Many second language teachers and researchers may think of the use of computer technology in language learning as a phenomenon arising in the mid-1990s with the advent of the World Wide Web. In fact, however, the idea of harnessing the capabilities of technology for language instruction was acted upon in the 1960s by individual teachers and a few researchers at universities. For the first two decades (1960s–1980s), researchers in this area were preoccupied with the question of whether learning might be better accomplished through computer-assisted instruction than in the classroom by, for example, measuring the acquisition rate and mastery of grammar and vocabulary taught in a computer-based language learning environment compared to a teacher-led classroom. Although such comparisons are relevant in some contexts, research attempting to understand what, why, how, and to what end technology leads to successful learning outcomes is the challenge for most applied linguists working in this area today. In addition to these comparative studies, some teachers, in contrast, were interested in developing programs that provided learners with some added features such as individualized instruction, interactivity, and record keeping. For instance, the PLATO project developed in the early 1970s included a reading course with interactive vocabulary and grammar drills and translation tests that measured students’ progress. It remains clear why such capabilities were of interest, but the materials from that period reflect the structural view of language dominant in North America at that time, and therefore, the beginnings of computer-assisted language learning (CALL) are typically shown as text-based interactive programs intended to teach grammar and vocabulary (Hart, 1981).
Such materials spanned the period when large mainframe computers were used (1960s–1980s), but the introduction of the microcomputer created an important sociological function of widening dramatically the number of teachers who had access to computer technology and therefore the creativity in thinking about the question of how computers might be used in language teaching (Ahmad et al., 1985). During this period, for example, Johns (1986) introduced the idea that learners’ might benefit by using computers to conduct their own corpus-based investigations—an idea that continues to grow in its relevance in view of the corpus of language data learners have access to on the Web and the growing corpus-based research in applied linguistics. Overall, CALL evolved through application of ideas from communicative language teaching to include interactive language games, and other activities in which language was not evaluated but rather interaction and conversation was stimulated (Higgins and Johns, 1984; Underwood, 1984). Such CALL activities extended beyond computer-learner interactions to include interactions among learners working together and using the computer as a catalyst for their conversation (Mohan, 1992).
Despite the creativity applied to the issues during this period, the fact remained that the first microcomputers were painfully lacking in the basic capabilities needed for teaching language such as adequate presentation of language characters on the screen, coordinated presentation of video with interactivity and sufficient processing size to conduct analyses of learners’ production beyond the word and phrase. By the late 1980s, microcomputers had become more sophisticated, making possible the introduction of multimedia materials and natural language processing (NLP) which aims at automated understanding and generation of natural human languages. With this generation of computers, authors were able to create interactive CALL programs with images, video, and sound thus providing opportunities for learners to obtain input from and practice with carefully selected and sequenced audio and video materials. Such technologies made it possible to develop input-based instruction, whereas the NLP capabilities allowed for the computer to provide an analysis of learners’ language and relevant feedback. The best examples of both types of CALL materials included interactive help for learners as well.
In the early 1990s, local area networks (LANs) had been established in many language learning centers and some teachers and researchers were exploring the possibilities for computer-mediated communication (CMC) among students within classrooms (Swaffar et al., 1998). Such communication, conducted using text chat, seemed to provide an effective addition to face-to-face conversation that was so painful for many students in the foreign language classroom. The written medium provided opportunity for useful reflection on linguistic form while prompting more even participation in classroom discussion. Moreover, evidence suggested that the linguistic functions students performed in interactive written communication expanded the scope and number of linguistic functions that students engaged in during class (Chun, 1994).
At the same time that LANs were being used in university laboratories, most university teachers had access to the next generation of electronic communication—the Internet. Before the Web popularized electronic communication, some teachers saw the potential of the electronic version of the pen pal, i.e., the key pal (e.g., Sanaoui and Lapkin, 1992). Such communication evolved beyond the face-to-face talk at the keyboard to CMC across distance and time (Thorne and Payne, 2005). Today, technology is used to connect learners with each other and other speakers of the target language through a variety of stationary and mobile devices (Sharples et al., 2007). These include asynchronous modes which function as electronic mailboxes where users can deposit text, audio, and video messages for each other to pick up at their convenience as well as synchronous interactive modes of conversing in real time through text, audio, and video, or some combination of the three. An important aspect of today's technology, called Web 2.0, is the participation of the users in creating and designing their own place in an ever more flexible and accessible virtual space of the Web. Beyond the language classroom, the Web of today is well populated and heavily traveled whereby students might work collaboratively with their peers or remain alone at the computer, which may be at an Internet café or at their home. The Web therefore provides an unprecedented amount and quality of target language opportunities for input, help, information, and interaction for learners who know how to use them.
The technology practices that have evolved throughout this period are important for understanding current practices and options, which today include various combinations of all facets of historically developed approaches including including “traditional” CALL, multimedia, artificial intelligence, communications software, Web 2.0 applications. Thus the acronym CALL nowadays refers to any environment in which a learner, alone or collaboratively with peers, uses technology in a second or other language. Throughout these changes in technological capabilities and teaching practices, however, several core issues have remained important from the perspective of studying how technology can help learners develop their language ability.
Computer technology represents a significant extension of the types of language experiences that learners engage in, and therefore researchers are keen to understand through research and development the extent to which computer technology can contribute to language learning. The research issues are numerous but we have clustered them into three areas of investigation that are related to and extensions of research issues in other areas of language learning research—the role of interaction, the challenge of individual differences, and the goals of autonomous learners.
Since interaction has been shown to be important for second language acquisition (SLA) (see the opening chapter of this volume), how beneficial are the interactions learners engage in through technology for language learning? Clearly, learners’ use of technology involves various forms of interaction, between the learner and the computer and between the learner and other people in CMC. Ellis (1999) takes a broad view of the ways in which interaction might benefit language learners by examining cognitive and social benefits of both interpersonal and intrapersonal interaction, and Chapelle (2003) extends that framework to include human-computer interaction as well. Of all of the types of interactions that occur, particularly interesting in CALL research are the instances where learners are able to get help with or feedback on their language as well as to engage in conversation and negotiation of meaning with another speaker of the language.
