structure and human computer
interaction levels of serious and commercial games and artificial intelligence techniques to a VLE’s
architecture. To attain this aim, this work introduces the Olympia architecture, which allows the
combination of VLEs or serious games with intelligent tutoring systems (ITS), and also provides the option
of creating hybrid learning environments (HLEs). The term HLE is used here to define a learning
environment that shares characteristics of VLEs, artificial intelligence and serious games. Olympia was
applied in a particular case study focused on teaching the topic of momentum to 20 students enrolled in a
Physics course at undergraduate level. This involved the implementation of a VLE and a HLE using Flash
and JAVA, the application of probabilistic relational models, (i.e. Bayesian networks), to infer the student’s
knowledge, and the statistical analysis of the results obtained to measure the effectiveness of Olympia. The
HLE and VLE, implemented using Olympia, have differences in their affective feedback, rendering
graphics and game mechanics modules. Experimental results show that students are motivated and attain
learning in a similar way with both the HLE and VLE, but with further experimentation different results
may be attained. However, the students’ perception is that they are more motivated while learning in the
HLE and Olympia is an effective guideline, since the students learned using both environments. Future
work would focus on repeating the experiment by comparing a serious game with a VLE implemented
through Olympia and also by improving the student’s probabilistic relational inference model.
Keywords
Artificial intelligence, emotions, generic architecture, hybrid learning environment, intelligent tutoring
system, serious games, virtual learning environments
1 Introduction
At the beginning of 2000s the information revolution and a new reform spirit in education occurred,
starting an innovative era in learning and the delivery of instruction. Since this time, the objectives have
focused on making the educational and training processes more effective, interesting, engaging,
personalized, interactive, and available anytime, anywhere (Steels & Tokoro, 2003). Accordingly, the
power of virtual learning environments (VLEs) and serious games has been utilized to enhance teaching
and learning experiences.
From their respective sides, VLEs and serious games for education, have proven educational effectiveness
as is perceived from the research performed by Cheng et al. (2007) and Sucar & Noguez (2008) but also, it
is noticeable that VLEs and serious games have still constraints that must be addressed. A current topic of
discussion has been which of the two educating media - VLEs or serious games - is more effective (Kuo,
2007). Researchers and commercial game companies are investigating ways to enhance the efficacy of
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serious games by modifying their architecture to add subliminal educational messages in areas of science
that many students find hard to comprehend (Williams, 2008).
Another challenge has been finding the most effective way in which VLEs and serious games can respond
to the students’ actions to extend the understanding of their actions in the context of specific situations.
Therefore, research has focused on analysing the advantages and results obtained from enhancing the
human computer interaction (HCI) of VLEs and serious games to monitor the student’s emotions, and
enabling machines to give an intelligent emotional response as feedback (Aist et al., 2002) since there is a
strong concern about knowing the influence that the student’s emotions play in their motivation and
education.
Here the Olympia architecture is discussed, which is an augmented version of the generic architecture
introduced for first time by Noguez & Sucar (2005) and refined in Sucar & Noguez (2008) with the
addition of features and elements present in the architectures of serious and commercial games described in
Bergeron (2005), Adams & Rollings (2007) and Sherrod (2007). Olympia uses probabilistic relational
models, (i.e. Bayesian networks) to infer the student’s knowledge from his or her interaction. Originally
Olympia could only combine VLEs with intelligent tutoring systems (ITS), therefore this architecture was
adapted to allow the combination of ITS with serious games to enhance the VLE’s HCI. This gives as a
result a HLE, which hypothetically will attain an improvement in the acquisition of the student’s
knowledge and understanding by incrementing the student’s motivation for learning.
Section 2 reviews literature on VLEs and serious games. In section 3, the hypothesis, aims and objectives,
rationale and methodology, analysis and design of Olympia architecture are discussed. In section 4, the
implementation of the HLE and the VLE using Olympia is described in detail. In section 5, the analysis and
results obtained from applying weighted hypothesis testing (Wasserman, 2004) to the research data
obtained from a specific case study for teaching Physics to 20 undergraduate students is explained. Section
6 compares Olympia to related work. Finally, in section 7, the conclusion and future work are discussed.
2 Background and related work
VLEs have still challenges to overcome with the objective of improving the attainment of the student’s
knowledge, understanding and motivation. Serious games, however, easily attain the student’s motivation
for learning. The most important conclusion obtained from analyzing and comparing some of the
architectures corresponding to VLEs (Noguez & Sucar, 2005; Neji & Ben Ammar,2007; Duarte et al.
