An Overview of First Generation STEAMiE Learning Objects Chad Mourning
[email protected] Scott Nykl
[email protected] David Chelberg
[email protected] Teresa Franklin
[email protected] Chang Liu
[email protected] Russ College of Engineering and Technology Ohio University United States
Abstract: Today’s educational games are often associated with specific stigmas connoting
boring and uneventful game play. When compared with today’s most popular recreational games, these prejudices may appear justified. Current educational software needs to enter the immersive 3D virtual worlds utilized by its recreational counter parts. The STEAMiE Educational Game Engine is a tool that enables a developer to rapidly create epistemic learning objects within such a virtual world. This paper presents a suite of learning objects created using the STEAMiE Educational Game Engine as well as actual data from the classroom showing the epistemic impact of these learning objects. The development time, budget, and objectives for each learning object are discussed.
Background and Motivation The current state of educational software lags behind its contemporary recreational counter parts. In 2008, the Entertainment Software Association announced that the video game industry generated $18.85 billion in sales throughout 2007; across all gaming platforms, 267.8 million games were sold (Bangeman, 2008) (Entertainment Software Association, 2008). Of this, less than 3% of all games sold were categorized as educational (Entertainment Software Association, 2008). Several factors can be attributed as to why educational games make such a small fraction of video games. First, the majority of today’s recreational games are highly immersive 3D worlds capable of rendering mesmerizing landscapes and other stimulating ‘eye candy’ with which the player can investigate and interact. Second, these games are created around a detailed plot line, much like a novel or movie, designed to draw the player into the game and keep them ‘hooked’. Third, the stigma of a game being labeled as ‘educational’ may deter players from potentially using the software; the potential player may infer that because a game is ‘educational’ the first two factors mentioned are not elements of this game. Based on the state of many of today’s 2D flash-based educational games, it is easy to see where this stigma arises. Based on the million-dollar production costs of most modern video games and the high popularity of other genres, it is clear why many publishers are choosing not to develop ‘educational’ titles (Bowditch, 2008).
Furthermore, as suggested by Shaffer (Shaffer et al., 2004), the majority of today’s educational games lack a fundamental pedagogical basis to promote learning. A simple 2D flash game cannot exploit the full potential of a well-designed virtual world; as a result, such a simple game may essentially equate to a digitized worksheet. For this reason, as argued by (Shaffer et al., 2004) (Squire, 2003), creating an epistemic game that enables a student to pool together knowledge from many contexts, digest, and conceive original solutions is where the greatest potential of educational software lies (Shaffer et al., 2004) (Squire, 2003). Creating such scenarios where epistemic learning takes place requires a more advanced simulation environment (Shaffer et al., 2004) (Squire, 2003). Unfortunately, the cost and time associated with developing such simulation environments can be prohibitively large. In this paper we present several learning objects created using the STEAMiE Educational Game Engine (Nykl et al., 2008). This engine enables developers to quickly create advanced simulation environments and game logic to generate epistemic learning scenarios. The engine utilizes the latest graphics technology immersing players in visually stunning worlds comparable to today’s modern games. Furthermore, we present several epistemic learning objects generated using the STEAMiE Educational Game Engine as well as data from these learning objects’ usage in the classroom. The STEAMiE Educational Game Engine and the learning objects we generate using it, represent our attempt at raising the technological state of educational games to that of recreational games while supporting epistemic learning by the player. Previous attempts to bridge this gap have been made (Sutherland, 2007). From 2004 – 2006, the University of Paisley attempted to create an immersive 3D Game Based Learning Environment (GBLE) to teach computer science topics such as database management (Sutherland, 2007). To do this, teams of four to six graduate students spent one academic year designing and implementing these educational virtual worlds. During the first year, the team used the Torque Game Engine (Sutherland, 2007) (Garage Games, 2008); during the second year the team used the Ogre Game Engine (Sutherland, 2007) (Ogre3D, 2008). Each team invested roughly one to two person years of labor at a budget of £50,000 – £100,000 (Sutherland, 2007). Ultimately, each of these teams failed to create a successful educational learning object (Sutherland, 2007). Sutherland et al gave several reasons for this failure; these reasons fall into two categories: motivational deficiencies and functional game engine deficiencies (Sutherland, 2007). The motivational deficiencies stemmed from the aforementioned stigma pertaining to educational games. As Sutherland stated: “It was difficult to motivate the students to develop the learning game. When compared to other offered student projects, such as space-wars or fighting mech games, it also proved impossible to persuade digital artists to get involved. For both programmers and artists the ‘boring’ word kept reappearing, despite their need to create a significant game piece as part of their future employment portfolio.”