A first type of interaction occurs when learners ask for help in their comprehension and production of the language (Rost and Fuchs, 2004). Such interactions can take place as learners read any electronic texts or use word processing software and consult interactive CALL materials. For example, learners who are reading online can move back and forth between the text and an online dictionary to get help with word meaning. Similarly, learners writing at the computer have the option of checking an electronic dictionary when they need help in choosing the wording that they need to express particular meanings or accessing a concordance which provides a means for learners to encounter and explore words in a variety of contexts by listing segments of texts containing the word. Such help is accessed while students are engaged in meaning-making, and therefore can be conceptualized similarly to the potentially valuable interactions that occur between students and teachers during face-to-face interactions (Gass, 2003). In a CALL environment such interactions can take place when learners ask for repetitions, modifications, or elaborations. Developers of CALL materials can intentionally provide these opportunities for learners, but learners who are able to use strategies for getting help on the Internet and in word processing software can create valuable computer-learner interactions beyond pedagogical software.
A second type of interaction in CALL appears when learners’ performance is followed by computer-generated feedback. Such feedback can come from instructional materials containing explicit exercises aimed at providing learners with practice on particular grammatical forms and meanings (e.g., Heift, 2010). Such materials, which can focus on specific areas of grammar or vocabulary, reading or listening, are aimed at providing learners with immediate feedback about the correctness of their responses to questions in a manner that engages learners in focused interactions that illuminate their gaps in their knowledge. For instance, this can be achieved with NLP software. CALL software that provides pronunciation feedback also exists and has shown promising results; however, feedback on learners’ spoken language is not yet sufficiently reliable to allow for free conversations. Important interactions can also occur if learners are able to use the feedback provided by word processing programs—both the word-level spelling mark-up and the sentence-level grammatical mark-up.
A third type of interaction occurs as students communicate with other learners through CMC. The study of learners’ interaction in oral and written CMC both in and outside the classroom has demonstrated expanded opportunities for interaction for language learners beyond face-to-face tasks. Some teachers and researchers have developed and investigated tasks for electronic communication based on the guidelines that come from work on face-to-face communication tasks (e.g., Blake, 2000). Others have expanded the types of tasks and perspectives on interaction studied (e.g., Belz, 2001; Lamy and Goodfellow, 1999). However, investigations based on each of these perspectives on interaction with and through technology do not suggest that students left on their own will engage in productive interactions through technology; instead, the research suggests the important role the teacher plays in demonstrating to students how to make the most out of such opportunities for interaction.
The second research issue is the evaluation of learner performance particularly in environments where the computer program can provide appropriate feedback or instructional decision-making. How can the computer be used to analyze learners’ language and behavior in a manner that increases capacity for assessment of individual characteristics beyond what can be done with current testing methods? In view of the variability in the abilities of any like-designated group of learners, the need for means of analyzing and recording specific learners’ knowledge is crucial in order to attempt to provide appropriate instruction. For instance, Heift (2008) investigated learner variability by examining learner usage of a variety of grammatical constructions in a beginner second language (L2) course for German. Her longitudinal data which were collected and analyzed by a NLP program emphasize the dynamic aspects, complexity, and non-linearity of interlanguage and, more generally, the variability of SLA: students not only acquire L2 constructs at a different rate but they also exhibit differences and variation in their own patterns of acquisition. Her study outcomes suggest an individualized learning environment capable of making distinctions at a very fine-grained level. While this is an issue that appears to be solvable only through the use of technology, in view of the complexity of analysis of learners’ language as well as the possibilities for individualization in instruction, much more research is needed, with regards to the kind of language analysis that needs be performed to achieve an individualized environment most conducive to language learning.
The third research issue in CALL arises from the dynamic that is evident if one considers the learner as an agent who chooses learning options, selects strategies, and constructs his or her own version of self in the target language (see Dörnyei and Ushioda, 2009). The study of possibilities the learner constructs for himself or herself is potentially as rich as the technologies themselves because these issues extend beyond the classroom to the life-long process of language learning. This fact creates both the need for learners to develop their autonomous strategies for language learning (Benson, 2001) and the opportunity for researchers to study how these strategies play out. Although the concept of autonomy does not have to be tied to technology, the reality today is that if autonomous learners are to take advantage of the resources and tools available for language learning, for example in a self-access lab (e.g., Milton, 1997), they are going to be working with technology.
Learners’ use of technology for language learning beyond the classroom raises another issue— the construction of their identity through participation in particular communities on the Web (Lam, 2000). This is a rich area for research as language learners of all ages see the Web as the natural place to get information, communicate with like-minded people, and learn things. The study of the role of learners’ identity and their use of the Web as a context for access to target language forums suggest the utility of a broad theoretical lens for viewing, describing, and explaining learners’ technology use (Warschauer, 2005).
In addition to computers providing a wide range of learning opportunities for language learners, they also constitute a powerful means for computer-aided data elicitation and collection for researchers. As such, they have had a significant impact on the study of language acquisition and use. Hulstijn (2000, p. 39), for instance, states that “until some 20 years ago, empirical research on language acquisition and use was restricted to the observation and measurement of language input and output. With these computer-aided tools, however, researchers have the means to get closer to the processes of language acquisition and use.” Ayoun (2000) summarizes the advantages of computer-based data elicitation and notes that, for example, a computer environment will expose all study participants to the stimuli and/or study treatment in the same way. Moreover, it eliminates or minimizes human error in data collection and facilitates data analysis and organization. Finally, it ensures a more complete data set because computer-based studies are generally designed in such a way that they require a response from study participants.
