Write An Essay On The Concept And Modes Of Cai

 

 

Editors’ Note:

Since it was published in 1980, a copy of Robert Taylor’s book, The Computer in the School: Tutor, Tool, Tutee, has had a place of honor on the shelf above my desk. This book of readings shaped the thinking of a generation of educational innovators.

We have previously republished seminal works from this text by Alfred Bork (http://www.citejournal.org/vol2/iss4/seminal/article1.cfm ) and by Arthur Luehrmann (http://www.citejournal.org/vol2/iss3/seminal/article1.cfm ), who coined the term “computer literacy.” We now have the pleasure of republishing the introduction to Tutor, Tool, Tutee by Taylor himself, who began in this fashion:

For the foreseeable future, computing will play an increasingly important role in human learning. However, no one yet knows exactly how great that role will eventually be, or precisely what form it will take. (p. 1)

Dave Moursund, last year’s recipient of the SITE Lifetime Achievement award, wrote the original introduction to Tutor, Tool, Tutee, concluding:

Anyone wondering either what role computers can play in education or why their incorporation into the curriculum should receive highest priority should read this book from cover to cover, as soon as possible. (Preface, p. viii).

Taylor framed potential uses of the computer as (a) tutor, computer assisted instruction in which the computer teaches the child, (b) tool, in which the computer amplifies ability to address academic tasks, and (c) tutee, in which students learn by programming (tutoring) the computer.

Nearly a quarter-century later, we are well into that future that Taylor envisioned, with a distance to go. This midpoint offers a useful vantage point for consideration of the roots of the discipline, and where we may wish to go in the future.

We also encourage you to read Taylor’s companion reflection piece, entitled “Reflections on The Computer in the School,” by clicking on the “Read related articles” link on this page.


Introduction

FOR THE FORESEEABLE future, computing will play an increasingly important role in human learning. However, no one yet knows exactly how great that role will eventually be, or precisely what form it will finally take.

Few people outside the computing community have anything but the vaguest concept of either that role or its form, for two reasons. First, technical innovation has come so fast in computing that even the expert can barely keep up with it. Second, the effort to apply computing to education is less than 25 years old and, though an immense body of work has already accumulated, it has been poorly publicized to the wider public. The media generally have tended to sporadically overrate a small subset of developments in this field while ignoring or giving only superficial treatment to the rest. Nevertheless, with the advent of microprocessors and the prospect they afford of widely available computing power, thousands of educators and parents are beginning to seriously ponder what the role of computing will be in human learning and what action they can and should take to affect it.

This book is meant to help them. It does so by making readily available a number of articles about the application of computing to education. Their authors, all pioneers in this field, have been directly or indirectly responsible for a great deal of work in this area in the past decade, and the articles included reflect upon and report that work. Despite the extensive innovation in computing, much remains the same—particularly in the way computer logic structures are related to human thought structures. Thus what has already been examined and implemented can be of surprising relevance. Teachers and other educators now entering this field may imagine they are breaking new ground when in fact they are not. Reading these essays will discourage such fantasies. By presenting past accomplishments, the essays will encourage the new entrant to use them and build upon them rather than to blindly create them anew. The articles are therefore a key to the future as well as a record of the past.

Such writings should be read by anyone interested in computing and education because they suggest what has already been accomplished. To know what has already been accomplished is the first step, whether one merely wishes to find out what the field is like or whether one wishes to determine the point from which to begin one’s own work. However, simply plunging into the field and attempting to assimilate the ideas may not work. Initially some conceptual help may be needed.

Approaching the Diverse, Technically Foreign Area of Computing in Education

The application of computing to education encompasses a range of complex activity, formidable in its apparent diversity even for those who are simultaneously both computer specialists and educators. Approaching such a complex area for the first time, especially as a computing novice, can be very confusing. This book attempts to minimize the unnecessary confusion three ways. First, by limiting the number of authors included, it arbitrarily limits the diversity of what is presented. Second, by presenting only articulate spokesmen, the issues and the work discussed are presented in an intelligible fashion. Third, by introducing a succinct framework (tutor/tool/tutee) for classifying all educational computing, the book provides the reader with a simple scheme for intellectually grasping a somewhat chaotic range of activities.

The major role of this introductory essay is to present the tutor/tool/tutee strategic framework. The basic framework and a summary set of comments on each of the five authors are presented. Then, the application of the framework is demonstrated, in terms of the work of those five.

To assist the reader interested in understanding more of the context of the author’s work, a brief biographical sketch precedes the presentation of that author’s articles. To assist the reader interested in reading more of a particular author’s work, a short selected biography for that author can be found at the end of the book

Tutor, Tool, Tutee — The Three Modes of Using Computing in Education

The framework suggested for understanding the application of computing in education depends upon seeing all computer use in such application as in one of three modes. In the first, the computer functions as a tutor. In the second, the computer functions as a tool. In the third, the computer functions as paychecks a tutee or student.

The Computer as Tutor

To function as a tutor in some subject, the computer must be programmed by “experts” in programming and in that subject. The student is then tutored by the computer executing the program(s). The computer presents some subject material, the student responds, the computer evaluates the response, and, from the results of the evaluation, determines what to present next. At its best, the computer tutor keeps complete records on each student being tutored; it has at its disposal a wide range of subject detail it can present; and it has an extensive and flexible way to test and then lead the student through the material. With appropriately well-designed software, the computer tutor can easily and swiftly tailor its presentation to accommodate a wide range of student differences.

Tutor mode typically requires many hours of expert work to produce one hour of good tutoring, for any or all of several reasons. (a) As intuitive beings, humans are much more flexible than any machine, even a computer. (b) Creating a lesson to be delivered by a human tutor requires less time because it omits much of the detail, relying upon the spontaneous improvisation and performance of the instructor to fill in both strategy and substance at the time of delivery. (c) Computers are still relatively crude devices and the only means we have of programming them are awkward and time-consuming. (d) Human instruction rarely aims to accommodate individual differences because the normal classroom situation prohibits such accommodation; hence lesson preparation and design are simpler and swifter. Because such accommodation is possible with the computer as tutor, the substantive and strategic details needed to individualize the lesson tend to get included, thus often greatly lengthening lesson design and preparation time.

The Computer as Tool

To function as a tool, the classroom computer need only have some useful capability programmed into it such as statistical analysis, super calculation, or word processing. Students can then use it to help them in a variety of subjects. For example, they might use it as a calculator in math and various science assignments, as a map-making tool in geography, as a facile, tireless performer in music, or as a text editor and copyist in English.

