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Editor’s Note: As commerce and communication move from face-to-face to web transactions, so is teaching and learning. And just as PCs replaced mainframe computers, the Web is displacing personal computing with locally stored programs and data to web based equivalents. It is no longer necessary to go to home, school, or to office to use your computer; all of these resources are available on the Web wherever you have access to a computer. This study applies such technology to courses in computer programming in Korea. Student-Centered Online Support for | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Figure 1. Gender Figure 2. Student classification
The majority of the students (92.7%, N=76) had computer programming experience, at least, with a semester course, and 7.3 % (N=6) had no experience. Most of them (86.6%, N=11) had some online experience from SYU campus, and 13.4% (N= 11) had no experience. Of them, most of the students, 72.0% (N=59) had some experiences, with at least 1 computer programming-related online course, 21.9% (N=18) with 2 courses, and the remaining 6.1% (N=5) had no experience.
The following data analysis attempted to see if any differences existed for demographic variables, illustrated earlier, among students, and were run to determine the differences in perceptions. The result indicates that Table 1 shows no significant differences in students’ perceptions toward online learning, in these demographic variables at the p=0.05 level. To put it shortly, the analysis showed no significant differences in gender, SYU online course, experience of other web sites (off-campus), computer programming experience, number of computer programming-rated online courses, type of internet mode, weekly internet hours used on campus (except class), and weekly Internet hours used at home.
The following data analysis is related to the topic of this study, containing 26 items (Q13-Q38) in the questionnaire, each item used a 5-scale Likert type. For each subject’s responses, 5 means strongly agree and 1 means strongly disagree.
Variables | Mean | S.D. | p |
Q1. Gender: | 0.56 | ||
1) female | 3.57 | 0.37 |
|
2) male | 3.64 | 0.50 |
|
Q5. SYU online course: | 0.09 | ||
1) yes | 3.64 | 0.47 |
|
2) no | 3.39 | 0.38 |
|
Q6. Experience at the other web sites | 0.25 | ||
1) yes | 3.65 | 0.48 |
|
2) no | 3.5 | 0.39 |
|
Q7. Computer programming experience: | 0.62 | ||
1) yes | 3.60 | 0.47 |
|
2) no | 3.71 | 0.39 |
|
Q8-1. Number of computer programming- | 0.35 | ||
1) 1 online course | 3.58 | 0.47 |
|
2) 2 online courses or more | 3.71 | 0.45 |
|
Q10. Type of Internet mode: | 0.19 | ||
1) cable (dedicated line) | 3.60 | 0.41 |
|
2) others | 3.28 | 0.15 |
|
Q11-1. Weekly Internet hours used on | 0.25 | ||
1) 1 hour | 3.55 | 0.4 |
|
2) 2 hours or more | 3.72 | 0.51 |
|
Q11-2. Weekly Internet hours used off- | 0.77 | ||
1) 1 hour | 3.52 | 0.24 |
|
2) 2 hours or more | 3.62 | 0.46 |
|
For data analysis, these 26 items were sub-grouped into five broad categories:
I. Convenience (Q18-Q21, Q25, Q28, Q38),
II. Flexibility (Q17, Q26, Q27, Q33),
III. Collaboration (Q16, Q29, Q36, Q37),
IV. Retention (Q13-Q15, Q22-Q24, Q30-Q32), and
V. Globalization (Q34, Q350).
Summary data is presented in Table 2.
A statistical technique was used for testing internal consistencies. Reliability statistics between these five subgroups showed Cronbach’s alpha values of 0.736, 0.615, 0.601, 0.719, and 0.778 for each group, respectively. These values were higher than at the 0.6 level which implies that these subgroups were moderate for internal consistencies.
N=82 | Number of | Average | | Reliability Statistics |
Sum-Scale | ||||
I. Convenience | 7 | 3.88 | 0.60 | 0.736* |
II. Flexibility | 4 | 3.76 | 0.59 | 0.615* |
III. collaboration | 4 | 3.68 | 0.59 | 0.601* |
IV. Retention | 9 | 3.21 | 0.59 | 0.719* |
V. Globalization | 2 | 3.95 | 0.79 | 0.778* |
Overall 26 3.41 0.46 | ||||
Cronbach’s α > 0.6 * | ||||
For the first sub-scale, the mean score of 7 items is 3.88, for the second, the mean of 4 items is 3.76, for the third the mean of 4 items is 3.68, for the fourth the mean of 9 items is 3.21, and for the last the mean of 2 items is 3.95. The overall mean of 26 item questionnaires is 3.41. In sum, it indicates that the students slightly agreed with the statements.
Next, the mean value shown in Table 3 indicates that many items have positive opinions toward online learning held by the students. Only a small portion (M=2.63, 3.01, 3.11, and 3.12 in the Q15, Q14, Q13, and Q30 of subgroup IV, in order) tended to hold negative opinions toward online learning. The S.D. (standard deviation) also reveals the spread of the score distribution toward the statements about online learning. The data collected also indicated the extent to which survey respondents provided similar responses in answering the questions. The S.D. value was small in the survey responses. In sum, the data analysis revealed that the students generally held positive opinions toward online learning.