Cognitive interactionist and socio-cultural SLA theories have provided the theoretical background and thus departure point for interaction-based research, that is, CALL studies that focus on aspects of human-computer interaction and CMC. Interaction-based research methods in CALL generally manipulate the types of interactions in which language learners are involved (see Chapelle, 2005a).
Learner-computer interactions. Data gathered automatically as learners interact with computers allow researchers to heed the advice of Swain (1998, p. 80) who recommends that researchers examine “what learners actually do, not what the researcher assumes instructions and task demands will lead learners to do” (see also Chapelle, 2001, 2005b). In a CALL environment, software features are designed with a particular pedagogical goal in mind but, due to a number of reasons, they may not be exploited to their best potential or, in the worst case, students may not even use them at all. For instance, Cobb and Stevens (1996) reported that even after students had learned in training sessions that certain help options in reading courseware were beneficial to their learning, they did not take advantage of the options when using the courseware outside of class (see also Grgurovic and Hegelheimer, 2007).
These important observations are made through the use of tracking procedures such as computer log files. A visit to a Website may be recorded in the server log with the date and time of the request, the originating Internet address, and the system response. More importantly, however, are the user selection of software features (e.g., frequency and sequence of access) and learner responses during task completion that can also be recorded and analyzed. For instance, a study on incidental vocabulary learning by Laufer and Hill (2000) investigated learner look-up preferences of additional information about new words and how well these words are remembered. The help options for the test items included an audio recording of the word, its first language (L1) translation, an L2 explanation of the test item as well as different types of morphological information. The CALL program recorded learners’ look-up behavior by means of a computer log file which then was examined and correlated with learner performance on a meaning recall test of the target words. Study results indicate that the additional information provided about the new words was useful, however, individual learner differences in look-up preferences were also noted.
In addition to computer log files that record learner-computer interactions, video-screen recordings and/or eye-tracking also provide insight into interaction-based learner behavior in CALL. For instance, Pujolà’s (2001) study with adult learners of Spanish examined learners’ use of various help facilities using ImPRESSions, a multimedia program for teaching listening and reading comprehension. In addition to observation and retrospection questions about the use of help facilities, learners’ screen movements were recorded and used to analyze learner responses to different types of system feedback. Due to eye-tracking technology, the study was able to determine whether students read, skimmed and/or ignored the feedback that was provided to them.
Computer-mediated interactions with peers. Researchers have also emphasized the importance of combining different data collection tools for interaction-based research (e.g., computer log files with video screen captures) to assess student learning behavior in computer-mediated interactions. For instance, Smith (2008) examined the use of self-repair among learners of German in a task-based synchronous computer-mediated communication (SCMC) environment. Using the chat function in Blackboard, paired study participants engaged in six jigsaw tasks over the course of one semester. For data collection, the chat sessions were not only recorded in a printed computer log file but, in addition, a video file of the screen captured the entire interaction. Study results indicate that by only considering the written computer log file one may gain the impression that learners do not self-correct very often in an SCMC environment. However, by also examining the video file, the data suggest that self-correction increased by over eight-fold of the amount of self-correction that was recorded by the written transcripts.
In addition to examining task types between individual learners, Lee (2004), for example, combined a number of data collection tools to examine the necessary learning conditions for satisfactory networked collaborative interaction between groups of learners. Lee collected data from online discussions, end-of-semester surveys, and final oral interviews between non-native and native speakers of Spanish. The combination of the distinct data sets provides evidence that network collaborative interaction, at the same time offering advantages and benefits, also causes notable difficulties for the non-native speakers with respect to finding a common time to be online and getting access to the network at the host institution in addition to age differences, learners’ language, and computer skills.
Interaction with corpora. Another application of interaction-based research can be found in the area of corpus analysis. In CALL research, we can distinguish between two main types of language corpora: Native corpora that have been collected from written and/or oral data of native speakers and learner corpora that provide a collection of data produced by language learners. Studies that employ native corpora primarily focus on learner interactions with concordancing tools with the ultimate goal to provide insight into teachers’ and learners’ use of corpora. For instance, Braun's (2007) case study with 25 EFL learners in Germany investigated the conditions and challenges of integrating corpus materials and corpus-based learning activities into secondary education. For data collection, Braun used a number of quantitative and qualitative data sources: a pre-test for overall proficiency assessment, computer log files, a post-study questionnaire, and comments and observations made by students and teachers during the study as well as during the final discussion of the teaching unit. Study results show that the experimental group outperformed the control group in their acquisition of lexical knowledge and proficiency. At the same time, the study highlights a number of challenges (e.g., skill-related as well as methodological and logistical problems) that have to be resolved in order to integrate learner corpora into the language learning classroom successfully.
In addition to interaction-based research that involves native corpora, CALL researchers have also examined learner corpora. The two main methodological approaches employed here are studies of Contrastive Analysis and Error Analysis, both ultimately aiming at testing and/or contributing to some aspect of SLA theory (see Granger et al., 2002). For studies of Contrastive Analysis, researchers investigate features present in learner language and compare and contrast them with those found in native language. On the contrary, studies of Error Analysis investigate interlanguage phenomena, for example, the order of acquisition and/or particular use of morphemes. For instance, Granger (2006) investigated the use of verb forms by L2 learners of English for Specific Purposes. By comparing the top 100 used words in two corpora (a learner and a native corpus), Granger found that the differences in learner usage of these words are less characterized by their frequency of use but more so by a distinctive lexico-grammatical patterning of these words, notably with respect to active-passive alternation.