Because of their immediate and practical utility, many such tools have been developed for business, science, industry, government, and other application areas, such as higher education. Their use can pay off handsomely in saving time and preserving intellectual energy by transferring necessary but routine clerical tasks of a tedious, mechanical kind to the computer. For example, the burdensome process of producing hundreds or even thousands of employee paychecks can be largely transferred to the computer through the use of accounting software; the tedious recopying of edited manuscripts of texts or even music can be relegated to the computer through word or musical notation processing software; the laborious drawing of numerous intermediate frames for animated cartoons can be turned over to the computer through graphics software; or the fitting of a curve to experimental data can be done by the computer through statistical software.

To use the computer as tutor and tool can both improve and enrich classroom learning, and neither requires student or teacher to learn much about computers. By the same measure, however, neither tutor nor tool mode confers upon the user much of the general educational benefit associated with using the computer in the third mode, as tutee.

The Computer as Tutee

To use the computer as tutee is to tutor the computer; for that, the student or teacher doing the tutoring must learn to program, to talk to the computer in a language it understands. The benefits are several. First, because you can’t teach what you don’t understand, the human tutor will learn what he or she is trying to teach the computer. Second, by trying to realize broad teaching goals through software constructed from the narrow capabilities of computer logic, the human tutor of the computer will learn something both about how computers work and how his or her own thinking works. Third, because no expensive predesigned tutor software is necessary, no time is lost searching for such software and no money spent acquiring it.

The computer makes a good “tutee” because of its dumbness, its patience, its rigidity, and its capacity for being initialized and started over from scratch. Students “teach” it how to tutor and how to be a tool. For example, they have taught it to tutor younger students in arithmetic operations, to drill students on French verb endings, to play monopoly, to calculate loan interest, to “speak” another computer language, to draw maps, to generate animated pictures, and to invert melodies.

Learners gain new insights into their own thinking through learning to program, and teachers have their understanding of education enriched and broadened as they see how their students can benefit from treating the computer as a tutee. As a result, extended use of the computer as tutee can shift the focus of education in the classroom from end product to process, from acquiring facts to manipulating and understanding them.

Five Pioneers of the Application of Computing to Education

Though many computer scientists have broad general interests, most have only a few really dominant specific interests and those few are typically shaped and informed by the individual scientist’s particular point of view. Before looking at the work of our five authors in terms of the tutor/tool/tutee framework, therefore, it may be helpful to summarize the dominant interests and point of view of each.

Alfred Bork

Bork is a physics professor at the University of California at Irvine where he has directed the Physics Computer Development Project for a number of years. That project produces computer-based material that can serve as the primary source from which first year physics is learned at Irvine. As this implies, Bork’s major interest is the application of computing to physics instruction. His work strongly emphasizes concept mastery, self-paced instruction, and computer-resident testing. Though his work beautifully demonstrates how computer/student dialogs can function and how graphics can be carefully and integrally used to enhance these dialogs, he does not argue that all instruction should be computerized, even in a subject like physics. Bork sees stand-alone computers as the major vehicle in the new generation of computer-assisted learning. He is also careful to point out repeatedly that good software in any reasonable quantity is more likely to be developed by software factories or institutes than by individual professors, teachers, or researchers.

Thomas Dwyer

Dwyer is a computer scientist and educator at the University of Pittsburgh, who for a number of years ran a series of projects involving high school and junior high school teachers and students. The projects were characterized by an exploratory approach to using computing, one which tended to depend upon and generate a new way of looking at learning in the school, one which Dwyer himself dubbed “Solo-Mode” learning. In such learning, the framework is provided by the teacher but the pupil must work autonomously, learning to “fly solo.” This mode Dwyer contrasts with the more usual classroom situation that keeps the teacher in complete control and has the student “flying dual.” Dwyer’s work stresses an heuristic, exploratory approach based on principles rather than a closed one based upon a formula of what to do. He places a heavy dependence upon the teacher as a supportive human being, stresses that the teacher is crucial, and addresses teacher education as a major concern of any attempt to use computing broadly and creatively in the schools. Though much of the solo work dealt with math and physical science, Dwyer’s work through solo and elsewhere also applied computing to other subjects, including music.

Arthur Luehrmann

Luehrmann is now associated with the Lawrence Hall of Science at Berkeley, where he is directing projects to integrate computing into museum science exhibits to make them interactive, and projects to teach computing to a broad, general public served by the museum. Prior to going to Berkeley, he was a professor at Dartmouth and was involved in many successful projects there, applying computing to instruction. As several of his article titles suggest, his strongest emphasis is upon the computer as a new and fundamental technology worthy of study on its own. He sees the mass impact of this new technology as very substantial and stresses the need for popular literacy, the need for everyone to acquire programming skills, and the need for a good stand-alone personal computer. Though trained as a physicist, Luehrmann’s work has dealt with applying computing in many instructional areas, not simply those related to the physical sciences.

Seymour Papert

A professor of mathematics and an educator at M.I.T., Papert is best known for his development of the LOGO language and its application to teaching computing and mathematics to young children. His major thrust definitely is to teach a way of mathematical thinking that young children can intuitively master. By encouraging anthropomorphizing, play, and intuitive guesswork he tries to capitalize upon the existing insights and mental frameworks of children. His strong attention to how and what children are thinking is in part based upon his extended association with Piaget in Switzerland. Papert’s work has been exploratory, centering on children’s use of computing, emphasizing almost exclusively the child learning to program. It has included imaginative use of robots, graphics, and sound as a child-attractive alternative to traditional textual output. Throughout, the computer tends to be used to create a problem-rich environment, presenting the child with interesting, challenging problems that require a computer for solution.

Patrick Suppes

Suppes is a philosophy and mathematics professor at Stanford, where he pioneered the development of computer-assisted instruction. His work stresses the applicability of the computer to skill areas such as mathematics, logic, and language. It aims to produce complete courses of instruction to be delivered by the computer. He has always stressed how little we know about learning but has carefully used what is available to design a considerable quantity of computer-assisted instruction. Much of this instruction is on the market and in wide use on minicomputers. For example, whole systems stressing mathematics and language arts skills are commercially available through the Suppes-founded Computer Curriculum Corporation. His work stresses individualized learning and increased educational productivity.

Using The Tutor/Tool/Tutee Framework

Now that the framework and the five pioneers have been introduced, let’s look at some of their writings in terms of the three modes of that framework. We will only cite a few of the articles included in this book and will discuss none in detail; our aim will be to merely suggest the framework’s utility. The authors speak well, even brilliantly, for themselves and do so clearly enough to need no explanation. The framework must be accurate enough for the reader to make the associations between it and the articles for himself or herself.

