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57.75 points which ranked her 5th across classes. In her survey she indicated appreciating all kinds of feedback. She pointed out that sometimes other students may be able to provide better explanations than the teacher. While she felt that corrections could be humiliating, she thought that they were the only way to learn. She was favorable towards computers in the language classroom and reported having used chatting in Spanish classes before. She reported that chatting was beneficial both in her prior Spanish classes and in the German class.

SSCGina participated in 8 chat sessions for a total of 3 hours and 35 minutes and 1 second during which she produced 786 words (i.e., 3.66 words per minute) and was exposed to 134 words by the teacher (i.e., 0.62 words per minute). She is 20 years old and has studied only German as a foreign language. She rates her German ability as that of a “beginning” to “intermediate” learner. On the pre-test without the writing section, she received 40 points which ranked her 7th across classes, and on the post-test she received

62.25 points which ranked her 1st across classes. In her survey she was favorable towards error correction and technology in the classroom. She did not explain her answers. In the guided question section she mentioned that the chat sessions were sometimes too long, leaving her without something to say.

ESCAmanda participated in 10 chat sessions for a total of 3 hours and 21 minutes and 26 seconds during which she produced 1038 words (i.e., 5.15 words per minute) and was exposed to a total of 357 words by the teacher (i.e., 1.77 words per minute). She is 20 years old and did not indicate having learned any foreign language prior to the class.

She rates her German ability as including good comprehension skills but limited productive skills. On the pre-test without the writing section, she received 37.5 points which ranked her 11th across classes, and on the post-test she received 49.25 points which ranked her 22nd across classes. In her survey she indicated mixed feelings about corrective feedback, often answering a 2 or a 3 on the 4-point Likert scale. She had a positive attitude towards technology and felt that she learned during the chat and that it was fun. Furthermore, she liked EveningTeacher’s participation style. The only caution she expressed was that chatting does not work if there is a mismatch in partners.

ESCVictoria participated in 10 chat sessions for a total of 3 hours and 14 minutes and 52 seconds during which she produced 694 words (i.e., 3.56 words per minute) and was exposed to a total of 418 words by the teacher (i.e., 2.15 words per minute). She is 21 years old and has studied only German as a foreign language. She rates her German ability as “improving.” On the pre-test without the writing section, she received 11.25 points which ranked her 44th across classes, and on the post-test she received 36.25 points which ranked her 41st across classes. In her survey she was favorable towards accuracy focused teaching. Her attitudes towards technology in the classroom changed slightly to the negative. However, her opinion of chatting was that it is a fun and beneficial activity.

She described her teacher’s role during chatting as “God”. This comment was interpreted as meaning that EveningTeacher was omnipresent and had all the answers.

After the above introduction to the nature of chat sessions and the case study subjects, the following sections will provide discussions of data analyses undertaken to address each of the research questions. Analyses for some research questions will use the data for all students in the classes, and others only use the data from the case study subjects. While it would be ideal to be able to use all students’ data for all research questions, simple time constraints did not allow for an in-depth analysis of all students’ chat transcripts. At the beginning of each discussion, it will be stated which data sample produce the results.

4.2.1 Research Question 1 The first research question stated: How do two case study teachers participate in foreign language classroom chatting? This research question emerged after the review of the computer-mediated communication literature. Many studies do not mention what the teacher was doing during the chatting. Furthermore, during the background study, when sample teacher turns were sought for the Instructor Manual (see Appendix V), it was discovered that chat transcripts in which the teacher was participating were hard to find.

Therefore, the qualitative analysis of teacher participation in foreign language chatting was considered an important component to investigate. However, only two teachers were analyzed, making this a case study which cannot be generalized beyond the two teachers involved. Rather, it provides two in-depth examples of teacher interaction during chatting.

As a reminder, the two teachers involved in this study carry the pseudonyms MorningTeacher and EveningTeacher. Both teachers are comfortable using technology, including chatting, on their own time according to their own reports. However, MorningTeacher expressed some reservations about using chatting in the classroom. On the other hand, MorningTeacher had experience with the chatting and this chat server in the classroom, while EveningTeacher did not. Furthermore, EveningTeacher was supported during her teaching by the developer of the chat program as her Lab Assistant, whereas MorningTeacher had no Lab Assistant in the No-Support Class (NSC) and a Lab Assistant who was not always able to help in her Some-Support Class (SSC). These may be the reasons for more frequent computer problems experienced by MorningTeacher than EveningTeacher. These computer problems resulted in partial and complete transcript loss in the NSC and SSC, which most likely affects the results of this study.

To answer the research question, the transcripts and the observation notes were consulted. First the results from the observation notes will be discussed. It should be mentioned here that the observation notes can only reflect the behaviors exhibited by the teachers while being observed, and cannot accurately state the behavior of the teacher while not observed, for there might be a difference.

The MorningTeacher spent some time walking around and assisting students in the physical space, which consequentially did not allow her to participate as much in the chat, as if she were at her keyboard at all times. Furthermore, the MorningTeacher appeared to be reading postings carefully, and posted her own messages often using the invisible function, i.e., she did not show up in the participant list, but could still post messages visibly. Here are some excerpts from the observation notes of each of MorningTeacher’s courses to illustrate her participation, though she interacted similarly

with both classes:

NSC – Observation Notes Observation 1 • “… MorningTeacher is reading….

• MorningTeacher started walking around looking over students’ shoulders and reading with them. She did assist some students with the chatting. “

SSC – Observation Notes Observation 1:

• “…MorningTeacher explained the activity to SSCSamantha in English. ….