2008), serious games (Bergeron, 2005) and video games (Adams & Rollings, 2007; Sherrod, 2007) was
that games contain principally several modules at the HCI level that allow a high level of interactivity, but
also at lower level they are able to support this interaction by the presence of the core mechanics module
that regulates and manages the challenges-actions relation. In a game architecture, the modules which are
mainly used to provide immediate feedback to the student’s actions are the rendering graphics and audio,
playback and control modules (Sherrod, 2007) which agrees with the idea that states that the human sight
and hearing senses are the key senses involved in communication.
2.1 Genesis of VLEs and serious games
VLEs and serious games have a common origin between 1930s and 1940s in the USA with the creation of
flight simulators to provide training for army pilots during the Second World War. Later in the 1960s and
1970s, the simulators were applied to train astronauts for the Apollo program. In 1980s and 1990s, the
Defence Advanced Research Project Agency (DARPA) started the Simulator Networking (SIMNET)
program. At the beginning of 2000s the information revolution and a new reform spirit in education
emerged and simulators started to merge with commercial video games (Kincaid, 2006; Steels & Tokoro,
2003). In 2002, The Woodrow Wilson International Center for Scholars founded the Serious Video Games
Initiative to face the actual world’s challenges in areas such as education, health-care, corporatemanagement and national defence through the implementation of a new series of policies focused on the
development of education, exploration and management tools using state of the art of computer game
technology (SGI, 2008). Accordingly, the exponential development of educational and computational tools
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and environments has changed the ancient form of delivering education, providing numerous advantages
such as online learning.
2.2 Features of serious games for enhancing VLEs
This section describes some of the main features of serious games that can be taken into account to enhance
the effectiveness of a VLE. A game is a human behaviour associated with the enhancement of human
survival. A methodology implicit in a game is problem-based learning (PBL), since a game teaches us how
to act in real life. Games suppose different forms and each one involves different types and degrees of
learning. Computer games cause an emotional state change in the player. According to Koster in Bergeron
(2005) even if the game is fun, entertaining or engaging, it is the emotional connection of the game that
enables the player to receive maximum benefit from the experience. The accuracy of the serious content
and the entertaining content also plays a main role in their effectiveness, since misinformation is worse than
no instruction. A game may use a combination of storytelling and risk and reward to make the process more
interesting and motivating for the user. Finally, a game may have elements of novelty, learning, creative
and expressive play immersion and socializing. In serious games, at the design stage, it is very important to
adapt the gameplay to the audience and sort the audience correctly, since it improves the reception of the
game (Adams & Rollings, 2007; Bergeron, 2005).
3 Design of Olympia
The Olympia architecture is a semi-open environment (Noguez & Sucar, 2006) where the students can
interact to achieve specific learning objectives. Olympia is flexible, since it can be used to implement a
VLE, a serious game or HLE. This section explains how Olympia achieves these features.
3.1 Hypothesis and statement of the aims and objectives
The aim of this research is to test the hypothesis that an enhancement in the HCI experienced whilst
learning attains an increment in the student’s motivation and consequently positively affects his or her
knowledge and understanding. An objective of this research is to design and implement Olympia, an
architecture that enhances the HCI of the learning environment at the required level. Olympia is then
applied in a particular case study with the objective of teaching Physics (e.g. momentum) to 20
undergraduate students. To test the hypothesis and the effectiveness of Olympia a VLE and a HLE are
implemented.
3.2 Rationale and methodology
As discussed in section 2 game architectures contain several modules at the HCI level that allow a high
level of interactivity but also at lower level they are able to support this interaction by the presence of the
core mechanics module that regulates and manages the challenges-actions relation. Accordingly, the chosen
methodology involves the creation of a HLE and a VLE with differences in Olympia’s affective feedback,
rendering graphics and game mechanics modules and the division of 20 undergraduate students in a control
(VLE) and experimental group (HLE) of the same size (10). One assumption made at the beginning of the
experiment was that the students do not posses knowledge about the topic, since the lecturers have not
delivered yet the lecture corresponding to it. At the end of the students’ interaction, the data obtained from
the knowledge inferred using Bayesian networks, the successful cases and total interactions per student can
be analysed by weighted hypothesis testing (Wasserman, 2004).
