KLAUS SCHWIENHORST
Trinity College, Dublin
ALEXANDRE BORGIA
Longueuil, Québec
Abstract:
Tandem learning is the collaborative learning partnership of two language learners with complementary language combinations, for example an Irish student learning German and a German student learning English. One of the major principles in tandem learning, apart from reciprocity and learner autonomy, is balanced bilingualism. While learners may find it relatively easy to control their bilingualism in email exchanges, it is not so easy to do so in synchronous text-based exchanges in object-oriented multi-user domains (MOOs), where measurements of bilingualism are often crude and inaccurate. The purpose of this paper is twofold. First, we wanted to develop and test a computerized tool, the Bilingual Tandem Analyser, that automatically analyzes and provides feedback on the languages that are used during a �live� exchange. Second, we wanted to implement the tool in a bilingual exchange between German and Irish students to see whether the balance in bilingualism improved. Our results show that the Bilingual Tandem Analyser is quick, reliable, and highly accurate for the four languages tested. When implementing the tool, we noticed that there is a noticeable improvement towards more balanced exchanges but that more work is needed on the pedagogical implementation.
KEYWORDS
Learner Autonomy, MOO, Tandem Learning, Bilingualism, Text-based Communication
INTRODUCTION
Tandem learning is the collaborative learning partnership of two language learners with complementary language combinations, for example an Irish student learning German and a German student learning English. One of the major principles in tandem learning, apart from reciprocity and learner autonomy, is bilingualism. The principle of bilingualism suggests that a successful partnership requires both
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learners to use both languages equally; on the one hand, to provide sufficient target language input for their partner and, on the other hand, to be “pushed” to produce target language output themselves. While learners may find it relatively easy to control their bilingualism in email exchanges, it is not so easy to do so in synchronous text-based exchanges in object-oriented multi-user domains (MOOs), where measurements of bilingualism are often crude and inaccurate.
The purpose of our research was twofold:
1. How accurately can a computerized tool analyze the language proportions in synchronous text-based, bilingual-learner exchanges, compared to manual analyses of transcripts?
2. When learners are given a tool that accurately analyzes their bilingual proportions in exchanges, is there a noticeable effect on their bilingual behavior, that is, do learners try to work towards a more balanced use of L1 and L2?
We have structured the paper in the following manner. First, we will give a short overview of tandem-learning principles and the rationale behind this study. Second, we will describe the design specifications for the Bilingual Tandem Analyser (BTA) and examine how accurately it is able to analyze text-based input in real time and provide feedback to learners. In our third section, we will look at the pedagogical framework for our study and the implementation of the BTA tool. We will focus on comparing bilingual proportions in two project groups, one that used the BTA and one that did not. In our conclusion, we will summarise the main results and suggest where the pedagogical and technological framework needs to be improved.
THREE PRINCIPLES OF TANDEM LEARNING AND THE RATIONALE BEHIND THE BTA
In a tandem learning partnership, two learners with complementary L1 and L2 combinations work together (e.g., an Irish learner of German works with a German learner of English), either face-to-face, via asynchronous email, or synchronous tools such as text-based object-oriented multi-user domains (MOOs). Tandem learning, as one of the pedagogical implementations of learner autonomy principles (see Little, 1991), is based on the three principles of (a) bilingualism, (b) reciprocity, and (c) learner autonomy (Little & Brammerts, 1996). Reciprocity requires both partners to help each other and adjust to each other's proficiency levels; this could take the form of error correction, modification of input, the use of repair strategies, and so forth. In such a partnership, learners need to understand that the success of the partnership relies on an equal effort by both partners. Learner autonomy, as a principle in tandem learning, implies that both learners need to take responsibility for their exchange (and, thus, their language learning) by negotiating topics, arranging working methods, and generally planning, monitoring, and evaluating learning processes and outcomes. In this way, learners are not only
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responsible for the success of their own learning but are also in part responsible for the success of their partner's learning. Face-to-face and email tandem learning partnerships have been organized and coordinated by the ETandem Network (see http://www.tcd.ie/CLCS/tandem), but in recent years, tandem partnerships have also been implemented using synchronous environments such as text-based object-oriented multi-user domains (MOOs) (see Kötter, 2002, 2003; Schwienhorst, 2003a, 2003b, 2004). MOOs have established themselves over the last few years as a valuable language learning tool (see Beatty, 2003; Shield, 2003; Shield, Davies, & Weininger, 2000; Sotillo, 2000; Trebbi, Jopp, & Coco, 2003).
