& Leitner, 2001)
Marks, Freeman, and Leitner (2001) employed case studies to teach two topics of image processing concepts: image enhancement and face recognition, to non CS-majors in college. Two lectures involved in the teaching of each case study: firstly presented an intuitive overview of the basic concept of image processing and the second concentrated on hands-on application operation. In the case study of image enhancement, instructor firstly introduced simple methods to modify the tonescale, sharpen and de-noise images. Students then enhanced several supplied images and simulated the image-processing steps by Adobe Photoshop to study these image-enhancement operations.
As in the case study of face recognition, many fundamental visual measurements including tracking, shape and object recognition, and motion analysis were firstly reviewed. Additionally, students also learned how to measure performance for recognition task. One face recognition software and a small database of 40 images of faces of volunteers from the class were provided in second lecture to study the effect of different image-similarity metrics on face-recognition performance. After the hands-on practices, students were demonstrated that the recognition performance mainly depends on lighting, facial expression, and head pose. This research has two purposes:
1) Students perform design and problem-solving tasks by hands-on software, therefore reinforcing the abstract concepts presented in first lecture. 2) Students conduct in-depth exploration and experimentation without writing any programs which is a difficult job for non-major students. The course involved these case studies has been offered for two years at the Harvard University Extension School. At the end of the project,
students showed their favor and gratitude on the learning experience.
Package 4: SIVA (Rajashekar, Panayi, Baumgartner, & Bovik, 2002)
Rajashekar, Panayi, Baumgartner and Bovik (2002) together developed SIVA (Signal, Image and Video Audiovisualization Demonstration Gallery) for signal- and image-processing courses in the University of Texas at Austin. SIVA comprised of two visualization modules: LabVIEW-based demonstration suite for an undergraduate course titled “Digital Image Processing and Video Processing” and a MATLAB demonstration package for a graduate course named “Digital Signal Processing”.
LabVIEW is a graphical programming language used as a powerful and flexible instrumentation and analysis software in industry and academic. Through the support of LabVIEW, SIVA developed many VIs (virtual instruments) for illustrating fundamental operations in image and signal processing procedure. The learning topics of VIs include analogy – digital conversion, binary image processing, histogram and point operations, Image Analysis (Frequency Interpretations), directional DFTs, image filtering, image compression, other advanced topics, such as linear and nonlinear filtering, lossy and lossless image compression schemes.
Putting on the Web learning management system, the demonstrations of SIVA are available over Internet. The image/video processing learning courses using SIVA as supplement attracted students from a variety of backgrounds at the University of Texas at Austin. Many other users from various countries also used SIVA for their projects and as a teaching tool for image/signal processing courses.
Package 5: IPT (Ageenko & La Russa, 2005)
IPT (Image Processing Toolkit) (Ageenko & La Russa, 2005) is a visualization tool to demonstrate various abstract concepts of image processing. Its point-and-click
interface is user-friendly. Users are not only provided many image processing algorithms to interactively apply to image, but also allowed to change the algorithm parameters. The produced effects of the algorithm on the image can be immediately observed and compared with the original one. By synchronous panning and zooming, IPT helps to demonstrate how algorithms and their properties differentiate on the image by giving an immediate comparison of the images. Different image types, binary, grayscale and color image, are supported in IPT. Importantly, IPT is a standalone tool and has open application interface based on Java platform. Therefore, new image processing algorithms implemented by any researchers can be added as plug-ins to the toolkit to enrich its features and functionalities.
As mentioned above, many common features are shared in these packages. First of all, many algorithms were provided in the packages or can be added into the system, to assist users to explore the image processing theory without programming. Secondly, user-friendly interface is necessary in the package environment. Last but not least, being able to execute in different platforms is inevitable. These features are worth to be considered in future studies. It is also worthy noticing that these packages were designed to facilitate image processing learning in undergraduate or graduate level.
The topics covered in the packages might be too advanced and less suitable used in high school level. More attention of image processing learning is needed to take for high school. In this thesis, development of image processing packages integrated in lab activities for high schools’ image processing teaching is one of our purposes.
2.5 Learners’ Characteristics
Learners’ characteristics usually influence their learning behavior. This section reviews related research on two types of characteristics: computer self-efficacy and
learning motivation.