(Sutherland, 2007) Had the developers believed in the possibility of creating an educational game with a captivating plot flowing within an immersive virtual world, the level of motivation would have been comparable to other groups with recreationally themed games, such as ‘space-wars’. In addition to lowered levels of motivation, the development teams faced functional game engine deficiencies by using the Torque and Ogre game engines (Sutherland, 2007)(Garage Games, 2008)(Ogre3D, 2008). Since these game engines are specifically designed for creating recreational games, aspects specific to educational requirements do not exist. According to Sutherland, the two most prominent technical issues related to development in the virtual world were 1) “…building the large and complex play-space, populated by a lot of NPCs, in a complex game level and with a large amount of AI scripting.”(Sutherland, 2007) And 2) “…the very different nature of the gameplay in the required GBLE involved a lot of additional coding and amendment of existing code. For example, the addition of logging and replay for later tutor/student reflection.” (Sutherland, 2007) The STEAMiE Educational Game Engine was designed to overcome these types of challenges (Nykl et al., 2008).
STEAMiE Educational Game Engine Background The STEAMiE Educational Game Engine was built explicitly for development of epistemic educational learning objects (Nykl et al., 2008). Its design and inherent flexibility enable the developer to rapidly create and modify all aspects of the virtual world; the engine abstracts away the complexities of simulating a virtual world (such as physics, 3D graphics, multi-player support, etc, see (Nykl et al., 2008)). The main intent of this abstraction
is to enable a developer to rapidly create highly immersive worlds with ease. As a result, the majority of one’s effort can focus on the plot line and epistemic educational content of the learning object. Aside from the features described in (Nykl et al., 2008), the STEAMiE Educational Game Engine contains many education-specific features that distinguish it from common recreational game engines. These features include real time physics introspection, force abstraction, data visualization, real time database interfacing, and rapid development framework for creation of new learning objects. Real time physics introspection allows a developer to query any object’s physics state at any given time; for example, a developer can collect information about an object’s collision with another object to show the actual forces and momentum acting on the bodies. Force abstraction enables a developer to simulate ‘complex’ forces that common recreational engines do not support. For example, a developer can model electromagnetism, buoyancy, force fields, and many other forces using this mechanism. The data visualization system enables rendering of data sets in a 2D or 3D environment; the user can navigate the data in 3-space and view it from any angle. Even 3D implicit functions can be visualized using algorithms such as marching cubes (Lorensen et al., 1987). These data sets can easily be placed within a virtual world and can be manipulated in real time by a user via interaction with other objects in the virtual world. Furthermore, these data sets can vary with time visualizing 4D phenomena interactively. For example, imagine a virtual world containing a user’s avatar and a 3D implicit function (perhaps visualizing a force field) that varies as a function of an independent variable, perhaps time, or another variable. The user could manipulate this variable via his avatar and observe the implicit function’s new visualization or even the change in visualization. Real time database interfacing allows a learning object to connect with a database at any time. The learning object can retrieve a set of questions from the database for use within the learning object. Furthermore, the learning object can send the results of each answered question back to the database for automatic grading and archiving. A web interface exists that allows real-time visualization of any user’s current progress through the question sets. In this way, a teacher can use a single web browser to track the progress of an entire class using a learning object. The rapid development framework provides a template from which a new learning object can be quickly created. This template provides a simple virtual world filled with examples showing how one can populate and manipulate the virtual world. Using this framework, non-computer science graduate students within Ohio University’s STEAM project have created their own epistemic virtual worlds (see Career Exploration and Stunt Park below) (Ohio University, 2006). Further compounding rapid development are the aforementioned features already implemented within the STEAMiE Educational Game Engine. As described below, many learning objects can be created within a short time frame.