A recurrent problem in both email and MOO tandem partnerships is that the more proficient L2 often takes over as the exclusive language of communication. However, a balanced and reciprocal partnership is vital for the success of these exchanges to ensure both exposure to comprehensible input and the production of “pushed” comprehensible output. It is common sense that the problem of imbalanced bilingualism is more prominent in synchronous exchanges than in asynchronous exchanges. Learners will naturally find it easier to arrive at a reasonable estimate of their bilingual proportions in an email to their partners and then act upon it. However, in transcripts of synchronous bilingual 1-hour sessions, learners (and even researchers) would find it difficult to arrive at a reasonable estimate regarding the proportions of the two languages used. In synchronous exchanges, the problem is also further complicated by the fact that code switching is much more likely to occur in synchronous exchanges than in asynchronous exchanges where learners have more time for editing and revising their utterances.
At the Centre for Language and Communication Studies (CLCS) at Trinity College, Dublin, we have conducted tandem exchanges in synchronous, text-based MOOs since 1998. My colleague Breffni O'Rourke and I have always noted that the stronger L2 was used much more than the weaker L2. In our German-Irish exchanges, this meant that English became the dominant language of communication between students (see O'Rourke, 2002, 2005; Schwienhorst, 2000). This is no surprise because students had no tool at their disposal to control their bilingualism other than a rough guess on whether they had used both languages equally.
For email, Christine Appel developed the Electronic Tandem Resources web site for e-mail tandem exchanges (Appel, 1999; Appel & Mullen, 2000), which uses n-gram analysis to analyze learners' emails and provide detailed feedback on language statistics. We wondered whether a combination of pedagogical and technological tools could alleviate the problem of imbalanced bilingualism in synchronous environments like MOOs. First, this required developing a system that can accurately analyse which language is being used at any given moment, and display this statistical information to each individual learner. Second, this system had to be accepted by students as a tool that would help them with their exchanges, that is, a tool that would give them control over their bilingualism at any time and then allow them to react accordingly rather than make them follow the dictates of a control mechanism imposed by the teachers/administrators.
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DESIGN SPECIFICATIONS FOR THE BTA
In developing, implementing, and evaluating the BTA, we had to take into account both pedagogical and technological considerations. On the one hand, we had to develop and implement a system that accurately and reliably analyzed which language was being used at any given time, store that information, and present feedback to learners on an individual basis. The statistical presentation of data should include “live” statistics (so learners could adjust their bilingualism during sessions with their tandem partner), as well as daily, weekly, monthly, and global statistics on their bilingualism. Also, the system should not be perceived by learners as an intruder in their tandem exchange but, instead, as a tool to help them with their exchange, not a mechanism created by the teachers/administrators of the MOO to control their behavior.
Nevertheless, we were certain that students had to be automatically presented with statistics on a regular basis. After all, this is not a tool found in traditional face-to-face communication, and learners need to become accustomed to it. We consider it important, especially in the framework of learner autonomy, that learners accept tools as personally meaningful, as devices that offer them better ways of learning a language. Only then can we expect learners to accept responsibility for and take control of their learning. In practice, the design and implementation of such a tool can be a balancing act, where, in one extreme, the tool is too far removed from learners' stage of autonomy that they do not use it, or, in the other extreme, the tool is forced on learners and impinges on their autonomy, in which case the tool may be used but without becoming personally meaningful and useful to the individual learner (for a more in-depth discussion of this issue, see O'Rourke, 2002; Schwienhorst, 2002; O'Rourke & Schwienhorst, 2003).
