The Relation between Willingness to Online ...

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The Relation between Willingness to Online Communicate and Learning ... Keywords: Platform of interactive learning, computer-supported collaborative ...
The Relation between Willingness to Online Communicate and Learning Achievement Chien-Hung Lai1, a, Wei-Xiang Chen2, b, Bin-Shyan Jong 3, c and Yen-Teh Hsia 4, d 1

2,3,4

Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan, Taiwan

Department of Information & Computer Engineering, Chung Yuan Christian University, Taoyuan, Taiwan a

[email protected], [email protected] , [email protected], d

[email protected]

Keywords:

Platform

of

interactive

learning,

computer-supported

collaborative

learning,

computer-mediated communication, willingness to communicate.

Abstract. The instructional systems of this study uses the functions of “chapter review” and “main point evaluation,” so that students can discuss online, and use the system to collect the content of information of online communication by students, which is then used to explore study communication types. Communication types are divided into active questions and passive responses, to explore the correlation between different communication types of learning achievement. Research results showed that communication intention has a significant effect on the elevation of learning performance and learning achievement. The enthusiastic responses by students with high preexisting knowledge to questions of their peers, and the active questions asked by students with low preexisting knowledge could enhance their learning achievement. Students who are not willing to ask questions or provide responses showed polarized learning achievement. It was also discovered that the correlation between communication content and knowledge exchange would affect learning performance and achievement. Introduction In the studies on collaborative learning, many studies have proposed various instructional strategies or instructional systems, which can benefit student learning. In a computer-assisted collaborative learning environment, students often need to discuss with peers and exchange opinions and knowledge. Monteserin proposed using argumentation plans to help students discuss evidence with peers in the collaborative learning environments [3]. Argumentation plans also provide students with a more intuitive and comprehensive angle to resolve problems. In addition, scholars have combined virtual laboratories and collaborative learning activities, to construct a dynamic online learning platform ─Easy Java Simulations (EJS). Students use this platform to learn Java language, and engage in synchronous communication and discussion with peers [1], but these studies do not spend too much time discussing the aspect of student interaction. Thus, this study

attempts to use the “online communication intention exploration system” to observe student learning behavior in an anonymous online environment, collect content of student interaction, and explore the correlation of different types of communication intention to learning performance. Experiment Procedure In this study, the experiment subjects are third year students in the Department of Information at Chung Yuan Christian University, to study the 2010 “System Programs” course. A total of 71 students participated in the experiment, and students who withdrew or missed half of their classes were eliminated from the study. Experiment results included 58 students who participated in the entire experiment, and 16 did not fill out the WTC questionnaire. The experiment is divided into two stages: First Stage. In the first stage, before the midterm exam, the WTC questionnaire was first used, with 42 subjects. The questionnaire uses the self- perception of communication competence scale by McCroskey [5] for analyzing the communication types of subjects, or the locations and others whom the subjects are more willing to communicate with. For each item, 0~100 were used as the quantified standards. If the number is large, it means that the subject is willing to discuss with other in the context. Second Stage. In the second stage, from after the midterm exam to before the final exam, students conducted learning activities online. Their student communication behavior and learning performance were collected on the learning platform to further analyze whether there are relationships between the two. In this stage, students engaged three learning sessions on the platform. Each student who participated in experiment had an hour of system operation practice in the first session, so that they could become familiar with the operational flow of the system. Each learning session was one hour. After logging into the system, the first 40 minutes are the “chapter learning point discussion” stage, and the system would limit the learning scope. This function is designed because an overly broad would make student discussion unfocused, or have too many questions but be unable to ask them all. If the scope is too narrow, it would decrease the number of questions asked by students, resulting in unenthusiastic communication with each other and waste of learning time. Thus, in each discussion, the system screen would note the course scope for this discussion so students can use it as a reference. A suitable discussion scope can make students achieve effective learning in a limited time, so that students would not have too many questions they want to ask due to large scope, and would not be able to gain the knowledge they want within limited time. Each discussion includes one or two concepts as the scope. After 40 minutes, the system automatically guides to the “chapter evaluations” stage. “Chapter evaluations” stage uses exams to evaluate the knowledge and learning reflections of students after discussion with peers in this chapter, deeming this to be learning performance in the chapter. In this