Examples of the Tutor Mode

Historically, this mode has its roots in programmed instruction. However, when properly deployed it is far more flexible than any book- or material- based programmed instruction. For one thing, in tutor mode, the material can be presented interactively, and dynamic graphics and other sophisticated teaching aids can be integrally used. For another thing (as pointed out earlier), in tutor mode the performance history of one or more pupils can be collected and stored, then subsequently used for evaluating the material and as a basis for routing a particular pupil through the material. At the same time, this mode can be designed to move the student at a wide range of speeds and to be interruptible more or less at the student’s convenience. Though the label has been applied to broader applications than just this one of using the computer as a tutor, this mode has often been called CAI (Computer-Assisted Instruction), probably because the ancillary tasks it performs are similar to those that could be performed by ideally competent teaching assistants.

The work of Bork and the work of Suppes both exemplify the tutor mode at its best. All of their included articles deal with this model. Bork has concentrated much of his thinking on how best to develop good tutor material for physics instruction, while Suppes has developed material for a wide range of subjects. Both have used the computer to store, analyze, and act upon student results, and both have used such sophisticated peripheral devices as audio or graphics to maintain student involvement and enrich the nature of the tutoring.

The time period encompassed by Suppes’ work alone reveals how many years have already gone into work on tutor mode computing applications. The breadth of those applications is suggested by his prepared statement for the Congressional hearings on Computers and the Learning Society, “The Future of Computers in Education.” No one who reads either that or his Marseilles conference article, “Impact of Computers on Curriculum in the Schools and Universities,” will mistakenly believe that CAI on microprocessors is completely new. One gets a sense of how much work goes into producing good material in this mode by reading Bork’s detailed, “Preparing Student-Computer Dialogs: Advice to Teachers,” or by reading his very thoughtful “Learning about Graphics.” Anyone who would produce good tutor mode material should certainly be thoroughly familiar with both these pieces.

This mode has had both its advocates and its critics. Criticism from those who are deeply involved in computing and education is usually directed at those making extreme claims about the positive benefits to be derived from tutor mode computing. Good criticism of this kind is exemplified by Luehrmann’s “Should the Computer Teach the Student or Vice-Versa?” Neither he nor any other pioneer, however, would argue that tutor mode computing should not have a significant place in education.

Examples of the Tool Mode

Tool mode is probably seen to be the major mode of computer use by most people outside computing and education. Because it receives considerable attention and encompasses such a wide range of activities, tool mode computing is popularly seen as synonymous with computer use, period. For example, most business data processing, whether routine accounting or word processing and office automation, uses the computer as a tool. Thus the school’s administrative activities use the computer in a tool mode, from payroll and inventory to pupil scheduling and grade reporting. Even library automation involves the computer merely as a tool.

In tool mode, the computer provides a service that the user needs and more or less knows how to use. It is not primarily a teacher or tutor as in the tutor mode; it is not user-alterable and not a set of raw building components as might be provided under tutee mode. Use of the computer in tool mode may teach the user something during use, but any such teaching is most likely accidental and not the result of any design to teach. In tool mode computing, the user can only explore activities and ideas for which the tool at hand is appropriate; one can explore musical inversion with a composition and playback tool, but not a word processing tool or a regression analysis tool.

Most people in computing and education frequently and creatively use the computer in a tool mode, because of their everyday familiarity with computing capabilities. However, possibly because they are familiar with such use, possibly for other reasons, most computing-and-education people do not see this mode as something they want to focus primary energy upon. All five pioneers whose work is included in this book fit this general position. All assume heavy use must be made in education of tool mode computing; none advocates it as most important or focuses his own major interest upon it.

All five have advocated the use of the computer as a calculator and a word processor, and all have advocated various other tool mode uses as well. Bork and Suppes, for example, suggest that the computer as a calculator and record keeper should be available simultaneously to anyone using the computer in the tutor mode. Bork further suggests that various graphic tool capabilities should be similarly available. Dwyer, Luehrmann, and Papert all argue that various tool capabilities should be available to be utilized by anyone who needs them in exploratory problem-solving, writing, or anything else. Dwyer, for example, argues that the student flying solo on the computer would freely utilize many of its tool capabilities as he pursued his overall project.

Examples of the Tutee Mode

The tutee mode is the one upon which Dwyer, Luehrmann, and Papert focus their energies. This book reflects that.

One of the early and still one of the best arguments for this mode of computer use is Luehrmann’s “Should the Computer Teach the Student, or Vice- Versa?” In it, he argues that in teaching the computer, the child learns more deeply and learns more about the process of learning than he or she does from being tutored by software written by others. Papert extends the argument by suggesting how children using the computer as a tutee may learn more of what they should be learning of mathematics than they can in classrooms without computers. This position is clearly articulated in both “Teaching Children Thinking” and “Teaching Children to Be Mathematicians vs. Teaching about Mathematics.” Dwyer extends the concept in a different way in “Some Principles for the Human Use of Computers in Education,” defining his now well-known concept of solo-mode computing and showing how it relates to the total curriculum question.

 

In these essays and others, all three suggest that in using the computer as tutee, the learning the child experiences is qualitatively different than he or she might otherwise experience in any school setting. None downgrades the role of the teacher in a tutee mode environment, but all see it as different from the teacher’s typical role now. Dwyer states the case very well for all three in his “Some Thoughts on Computers and Greatness in Teaching.”

Papert suggests that the computer as tutee can, with appropriate graphic and robotic capabilities, serve as a means to enable the child to link his or her experience to the deep, fundamental mathematical ideas we most want children to learn. In his “Personal Computing and Its Impact on Education,” he suggests that this may be the only way to avoid having most children spend most of their time struggling within the dismal reality of dissociate learning. Both in that essay and in “Computer-based Microworlds as Incubators for Powerful Ideas,” Papert contrasts dissociate learning, the attempt to somehow internalize great quantities of information apparently of no use in the child’s world, with a more natural learning that resonates with the child’s experience.

Using the Framework Without Becoming Blinded by It

The tutor-tutee-tool framework has been presented to help those who would like to get an organized initial grasp on an apparently complex field. It will serve to overcome hesitation and initial trauma. Of course it can and should be used later, so long as it conveniently provides insight. It is a reasonably broad framework and suffers from no more shortcomings than any other schema or typology. As such a schema though, it can divert attention from relevant insights when used too slavishly.

Reasonable alternatives to this framework certainly can be advanced. They might be entirely different or be simple extensions of it. For example, I seriously considered extending the tutor, tool, tutee framework to include a fourth mode, making it tutor, tool, tutee, toy. There are numerous games, simulations, and models of many sorts that one spontaneously classifies as toys—they appear to have been created above all to play with and enjoy, whatever other merits they might possess. I finally decided against that extension, though, believing that such software is just as well subsumed under one or more of the earlier three modes. The point is, one need not be bound by this framework. If modifying it or replacing it by an alternative framework helps with the process of internalizing the ideas advanced in the various articles, then such modification or replacement is in order.