In CALL research, Hubbard (2005) found that questionnaires have decreased in use over the past decade. This shift in data collection tools since the mid-1990s (see Jung, 2005) might be, at least partly, due to the fact that research in CALL has shown significant discrepancies between tracked learner use of software in the form of computer log files and learner self-reports of system use (see e.g., Fischer, 2004). Hubbard (2005) reviewed 78 studies reported in CALL journals from 2000 to 2003 and found that only 15 percent of the studies he surveyed relied exclusively on questionnaire or survey sources for student data (compared with 75 percent that included behavioral data). For instance, Chen et al. (2004) investigated the effectiveness of mode of interaction for listening comprehension tasks in a Web-based course, Academic English, with the aim to measure learning outcomes and learners’ attitudes toward the Web-based course. Learning outcomes were measured by means of computer log files and a pre-treatment questionnaire that examined participants’ prior knowledge of the topic of instruction. A post-treatment questionnaire rated participants’ attitudes toward the Web-based course. The combination of interaction-based research methods with questionnaires allowed the researchers to not only assess learners’ performance during system interaction in a quantitative way but also to determine learners’ motivation and attitudes toward the Web-based course.
CALL studies also make use of introspective methods—retrospective interviews and think-alouds to examine students’ and teachers’ perceptions and attitudes toward learning materials during and/or after CALL use. According to Mackey and Gass (2005), one advantage of conducting interviews is the fact that they provide an opportunity for the researcher to observe phenomena that are not readily observable by other research methods. For instance, Meskill et al. (2002) studied the “technology talk” of novice and expert teachers of language and literacy in kindergarten and grades 1 through 8 (K-8). Interview data with eight teachers provided insights into the conceptual and practical differences between teachers with varying degrees of experience with technology. Study results show that classroom experience has a stronger impact on the prediction of teachers’ comfort level with implementing technology than training in classroom technologies.
Think-alouds aim to provide insights into learners’ mental processes and strategies that they employ when using a CALL program. For example, in a study by Vinther (2005) students were asked to verbalize their thoughts while using a CALL program to learn English syntax. By considering both learners’ think-alouds and quantitative data, the study provides evidence that, in the case of low-achieving students, the computer can help learners to improve their cognitive strategies.
Computer-aided research allows us to examine aspects of SLA and use that goes beyond the possibilities found with classroom-based research. The degree of control over instructional and testing decisions, the minimization of human error rate in data collection as well as the vast data samples that can be elicited, collected, and analyzed contribute to making this a unique research environment.
Instructional relevance is the raison d’être for CALL research, which aims to discover and demonstrate how technology can be used to create optimal instructional practices. In view of the range of factors involved in second language learning, and the challenge in finding evidence that learners’ language competence has improved as the result of any one particular instructional intervention, the overall objective is addressed from a number of different angles. This leads to a wide range of claims about CALL use. Researchers tend to state their claims in terms of factors that are expected to create a positive learning experience, particularly drawing on constructs, such as interaction, individualization, and autonomy, that we outlined above (Core Issues). Whereas claims about learning conditions in terms of CALL design and its learning outcomes are the most commonly found in research in this area, some researchers are also concerned with claims about the relative value of CALL when compared with classroom learning.
The claim that some CALL researchers want to be able to make is that the use of technology for language learning or a technology-plus-classroom blend is at least as—if not more—effective than use of classroom instruction alone. Nutta (1998), for instance, conducted a study on the acquisition of verb tenses by post-secondary ESL students to determine the impact of two distinct methods of instruction, computer-based and teacher-directed. Study results indicate that for all proficiency levels, the computer-based students significantly outperformed the teacher-directed students on open-ended tests. No significant differences, however, were found between the computer-based and teacher-directed student scores on multiple choice or fill-in the-blank tests. Similarly, Torlakovic and Deugo (2004) studied ESL learners in their acquisition of the positioning of adverbs by comparing a teacher-fronted group with a CALL group. Study results show a significant improvement for the computer group on both learners’ performance and confidence in positioning adverbs while no significant gains for the teacher-fronted group were recorded.
Whereas these comparative questions are of interest for making particular curricular decisions, researchers interested in better understanding CALL, pointed out years ago that such research is limited because the findings cannot be generalized due to the lack of specification of what the technology consists of (e.g., Pederson, 1987). A more pointed approach to investigating learning outcomes in CALL is to examine the effects of various design and activity features (e.g., help options) on CALL use and learning.
Research on specific features of design yields evidence aiming to increase professional knowledge in CALL that can inform materials developers, teachers, and learners. For example, if evidence suggesting that the help and feedback learners get from the computer is beneficial for comprehension, learning, or both, then developers know that this feature is worthwhile to add to CALL tasks, and teachers know that they should encourage students to use help and feedback if they are available. In fact, such evidence has been found to support the value of learners’ interaction with the computer while working on CALL tasks online. An examination of both help options and feedback shows that research yields evidence about even more specific claims about the types of help and feedback.
Help with comprehension. With respect to students’ aural comprehension, many studies have shown that pictorial or written information enhances students’ comprehension of a written text (e.g., see Chun and Payne, 2004; Guillory, 1999; Jones and Plass, 2002; Lavine, 1992; McGrath, 1992; Plass et al., 1998, 2003; Weinberg, 2002; Zhao, 1997). These studies have also shown that help with comprehension in reading can assist students to understand the text and learn vocabulary; moreover, in studies comparing different modes of help (e.g., L1, L2, images, text, aural, written), the finding is that the more modes the help appears in the better for vocabulary learning.
The claims one can make about the role of help on comprehension and learning of aural input are less clear. Gruba (2004), however, found that the importance of visual information on students’ aural comprehension changes dependent upon students’ comprehension of the material presented. Moreover, if left to their own devices, students exhibit preferences for certain help options over others and learner differences exist in the use of help options. Grgurovic and Hegelheimer (2007), for instance, designed a multimedia listening activity that contained a video of academic lectures with the goal to offer help in the form of target language subtitles and lecture transcripts in cases of comprehension breakdowns. Study results indicate that overall, participants interacted with the subtitles more frequently and for longer periods of time than with the transcript, but some students used only subtitles, only transcripts, both, or none at all. These results also support previous findings by Hoven (2003), for example, who found that high proficiency learners used multimedia-based help tools more often than did low or medium proficiency learners (see also Cárdenas-Claros, 2005; Desmarais et al., 1998; Hegelheimer and Tower, 2004; Jones, 2003, 2006; Pujolà, 2002). These results suggest that help options are generally beneficial to the learner, but the studies also show that learner training is central, in particular, in the case of low proficiency learners.