stage, the exam questions are derived or modified from course points in the “chapter learning point discussion” stage. This not only avoids loss of focus on learning objectives and being confused about the learning process by students, and this can be used to observe the effect of student communication ability on learning. This stage are answers for the formal exam, and the scores are calculated based on 100 points, students have to use discussion to solve the problems. Meanwhile, the teacher can use this to evaluate student learning performance. Experimental Result Online Anonymous Peer Discussion. Cho [2] proposed that students with high WTC also have enhanced learning performance. Therefore, this study used the results of the WTC questionnaire [4] to show that there is a positive correlation between student willingness to communicate with “friends” and learning achievements. Thus, this study further explored the correlation between WTC questionnaire results and online anonymous communication intention. The results indicated that there is no significant correlation between the two. Thus, this study used K-means grouping methods to divide students into a high group and low group based on the number of questions asked and response rates. The numbers of questions asked and replies were used to divide communication behavior into four quadrants (groups): 1. Quadrant I: The High Reply High Ask (HRHA) group. This group of students is not only in the top 50% for number of questions asked but also for the response rate; this is a group with more response rate and number of questions asked. 2. Quadrant II: The Low Reply High Ask (LRHA) group. This group of students has a response rate in the bottom 50% of the class, but top 50% for the number of questions asked, inclined toward active communication behavior; this is a group with lower response rate but higher number of questions asked. 3. Quadrant III: The Low Reply Low Ask (LRLA) group. This group of students is not only in the bottom 50% for number of questions asked but also for the response rate, low active and passive communication behavior; this is a group with lower response rate and also lower number of questions asked. 4. Quadrant IV: The High Reply Low Ask (HRLA) group. This group of students has a response rate in the top 50% of the class, but the number of questions asked are in the bottom 50%, inclined toward passive communication behavior; this is a group with higher response rate but lower number of questions asked. The Relation of Communication Willingness and Learning Performance. Table 1 shows the average scores of the number of people in groups, and the three quizzes they took. Table 2 shows the ANOVA tests for quiz scores of each group. ANOVA tests showed that there are no significant differences among quiz scores of the groups. However, Table 1 shows that students in the “LRHA” group have the highest quiz averages, and the quiz averages of the first and fourth groups are also

not very different from that of the “LRHA” group, students in the “LRLA” group have quiz averages far lower than those of the other three groups. This shows that students with low communication intention also tend to have worse learning performance than their peers. Table 1 The distribution of the number of each group and the average of the three quiz Group