The articles are the main thing. They provide a good introduction to computing and education. If you prefer a different order, follow it. If the framework gets in your way, disregard it. But do read the articles—all 19.

Computer game-based and traditional learning method: a comparison regarding students’ knowledge retention

1Department of Physiotherapy, Speech-Language and Hearing Sciences and Occupational Therapy, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil

2Rua Cipotânea, 51 – Cidade Universitária, CEP: 05360-160, São Paulo/S.P, Brazil

Corresponding author.

Silmara Rondon: rb.psu@nodnor.aramlis; Fernanda Chiarion Sassi: rb.psu@issasf; Claudia Regina Furquim de Andrade: rb.psu@naualc

Author information ►Article notes ►Copyright and License information ►

Received 2012 Sep 5; Accepted 2013 Feb 20.

Copyright ©2013 Rondon et al; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

This article has been cited by other articles in PMC.

Abstract

Background

Educational computer games are examples of computer-assisted learning objects, representing an educational strategy of growing interest. Given the changes in the digital world over the last decades, students of the current generation expect technology to be used in advancing their learning requiring a need to change traditional passive learning methodologies to an active multisensory experimental learning methodology. The objective of this study was to compare a computer game-based learning method with a traditional learning method, regarding learning gains and knowledge retention, as means of teaching head and neck Anatomy and Physiology to Speech-Language and Hearing pathology undergraduate students.

Methods

Students were randomized to participate to one of the learning methods and the data analyst was blinded to which method of learning the students had received. Students’ prior knowledge (i.e. before undergoing the learning method), short-term knowledge retention and long-term knowledge retention (i.e. six months after undergoing the learning method) were assessed with a multiple choice questionnaire. Students’ performance was compared considering the three moments of assessment for both for the mean total score and for separated mean scores for Anatomy questions and for Physiology questions.

Results

Students that received the game-based method performed better in the pos-test assessment only when considering the Anatomy questions section. Students that received the traditional lecture performed better in both post-test and long-term post-test when considering the Anatomy and Physiology questions.

Conclusions

The game-based learning method is comparable to the traditional learning method in general and in short-term gains, while the traditional lecture still seems to be more effective to improve students’ short and long-term knowledge retention.

Keywords: Speech, Language and hearing sciences, Anatomy, Physiology, Stomatognathic system, Learning, Computer-assisted instruction

Background

Computer-Assisted Instruction (CAI) is an additional modality of teaching methods that incorporate multimedia to present knowledge [1]. Features such as the functionality to incorporate multimedia, to present knowledge in a setting similar to that in which it will be used [2], to provide access to learning materials in a time and place convenient for the learner [3], and to provide interactive feedback critical for self-assessment [4] should be considered when using CAI, especially in medical/health education. Given the changes in the digital world over the last decades, students of the current generation expect technology to be used in advancing their learning requiring a need to change traditional passive learning methodologies to an active multisensory experimental learning methodology [5,6]. However, one should always have in mind that it is important to consider that the quality of the teaching provided by the CAI remains more important to both student satisfaction and learning than technology [7].

Knowledge related to head and neck Anatomy and Physiology is complex and extremely important for guidance in the processes of assessment, diagnosis and intervention in the field of the Speech-Language and Hearing Science [8-10]. When these concepts are well assimilated in the early stages of formation it is possible to avoid situations where the student only realizes the importance of these concepts when he is put ahead of his first patient [11]. Well-designed CAI has been shown to be effective in producing lasting clinical skills in Health Sciences education, although literature provides little guidance on the relative advantages of specific instructional and technical design features to maximize learning [12-15].

With the advance in information technology, computer-assisted learning environments and objects have been incorporated to Anatomy and Physiology laboratories and classes to enhance learning [16,17]. Ohrn, van Oostrom and van Meurs [18] performed a comparison of traditional textbook and interactive computer learning of neuromuscular blockade among first-year anesthesia residents. The results presented by the authors indicated that the improvement in test scores was significantly greater for the computer group than for the textbook group. Also, differences in the enjoyment and quantity learned rankings of the two groups were found to be significant in favor of the computer program. In a different study, Goldberg and McKhann [17] investigated the performance of students in a virtual learning environment to learning topics in Neuroscience and compared with that of students in a conventional lecture. The results consistently demonstrated higher test scores in the virtual learning environment as opposed to the conventional lecture, regardless of the time in the semester at which the knowledge tests were given.

Studies have shown that learning objects that provide educational alternatives for reasoning involving a problem solving situation (i.e. considering the student’s prior knowledge and cognitive architecture) are more appropriate for learning [19-23]. According to these studies, this occurs because learning methods that use these types of objects may reduce the working memory cognitive load and therefore facilitate the learning process. Educational computer games are examples of this type of learning object [24,25] which represents an educational strategy of growing interest [26]. These games have characteristics related to problem solving, providing the student with different possibilities to elaborate strategies and to achieve their predetermined goals [27].

Prensky [28] stressed that the key to learn about the effectiveness of digital/computer games lay in their design. The design of computer game-based learning methods should include clear rules and goals, and most importantly, the game must be fun to play and gain values [29]. Computer games should contain questions with complexities that favors students’ performances and their learning process [30,31]. Additionally, good quality games should give the player feedback about his/her actions and new problems to be solved [32].

Students can develop cognitive skills such as memory, attention and critical thinking through the use of computer games, besides being able to elaborate and confirm their hypothesis [32-35]. In addition, students can construct their knowledge in a more integrative way (i.e. integrating their knowledge with their actions), with higher motivation to learn [27,29,36-38].

In response to the lack of empirical studies examining the differential effects of computer games on the academic performance of diverse learners, and the lack of consensus that had not been reached on the effects of computer games on student achievement, Kim and Chang [38] empirically examined the effects of math computer games on the math performance of 4th grades with focused attention to differential effects for gender and linguistic groups. The results showed that English-speaking students who played computer math games in school everyday displayed significant lower math achievement than those who never played. Contrastingly, positive effects of daily computer use were noted among male students whose first language was other than English. Male language minority students who daily played computer games in math demonstrated higher math performance scores compared with their male English-speaking counterparts who never played.

Positive results have been found and different types of games have been incorporated in Higher Education, including Health Sciences courses [39]. A pioneering study indicated that medical students who used a computer game about the administration of a particular drug, presented higher percentages of correct decision making actions related to the covered topic [40]. Another study, developed in Civil Engineering area, identified that playing an educational computer game leads to equivalent learning results as participating of a traditional method and the game environment leads to increasing motivation – the learner plays the game again [27]. The study of Kanthan and Senger [41] provided insight that specially designed content-relevant digital games can be used as an additional, e-teaching/learning resource for the teaching of pathology in undergraduate medical education; improve academic performance on examination test scores; increase student engagement, promote student satisfaction and reduce student stress; and foster an improved, facilitated, fun, nonthreatening, extended study learner environment.