Feedback. Research has sought evidence that feedback in CALL makes a difference, and more specifically what kind of feedback makes a difference. One of the early studies investigating different feedback types for Japanese grammar instruction found that “intelligent” feedback (with a metalinguistic explanation) was more effective than traditional feedback (e.g., wrong, try again!) (Nagata, 1993). A number of studies followed (e.g., see Bowles, 2005; Brandl, 1995; Clariana and Lee, 2001; Gaskell and Cobb, 2004; Heift, 2002, 2004, 2010; Murphy, 2007; Nagata and Swisher, 1995; Nagata, 1996; Petersen, 2010; Pujolà, 2002; Rosa and Leow, 2004; Vinther, 2005) and the results generally support the claim that students benefit from explicit feedback because they subsequently perform better on particular target language structures and/or because students’ grammatical awareness is subsequently raised. Evidence for effects of computer-generated feedback has also been sought in studies examining learner error correction behavior, referred to as learner uptake (Lyster, 2007), in response to distinct feedback types (e.g., Heift and Rimrott, 2008). Here, the findings report significantly more learner uptake for feedback that provides detailed corrections.
Effects of feedback obtained from interlocuters during CMC tasks have also been investigated. For example, Sauro (2009) examined the impact of two types of computer-mediated corrective feedback, recasts and metalinguistic feedback, on the development of adult ESL learners’ during task-based interaction via text-chat. While study results indicate an improvement in mean scores for both feedback groups compared to the control group, only the metalinguistic group showed significant gains (see also e.g., Loewen and Erlam, 2006; Ware, 2008).
Researchers seek to test the claim that learners engage in meaning-based communication in written CMC including tasks with opportunities for beneficial metalinguistic reflection on the language. Fernández-Garciá and Martínez-Arbelaiz (2002), for instance, investigated whether or not negotiation of meaning would take place in text chat discussion among third-year university students studying Spanish. The authors found some instances of L2 negotiation as learners discussed questions about readings, but they also found many instances of resolution of communication breakdown through the use of the L1 (see also Fernández-Garciá and Martínez-Arbelaiz, 2003; Jepson, 2005). Research in CMC has also examined different task types to determine their impact on learner's discourse with respect to negotiation of meaning. For instance, Blake (2000) compared types of communication tasks constructed in accordance with those designed by researchers investigating face-to-face communication. In his study, dyads of L2 learners of Spanish carried out a series of online tasks (jigsaw, information-gap, decision-making) using a synchronous chat program. The study found that the jigsaw tasks in written synchronous CMC seem to prompt negotiation of meaning best thus confirming previous results found in face-to-face communication (see also Pellettieri, 2000; Warschauer, 1996).
That CALL activities can meet the individual needs of learners is a claim that has been made since the earliest work on CALL. The goal was to use the computer to support classroom instruction in a way that would provide individualization to meet learners’ needs by identifying specific areas of knowledge (e.g., placement of the indirect object in French), providing exercises on these areas of knowledge for learners to complete, and tabulating learners’ successes and errors within the knowledge categories. This work has become more sophisticated as CALL developers explored more sophisticated exercise types requiring NLP (Heift and Schulze, 2007). A more delicate and potentially more useful analysis can be made with NLP software that can analyze learners’ language rather than simply categorizing their responses on selected-response items. Such a program is also useful for modeling what the student knows based on the evidence found in his or her writing, and such models can be used for making suggestions about useful areas of instruction.
It has been claimed that learners expand their autonomous learning strategies and opportunities for development of their intercultural competence as they work on some CALL tasks. Many language learners look to the Web for language resources such as dictionaries and concordances, language and cultural materials to read, listen to, and watch (e.g., newspapers and YouTube), and conversations to participate in (e.g., special interest forums). As a result, learners have many opportunities to engage in collaborative development of language (e.g., wikis) and cultural content. Teachers who struggle to get students to use the target language in the classroom see students participating in the social world of the Web through wikis, blogs, and social bookmarking (e.g., Facebook). There is much in this domain to be studied if researchers are to get a better understanding of the new contexts of linguistic input for learners provided by computer technology.
In view of the scope of such claims about technology use, research framed in socio-cultural theory has been useful in seeking evidence concerning how collaborative CMC activities can support language development and intercultural understanding in telecollaborative language learning (Belz, 2002, 2003; Kern, 2000; Meskill and Anthony, 2005; Thorne, 2003; Thorne and Payne, 2005; Ware and Kramsch, 2005). Warschauer (1998, p. 760), for example, notes that in order “to fully understand the interrelationship between technology and language learning, researchers have to investigate the broader ecological context that affects language learning and use in today's society, both inside and outside the classroom” (see also Chapelle, 2000; Salaberry, 1999, 2000). Belz (2006), for instance, investigated the kinds of interactional patterns and norms that are jointly constructed by L2 learners, finding that while learners construct their learning contexts as active social agents, the teacher, nevertheless, plays an important role in determining the appropriateness of a selected CMC tool for the purpose of an activity (see also Fiori, 2005).
This brief overview of CALL reveals a rich new set of opportunities for language learners, teachers, and second language researchers. For learners, there are new options for accessing the target language, connecting with users of the language in virtual spaces, and obtaining online help. These opportunities radically change the language learning landscape. As for teachers, their involvement can trigger, stimulate, monitor, and guide online as well as offline activities conducive to learning. The primary goal for researchers in CALL is to better understand the new landscape with its implications for language teaching and impact on language learning. The research findings suggest some instructional practices that can guide materials developers, teachers, and learners and it is to these we turn next.