STEAMiE Learning Objects The various STEAMiE learning objects are intended for practical assimilation into the classroom; therefore, certain design constraints have been developed to ensure that this is an easy process. There is a great amount of diversity in the size, style, and scope of the STEAMiE learning objects, but these criteria should always be addressed. First, the maximum play time for one setting is roughly equal to one class period. This implies that learning objects must either be able to be completed by a reasonable percentage of the students in one class period, or some mechanism must exist so that students can save their progress and resume at a later time. Second, all learning objects should be specifically tailored to teach a single topic or set of topics. The STEAMiE learning objects developed at this time have all been designed with Ohio Department of Education standards, and national standards in mind. Third, replayability is especially desirable in a game of an educational nature. Most people do not learn a specific concept the first time they are exposed to it, so repeated use of a learning object yields repeated exposure that should in turn increase learning. Fourth, student motivation is always a high priority and incorporating video games into the classroom tends to bolster this, but it can be further augmented by increasing the level of immersion in games or giving the student another reason to connect with the game. (continue list for 4.1) A subset of the completed STEAMiE learning objects are presented in this paper. A brief description of the learning object will be given, along with the following: a list of the unique features of that specific learning object, a captured image, an approximation of how long it would take a competent STEAMiE developer to make the learning object, and the art budget spent on developing multimedia for that learning object. This last point is important because most programs developing digital educational content do not have large developmental budgets.
Force and Momentum Force and Momentum, created by Scott Nykl, is a simulation of a user-controlled car that can be driven around a three dimensional playground. Within the playground, several cubes of different volume and mass exist. The user can crash the car into these boxes and the simulation automatically records the force and momentum of the collision. This learning object is specifically tailored to teach that f = ma ( force = mass * acceleration ) and p = mv ( momentum = mass * velocity ). Force and Momentum was the first STEAMiE learning object to take advantage of STEAMiE’s physics introspection. Having the ability to inspect the physics engine at any time and query data is integral in the design of a simulation intended to teach this type of material in a free form and flexible way. A competent STEAMiE developer could recreate Force and Momentum in twenty (20) hours. Art requirements were low and retail value for 3D models and textures would be less than $100.