Games may have potential in improving learners’ knowledge, skills, attitudes and behaviours [42]. Therefore, computer simulation games, for instance, may have multiple effects on problem solving with computer programs. Kiili [43] has argued that games can be a vehicle for engaging students in a “flow”. Flow is a consciousness state during which an individual is in control of his actions, and in which there is little distinction of self and environment, between stimulus and response [44,45]. Liu, Cheng, Tsai and Huang [37] considered flow as an useful construct for improving problem solving. These authors analyzed the feedback and problem solving of undergraduate students in a simulation game, designed to assist them to learn computational problem solving. It was found that students when learning computational problem solving with the game were more likely to perceive a flow learning experience than in traditional lectures. In particular, the results of the study indicated a close association between the students’ learning experience and their problem solving strategies.

Although evidence exists on the benefits of using games (including computer games) in the education of Health Sciences students, more studies are necessary about how to conduct pre and post intervention assessments (i.e. conducting a baseline assessment in addition to the post-test assessment), considering educational and clinical aspects [39]. The main objective of the assessments should be to measure the results of using computer games in terms of learning performance and knowledge retention [39]. Until this date, there are no related studies in the field of the Speech-Language and Hearing Science about the use of computer game-based learning methods.

The purpose of this study was to compare a computer game-based learning method with a traditional learning method, regarding learning gains and knowledge retention, as means of teaching head and neck Anatomy and Physiology to Speech-Language and Hearing pathology undergraduate students. We set the following hypothesis for our present study:

1) The game will be as effective as traditional learning method concerning the gain of knowledge that the game is supposed to reinforce and integrate, if measured immediately after the conclusion of the exposure.

2) The long-term knowledge retention will be higher in the game group.

Methods

This study was conducted with second-year Speech-Language and Hearing Science students of the School of Medicine of University of São Paulo, who were undertaking a head and neck Anatomy and Physiology class. This included weekly teaching sessions and a study schedule developed in the classroom environment.

To be included in the study, students should have successfully completed the introductory classes of Anatomy and Physiology and had to be proficient in English reading comprehension.

Each student was randomly allocated in one of two groups: Group I (GI)– 15 students who were submitted to the computer game-based learning method (CGBLM); Group II (GII) – 14 students who were submitted to the traditional learning method (TLM). Both methods had the same duration (one-hour, once a week), and were delivered by the same tutor. The tutor was blinded to the random allocation process.

The study design was approved by the Ethics Committee for the Analysis of Research Projects (CEP FMUSP no. 080/10). Prior to their enrolment, all participants were informed of the purpose and procedures, after which all gave informed consent.

The application of the learning methods was developed over nine weeks. The content of the learning methods was the same for both groups. For GI, the quiz section of the software Anatesse 2.0[45] was used. For GII, short scientific texts related to the topics discussed in class were used. The topics selected for both learning methods were the same.

In the CGBLM, a notebook integrated with a multimedia projector was used to play the quiz in Anatesse 2.0. Anatesse 2.0 is interactive student learning software containing animations, chapter support, and self-study quizzes to aid learning and augment understanding of anatomy and physiology of the speech, language, hearing, and swallowing mechanisms which integrate a section of The Electronic Classroom Manager to accompany the book Anatomy & Physiology for Speech, Language and Hearing, Third Edition. This material is presented in a CD-ROM format and it is divided into these major sections: the ExamView® Computerized Test Bank contains over 1000 questions. These questions can be used for the teacher to create their own review materials or tests; the Instructor’s Manual includes a wide variety of valuable resources to help with planning the course and implementing activities by chapter for classroom use – the availability of this manual in an electronic format increases its ease of use and value as a teaching resource; an Image Library containing electronic versions of some images from the book that can be used to develop handouts; and Anatesse 2.0.

For our study, only the quiz section was used. The quiz section (i.e. computer game) contains multiple-choice questions, involving text and figures, and is divided by topic (e.g. bones of the head, muscles of the face, muscles of the tongue etc.). Feedback was given by the software immediately after each answer. If the answer was correct, a picture representing a happy face appeared on screen; if the answer was incorrect, a picture representing a sad face was shown. Students were given a total score at the end of each quiz topic. Each week one quiz topic was selected, according to what was covered in class. During each week, one student was chosen by his/her classmates to operate the software system (i.e. register answers after group discussion). There was only one computer for the whole group. Each quiz topic, containing several questions, was played twice. The first time students answered the questions, only a happy or sad face feedback was given. During the second round, the correct answer was given automatically, in order to enhance the feedback of performance.

In the TLM students were given short scientific texts, one per week, containing relevant information and pictures. Students were instructed to perform their study in the same way they were used to when studying at home. This could be done individually or in groups.

To assess students’ prior knowledge, short-term knowledge retention and long-term knowledge retention, a fifty multiple choice questionnaire (containing four alternatives each) was developed and applied in three different points: before the application of the learning methods (pre-test), immediately after the class conclusion(post-test) and six months after class conclusion (long-term post-test). The questionnaire was specifically designed for this research, since there are no standardized materials for testing knowledge about head and neck Anatomy and Physiology in the field of the Speech-Language and Hearing Science. Questions were classified as being related to Anatomy or to Physiology. To ensure the quality and relevance of the questions, the questionnaire was submitted to two independent judges, speech-language pathologists, with a Ph.D in the covered topics. Interjudge reliability was of .96, representing an excellent level of agreement.

The performance between the groups was compared, considering the three points of assessment. Comparisons were made using both the total number of correct answers, as well as the number of correct answers regarding Anatomy and Physiology.

Data analysis

One-way ANOVA with two factors was used to perform between group comparisons (i.e. for the mean total score and for the mean scores obtained in each section of the questionnaire, in the three moments of assessment) [46]. Bonferroni correction for multiple comparisons was used to ensure .05 level of significance and to verify where the significant differences occurred. One-way ANOVA with one factor was used to perform within group comparisons [46].

Results

Twenty nine students were randomized to an intervention - one student from GI was excluded for not having completed the multiple choice questionnaire for the long-term knowledge retention assessment; three students from GII were excluded for not having completed the multiple choice questionnaire for the prior knowledge assessment. Thirteen students played the computer game and twelve students were given thetraditional lecture.

Most students were female (92.0%), with mean age of 23.0 years (SD = 6.2). There was no statistically significant difference for age when comparing both groups (Mann–Whitney test, p = .062). Our study did not consider gender aspect as a variable of investigation. It is important to highlight at this moment that in Brazil, approximately 95% of the Speech-Language and Hearing Science undergraduate students are female. For this reason, it would not to be possible to have a balanced sample containing an equivalent number of males and females.

