Effectiveness of Multimedia In Learning & Teaching Data Structures Online

Turkish Online Journal of Distance Education-TOJDE October 2008 ISSN 1302-6488 Volume: 9 Number: 4 Article 7 EFFECTIVENESS OF MULTIMEDIA IN LEARNING & TEACHING DATA STRUCTURES ONLINE ABSTRACT Sahalu JUNAIDU Information & Computer Science Department King Fahd University of Petroleum & Minerals Dhahran, KINGDOM OF SAUDI ARABIA Online electronic education is now being widely accepted as a major viable component of higher education. This is fuelled by the emergence of worldwide information and computer communications technologies. However, online education is not being adopted in science and engineering subjects as widely as in other fields because of the idiosyncrasies of some science and engineering-based courses. For online engineering education to be broadly accepted and utilized, the quality of online courses must, amongst other things, be comparable to or better than those of traditional face-to-face classroom education. This paper explores and reports on the importance of creating multimedia-rich course content and the important role that animations can play in creating a successful online learning experience. Results of our study on an online data structures course over five years offerings show that students consistently perform much better in questions requiring application of material taught in carefully animated algorithms. These results should carry over to other educational environments. Keywords: Multimedia; e-Learning; data structures INTRODUCTION The acceptance of online electronic education in colleges, universities and corporate organizations is now pervasive. This is made possible largely by the emergence and rapid development in worldwide information and computer communications technologies. The initial skepticism with which online electronic education was greeted is now waning away. We are now witnessing not only the offering of a course or two online in traditional universities but the establishment of full-fledged degree programs online and online universities (Phoenix, 2006; Cardean, 2006; Colorado, 2006). Even with these developments, online electronic courses in science and engineering are not as widespread as courses in other disciplines in higher education. The reasons for this are that, science and engineering education has, traditionally, been contentcentered, design-oriented, and is permeated by the development of problem-solving skills (Bourne, 2005). It is further argued that some of the special needs of undergraduate science and engineering education have not been well served by methods of online education. 97 Specifically, laboratories are a mainstay of engineering education, as are mathematical foundations and design tools. Laboratories (Grose, 2003; Peterson, 2002) are notably difficult to provide online because of the traditional desire for the direct operation of instruments. Similarly, course materials that require significant use of mathematics have not been as easy to implement as topics that require only text-based discussion (Bourne, 2005). For online science and engineering education to be broadly accepted and utilized; ¾ ¾ ¾ the quality of online courses must be comparable to or better than the traditional classroom, courses should be available when needed and accessible from anywhere by any number of learners, and topics across the broad spectrum of engineering disciplines should be available. One way of meeting the first requirement is through the use of multimedia in creating interactive courseware that gives learner control leading to potentially better learning experiences. The potential of multimedia in education does have a theoretical foundation. Bagui (1998) and Daniels (1995) summarized the theory of multi-channel communication in support of the potential for multimedia. According to this theory, humans have several channels by which data is communicated. If information is presented via two or more of these channels, there will be additional reinforcement and, consequently, greater retention, thereby improving learning (Ellis, 2004). Further support for the potential benefits of multimedia is offered by research in learning styles. McCarthy (1997) explored learning styles and identified four distinct approaches to learning: the feeler, the analyzer, the doer, and the creator. A multimedia approach presents the potential to address these different approaches to learning, as was suggested by the research of (Riding and Grimley, 1999). This paper explores the effectiveness of multimedia in helping students learn in an online undergraduate Data Structures course at our university. By the time of this study, the course has been offered completely online for four years, except for the laboratory component of the course which was instructor lead. I make use of Ellis’s model for testing the effectiveness of multimedia in this study. Ellis’s model is discussed in the next section. The rest of the paper details the purpose of the study, the algorithms selected for the study, the student population, data collection and analysis, and conclusions of the study. ELLIS’S MODEL FOR MULTIMEDIA EFFECTIVENESS Our work follows the model of (Ellis, 2004) for establishing precisely the value of multimedia in enhancing learning. According to this model, any study of the effectiveness of multimedia as a tool to enhance learning must specify: ¾ ¾ ¾ ¾ learning in a manner that is consistent with accepted learning theory the student population under consideration the subject matter being studied which media elements are being studied, at what level of interactivity, and toward what end 98 A typical Data Structures course like ours normally covers level 2 (comprehension), level 3 (application), level 4 (analysis) and level 5 (synthesis) competency levels of the Bloom’s taxonomy (Bloom, 1956). Level 2 competency is covered in the requirement for learners to be able to describe common applications for each data structures, describe how the data structures are allocated and used in memory, and explain the use of big O to describe the amount of work done by an algorithm. Level 3 competency is covered in the requirement for learners to be able to perform tasks illustrated in our animations. They should also be able to write programs that use arrays, strings, linked lists, stacks, queues, hash Tables, trees, and graphs. Level 4 competency is covered in the requirement for learners to be able to compare alternative implementations of data structures with respect to performance and also learners’ ability to compare and contrast the costs and benefits of dynamic and static data structures implementations. Level 5 competency is covered in the requirement for learners to be able to choose the appropriate data structure for modeling a given problem. For the purpose of the study in this paper, the aspect of learning under consideration is learners’ ability to quickly acquire information and to apply the newly acquired information to solve probl (...truncated)