• MorningTeacher is reading something on the computer ….

• MorningTeacher is chatting by being invisible. ….

• Some students just figured out that the teacher can type something and not be visible, because a message from MorningTeacher popped up, but she was not in the participants list. They commented this occurrence with “Hey, you ARE here!”….

• One channel with SSCSamantha and SSCBob crashed and they had to be moved to another channel. What they were typing did not show up on the screen. For all of them. …

• MorningTeacher is reading again but I am not sure if she is reading the transcripts. I can’t see her anywhere in the channels next to me. But then again, she set it to invisible.” EveningTeacher spent most of her time after introducing the activities in her seat at the computer. As far as I know, she did not use the invisible function. She typed frequently and she also commented out loud in the physical space. Furthermore, she used the “to all” function, sending one message into all chat rooms simultaneously. Here are

some excerpts from the ESC Observation notes:

ESC Classroom Observation Notes Observation 1:

• “EveningTeacher broke off everybody and told them out loud that they need to talk about “Kinder” and not to chat about “where are you from”

• Then she asked them more questions for the chat activity. …

• EveningTeacher keeps commenting out loud on the stuff students write in class.

• EveningTeacher is typing….”

ESC Classroom Observation Notes Observation 4:

• “… Lab Assistant sets up chat from teacher station. Lab Assistant moves away from the teacher station and EveningTeacher finally sits down….

• EveningTeacher apparently wrote something to all, and students laughed. One student said out loud that her daughter would not approve. …

• EveningTeacher laughs out loud … at something that one of the students at pod 1 wrote. One of the students at the middle pod rolls her eyes….” To summarize, during the classroom observations it appeared that the MorningTeacher was typing less and reading more than EveningTeacher. While MorningTeacher laughed sometimes also, she did not laugh as much as EveningTeacher.

MorningTeacher walked up to individual students to assist with problems and questions.

EveningTeacher did not walk around the classroom, once she sat down. Furthermore, EveningTeacher shouted across the room to provide further instructions or to comment on the conversation in a chat room.

In reviewing the transcripts, differences between the teachers were also found. To discuss the nature of the teacher participation in the chat room, the following factors will be presented: teacher word count, teacher words per minute, teacher error rate, teacher target language use, teacher feedback moves, and teacher moves. Teacher word count and teacher words per minute will be discussed using data from all transcripts, whereas teacher error rate, teacher target language use, teacher feedback moves, and teacher moves will be discussed using the transcripts from the case study subjects only.

First, it was measured how much teacher output the students were exposed to. The rationale for this measure stems from several sources. First, research on CMC has claimed that CMC exhibits an equalization or democratization effect in participation (see for example Beauvois, 1998). Furthermore, when talking with colleagues opposed to CMC in the classroom, one argument that is often presented is that the students are exposed to so much non-target like language from their peers and less target-like language from their instructors. Therefore, investigating the number of teacher words was important. Words in German excluding names were counted as words. For comparability purposes the words per minute produced by the teacher were established. These teacher words include words addressed to the subject as well as those that were not. Furthermore, since one teacher turn most likely appeared on more than one transcript, especially in the case of EveningTeacher who used the “to all” function, teacher turns were counted multiple times. Teacher words were counted from the perspective of the student, i.e., how many words per minute was each subject exposed to by the teacher, whether addressed to him or her directly, or not.

Table 4.3 Teacher Word Count and Teacher Words per Minute

–  –  –

Table 4.3 shows the descriptive statistics for exposure to teacher output as measured in word count, words per minute, and ratio between teacher and student words per minute.

All subjects except for ESCTiffany produced more words per minute on average than their teacher. Students in NSC were exposed to an average number of total teacher words of 71.67 ranging from 12 to 101 words. For comparability between classes and teachers, the numbers were also presented as words per minute and teacher/student output ratio. Students in NSC were exposed to an average of 0.45 words per minute from MorningTeacher ranging from 0.29 to 0.76. The class average teacher-to-student output ratio is 0.12. This means that for every word produced by a subject, he or she was exposed to 0.12 words from the teacher. Students in SSC were exposed to an average number of total teacher words of 86.44 ranging from 15 to 155 words. Students in SSC were exposed to an average of 0.62 words per minute from MorningTeacher ranging from 0.22 to 0.90. The class average teacher to student output ratio is 0.19. Students in ESC were exposed to an average number of total teacher words of 352.10 ranging from 242 to 488 words. Students in ESC were exposed to an average of 2.12 words per minute from EveningTeacher ranging from 1.64 to 2.48. The class average teacher to student output ratio is 0.52.

Analyses of Variance (ANOVAs) were used to establish potential significant differences between student groups and between teachers, as ANOVA is the statistical procedure that can determine if there is a statistically significant difference between group means. First the data were analyzed using a one factor between subjects ANOVA, with teacher as the factor with the levels MorningTeacher and EveningTeacher. Since no student had both teachers, a between subjects procedure was chosen. The dependent variable is the average words per minute of teacher output seen by the individual student.

The main effect of teacher is significant (F(1,44)=357.98, p.001) (see also table 4.4.).

–  –  –

The average words per minute for the MorningTeacher was 0.55 and for the EveningTeacher was 2.12, which means that the students who had MorningTeacher received significantly more words per minute input from their teacher. This could mean that the students in the ESC were exposed to more target-like language, hence increasing their opportunity to learn. On the other hand, an increase in teacher input, may decrease the opportunity for student output.

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