Descriptive analyses of the students’ scores obtained in the knowledge assessment according to the group, the category of questions and the point of assessment are presented in Table 1.

Table 1

Descriptive statistics of knowledge assessment

Considering the between group analysis, a statistically significant difference was observed for both groups when comparing results obtained in the three points of assessment (p < .001). However, there were no significant differences between the groups related to the learning methods (p = .176), neither in terms of the mean scores obtained in the knowledge questionnaire, nor when considering the three points of assessment (p = .699) (Figure 1).

Figure 1

Total results obtained (mean ± CI 95%) according to learning method and moment of assessment.

The Tukey test indicated that, for both groups, pre-test scores were significantly lower when compared to the post-test and the long-term post-test. Although it is possible to observe a trend towards a better long-term retention for GII (Figure 1), no significant difference was observed when comparing the performance in the post-test and long-term post-test (Table 2).

Table 2

Between group comparisons regarding total scores in the three points of assessment

There was a statistically significant difference in the results obtained along the research period for the Anatomy (p < 0.001) and Physiology (p = .001) questions. However no significant difference between learning methods was observed, even when considering the comparison group x point of assessment along the complete period of the research (Table 3).

Table 3

Between group comparisons regarding the total score in anatomy and physiology questions and the point of assessment

Within group comparisons indicated overall significant differences for both groups when comparing the scores obtained in the pre and post-test. Comparison between pre and long-term post-test scores indicated significant differences only for GII. No significant difference was observed between the students’ total scores obtained in the post-test and in the long-term post-test (Table 4).

Table 4

Within group comparisons regarding the total scores according to the point of assessment

Results obtained from Anatomy section of questionnaire indicated that GI obtained better scores in the comparison between pre and post-test (p < .001) and GII obtained better scores in the comparison between pre and long-term post-test (p = .042). Results obtained from the scores related to the Physiology section of the questionnaire indicated that only GII presented significant difference when comparing pre and post-test scores (GI – p = .064; GII – p = .019).

Discussion

In the present study we compared a computer game-based learning method with a traditional lecture as means of teaching head and neck Anatomy and Physiology to second-year Speech-Language and Hearing Pathology undergraduate students. Both methods were compared considering students’ learning gains and knowledge retention. The results showed that the CGBLM is comparable to the TLM concerning knowledge gains when measured immediately after the learning method application. This result agrees with the results of previous studies that investigated the effects of educational computer games in students’ knowledge reinforced and integrated by a computer game, as well as knowledge learnt during expository lectures but not strengthened by a computer game [27,47].

There are many studies regarding the impact of using different computer games (i.e. in terms of the complexity and the type of games) in students’ motivation and engagement to learn [27,37,39,41]. However, the data about knowledge retention are thin [36]. Studies have shown that students demonstrate a positive effect size regarding knowledge retention from computer game-playing, when assessed immediately after or up to one month after game exposure [41,48]. In the present study, the same result was found in terms of students’ short-term knowledge retention, confirming the first hypothesis of the study.

Egenfeldt-Nielsen [49] found a small gain in terms of long-term knowledge retention when students were assessed five months after game exposure. In our study, only students in the TLM group presented gains in terms of long-term knowledge retention (i.e. within group analysis), assessed six months after the application of the learning method, when comparing pre and long term post-test performances. Although the literature reports that the use of computer games increases the engagement and the motivation to learn [27], in some situations people still seem to be more comfortable with printed out texts. The reading activity gives students the possibility to pause, resume and cover the ideas presented [50]; during the computer game-playing the students may have other distractions [51]. In our study, during the computer game-playing, the feedback was given immediately after each question (i.e. if answer was correct or not), in order to reinforce the content presented. However, the order in which the questions were presented in the screen was determined by the software system, and it was not possible for students to go back and review their answers and contents of interest. In our study, the possibility to review information could only be done by the students in the TLM group.

Our study had a few limitations. We have to consider that the group of students who underwent the CGBLM faced as a limitation the existence of only one computer for the whole group. This may have interfered in the students’ interest in the computer game or even in their attention span.In general, studies that have presented good results for computer game-based learning methods refer to the use of one computer per student [27,37,38,41,48]. Also our study did not investigate differences in performance related to gender. Previous researches suggest that is almost impossible to separate students’ experience with video games from gender issues as male not tend only tend to play games more often, but they also play different types of games [52,53] and they hold significant attitudes toward the use of video games [54]. However, some studies indicated that games can be equally effective and motivating for both male and female. These studies suggest that the impact of gender on acceptance tends to disappear during the implementation phase [55,56]. In a recent study regarding medical student attitudes toward video games and related new media technologies in medical education [57] the results indicated that men and women agreed that they were most inclined to use multiplayer simulations if they were fun, and if they helped to develop skill in patient interactions. Significant gender dissonance was observed over types of favorite games, the educational value of video games, and the desire to participate in games that realistically replicated the experience of clinical practice. This point should be considered in future investigations.

Further studies need to be carried out considering other factors that interfere with learning through computer games, as the motivation for learning and the type of computer game used, according to their educational objectives. As indicated by the literature, these variables can have an influence in the performance of students [27,41,47], not only in terms of knowledge gains but also when considering clinical practice (i.e. in the case of the Health Sciences) [39].

The use of computer games in the classroom environment is a novel proposal in in the field of the Speech-Language and Hearing Sciences. Using computer games as a complementary educational resource in Higher Education is an innovative proposal, and many challenges must be addressed, particularly regarding the development and the application of a type of learning object whose use is still far from the educator reality [58], especially in Higher Education [59].

With the increasing development of educational computer games and their use in Higher Education, further studies are necessary for rigorous evaluation of the computer games effectiveness in improving educational and clinical outcomes [26]. This should be performed by developing methods for the assessment of students' knowledge retention [37,41].

The present study represents a first initiative to investigate the use of computer games in the field of Speech-Language and Hearing. The follow up proposal for this study is to increase sample size, to assess learning motivation when using computer gamesand to investigate the effects of computer games on clinical reasoning and decision making in the field of the Speech-Language and Hearing Sciences.

Conclusion

The results of the present study showed that the computer game-based learning method is comparable to the traditional learning method concerning knowledge gains when measured immediately after the learning method application (short-term knowledge retention). Moreover, the traditional lecture seems to be more effective to improve students’ long-term knowledge retention. In general, this finding is more important than the first one because long-term effects of curricular education are more crucial than short-term effects.

It is important to point that the results presented in this study should not undermine the use of computer games in classroom environment even in Higher Education. Rather, it helps reinforce the critical need for further research aimed at assessing the educational value of computer games in students’ learning and knowledge retention.

Competing interest

The authors declare that they have no competing interest.