Q# |
Statement | Students’ Response | Sub-Group | ||
N | Mean | S.D. | |||
Q18 | unbound by place | 82 | 4.07 | 0.90 |
I
|
Q28 | less online learning cost (tuition fee, .) | 80 | 3.99 | 1.01 | |
Q38 | all course-related materials, handouts, etc. | 82 | 3.93 | 0.83 | |
Q20 | do not require student’s physical attendance | 80 | 3.85 | 1.01 | |
Q19 | self-paced (any time, any place, any pace) | 82 | 3.84 | 1.00 | |
Q25 | various learning styles (lecturing; hands-on | 82 | 3.80 | 0.89 | |
Q21 | on-demand access to learning content | 82 | 3.72 | 1.06 | |
Q27 | self-directed, different needs, interest | 80 | 4.15 | 0.70 |
II
|
Q26 | various available modes of learning | 80 | 3.94 | 0.85 | |
Q17 | look up the information I need | 80 | 3.59 | 0.82 | |
Q33 | learn in greater depth. | 82 | 3.50 | 0.86 | |
Q29 | interaction and collaboration. | 80 | 3.90 | 0.77 |
III
|
Q16 | working with my teammates | 82 | 3.67 | 0.89 | |
Q37 | necessary to communicate to and interact | 80 | 3.49 | 1.03 | |
Q36 | a member of a team in virtual collaborative . | 80 | 3.45 | 0.95 | |
Q32 | attractive audio- and video lectures | 80 | 3.61 | 0.86 |
IV
|
Q31 | hyperlinks, graphics, animations, etc. and/ | 80 | 3.55 | 0.99 | |
Q24 | a class learning style | 80 | 3.49 | 0.97 | |
Q22 | prefer online courses to traditional course | 81 | 3.46 | 1.15 | |
Q23 | 40% online course + 60% with lecture | 82 | 3.39 | 1.11 | |
Q30 | active online multimedia course | 82 | 3.12 | 0.96 | |
Q13 | feel comfortable taking course online. | 82 | 3.11 | 1.01 | |
Q14 | learn more through on-line material | 82 | 3.01 | 1.01 | |
Q15 | immediate feedback through chats | 82 | 2.63 | 1.07 | |
Q34 | global resources of knowledge | 78 | 3.99 | 0.85 | V
|
Q35 | no geographic isolation | 80 | 3.93 | 0.87 | |
Q#: Question# N: number of respondents S.D.: Standard Deviation Ave. Mean: Average Mean |
| ||||
The results of this investigation revealed commonly held opinions among students on online learning and indicate that students tend to view online learning positively. Students were of the opinion that online learning benefits students and assists learning in general. Results of this study echo earlier studies by Yang (2006), Smart (2006), Maltby (2000), and O’Malley (1999).
Results of this study also revealed other widespread opinions on advantages and benefits displayed in online learning, as follows:
§ Online can encourage more independent and active learning for students to do programming activities.
§ Online saves students and faculties time and effort.
§ Information or class materials for programming learning can vary in quality and level. So accurate guidance and preparation is needed.
§ Resources can be accessed from any location and at any time.
§ Online learning is able to link resources to many different formats.
§ Online learning can be an efficient way of delivering programming course materials.
§ Online learning can make teaching more efficient.
§ Online learning provides students greater access to educational opportunities.
§ Online learning can provide useful supplementary materials to conventional classes in computer programming.
In addition, whether based on the commonly held opinion resulting in each subgroup and reality of programming class in traditional classroom, the following draws the major components of online environments that can enhance and improve the learning of programming in online learning, and that can make the learning environment as smooth as possible.
Advanced online system with multimedia system and visual tools
Many students are interested in an online environment that uses visualization to display class structure. They spend lots of time discussing class structure in object-oriented programming. In fact, visuals have been an important pedagogical tool for a long time in computer programming. The online system can provide opportunities to expand availability of visualization-based programming learning tools. Some tools such as BlueJ and Jeliot are recommended for activating learning tools (Maria and Luis, 2005), (Buabeng, et al. 2002).
BlueJ is a visual programming environment designed to teach object-oriented programming using Java. It is an integrated teaching environment and language, developed at Sydney University and Monash University in Australia. This program helps students to develop an understanding of object-oriented concepts such as objects and classes, message passing, method invocation and parameter passing. Jeliot emphasizes program animation to demonstrate the execution of input-output, assignment, selection and loop statements (Haataja, et al., 2000).
In addition to these tools, Rational Rose and Together are powerful visual modeling tools for object-oriented system development using UML (Unified Modeling Language). They support full round trip engineering, allowing reverse engineering and the generation of UML diagrams from source programs. These are not suitable for use as a tool for beginners to learn object-oriented programming (Cheung, 2006). Some functions can compare with other OOP visual packages: BlueJ, Sun ONE Studio, Rational Rose, etc. (Cheung, 2006).
The online system can provide an integrated collaborative and interactive environment for students to do programming activities at anytime and anywhere, including the following activities:
1. Building effective interactions among students is important in the learning process. When students are geographically remote from each other, the system is capable of accessing a database server that stores students’ work and logs their behavior while doing programming activities online.
2. Another important function in learning programming online is to give instant help for those students who need it. Students can use e-mail or bulletin board messages to contact their peers or instructors. In the online system, the main idea is to answer the questions as quickly as possible so that the upcoming problems do not hinder the learning process (Haataja, et al., 2000).
3. Students do not learn in isolation. Learning is no longer bounded by the closed wall of the classroom but transcends the limits of time and location. They are often involved in learning activities that require them to work with their peers in small groups and teams, both inside and outside the classroom (Cheung, 2006).
4. The students can be engaged in different kinds of out-of-class activities, and can work in groups that need to communicate, debate, and give opinions to other group members, encouraging the kind of reflection that leads to learning, through various technology-enhanced forums and interactions.
5. The system also allows instructors to effectively monitor the learning progress of students and provide timely feedback to them (Sheung-On, et al., 2004; Ellis, 2001; Esteves, et. al., 2006).