3.3 Requirements analysis
From an interview performed in 2007 of the Head of Department, Dr. Luis Jaime Neri Vitela, of Basic
Sciences at Tecnológico de Monterrey University, Mexico City campus (ITESM-CCM), it was deemed a
necessity to find new ways of engaging, motivating and challenging students whilst teaching Physics. In
addition, he proposed that the main constraint was the lack of time and place for teaching. The possibility
of creating intelligent learning environments that can be accessed online by students and can adapt to each
student was suggested. The environment would be accessed by lecturers and students. The latter are
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between 18 and 24 years old and are familiar with using electronic tools such as Blackboard and Lotus
Notes. The learning environment must provide service on the Internet 7 days a week, 24 hours a day, its
graphic user interface (GUI) must be friendly and intuitive to use and it has to provide security and allow
the access to the users, according to their role and permissions in the system. In the learning environment’s
implementation scalability and portability are desired. Also the Head of Department and lecturers must be
able to consult essential information about the student’s performance, such as the deficiencies identified in
the student’s knowledge and understanding and the final result of the interaction.
3.4 Architecture of Olympia
Olympia, shown in Figure 1, is comprised of the interaction modules, shown in Figure 2, which were added
based on the analysis of game engines performed in Sherrod (2007) and the commercial and serious game
architectures presented in Adams & Rollings (2007) and Bergeron (2005). A combination of these modules
should be selected and implemented according to the enhancement required in the interaction. The physics
and collisions module contains all the physics-and-maths driven objects used to increase the level of
realism during simulation and the emotional feedback module the sound that could be used to set the
student’s mood. The interactive AI module is the artificial intelligence, such as intelligent agents (IA), used
to make a learning environment believable and to pose a challenge to students. The input detection module
detects and handles the input; the networking module transmits data across the network and the Utilities
module contains other tools or objects that complete the task in the most efficient way, such as timers and
resource management. The scripting module allows the control of the application from outside it. The
Graphics Rendering module contains all the graphic resources, manages them and the scenes in real-time.
The game mechanics module manages the challenges-actions relation. Finally, the teaching and learning AI
module consists of an ITS, which is based on Bayesian networks (Noguez & Sucar, 2006). The feedback
provided depends on the pedagogical actions available on the system, which could be tutorials or even
complex animated pedagogical avatars. Olympia’s ITS can be modified with the objective of also keeping
track of the student’s emotions while interacting to give an intelligent cognitive or emotional response (Aist
et al., 2002).
Figure 1. Architecture of Olympia
Figure 2. Interaction modules of Olympia
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3.5 Unified modelling language, relational model and GUI design
Unified modelling language (UML) was used to outline the functionality that Olympia must provide to
each one of the users identified (Administrator, Head of Department, Lecturer and student) and the way in
which the interaction occurs. Olympia must execute activities such as analyzing the student’s interaction
and behaviour by inferring the student’s knowledge while he is solving the problem and choosing the best
pedagogical action to give feedback. To infer the student’s knowledge a relational model (Bayesian
network) was defined, which corresponds to the case study of an astronaut who must arrive at his spaceship
before his secondary source of oxygen depletes. The only tools available in the scene are a screwdriver,
pipe wrench and adjustable wrench that the student must link with concepts related with the topic of
momentum to attain the main goal of the experiment, which is to save the life of the astronaut. To pose a
problem challenging to the student, the time left to finish the oxygen and the distance to the spaceship are
initialized randomly. Figure 3 shows the GUI control designs of the HLE and the VLE. The HLE is
manipulated by keyboard and mouse events and the VLE is managed by buttons and sliders, which are
translated differently between their game mechanics modules. Additionally, the HLE has higher quality
graphics and uses sound to give feedback when throwing the tools and to let the student know the final
result of the interaction.
Figure 3. HLE (foreground) and VLE (background) GUI design
4 Implementation of Olympia HLE and VLE
The Olympia HLE and the VLE were implemented using JAVA and Flash. Additionally, the Elvira tool
(The Elvira Project, 2005) was utilized to propagate to, and obtain information from, the Bayesian network.
A Tomcat Apache server was used to deploy the web application Olympia and the knowledge base where
the students’ interaction information is kept was implemented with MySQL. Communication between
JAVA and Flash involved a combination of passing session parameters by the HTML POST method and
returning XML messages by the HTML GET method. Screenshots of the VLE and the HLE in real time are
shown in Figures 4. The ITS was implemented in JAVA and the emotional feedback, physics, graphics
rendering, scripting and game mechanics modules were implemented in Flash. The student explores the
effects of modifying parameters related to interactive objects in the scene then he or she decides to run the
simulation. At the end the corresponding learning environment provides feedback from the interaction by
displaying messages.