Self-efficacy is defined as an individual’s judgment of his or her own capability to perform a particular task (Bandura, 1977). It might have an impact on how much effort an individual is willing to invest in and what strategies to take when encountering challenges or difficult problems that may affect students’ academic achievement (Bandura, 1996). Computer self-efficacy includes general computing knowledge and some specific application skills. It has been found to be positively related to performance in software training (Martocchio, 1992, 1994; Rozell & Gardner, 1999;
Webster & Martocchio, 1992, 1993, 1995), academic performance in introductory MIS classes (Karsten & Roth, 1998; Rozell & Gardner, 1993), and adaptability to new computing technology (Burkhart & Brass, 1990). Application-specific skill is one’s perception of efficacy in using a specific computer application or system (Marakas, &
Johnson, 1998). Previous research (Ames, 1992; Brown, 2002; Tsai, & Tsai, 2003; Yi,
& Hwang, 2003) revealed that application-specific self-efficacy has a positive effect on using information technology for learning.
Application-specific self-efficacy is particularly important in the online distance courses. Because students are physically separated from the instructor and peers in this learning environment, they take more responsibility for their learning, including handling of any technical difficulties. Students with greater application-specific self-efficacy are more likely to have more confidence in handling these difficulties on their own. Consequently, students with higher computer self-efficacy might have positive attitudes toward online distance courses. For such reason, in this study, application-specific self-efficacy is used as a scale to measure a student’s individual characteristics when using an online lecture video browsing system. The relationship between application-specific self-efficacy and lecture video viewing behaviour is a focus of this study.
Learners’ motivation to learning is another learner characteristic that can have much weight on learner’s learning. Many studies have reported a positive correlation between learning motivation and achievement in traditional learning environment (Lukmani, 1972; Noels, Clement, & Pelletier, 2001; Peng, 2002). Researchers (MacIntyre & Noels, 1996; Peng, 2002) further found that motivated learners made better use of learning strategies and achieved higher proficiency.
In the research domain of web-based learning system, students’ use of such systems for learning are usually voluntary or not required, so that students’ learning motivation might be related to their system usage and achievement. In the research of Oxford, Park-Oh, Ito, and Sumrall (1993) indicated that motivation affected performance in a foreign language course delivered by distance education. Shih and Gamon (2001) analyzed the relationships among students’ motivation, attitude, learning styles and achievement in two web-based courses. They found that motivation was the only significant factor that determined student achievement. The research in Grabe (2005) examined the differences in students’ learning motivation in the context of voluntary use of online notes. The results showed that frequency users of voluntary use of online lecture notes in an Introductory Psychology course in the college had significantly higher extrinsic learning motivations toward the course than those who frequently used notes as a replacement for class attendance. Roberts and Dyer (2005) explored the relationship between self-efficacy, motivation, and critical thinking disposition to achievement and attitudes when a web lecture is used in an online learning environment. The results indicated that motivation and computer proficiency tend to influence attitudes and that motivation and prior knowledge influence achievement. It was concluded that when a web lecture is used to deliver content, students with higher levels of motivation tend to exhibit higher achievement and more favorable attitudes. Although the previous studies have analyzed the relationship
between learning motivation and web use or achievement in web-based learning environment, whether students’ learning motivation affected their online lecture video viewing behaviour was not examined. More research is needed to investigate how learning motivation influence students’ intention to view lecture videos before class.
Thus, in addition to application-specific self-efficacy, students’ achievement goals are also to be identified in this study.
In the present study, achievement goals were used to measure students’ learning motivation. . Achievement goals include mastery goals and performance goals. The former is an individual’s desire to master the domain knowledge whereas the latter emphasizes the degree to which someone focuses on his or her performance capabilities relative to others (Brown, 2002). These two goals may influence students’ motivation to learn. Students with high mastery goals would like to develop new skills, try to understand their work, or achieve a sense of mastery based on self-referenced standards (Brophy, 1983; Harackiewicz, Barron, Tauer, Carter, & Ellio, 2000). In contrast, students with high performance goals focus on doing better than others who may apply short-term learning strategies and avoid challenging tasks (Brophy, 1983; Harackiewicz, Barron, Tauer, et al., 2000). It has been shown that mastery goals can positively predict subsequent interests in a course and the performance goals can positively predict grades, but not interests (Harackiewicz, Barron, Tauer, et al., 2000).