Figure 1: Force and Momentum (left), Mass vs. Volume (right) Mass vs. Volume Mass vs. Volume, created by Chad Mourning, is a game designed to teach the user that there is no direct correlation between mass and volume. In other words, just because one object is more voluminous than other objects does not imply that it is more massive. The user is given a set of objects and is tasked to sort them in order of increasing mass. Each level contains at least five (5) objects selected randomly from ten (10) shapes and twelve (12) materials with enormously varying combinations of dimensions. This allows for the creation of millions of possible scenarios for the user to overcome, significantly increasing replayability. The user is assisted in determining his/her solution with a balance that will determine which of two objects is more massive, as well as, a table of material densities and volume formulae if the user chooses to find the masses and sort by hand. Mass vs. Volume was the first STEAMiE learning object to take advantage of STEAMiE’s ‘hover over’ tool-tip system. Steep learning curves often discourage the user and decrease motivation, thereby decreasing replayability. Having a rich, flexible graphical user interface system decreases the overall learning curve the product’s user will require to properly use the product, thereby increasing the likelihood for repeated use. A competent STEAMiE developer could recreate Mass vs. Volume in eight (8) hours. 2D and 3D art requirements were very low and could be obtained for less than $50. Space Racer Space Racer, created by Chad Mourning and Scott Nykl, is a game designed to be able to teach content for any subject by exploiting the inherent competitiveness of head-to-head multi-player games. Space Racer is a 3D
racing game where each player controls a separate spacecraft with the goal of passing through a sequence of rings along a predetermined course. During the course of the race, the player’s ship will stop and a question will be asked. If the question is answered correctly, the player resumes normal speed and continues. If the question is answered incorrectly, the player is asked another. Players who can correctly answer questions on a first attempt are much more likely to resume play faster than those choosing at random averaged over the whole race, so players will soon realize that actually attempting to solve the questions is the optimal solution. The game features three (3) distinct levels intended to be completed in one sitting. Space Racer was the first STEAMiE learning object to use STEAMiE’s ability to bind to an external question database. This allows STEAMiE games to dynamically choose which questions should be used in a game, allowing Space Racer to be used to teach any topic. Alternatively, questions can be loaded locally and easily for those without the technical resources to set up an external database. Similarly, Space Racer was the first STEAMiE learning object to take advantage of STEAMiE’s clientserver networking capability. Space Racer has been tested in instances of up to twenty-two (22) players. The ability to allow for competitive or cooperative multi-player gaming adds an extra dimension to game play that is often lacking in educational gaming today. These features can be easily exploited to enhance exposure through repeated use and increased motivation. A competent STEAMiE developer could recreate Space Racer in forty (40) hours. The 2D and 3D art budget was larger for this game than most of the other STEAMiE learning objects. The 2D and 3D art used in this learning object could probably be obtained for around $200. Extensive use of free NASA images can reduce this cost.
Figure 2: Space Racer (left), Tide Island (right) Tide Island Tide Island, created by Chad Mourning, Scott Nykl, and Mitchell Leitch, is an exploration based adventure game intended to teach users about the relationship between the positioning of the sun, moon, earth and the height of tides. Additionally, Tide Island also includes content related to the phases of the moon that will be experienced during the course of the game. Tide Island begins with the protagonist stranded on a desert island where he must control the tides to gather wood to build a boat to progress to the next island. On the second island, there are two puzzles related to the phases of the moon that must be solved in order to complete the game. Tide Island was the first STEAMiE learning module to utilize the STEAMiE force abstraction system to model realistic buoyancy when the boat sails and the wood floats on the ocean. This increases realism and enhances immersion thereby strengthening motivation for the user to continue using the object. Tide Island was also the first STEAMiE learning module to contain voice acted recordings for the text. Many educational games today have sound effects and music, some even have prerecorded readings of the text, but few use truly voice acted content. STEAMiE supports all of these and Tide Island itself has almost four (4) minutes of voice acted dialog. Realistic voice acting also increases realism, enhances immersion, and thereby strengthens motivation to continue playing the game.
A competent STEAMiE developer could recreate Tide Island in sixty (60) hours. The 2D and 3D art used in this game would probably retail for around $200. Mystery Minerals Mystery Minerals, created by Chad Mourning and Steve Caroll, is a digital representation of a geology lab that provides easy, low cost access to realistic geological experiments to schools that might otherwise not be able to afford them. Mystery Minerals contains eighteen (18) different minerals, as well as, ten (10) different tests to perform on them. The object of Mystery Minerals is to perform the tests on the minerals to gather enough information so that they can be uniquely identified. A dichotomous key is also available to help the user process the data he has collected to determine the true identity of the minerals. Once the user has attempted to identify all the minerals they are given a score based on correctness and time taken. Mystery Minerals was the first STEAMiE module designed specifically to be a cost effective digital alternative to a common tangible classroom activity. Some schools may not be able to afford a mineral testing kit for every student, and those that can may not be able to afford a wide variety of minerals for the students to test on. Mystery Minerals resolves these issues by serving as a low cost, reusable geological test kit. A competent STEAMiE developer could recreate Mystery Minerals in thirty (30) hours. 2D and 3D art resources for this module would retail for roughly $50.