At least two large issues underlie the suggestions. One concerns the type of professional knowledge required of language teachers in view of the changed landscape for L2 learning. A new set of teaching competencies is required for helping learners to take advantage of technology as well as to plan and work within new types of language curricula. Language teaching, like other professions, is changing as the result of pervasive use of technology and therefore teachers need to be able to use and develop technology-based learning materials most conducive to language learning.
A second large issue pertaining specifically to SLA is the significant change in the environments in which language is learned today. The precious, and in many contexts rare, face-to-face conversations that formed the basis of important theoretical perspectives on SLA have been supplemented, diversified, and extended beyond the physical boundaries that separated learners from their target language in the past. Perspectives on the psycholinguistic dimensions of SLA can be expanded to theorize and study the new types of interactions that learners engage in and their effects on the learners’ language and identity. Perspectives on the social contexts of SLA need to encompass computer technology as an important player in these contexts. Despite the utility of existing knowledge and research directions in SLA for the study of CALL, the need exists to better understand the new conditions for SLA brought about by the real language-related capabilities of technologies that many learners have access to on a daily basis.
Ahmad, K., Corbett, G., Rogers, M., and Sussex, R. (1985). Computers, language learning, and language teaching. Cambridge: Cambridge University Press.
Ayoun, D. (2000). Web-base elicitation tasks in SLA research. Language Learning and Technology, 3(2), 77–98. Belz, J. A. (2001). Institutional and individual dimensions of transatlantic group work in network-based language teaching. ReCALL, 13(2), 213–231.
Belz, J. A. (2002). Social dimensions of telecollaborative foreign language study. Language Learning and Technology, 6(1), 60–81.
Belz, J. A. (2003). Linguistic perspectives on the development of intercultural competence in telecollaboration. Language Learning and Technology, 7(2), 68–117.
Belz, J. A. (2006). ESL students’ computer-mediated communication practices: Context configuration. Language Learning and Technology, 7(2), 65–84.
Benson, P. (2001). Teaching and researching autonomy in language learning. Harlow, England: Pearson Education.
Blake, R. (2000). Computer-mediated communication: A window on L2 Spanish interlanguage. Language Learning and Technology, 4(1), 120–136.
Bowles, M. (2005). Effects of verbalization condition and type of feedback on L2 development in a CALL Task. Washington, D.C.: Georgetown University.
Brandl, K. K. (1995). Strong and weak students’ preferences for error feedback options and responses. The Modern Language Journal, 79(ii), 194–211.
Braun, S. (2007). Integrating corpus work into secondary education: From data-driven learning to needs-driven corpora. ReCALL, 19(3), 307–328.
Cárdenas-Claros, M. (2005). Field dependence/field independence: How do students perform in CALL-based listening activities? Unpublished MA thesis. Iowa State University, Ames.
Chapelle, C. (2000). Is network-based learning CALL? In M. Warschauer and R. Kern (Eds.), Network-based language teaching: Concepts and practice (pp. 204–228). New York: Cambridge University Press.
Chapelle, C. A. (2001). Computer applications in second language acquisition: Foundations for teaching, testing, and research. Cambridge: Cambridge University Press.
Chapelle, C. A. (2003). English language learning and technology: Lectures on applied linguistics in the age of information and communication technology. Amsterdam: John Benjamins.
Chapelle, C. A. (2005a). Interactionist SLA theory in CALL research. In J. Egbert and G. Petrie (Eds.), Research perspectives on CALL (pp. 53–64). Mahwah, NJ: Laurence Erlbaum Associates.
Chapelle, C. A. (2005b). Computer-assisted language learning. In E. Hinkel (Ed.), Handbook of second language teaching and learning (pp. 743–755). Mahwah, NJ: Lawrence Erlbaum.
Chen, J., Belkada, S., and Okamoto, T. (2004). How a web-based course facilitates acquisition of English for academic purposes. Language Learning and Technology, 8(2), 33–49.
Chun, D. M. (1994). Using computer networking to facilitate the acquisition of interactive competence. System, 22(1), 17–31.
Chun, D. C. and Payne, J. S. (2004). What makes students click: Working memory and look-up behavior. System, 32(4), 481–503.
Clariana, R. B. and Lee, D. (2001). The effects of recognition and recall study tasks with feedback in a computer-based vocabulary lesson. Educational Technology Research and Development, 49(3), 23–36.
Cobb, T. and Stevens, V. (1996). A principled consideration of computers and reading in a second language. In M. Pennington (Ed.), The Power of CALL (pp. 115–136). Houston, TX: Athelstan.
Desmarais, L., Laurier, M., and Renie, D. (1998). The analysis of navigation patterns in CALL. Computer Assisted Language Learning, 11(3), 309–315.
Dörnyei, Z. and Ushioda, E. (Eds.) (2009). Motivation, language identity and the L2 self. Bristol: Multilingual Matters.
Ellis, R. (1999). The study of second language acquisition. Oxford: Oxford University Press.
Fernández-Garciá, M. and Martínez-Arbelaiz, A. (2002). Negotiation of meaning in nonnative speaker-nonnative speaker synchronous discussions. CALICO Journal, 19(2), 279–294.
Fernández-Garciá, M. and Martínez-Arbelaiz, A. (2003). Learners’ interactions: A comparison of oral and computer-assisted written conversation. ReCALL Journal, 15(1), 113–136.
Fiori, M. L. (2005). The development of grammatical competence through synchronous computer-mediated communication. CALICO Journal, 22(3), 567–602.
Fischer, R. (2004). How do we know what students are actually doing? Monitoring students’ behavior in CALL. Computer Assisted Language Learning, 20(5), 409–442.
