Abstract
3D visualization specifically has been widely applied in a broad range of fields, including computer science, pedagogy, and so forth. 3D visualization instruction has become the essential tool that uses computer programs to generate 3D representations of manmade objects. For users, 3D visualization instruction can be manipulated, altered and efficiently communicated to others, and it is efficient for teaching and learning. The aim of this study is investigating students' perception toward 3D visualization instruction, and the influence of learning-style preferences on learners' intentions to use 3D visualization instruction. We are trying to develop the experiment which undergraduate students participated in this study, the purpose of which was to investigate the utilize 3D visualization instruction access to the single learning style and multiple learning styles. Data mining technology was employed in this study to identify multiple learning styles. The result showed that high visual and high sensing learning style has potential of using 3D visualization instruction.
TopIntroduction
The use of visualization tools for representing data relations and in other types of demonstrations has become popular in recent years. Data visualization representations have become essential in many fields with the recent flourishing of information and communication technology, and visualization technology has been widely applied to teaching and learning (Avgoustinov, 2000; Cheng, Chiang, Ye, & Cheng, 2010; Ieronutti & Chittaro., 2007; Keefe et al., 2008). One example is the Collaborative Virtual Environment, which creates virtual characters to help students with Autism Spectrum Disorder acquire social skills (Cheng et al., 2010). Various types of 3D visualization have also been successfully employed in educational applications (Dalgarno & Lee, 2010).
A learning style is an individual’s habitual pattern of acquiring and processing information in learning situations (James & Gardner, 1995). Few studies have considered whether learning styles have a direct relationship to user attitudes toward 3D visualization instruction. The present study examined how individual learning styles influence attitude toward 3D visualization instruction. The Index of Learning Style (ILS) questionnaire (Felder & Soloman, 2003) was used to measure participants’ learning styles, and data mining techniques were employed to identify the presence of multiple learning styles. Moreover, questionnaires on learning attitude, technology acceptance, and cognitive load were used to evaluate learners’ perceptions of 3D visualization instruction. The purpose was to determine what approaches can best enhance the motivation of various types of learners to benefit from 3D visualization instruction. The overall goals of this study are as follows:
- 1.
To explore whether learning styles can influence the use of 3D visualization instruction, and to determine what relationships may exist between learning styles and intention to use this tool.
- 2.
To investigate information on learners’ experiences and motivations for tool usage (also provide 3D visualization instructions and suggestions how to make the tool most effective).
- 3.
To assess students’ learning attitude, perceived cognitive load, and level of acceptance with regard to 3D visualization instruction technology.
- 4.
To consider which learning approaches can best assist users of 3D visualization instruction and enhance their learning motivation.
Key Terms in this Chapter
Learning Style: A personal traits refers to an individual’s approach to learning based on their preferences, strengths and weaknesses.
Query-Based Learning: A learning approach where the learner has a dialogue with a teacher, which provides on request learning suggestions about the concept to be learnt.
Educational Technology: A concept that applying technological tools in pedagogic environment.
Computer-Assisted Instruction (CAI): An instruction that used computer or digital device to monitor the learning that takes place and present the instructional material.
Data Mining: A technological method that is used to explore hidden patterns and relationships in strategy making.
Adaptive Learning: A pedagogic method which uses computers modifies the presentation of material in response to the unique needs of each learner.
3D Visualization Instruction: An instruction that use a three-dimensional representation of data.