The first problem we encountered in the design phase was deciding on what basis we should assess bilingualism. There are three aspects that should be mentioned. First, in previous projects, we had simply told our students to spend 30 minutes talking in one language and 30 minutes in the other. However, that did not work since students came in late, took some time to find their partner, left early, and so on. In addition, the better L2 speaker would, of course, produce much more text in half an hour than the weaker L2 speaker (and there usually were big differences in L2 proficiency between German students of English and Irish students of German). One of the decisions that had to be made was, therefore, whether we should measure bilingualism by the amount of text students produce or the time they spend processing and producing utterances in the L2. We decided that the amount of text produced was a more reliable and useful option because it was less dependent on network delays, time spent on looking up words in online dictionaries, and other external factors. Second, O'Rourke (2002), which provided comparison data for our BTA, had analyzed bilingualism on the basis of partnerships rather than individual learners. For our purposes, we found it more useful for the system to analyze bilingualism in individual learners; otherwise, the extreme case of one learner speaking one language and the other speaking the other language all the time would end up in a 50:50 balance. Third, bilingualism could be measured by counting words or utterances. From a sociopragmatic perspective, we
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thought it was more useful to measure bilingualism on the basis of utterances. As mentioned earlier, code switching would negatively influence a word-by-word analysis, so we decided for the utterance as the framework for analysis.
On the basis of these three decisions, we developed the BTA tool. The main components of the system are the Language Categorisation Tool (LCT) and the Student Journal. The LCT is the “heart” of the BTA because it is able to guess which language is being used at any given time in whole MOO utterances. The LCT was inspired by TextCat (Van Noord, 1999) and follows n-gram analysis (Appel & Mullen, 2000; Cavnar & Trenkle, 1994). Although we decided to start with four languages (English, German, French, and Italian), the LCT allows for new language profiles to be developed and parameters to be adjusted (e.g., if the accuracy of the tool is affected).
The Student Journal simply displays the results of the LCT analysis to the learners in the form of overall (or global) statistics, monthly, weekly, daily, and “live” statistics—to check bilingualism during a MOO session.
Figure 1
Display Options of Language Statistics in the Student Journal
0x01 graphic
The Student Journal is displayed automatically with global statistics whenever learners connect to the MOO. After that, it is completely up to students when and how often they want to display it by typing in <@journal>. The Student Journal is also available to the MOO administrators and teachers for research purposes, and administrators can receive regular overviews by email of how the LCT analyzes input, as shown in Figure 2.
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Figure 2
Excerpt from Administrator's Log File
** [Irish student] - Thu Mar 27 11:40:34 2003 GMT
** German
(bleh! wir haben kein umlauten )“
** [Irish student] - Thu Mar 27 11:40:36 2003 GMT
** English
heh heh cool snow :)“
** [Irish student] - Thu Mar 27 11:40:39 2003 GMT
** German
Hi Stephanie wie gehts?“
** [Irish student] - Thu Mar 27 11:40:45 2003 GMT
** English
Hmm, would it be Ok if we changed the language now?“
** [Irish student] - Thu Mar 27 11:41:33 2003 GMT
** too short
Ok“
** [Irish student] - Thu Mar 27 11:42:12 2003 GMT
** ambiguous
yah ! Umlauten!“
Our first question was whether the LCT can perform the analysis accurately and efficiently. The next section examines in more detail the concepts behind n-gram analysis and a comparison between machine analysis and manual (human) analysis of bilingual input.
THE LANGUAGE CATEGORIZATION TOOL
N-gram analysis is a fast and adaptive way to determine general characteristics of unknown media (Cavnar & Trenkle, 1994). Since n-gram analysis has already been proven to be efficient for document analysis over the years, this section will focus on the main concerns when implementing it for synchronous chat environments, starting with a brief overview of the method. The Language Categorization engine works by doing statistical comparisons between texts of a known and unknown nature. Profiles are built by parsing documents into small tokens that are sorted by frequency of appearance, basically resulting in a list of most commonly used combinations of letters. Similarities found between such profiles suggest similarities between their original documents: a common topic, for instance, if looked at closely—but more obviously, a common language. The actual categorization is done by simply gathering a database of representative languages using literary works as a default comparison base. The engine queues user inputs and processes
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them one after another against all previously built language profiles, keeping individual statistics of the most relevant matches. The engine is fast enough to provide real-time feedback to the Student Journal, inputs taking about one second each to process on a standard workstation with four concurrent languages.