Authors’ contribution

SR contributed to data collection and analysis, to the interpretation of the results, to the manuscript writing and provided substantial scientific contribution. FCS contributed to the interpretation of the results, to the manuscript writing and provided scientific contribution. CRFDA contributed to the research and experimental design. All authors read and approved the final manuscript.

Authors’ information

Silmara Rondon is a speech pathologist at the Department of Physiotherapy, Speech-language and Hearing Sciences and Occupational Therapy. School of Medicine, University of São Paulo, Brazil.

Fernanda Chiarion Sassi has a Ph.D. in Science and is a speech pathologist at the Department of Physiotherapy, Speech-language and Hearing Sciences and Occupational Therapy. School of Medicine, University of São Paulo, Brazil.

Claudia Regina Furquim de Andrade is a Full Professor at the Department of Physiotherapy, Speech-language and Hearing Sciences and Occupational Therapy. School of Medicine, University of São Paulo, Brazil.

References

  • Berman NB, Fall LH, Maloney CG, Levine DA. Computer-assisted instruction in clinical education: a roadmap to increasing CAI implementation. Adv Health Sci Educ. 2008;13:373–383. doi: 10.1007/s10459-006-9041-3.[PubMed][Cross Ref]
  • Norman G, Schmidt H. The psychological basis of problem-based learning: a review of the evidence. Academ Med. 1992;67(9):557–565. doi: 10.1097/00001888-199209000-00002.[PubMed][Cross Ref]
  • Piemme T. Computer-assisted learning and evaluation in medicine. J Am Med Assoc. 1988;260:367–372. doi: 10.1001/jama.1988.03410030083033.[PubMed][Cross Ref]
  • Clayden G, Wilson B. Computer-assisted learning in medical education. Med Educ. 1988;22(5):456–467.[PubMed]
  • Prensky M. Digital natives, digital immigrants. On The Horizon. 2001;9(5):1–6. doi: 10.1108/10748120110424816.[Cross Ref]
  • Deshpande AA, Huang SH. Simulation games in engineering education: a state-of-the-art review. J Comput Appl Eng Educ. 2009. [Cross Ref]
  • Vogel M, Wood D. Love or hate it? medical students’ attitudes to computer-assisted learning. Med Educ. 2002;36(3):214–215. doi: 10.1046/j.1365-2923.2002.01181.x.[PubMed][Cross Ref]
  • Zemlin WR. Princípios de anatomia e fisiologia em fonoaudiologia. Porto Alegre: Artmed; 2000.
  • Felício CM. In: Tratado de fonoaudiologia. 2. Fernandes FDM, Mendes BCA, Navas ALPGP, editor. São Paulo: Roca; 2009. Desenvolvimento normal das funções estomatognáticas; pp. 17–27.
  • Yuen HK, Fallis M, Martin-Harris B. A survey of head and neck cancer curriculum in United States speech language pathology masters programs. J Cancer Educ. 2010;25(4):556–9. doi: 10.1007/s13187-010-0106-x.[PubMed][Cross Ref]
  • Gardner E, Gray DJ, O’Rahilly R. Anatomia Geral – Introdução. Rio de Janeiro: Guanabara-Koogan; 1971. pp. 3–9. (Anatomia - Estudo Regional do Corpo Humano).
  • Jang KS, Hwang SY, Park SJ, Kim YM, Kim MJ. Effects of a web-based teaching method on undergraduate nursing students’ learning of electrocardiography. J Nurs Educ. 2005;44:35–39.[PubMed]
  • Schneider PJ, Pederson CA, Montanya KR, Curran CR, Harpe SE, Boheneck W, Perratto B, Swain J, Wellmann KE. Improving the safety of medication using an interactive CD-ROM programme. Am J Health-Syst Ph. 2006;63:59–64. doi: 10.2146/ajhp040609.[PubMed][Cross Ref]
  • Criley JM, Keiner J, Boker JR, Criley SR, Warde CM. Innovative web-based multimedia curriculum improves cardiac examination competency of residents. J Hosp Med. 2008;3(2):124–133. doi: 10.1002/jhm.287.[PubMed][Cross Ref]
  • Kalet AL, Song HS, Sarpel U, Schwartz R, Brenner J, Ark TK, Plass J. Just enough, but not too much interactivity leads to better clinical skills performance after a computer assisted learning module. Med Teach. 2012;34:833–839. doi: 10.3109/0142159X.2012.706727.[PMC free article][PubMed][Cross Ref]
  • Paalman MH. Why teach anatomy? anatomists respond. Anat Rec B New Anat. 2000;261(1):1–2. doi: 10.1002/(SICI)1097-0185(20000215)261:1<1::AID-AR1>3.0.CO;2-3.[PubMed][Cross Ref]
  • Goldberg HR, McKhann GM. Student test scores are improved in a virtual learning environment. Adv Physiol Educ. 2000;23:59–66.[PubMed]
  • Ohrn MA, van Oostrom JH, van Meurs WL. A comparison of tradicional textbook and interactive computer learning of neuromuscular block. Anesth Analg. 1997;84(3):657–661.[PubMed]
  • Sweller J. Cognitive load during problem solving: effects on learning. Cognitive Sci. 1988;12:257–285. doi: 10.1207/s15516709cog1202_4.[Cross Ref]
  • Sweller J, van Merriënboer JJG, Paas FGWC. Cognitive architecture and instructional design. Educ Psychol Rev. 1998;10(3):251–296. doi: 10.1023/A:1022193728205.[Cross Ref]
  • Rummel N, Spada H. Learning to collaborate: an instruction approach to promoting collaborative problem solving in computer-mediated settings. J Learn Sci. 2005;14(2):201–241. doi: 10.1207/s15327809jls1402_2.[Cross Ref]
  • Pearson J. Investigating ICT, using problem-based learning in face-to-face and online learning environments. Comput Educ. 2006;47(1):56–73. doi: 10.1016/j.compedu.2004.09.001.[Cross Ref]
  • Kester L, Lehnen C, Van Gerven PWM, Kirschner PA. Just-in-time schematic supportive information presentation during cognitive skill acquisition. Comput Hum Behav. 2006;22(1):93–116. doi: 10.1016/j.chb.2005.01.008.[Cross Ref]
  • Silva AS, Delacruz GC. Hybrid reality games reframed – potencial uses in educational contexts. Game Cult. 2006;1(3):231–251. doi: 10.1177/1555412006290443.[Cross Ref]
  • Thomas D, Brown JS. The play of imagination – extending the literary mind. Game Cult. 2007;2(2):149–172. doi: 10.1177/1555412007299458.[Cross Ref]
  • Akl EA, Mustafa R, Slomk T, Alawneh A, Vedavalli A, Schünemann HJ. An educational game for teaching clinical practice guidelines to internal medicine residents: development, feasibility and acceptability. BMC Med Educ. 2008;8:50. doi: 10.1186/1472-6920-8-50.[PMC free article][PubMed][Cross Ref]