Figure 3: Mystery Minerals (left), Digital Gallery Walk (right) Digital Gallery Walk The Digital Gallery Walk, created by Chad Mourning, is STEAMiE’s first application of the “gallery walk” concept, where student’s work is displayed for other students to observe. In this instance, the work of thirty-five (35) Roseville Middle School students was incorporated into the game. As opposed to a traditional real-world gallery walk, a digital version allows the work to be displayed persistently and redistributed efficiently. The Digital Gallery Walk was the first STEAMiE module to incorporate student’s work inside the game. The game contained one (1) picture, one (1) page of text, and the student’s recorded reading of that text, for each of thirty-five (35) students. A school-issue picture of each student was also placed next to that student’s work inside of the game. Knowing that their work will be incorporated inside of a game significantly increased the motivation of the students to do the assignment. A competent STEAMiE developer could recreate the Digital Gallery Walk in five (5) hours. In this particular instance, all 2D art was provided by the students, and all 3D art was stock models included with the engine.
Career Exploration Career Exploration, created by Bill Young and Juan Flores, is a 3D puzzle game where the user attempts to solve various puzzles to unlock biographical information about STEAM personnel and alumni. This module was intended to give the user, typically middle schoolers, an idea of what people in the STEM fields do in their careers. There are five (5) zones with multiple biographies for the user to examine in each zone. A competent STEAMiE developer could recreate Career Exploration in fifteen (15) hours. 2D and 3D art resources for this module would retail for roughly $100.
Figure 4: Career Exploration (left), Stunt Park (right) Stunt Park Stunt Park, created by Bill Young and Jason Yerardi, is a free form exploration game that contains several different stunts that players can perform and upon successful completion of a stunt they are given a question, that if answered correctly rewards the player with full points. The questions are selected dynamically from a question database, or selected from a local file. Stunt Park is the first STEAMiE module to be developed by a non-computer science developer. This should be an indication that, even in its current state, the learning curve is not too steep and the usability is high. More tools are currently in development to make creating games, simulations, and other learning objects in the STEAMiE engine even easier. A competent STEAMiE developer could recreate Stunt Park in fifteen (15) hours. 2D and 3D art resources for this module would retail for roughly $75.
STEAMiE Learning Object Educational Gains Several of the first generation STEAMiE learning objects have been field tested in classrooms throughout Appalachian Ohio. Each of the tested learning objects had associated pre- and post-tests to be used before and after the students used the corresponding learning object. The students were divided into two convenient groups trying to maintain a balance between boys and girls, and students of different abilities. Group one took the pre-test, used the learning object, and took a post-test. Group two, however, took both the pre-test and post-test, but did not use the learning object. Subsequently, group two was allowed to play the game (after the post-test) so they would have access to any learning benefit the games provide. In the next several sections we will present some of the research results gathered from the testing. Force and Momentum Force and Momentum was tested at Athens Middle School in Athens, Ohio by Scott Nykl under the supervision of Kurt Nostrant. Eighty-four (84) students took the associated tests while forty-four (44) of the
students used the learning object (Group 1). The mean test score among all students who had not used the learning object was 4.44 out of 10. The mean pre-test score of only Group 1 was slightly higher at 4.52. After using the learning module, the mean post-test score of Group 1 rose to 6.81 out of 10. The learning associated with Force and Momentum i.e. the difference between pre-test and post-test scores by those that used it was shown to be significant at the 95% significance level. Educational Gains for Force and Momentum