By abstracting language into statistics, the determination process can handle streams of text very well even if the stream contains broken syntax, misspellings, or typographical mistakes, all of which are commonly found in chat transcripts. There are, however, a few drawbacks in not using sets of rules or a lexicon for formal analysis. Since the engine relies on accumulation and comparison of data to make decisions, it can only analyze text as blocks and will usually remain clueless when dealing with very short inputs. This is a main concern when monitoring chat activities because the speakers will often break up their sentences into smaller utterances, providing sparse information about logical breakpoints (e.g., uppercase characters or punctuation marks) to combine them into bigger, less ambiguous blocks that are more likely to contain a single language.
One way around this problem was to reinforce the recognition scheme with mechanisms that keep erroneous matches out of the analysis. Small utterances (less than around 10 characters long) are automatically blocked by the engine; also, at some point, matches will be considered ambiguous if the results are too close between languages. Furthermore, if users are known only to deal with specific languages in their learning activities, the other languages will be ignored along with those already marked erroneous when balancing user statistics. As shown by the results in Table 1, this method is quite efficient and accurate, the ratio of language usage among tandem users in session transcripts being similarly detected by automated and human analysis, with a difference equal to or less than 2%. This difference is not statistically significant (p > 0.05).
Table 1
Comparison of Machine and Manually Parsed Results (O'Rourke, 2002)*
Session
# English utterances
English (%)
# German utterances
German (%)
Automatic
Manual
Automatic
Manual
A
460
75%
76%
143
24%
24%
B
440
73%
73%
166
26%
27%
C
331
86%
87%
48
13%
13%
D
604
96%
97%
20
3%
3%
E
416
84%
86%
69
15%
14%
F
292
87%
90%
33
12%
10%
Pooled across sessions
2,543
83%
84%
479
16%
16%
*Analysis based on 97 transcripts.
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Language activity monitoring has thus been successfully tested in a real-time chat application. The Language Categorization Tool is currently available for use in a MOO environment, bundled with the Journal service, and provides easy commands to create and manage language databases and to fine tune resource consumption (for free download and documentation, see Borgia, 2003). Due to the nature of the parsing, the engine is also able to gather basic sentence statistics, such as word count or average word length, along the way which are available for display. These statistics may prove useful for future research agendas.
PEDAGOGICAL FRAMEWORK FOR THE IMPLEMENTATION OF THE BTA AND METHODOLOGY
Before looking at the implementation of the BTA in our tandem projects, it would be helpful to describe the pedagogical framework of our language courses in which the projects took place. The subjects in our study consisted of Information and Communication Technology students from Trinity College Dublin, who were studying German as part of their degree program and Information Technology students from the Fachhochschule Bonn-Rhein-Sieg, who were studying English as part of their degree program. The German students had completed an English entry exam and could be rated as lower advanced English speakers, while the Irish students' proficiency in German could be described as higher beginning or lower intermediate. The students formed learning partnerships on the basis of introductory emails sent by the Irish students to the German students and from which the students selected their partners. Since the number of German students was much higher than the number of Irish students, some 1+2 partnerships had to be formed in addition to the 1+1 partnerships. Students worked together with their selected partner(s) for 1 hour per week in scheduled class sessions. We compared bilingualism in this exchange (2002-03) with similar exchanges from 2000-01 (see Table 2), mainly for two reasons. First, the number of transcripts was almost identical and therefore provided us with a reasonable amount of comparable data. Second, the 2000-01 exchanges had been the subject of O'Rourke's (2002) manual analysis of bilingualism, and the original data were available to us with permission by the students.
Table 2
Overview of the Subjects in the 2000-01 and 2002-03 Studies
Year
Student groups
Tandem set up
Number of weeks/transcripts
2000-01
26 Irish, 34 German
26 pairs, 8 extra German
6/298
2002-03
12 Irish (1 native German) 18 German
6 pairs, 6 groups of 1+2
12/286
Students in all groups worked on bilingual tasks with a final assessed product, which they were able to access in the MOO and on a paper print out (see Table 3)
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Table 3
Sample Activity and Task
Topic 5: Work experience
Thema 5: Erfahrungen in der Arbeitswelt
AIM:
ZIEL:
to discuss previous work experience
Jobs diskutieren
to develop vocabulary for a CV
Vokabeln für einen Lebenslauf sammeln
TIME: 1-2 sessions
ZEIT: 1-2 Stunden
Discuss with your partner:
Diskutiere mit Deinem Partner:
What kind of jobs have you been doing?