Gaskell, D. and Cobb, T. (2004). Can learners use concordance feedback for writing errors? System, 32(3), 301–319.
Gass, S. (2003). Input and interaction. In C. J. Doughty and M. H. Long (Eds.), The handbook of second language acquisition (pp. 224–255). Malden, MA: Blackwell Publishing.
Granger, S. (2006). Lexico-grammatical Patterns of EAP verbs: How do learners cope? Paper presented at the exploring the lexis-grammar interface conference, Hanover (Germany), 5–7 October, 2006.
Granger, S., Hung, J., and Petch-Tyson, S. (Eds.). (2002). Computer learner corpora, second language acquisition, and foreign language. Vol. 6: Language Learning and Language Teaching. Amsterdam and Philadelphia: John Benjamins.
Grgurovic, M. and Hegelheimer, V. (2007). Help options and multimedia listening: Student's use of subtitles and transcripts. Language Learning and Technology, 11(1), 45–66.
Gruba, P. (2004). Understanding digitized second language video text. Computer Assisted Language Learning, 17(1), 51–82.
Guillory, H. G. (1999). The effect of keyword captions to authentic French video on learner comprehension. CALICO Journal, 15(1–3), 89–108.
Hart, R. S. (Ed.). (1981). Studies in language learning. special issue on the PLATO system and language study (Vol. 3). Urbana, IL: Language Learning Laboratory, University of Illinois at Urbana-Champaign.
Hegelheimer, V. and Tower, D. (2004). Using CALL in the classroom: Analyzing student interactions in an authentic classroom. System, 32(2), 185–205.
Heift, T. (2002). Learner control and error correction in ICALL: Browsers, peekers and adamants. CALICO Journal, 19(3), 295–313.
Heift, T. (2004). Corrective feedback and learner uptake in CALL. ReCALL, 16(2), 416–431.
Heift, T. (2008). Modeling learner variability in CALL. Computer-Assisted Language Learning, 21(4), 305–321.
Heift, T. (2010). A longitudinal study of learner uptake. The Modern Language Journal, 94(2), 198–216.
Heift, T. and Rimrott, A. (2008). Learner responses to corrective feedback for spelling errors in CALL. System, 36(2), 196–213.
Heift, T. and Schulze, M. (2007). Errors and intelligence in computer-assisted language learning: Parsers and pedagogues. London: Routledge.
Higgins, J. and Johns, T. (1984). Computers in language learning. Reading, MA: Addison-Wesley.
Hoven, D. (2003). Strategic uses of CALL: What learners use and how they react. Australian Review of Applied Linguistics, 17(1), 125–48.
Hubbard, P. (2005). A review of subject characteristics in CALL research. Computer Assisted Language Learning, 18(5), 351–368.
Hulstijn, J. (2000). The use of computer technology in experimental situations of second language acquisition: A survey of some techniques and some ongoing studies. Language Learning and Technology, 3(2), 32–43.
Jepson, K. (2005). Conversations—and negotiated interaction—in text and voice chat rooms. Language Learning and Technology, 9(3), 79–98.
Johns, T. (1986). Micro-Concord, a language learner's research tool. System, 14(2), 151–162.
Jones, L. (2003). Supporting student differences in listening comprehension and vocabulary acquisition with multimedia annotation: The Students Voice. CALICO Journal, 21(1), 41–65.
Jones, L. (2006). Effects of collaboration and multimedia annotations on vocabulary learning and listening comprehension. CALICO Journal, 24(1), 33–58.
Jones, L. and Plass, J. (2002). Supporting listening comprehension and vocabulary acquisition in French with multimedia annotations. The Modern Language Journal, 86(4), 546–561.
Jung, U. (2005). CALL: Past, present and future—A bibliometric approach. ReCALL, 17(1), 4–17.
Kern, R. (2000). Literacy and language teaching. Oxford: Oxford University Press.
Lam, W. S. E. (2000). L2 literacy and the design of the self: A case study of a teenager writing on the internet. TESOL Quarterly, 34(3), 457–482.
Lamy, M. -N. and Goodfellow, R. (1999). “Reflective conversation” in the virtual language classroom. Language Learning and Technology, 2(2), 43–61.
Laufer, B. and Hill, M. (2000). What lexical information do L2 learners select in a CALL dictionary and how does it affect word retention? Language Learning and Technology, 3(2), 58–76.
Lavine, R. (1992). Rediscovering the audio language laboratory: Learning through communicative tasks. Hispania, 75(5), 1360–1367.
Lee, L. (2004). Learners’ perspectives on networked collaborative interaction with native speakers of Spanish in the U.S. Language Learning and Technology, 8(1), 83–100.
Loewen, S. and Erlam, R. (2006). Corrective feedback in the chatroom: An experimental study. Computer Assisted Language Learning, 19(1), 1–14.
Lyster, R. (2007). Learning and teaching languages through content: A counterbalanced approach. John Benjamins.
Mackey, A. and Gass, S. (2005). Second language research: Methodology and design. New Jersey: Lawrence Erlbaum Associates.
McGrath, D. (1992). Hypertext, CAI, paper, or program control: Do learners benefit from choices? Journal of Research on Computing in Education, 24(4), 513–532.
Meskill, C. and Anthony, N. (2005). Foreign language learning with CMC: Forms of online instructional discourse in a hybrid Russian Class. System, 33(1), 89–105.
Meskill, C., Mossop, J., DiAngelo, S., and Pasquale, R. K. (2002). Expert and novice teachers talking technology: Precepts, concepts, and misconcepts. Language Learning and Technology, 6(3), 46–57.
Milton, J. (1997). Providing computerized self-access opportunities for the development of writing skills. In P. Benson and P. Voller (Eds.), Autonomy and independence in language learning (pp. 237–248). London: Longman.
Mohan, B. (1992). Models of the role of the computer in second language development. In M. Pennington, and V. Stevens (Eds.), Computers in applied linguistics: An international perspective (pp. 110–126). Clevedon, UK: Multilingual Matters Ltd.