  • Ebner M, Holzinger A. Successful implementation of user-centered game based learning in higher education: an example from civil engineering. Comput Educ. 2007;49:873–890. doi: 10.1016/j.compedu.2005.11.026.[Cross Ref]
  • Prensky M. Digital Game-Based Learning. New York: McGraw-Hill; 2001.
  • Hong J-C, Cheng C-L, Hwang M-Y, Lee C-K, Chang H-Y. Assessing the educational values of digital games. J Comput Assist Lear. 2009;25:423–437. doi: 10.1111/j.1365-2729.2009.00319.x.[Cross Ref]
  • Kalyuga S. Instructional designs for the development of transferable knowledge and skills: a cognitive load perspective. Comput Hum Behav. 2009;25:332–338. doi: 10.1016/j.chb.2008.12.019.[Cross Ref]
  • Huang WH. Evaluating learners’ motivational and cognitive processing in an online game-based learning environment. Comput Hum Behav. 2011;27:694–704. doi: 10.1016/j.chb.2010.07.021.[Cross Ref]
  • Gee JP. Good video games and good learning: collected essays in video games, learning and literacy. New York: Peter Lang Publishing; 2007.
  • Driskell JE, Willis RP, Cooper C. Effect of over-learning on retention. J Appl Psychol. 1992;77:615–622.
  • Walliser B. A spectrum of equilibration processes in games. J Evol Econ. 1998;8:67–87. doi: 10.1007/s001910050056.[Cross Ref]
  • Coyne R. Mindless repetition: learning from computer games. Design Stud. 2003;24:199–212. doi: 10.1016/S0142-694X(02)00052-2.[Cross Ref]
  • Shaffer DW, Squire KR, Halverson R, Gee JP. Video games and the future of learning.http://www.academiccolab.org/resources/gappspaper1.pdf.
  • Liu C-C, Cheng Y-B, Tsai C-C, Huang C-C. The effect of simulation games on the learning of computational problem-solving. Comput Educ. 2011;57(3):1907–1918. doi: 10.1016/j.compedu.2011.04.002.[Cross Ref]
  • Kim S, Chang MD. Computer games for the math achievement of diverse students. Educ Technol Soc. 2010;13(3):224–232.
  • Akl. The effect of educational games on medical students’ learning outcomes: a systematic review: BEME guide No 14. Med Teach. 2010;32:16–27. doi: 10.3109/01421590903473969.[PubMed][Cross Ref]
  • Boreham N, Foster R, Mawer G. The phenytoin game: its effect on decision skills. Simulat Games. 1989;20(3):292–299.32. doi: 10.1177/104687818902000304.[Cross Ref]
  • Kanthan R, Senger JL. The impact of specially designed digital game-based learning in undergraduate pathology and medical education. Arch Pathol Lab Med. 2011;135:135–142.[PubMed]
  • Papastergiou M. Exploring the potential of computer and video games for health and physical education: a literature review. Comput Educ. 2009;53(3):603–622. doi: 10.1016/j.compedu.2009.04.001.[Cross Ref]
  • Kiili K. Digital game-based learning: towards an experiential gaming model. Internet High Educ. 2005;8(1):12–24.
  • Csikszentmihalyi M. Beyond boredom and anxiety. San Francisco: Jossey-Bass Publishers; 1975.
  • Seikel JA, King DW, Drumright DG. Anatesse 2.0: Electronic classroom manager to accompany Anatomy and Physiology for Speech, Language and Hearing. [CD-ROM] 3. United States of America: Thomas Delmar Learning; 2005.
  • Neter J, Kutner MH, Nachtsheim CJ, Wasserman W. Applied Linear Statistical Models. 4. Ilinois: Richard D. Irwing; 1996.
  • Annetta LA, Minogue J, Holmes SY, Cheng MT. Investigating the impact of video games on high school students’ engagement and learning about genetics. Comput Educ. 2009;53:74–85. doi: 10.1016/j.compedu.2008.12.020.[Cross Ref]
  • Brom C, Preuss M, Klement D. Are educational computer micro-games engaging and effective for knowledge acquisition at high-schools? a quasi-experimental study. Comput Educ. 2011;57:1971–1988. doi: 10.1016/j.compedu.2011.04.007.[Cross Ref]
  • Egenfeldt-Nielsen S. Beyond edutainment: Exploring the educational potential of computer games. PhDthesis. Copenhagen: University of Copenhagen; 2005.
  • Wong WL, Shen C, Nocera L, Carriazo E, Tang F, Bugga S, Narayanan H, Wang H, Ritterfeld U. Serious video game effectiveness. New York: ACM Digital Library; 2007. pp. 49–55. (Proceedings of the International Conference on Advances in computer entertainment technology).
  • Selby G, Walker V, Diwkar V. A comparison of teaching methods: interactive lecture versus game playing. Med Teach. 2007;29:972–974. doi: 10.1080/01421590701601584.[PubMed][Cross Ref]
  • Bonanno P, Kommers PAM. Gender differences and styles in the use of digital games. Educ Psychol. 2005;25(1):13–41. doi: 10.1080/0144341042000294877.[Cross Ref]
  • Jean JD, Upitis R, Koch C, Young J. The story of phoenix quest: how girls respond to a prototype language and mathematics computer game. Gender Educ. 1999;11(2):207–223. doi: 10.1080/09540259920708.[Cross Ref]
  • Bonanno P, Kommers PAM. Exploring the influence of gender and gaming competence on attitude towards using instructional games. British J Educ Technol. 2008;39(1):97–109.
  • Fengfeng K. A case study of computer gaming for math: engaged learning from gameplay? Comput Educ. 2009;51(4):1609–1620.
  • Papastergiou M. Digital game-based learning in high school computer science education: impact on educational effectiveness and student motivation. Comput Educ. 2009;52(1):1–12. doi: 10.1016/j.compedu.2008.06.004.[Cross Ref]
  • Kron FW, Gjerde CL, Sen A, Fetters MD. Medical student attitudes toward video games and related new media technologies in medical education. BMC Med Educ. 2010;10:50. doi: 10.1186/1472-6920-10-50.[PMC free article][PubMed][Cross Ref]
  • Ketelhut DJ, Schifter ER, Kidder LH. Teachers and game-based learning: improving understanding of how to increase efficacy of adoption. Comput Educ. 2011;56:539–546. doi: 10.1016/j.compedu.2010.10.002.[Cross Ref]
  • Zayim N, Yildirim S, Saka O. Technology adoption of medical faculty in teaching: differentiating factors in adopter categories. Educ Technol Soc. 2006;9(2):213–222.

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