HRHA

LRHA

LRLA

HRLA

Number of students

26

4

11

17

Average score of quiz

54.09294872

55.75

48.21212121

54.95588235

Table 2 The ANOVA tests for quiz scores of each group Group

Number of groups

Average

Variance

HRHA

26

54.09295

136.5591

LRHA

4

55.75

259.5417

HRHA

26

54.09295

136.5591

LRLA

11

48.21212

498.4324

HRHA

26

55.92308

312.4738

HRLA

17

54.95588235

128.0912

LRHA

4

55.75

259.5417

LRLA

11

48.21212

498.4324

LRHA

4

55.75

259.5417

HRLA

17

54.95588235

128.0912

LRLA

11

48.21212

498.4324

HRLA

17

54.95588235

128.0912

F

Critical value

0.063571

4.195972

1.114084

4.121338

0.057442

4.078546

0.375974

4.667193

0.013719

4.38075

1.122722

4.225201

Fig.1 is the quiz score distribution for each group of students. It is obvious that the quiz scores for students in the first, second, and fourth groups are generally concentrated around the mean (53.09); while the “LRLA” group is concentrated on the two sides of the mean. Half of the students are above the mean, and half are below the mean. Because there are more below the mean and concentrated on low scores, it resulted in lower average quiz scores for students in the “LRLA” group. This shows that students with low communication intention do not show an absolute effect on preexisting knowledge or learning performance. Exploration of the reason shows that even though students in the “LRLA” group rarely interact with other peers, the paths for obtaining knowledge are not limited to exchange with peers. Students in this group may use other methods, such as: self-study, searching for reference data online, to obtain knowledge and resolve problems. Thus, in the process of learning activities, even though this group of students is unwilling to interact with peers, they may have a certain degree of understanding for problems on the quizzes. Conversely, the method of not interacting with peers and relying on own study also has the risk of gaining incorrect concepts or not gaining the necessary knowledge. Because they do not interact with peers, it is

difficult for them to know whether their concepts are correct. Moreover, when they answer quizzes, they use their own understanding to answer the questions, resulting in poor scores on quizzes. Fig.1 also shows that students in the “HRHA” group are generally concentrated at the average of the whole class, which means that most students in this group have medium learning performance in the class. Moreover, with higher communication intention, in the process of interacting with peers, they would not only actively supplement their own inadequacies, but also answer the questions and needs of others based on their own knowledge.

Fig.1 The quiz score distribution for each group of students Conclusion This study categorized students’ communication intention, and explored the effect of different types of communication intention on learning achievement. After three sessions of online platform learning activities, the content of interactive messages of students was collected, categorized based on active and passive communication methods, forming four different active and passive communication intention clusters. Experiment results showed that students in the “HRHA” group do have significantly enhanced learning achievement, and students of the “HRHA” group also have more questions and answers for the other three groups. Thus, it is inferred that not only face-to-face communication intention in a collaborative learning environment would affect student learning achievement, but also that in the online anonymous context, communication intention would affect learning achievement. Thus, regardless of whether the information content is filtered, students in the “HRLA” group have more preexisting knowledge on average than the other three groups. Thus, it is possible to understand that even though communication intention would indeed affect student learning achievement, but students’ preexisting knowledge would also affect communication intention. If students have more preexisting knowledge, in the process of collaborative learning, since there are

fewer questions about the main points of the course, they would place the focus of interaction on answering or resolving the questions of peers. Conversely, students in the “LRHA” group have lower preexisting knowledge than the other three groups, in the process of collaborative learning there are more questions that need answering regarding the points of the course, so they would put the focus of interaction on seeking answers from peers. This study found that there is a significant effect of communication intention on learning achievement, and preexisting knowledge would also affect communication intention, so that students in the process of collaborative learning, approach different communicative aspects. Reference [1] C. A. Jara, F. A. Candelas, F. Torres, S. Dormido, F. Esquembre and O. Reinoso, Real-time collaboration of virtual laboratories through the Internet, Computers & Education, 52(1), 126-140. (2009). [2] H. Cho, G. Gay, B. Davidson and A. Ingraffea, Social networks, communication styles, and learning performance in a CSCL community, Computers & Education, 49(2), 309-329. (2007). [3] A. Monteserin, S. Schiaffino and Amandi, A. Assisting students with argumentation plans when solving problems in CSCL, Computers & Education, 54, 416-426. (2010). [4] J. C. McCroskey, Willingness to communicate, communication apprehension, and self-perceived communication competence: conceptualizations and perspectives. In J. A. Daly, J. C. McCroskey, J. Ayres, T. Hopf, & D. M. Ayres (Eds.), Avoiding communication: shyness, reticence, and communication apprehension (second ed., pp. 75–108). Cresskill, NJ: Hampton Press Inc. (1997). [5] J. C. McCroskey, Personality and interpersonal communication. Newbury Park: Sage. (1987).