Welche Jobs hast Du schon gehabt_ Als was hast Du schon gearbeitet?
Which jobs did you particularly like/dislike? Why?
Welche Jobs haben Dir besonders gefallen/nicht so gut gefallen? Warum?
Do you have plans for other jobs? Describe them!
Hast Du schon Pläne für andere Jobs? Beschreibe sie!
What information is vital for a CV? Are there differences between German and Irish CVs?
Welche Information sollte in einem Lebenslauf stehen? Gibt es Unterschiede zwischen deutschen und irischen Lebensläufen?
Final task: write a CV and send it to your partner for correction.
Abschluss: schreibe einen Lebenslauf und schicke ihn an Deinen Partner zur Korrektur.
Within class sessions, we organized training sessions to introduce learners to the tools and tandem principles and, half way through the project, reflection sessions to have them reflect on their behavior. We further encouraged learners to keep online learner diaries in the form of Dam's class diaries (Dam, 1995) and to work on their individual session transcripts that were sent to them automatically after each session.
We accessed the statistics for all students taking part in the 2002-03 exchange directly in the MOO database. For the 2000-01 exchange, transcripts were edited using Textpad (http://www.textpad.com) and then individually sent through the MOO for analysis by the BTA. Thus, the transcripts from the 2000-01 group were analyzed in the same way as “live” data from the 2002-03 group, and we were able to collect the data for individual and global statistics. (We did not simulate weekly or monthly statistics because we did not consider them relevant for the analysis here.)
We determined a threshold of 70% to 30% (or 30% to 70%) as a useful measure for balanced bilingualism, that is, if an individual MOO session or the global statistics of a student were 64% English and 38% German, it was deemed balanced, whereas 74% English and 26% German were deemed imbalanced. We are aware that setting this threshold could be considered arbitrary, but it seems like a useful threshold for our purposes. We then counted the amount of individual MOO sessions and global statistics that were within that range and the average percentages across all sessions/global statistics.
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COMPARING THE RESULTS OF BILINGUALISM IN TWO MOO TANDEM PROJECTS
We examined in detail the individual sessions by learners and their global statistics (i.e., their bilingual proportions over the whole exchange). In this section, we will first present the analysis of the individual sessions and then look at the global statistics.
Table 4 presents an overview of bilingualism in the individual MOO sessions. We can see that the 2002-03 average of all sessions, with the BTA in use, approached balanced bilingualism and was just within the 70:30 range, whereas the 2000-01 group without the BTA was considerably imbalanced in favor of the stronger L2 (English). The number of sessions that were within the range is slightly higher in the 2002-03 group (32%) than that in the 2000-01 group (25%).
Table 4
Bilingual Proportions of English (E) and German (G) in Individual MOO Sessions
Student group
Individual sessions average (E%:G%)
Number of individual sessions in 70:30 range
2000-01
78%:22%
75 out of 298 (25%)
2002-03
67%:32%
92 out of 286 (32%)
We then looked at the results in the global statistics (i.e., daily, weekly, and monthly statistics). These were the statistics that were presented to the students automatically when they connected to the MOO. These more broadly based statistics can be considered more reliable than the results from individual sessions (see Table 5).
Table 5
Bilingual Proportions of English (E) and German (G) in the Global Statistics
Student group
Global statistics average (E%:G%)
Number of global statistics
in 70:30 range
2000-01
76%:23%
15 out of 60 (25%)
6 Irish (23%)
9 German (26%)
2002-03
63%:36%
16 out of 30 (53%)
8 Irish (67%)
8 German (44%)
Similar to the results of the analysis of the individual sessions, the results of the analysis of the global statistics show a greater balance of bilingualism in the 2002-03 group than in the 2000-01 group, but the improvement is dramatically greater: 16 out of 30 sessions (53%) in 2002-03 versus 15 out of 60 sessions (25%) in 2000-01. Considering the 70:30 ratio as a criterion of success, the difference
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between the groups is significant (chi square = 7.11, p = .008). Although the German students improved in the number of successful sessions, the far greater contribution to improvement came from the Irish students. Finally, when we looked at the individual partnerships, we noticed that in three out of the six 1+2 partnerships all participants had imbalanced bilingualism, whereas in the 1+1 partnerships there was at least always one learner within the 70:30 range, suggesting that 1+1 partnerships offer the most effective framework for balanced bilingualism in the MOO.