Figure 4. Screen shots of interaction with the HLE (foreground) and VLE (background)
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5 Results and evaluation
20 undergraduate students from the Department of Engineering and Architecture at ITESM-CCM enrolled
in a Physics course were divided equally into a control group using the Olympia VLE and an experimental
group using the Olympia HLE. The experimental data was acquired from the students’ interaction and
comprises the average probabilities corresponding to each student’s knowledge of the topics of velocity and
rectilinear uniform movement, linear momentum and conservation of momentum. The number of
successful cases per student and the total number of interactions were also obtained. This information was
ordered and analysed using weighted hypothesis testing (Wasserman, 2004) using the statistical function Z0
as shown in Eqn. (1) to validate hypotheses with a small quantity of data where the median of the control
group, x , the median of the experimental group, n , the total population and  the standard deviation of
the control group are known.
x
Eqn. (1)
Z 
0
 /
n
To attain the aim of this research the hypothesis was divided in two alternative (HA) and two null (H0)
hypotheses:
H0 -“The students acquire the same quantity of knowledge in the two learning environments”
HA -“The HLE students acquire more knowledge than the VLE students”
H0 -“The students have the same motivation whilst interacting in the two learning environments”
HA -“The HLE students have more motivation whilst learning than the VLE students”
The data analysed had behaviour of right tail. The , the risk of wrongly rejecting the null hypothesis is
chosen as 0.05 for standard convention, which corresponds to a Z 0.95, and is equal to 1.645. To reject the
hypothesis the null hypotheses Z0 must be bigger than 1.645. As a result, the two null hypotheses could not
be rejected with the data obtained from this experiment, as is seen from the data in Tables 2 and 3. One
explanation for this could be the small size of the population, which was 10 students in each group, since Z0
is a measure of the existent evidence against the null hypothesis. Also, problems were experienced inferring
the student’s knowledge because the result of saving the astronaut was located at an incorrect level of the
hierarchy in the Bayesian network - which had to be at the level of the network leaves. Consequently the
learning environments sometimes did not allow the student to win even though he or she set the parameters
appropriately. Additionally, the HLE has a less intuitive GUI while compared with the VLE, which was
indicated by some students and the differences on their design were kept minimal for this preliminary
research. The students were asked to complete a questionnaire after interacting with the system. This was
an evaluation of the learning environment using a scale from 2 to 10, where 2 means completely disagree
and 10 completely agree. The evaluation of this questionnaire shows that the students’ perception is that
they are more motivated whilst learning in the HLE.
Table 2. Data on student’s knowledge
Table 3. Data on student’s motivation
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6 Relation to previous work
Table 4 shows a comparison of Olympia with 6 architectures of commercial games, serious games and
VLEs discussed in section 2. It can be seen that commercial games are focused on enhancing HCI and
VLEs, though initially focused mainly on learning, are nowadays focused on enhancing emotional
interaction. Serious games enhance both HCI and learning. Olympia brings the option of enhancing HCI,
emotional interaction and learning and is the first architecture that can be used to implement a VLE, a
serious game or a HLE. The AI teaching tool implemented with Olympia is an ITS, which provides several
advantages over intelligent agents (IA), such as keeping track of the students’ progress and implementing
the Student model with Bayesian networks. Finally, like the others Olympia is a generic architecture that
can be applied to different educational domains.
Table 4. Comparison between architectures of serious and commercial games and VLEs
7 Conclusion and future work
This work focused on testing the hypothesis that an enhancement in a VLE’s HCI would attain an
increment in the student’s motivation for learning and consequently a positive effect on the student’s
knowledge and understanding. To test this hypothesis the Olympia architecture was used to implement a
HLE and a VLE that were applied to teaching the topic of Physics (momentum) to 20 undergraduate
students. The students were divided equally into two groups. The control group interacted with the VLE
and the experimental group with the HLE. The main differences between the HLE and VLE are located in
their affective feedback, rendering graphics and game mechanics modules. Experimental results show that
students are motivated and attain learning in a similar way with both the VLE and the HLE, but with
further experimentation different results may be attained. However, the students’ perception is that they are
more motivated whilst learning in the HLE. Olympia is an affective architecture since students learned with
both environments and joins artificial intelligence serious games and VLEs. Work in progress of this
research includes the application of a general knowledge questionnaire involving the concepts learned in
the two environments. The results will be analysed by using weighted hypothesis testing and will be
compared with the results already obtained. Future work will focus on improving the relational model used
to infer the student’s knowledge and the development of a serious game using Olympia to test again its
effectiveness and the same hypothesis on a larger population.
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