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Figure 5: In the above images, blue bars represent the overall mean scores of students that had not used the module. In the above images, green bars represent the mean test scores of a group of students that had not used the learning object, but would at a later time. In the above images, yellow bars represent the mean test scores of a group of students after they had used the learning object, but had changes that fell below the 95% significance level. In the above images, red bars represent the mean test scores of a group of students after they had used the learning object, and had changes that were above the 95% significance level. Educational gains for Force and Momentum indicate that students who used the learning object had a mean test score increase from 4.52 to 6.81 out of 10. This indicates a mean test score increase of 2.37 (>50% improvement) with significance greater than 95% (left). Educational Results for Mass vs. Volume show overall increases in mean test scores for the study group, as well as, the medium and high ability groups. The gains of the medium ability group were shown to be above the 95% significance level (right). Mass vs. Volume Mass vs. Volume was tested at Roseville Middle School in Roseville, Ohio by Steve Caroll under the supervision of Tim Taylor. The students were broken up into three ability groups, and each ability group was divided into the two sub-groups as outlined in Figure 6. The mean test score among all students who had not used the learning object was 3.62 out of 10. The mean pre-test score of only those students in Group 1 was 3.73. The mean pre-test scores of the Group 1 students in the low, medium, and high ability groups before using the learning object were 3.44, 3.12, and 4.5, respectively. After using the learning object, the mean post-test scores of all tested students rose to 4.39. The mean post-test scores of the low, medium, and high ability group tested students were 3.12, 4.86, and 5.1, respectively. These results show a certain trend in support of student achievement and the learning of content through the use of STEAMiE learning objects. The mean increase for the medium ability group was shown to be above the 95% significance level; however, the changes for the other two ability groups and the overall change were not. Further testing is being conducted to achieve a larger sample size, and increased significance is expected. There may also be some deeper correlation between the higher rise in test scores for the medium ability group, in contrast to the high and low ability groups. However, this is beyond the scope of this paper. Low Ability Group Medium Ability Group High Ability Group
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Figure 6: Student Distribution at Roseville Middle School ’08-’09 for Mass vs. Volume
Mystery Minerals Mystery Minerals was tested at Roseville Middle School in Roseville, Ohio by Steve Caroll under the supervision of Tim Taylor. The students were broken up into three ability groups, and each ability group was divided into the two sub-groups as outlined in Figure 7. The mean pre-test score of all students in Group 1 was 4.79 out of 10. The mean pre-test scores of the Group 1 students in the low, medium, and high ability groups before using the learning object were 3.56, 2.83, and 7.33, respectively. After using the learning object, the mean post-test scores of all tested students rose to 5.83. The mean post-test scores of the low, medium, and high ability group tested students were 5.11, 4.14, and 7.89, respectively. The mean test scores for the testing group and the subgroups after use, while increasing, fell below the 95% significance level. Further research with increased test sample sizes should help increase overall significance. Low Ability Group
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Figure 7: Student Distribution at Roseville Middle School ’08-’09 for Mystery Minerals Educational Gains for Mystery Minerals
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Figure 8: In the above image, green bars represent the mean test scores of a group of students that had not used the learning object, but would at a later time. In the above image, yellow bars represent the mean test scores of a group of students after they had used the learning object, but had changes that fell below the 95% significance level. Mean test scores increased for both the overall group and all tested sub-groups using Mystery Minerals. However, all gains fell below the 95% significance level.
Conclusion Today’s virtual worlds have captivated the imaginations of both children and adults, alike. If educational content can tap into this vast resource and harness this captivation for epistemic learning, a powerful new learning model would emerge. The STEAMiE Educational Game Engine is our attempt to unite captivation and learning. We presented several of the first generation learning objects created using STEAMiE and shown they achieved statistically significant gains in learning within the classroom. We have also discussed many of the specific educational features provided to developers who use STEAMiE, the shortened development time, and reduced budget requirements for development. As development of STEAMiE continues, we expect the next generation of learning objects will be even easier to develop, even more immersive, and therefore, even more educational.
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Acknowledgments This material is based upon work supported by the National Science Foundation under Grant No. 0538588. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