CONCLUSION
In our design, implementation, and evaluation of the BTA we learned some valuable lessons for future bilingual exchanges in text-based synchronous environments. The major results of our study are
1. the BTA provides learners with a very accurate tool to control bilingualism in synchronous text-based environments,
2. L1/L2 proportions are more balanced with the BTA tool than without it,
3. global and individual stats are substantially more balanced with the BTA tool, and
4. closer examination showed that three out of six 1+2 groups in 2002-03 were all imbalanced, whereas in pairs at least one learner was balanced.
Still, we need to consider other factors that may have played a role and ask how we can further improve balanced bilingualism in tandem exchanges. Thus, for example, training and reflection sessions can raise learners' awareness of the principles of tandem learning and subsequently influence their bilingualism, but the number of sessions needed remains an unanswered question. In order to increase the balance of bilingualism, we could implement technological improvements, such as “friendly reminders” that caution students when they leave the specified zone of balanced bilingualism. These reminders have already been built into the email web site by Appel (personal communication), and it would be easy to do the same for synchronous MOOs. We could also experiment with the default display of the statistics: would it maybe be more beneficial to students to always or periodically be informed of statistics during a session, after a certain time, or after students leave a certain zone of balanced bilingualism? We also need to consider other forms of support for the weaker L2 speakers such as integrating dictionaries into the MOO. In terms of pedagogical improvements, we should definitely avoid 1+2 partnerships in future projects. It appears that one student is simply overwhelmed by the input and the decisions of two other students.
In summary, we can say that the development of the BTA has been successful, although the results of its implementation suggest the need for more research to increase its instructional value. Now, to our knowledge, for the first time, learners have an accurate tool (so far, for English, German, French, and Italian) that gives them full control over their bilingualism in synchronous text-based tandem exchanges. The tool is freely available on our web site (http://kontakt.tcd.ie/BTA)
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and may become an integrated part of the EnCore MOO database in the future. The fact that learners use the tool to adjust their language proportions indicates that they see it as a valuable addition to the language learning tools at their disposal during exchanges (see also Kapec & Schwienhorst, 2005). As such, the BTA can become a useful tool to develop learner autonomy because it gives learners control over an important aspect of bilingual language learning exchanges.
REFERENCES
Appel, M. C. (1999). Tandem learning by e-mail: Some basic principles and a case study (Vol. 54, CLCS Occasional Paper). Dublin: Trinity College, Centre for Language & Communication Studies.
Appel, C., & Mullen, T. (2000). Pedagogical considerations for a web-based tandem language learning environment. Computers and Education, 34 (3-4), 291-308.
Beatty, K. (2003). Teaching and researching computer-assisted language learning. Harlow: Longman.
Borgia, A. (2003). Moo Utilities [Web page]. Retrieved August 24, 2003, from http://kontakt.tcd.ie/BTA
Cavnar, W. B., & Trenkle, J. M. (1994). N-gram-based text categorization. In Proceedings of third annual symposium on document analysis and information retrieval (pp. 161-175). Las Vegas, NV: UNLV Publications/Reprographics.
Dam, L. (1995). Learner autonomy 3: From theory to classroom practice. Dublin: Authentik.
Kapec, P., & Schwienhorst, K. (2005). In two minds? Learner attitudes to bilingualism and the Bilingual Tandem Analyser. ReCALL, 17 (2), 254-268.
Kötter, M. (2002). Tandem learning on the Internet. Frankfurt: Peter Lang.