Murphy, P. (2007). Reading comprehension exercises online: The effects of feedback, proficiency and interaction. Language Learning and Technology, 11(3), 107–129.
Nagata, N. (1993). Intelligent computer feedback for second language instruction. The Modern Language Journal, 77, 330–338.
Nagata, N. (1996). Computer vs. workbook instruction in second language acquisition. CALICO Journal, 14(1), 53–75.
Nagata, N. and Swisher, M. V. (1995). A study of consciousness-raising by computer: The effect of metalinguistic feedback on second language learning. Foreign Language Annals, 28(3), 337–347.
Nutta, J. (1998). Is computer-based grammar instruction as effective as teacher-directed grammar instruction for teaching L2 structures? CALICO Journal, 16(1), 49–62.
Pederson, K. M. (1987). Research on CALL. In W. F. Smith (Ed.), Modern media in foreign language education: Theory and implementation (pp. 99–132). Lincolnwood, IL: National Textbook Company.
Pellettieri, J. (2000). Negotiation in cyberspace: The role of chatting in the development of grammatical competence in the virtual foreign language classroom. In M. Warschauer and R. Kern (Eds.), Network-based language teaching: Concepts and practice (pp. 59–86). Cambridge: Cambridge University Press.
Peterson, K. (2010). Implicit corrective feedback in computer-guided interaction. Does mode matter? PhD dissertation. Georgetown University, Washington, D.C., USA.
Plass, J., Chun, D., Mayer, R., and Leutner, D. (1998). Supporting visual and verbal learning preferences in a second language multimedia learning environment. Journal of Educational Psychology, 90(1), 25–36.
Plass, J., Chun, D., Mayer, R., and Leutner, D. (2003). Cognitive load in reading a foreign language text with multimedia aids and the influence of verbal and spatial abilities. Computers in Human Behavior, 19(2), 221–243.
Pujolà, J. -T. (2001). Did CALL feedback feed back? Researching learners’ use of feedback. ReCALL, 13(1), 79–98.
Pujolà, J. -T. (2002). CALLing for help: Researching language learning strategies using help facilities in a web-based multimedia program. ReCALL, 14(2), 235–262.
Rosa, E. and Leow, R. (2004). Computerized task-based exposure, explicitness and type of feedback on Spanish L2 development. The Modern Language Journal, 88, 192–217.
Rost, M. and Fuchs, M. (2004). Longman English interactive. New York: Pearson Education.
Salaberry, M. R. (2000). L2 morphosyntactic development in text-based computer-mediated communication. Computer Assisted Language Learning, 13, 5–27.
Salaberry, R. (1999). CALL in the year 2000: Still developing the research agenda. A commentary on Carol Chapelle's CALL in the year 2000: Still in search of research paradigms. Language Learning and Technology, 3(1), 104–107.
Sanaoui, R. and Lapkin, S. (1992). A case study of an FSL senior secondary course integrating computer networking. The Canadian Modern Language Review, 48(3), 525–552.
Sauro, S. (2009). Computer-mediated corrective feedback and the development of L2 grammar. Language Learning and Technology, 13(1), 96–120.
Sharples, M., Taylor, J., and Vavoula, G. (2007). A theory of learning for the mobile age. In R. Andrews and C., Haythornthwaite (Eds.), The Sage handbook of E-learning research (pp. 221–247). Los Angeles: Sage Publications.
Smith, B. (2008). Methodological hurdles in capturing CMC data: The case of the missing self-repair. Language Learning and Technology, 12(1), 85–103.
Swaffar, J., Romano, S., Markley, P., and Arens, K. (Eds.). (1998). Language learning online: Theory and practice in the ESL and the L2 computer classroom. Austin, TX: Labyrinth Publications.
Swain, M. (1998). Focus on form through conscious reflection. In C. Doughty and J. Williams (Eds.), Focus on form in classroom second language acquisition (pp. 64–81). Cambridge: Cambridge University Press.
Thorne, S. L. (2003). Artifacts and cultures-of-use in intercultural communication. Language Learning and Technology, 7(2), 38–67.
Thorne, S. L. and Payne, J. S. (2005). Evolutionary trajectories, internet-mediated expression, and language education. CALICO Journal, 22(3), 371–397.
Torlakovic, E. and Deugo, D. (2004). Application of a CALL system in the acquisition of adverbs in English. Computer-Assisted Language Learning, 17(2), 203–235.
Underwood, J. H. (1984). Linguistics, computers, and the language teacher: A communicative approach. Rowley, MA: Newbury House.
Vinther, J. (2005). Cognitive processes at work in CALL. Computer Assisted Language Learning, 18(4), 251–271.
Ware, P. (2008). Peer feedback on language form in telecollaboration. Language Learning and Technology, 12(1), 43–63.
Ware, P. and Kramsch, C. (2005). Toward an intercultural stance: Teaching German and English through telecollaboration. The Modern Language Journal, 89(2), 190–205.
Warschauer, M. (1996). Comparing face-to-face and electronic discussion in the second language classroom. CALICO Journal, 13(2and3), 7–25.
Warschauer, M. (1998). Researching technology in TESOL: Determinist, instrumental, and critical approaches. TESOL Quarterly, 32(4), 757–761.
Warschauer, M. (2005). Sociocultural perspectives on CALL. In J. Egbert and G. Petrie (Eds.), Research perspectives on CALL (pp. 41–51). Mahwah, NJ: Laurence Erlbaum Associates.
Weinberg, A. (2002). Virtual misadventures: Technical problems and student satisfaction when implementing multimedia in an advanced French listening comprehension course. CALICO Journal, 19(2), 331–357.
Zhao, Y. (1997). The effects of listeners’ control of speech rate on second language comprehension. Applied Linguistics, 18(1), 49–68.