Kötter, M. (2003). Negotiation of meaning and codeswitching in online tandems. Language Learning & Technology, 7 (2), 145-172. Retrieved October 12, 2005, from http://llt.msu.edu/vol7num2/kotter/default.html
Little, D. (1991). Learner autonomy 1: Definitions, issues, and problems. Dublin: Authentik.
Little, D., & Brammerts, H. (Eds.). (1996). A guide to language learning in tandem via the Internet (Vol. 46, CLCS Occasional Paper). Dublin: Trinity College, Centre for Language & Communication Studies.
O'Rourke, B. (2002). Metalinguistic knowledge in instructed second language acquisition: A theoretical model and its pedagogical application in computer-mediated communication. Unpublished doctoral dissertation, Trinity College, Dublin.
O'Rourke, B. (2005). Form-focused interaction in online tandem learning. CALICO Journal, 22 (3). 433-466
O'Rourke, B., & Schwienhorst, K. (2003). Talking text: Reflections on reflection in computer-mediated communication. In D. Little, J. Ridley, & E. Ushioda (Eds.), Learner autonomy in the foreign language classroom: Teacher, learner, curriculum and assessment (pp. 47-60). Dublin: Authentik.
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Schwienhorst, K. (2000). Virtual reality and learner autonomy in second language acquisition. Unpublished doctoral dissertation, Trinity College, Dublin.
Schwienhorst, K. (2002). Pressures, potentials, and affordances: The role of tools in CALL environments. Communication & Cognition—Artificial Intelligence, 19 (3-4), 133-149.
Schwienhorst, K. (2003a). Learner autonomy and tandem learning: Putting principles into practice in synchronous and asynchronous telecommunications environments. Computer-Assisted Language Learning, 16 (5), 427-443.
Schwienhorst, K. (2003b). Neither here nor there? Learner autonomy and intercultural factors in CALL environments. In D. Palfreyman & R. C. Smith (Eds.), Learner autonomy across cultures: Language education perspectives (pp. 164-180). London: Palgrave Macmillan.
Schwienhorst, K. (2004). Native-speaker/non native-speaker discourse in the MOO: Participation and engagement in a synchronous text-based environment. Computer-Assisted Language Learning, 17 (1), 35-50.
Shield, L. (2003). MOO as a language learning tool. In U. Felix (Ed.), Language learning online: Towards best practice (pp. 97-122). Lisse: Swets & Zeitlinger.
Shield, L., Davies, L. B., & Weininger, M. J. (2000). Fostering (pro)active language learning through MOO. ReCALL, 12 (1), 35-48.
Sotillo, S. M. (2000). Discourse functions and syntactic complexity in synchronous and asynchronous communication. Language Learning & Technology, 4 (1), 82-119. Retrieved October 12, 2005, from http://llt.msu.edu/vol4num1/sotillo/default.html
Trebbi, T., Jopp, C., & Coco, M. (2003, 16-18 October). Didaktisk rollespill i en MOO: Er læringen satt på spill? Paper presented at the Digital Dannelse Conference, Oslo.
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AUTHORS' BIODATA
Klaus Schwienhorst works as co-ordinator of extracurricular language modules and Lecturer in Applied Linguistics at the Centre for Language and Communication Studies at Trinity College, Dublin, Ireland. He has published mainly in the area of learner autonomy and synchronous text-based communication tools. His main research interests lie in virtual reality, computer mediated communication, and learner autonomy for second language acquisition.
Alexandre Borgia is an independent software developer whose main interests revolve around computer-assisted pedagogy. He has worked on many private eLearning solutions and research tools for language studies in collaboration with Trinity College Dublin. He is co-founder of MOOFrançais, a virtual French community, which has the goal of promoting French and helping second language learners.
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AUTHORS' ADDRESSES
Klaus Schwienhorst
CLCS, Arts Building
Trinity College Dublin,
Dublin 2
Ireland.
Phone: +353-1-608 3316
Fax: +353-1-608 2941
Email: kschwien@tcd.ie
Alexandre Borgia
3554 Rigaud
Longueuil
QC
Canada
J4L 4K7
Phone: 450/442-9168
Fax: 450/442-4301
Email: alexborgia@hotmail.com
Retrieved on December 20, 2010 from https://www.calico.org/memberBrowse.php?action=article&id=122
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