In this episode of Voices from LLT, Hayo Reinders speaks with Robert Godwin-Jones, contributor over a period of 25 years of some of the most widely-read and appreciated articles in the journal.
Host [H]: Hello, everybody, you lovely language and terrific technology people, to a new episode of Voices from LLT, the podcast of Language Learning and Technology. I’m your host, Hayo Reinders, and today, I have a very special guest, a rockstar in our field, Robert (Bob) Godwin-Jones. Welcome, Bob!
Robert Godwin-Jones [RGJ]: Thanks, Hayo.
H: It’s wonderful to be able to speak with you. I don't think you and I've ever actually met. I have, of course, like every other person in this field in the world, been familiar with your work for a very long time. Your contributions to Language Learning and Technology span many, many years, and I think it's safe to say that they are some of the most highly cited and most often referred to resources published in the journal. For the two-and-a-half people in the world remaining who don't know, Bob publishes a very regular–I was trying to see if it was a perfect record–but certainly a very regular, ongoing feature called Emerging Technologies that really does an amazing job of synthesizing what is known about a particular topic at the time. So if you're not already familiar with Bob's work there, please do have a look. Bob, my first question to you is, I had of course a little bit of a look at the old Google and found that your background is actually in French and German. So how did you end up in technology?
RGJ: Actually, my PhD is in comparative literature, but I studied, as an undergraduate, French and German predominantly and also British literature. So when I was in graduate school, I taught German and my first job at the University of Wisconsin was teaching French and German. At that time, I wasn't doing much with technology, playing reel to reel tapes maybe for my students. When I moved from Wisconsin to Virginia, now it's Virginia Commonwealth University–I’ve been there since 1979 so quite a long time–we didn't even have a language lab and so at one of the first departmental meetings, I said to the chair, “Maybe it would be nice to have a lab and students could listen to authentic voices on tape.” And the chair at the time said, “Bob, you must not know about language pedagogy because nobody uses language labs anymore.” But despite that I wrote a grant to get a cassette-based language lab, that we got, and I was the lab director. And then I became chair of the Department of Foreign Languages and we had a new dean coming in. The first meeting with the dean, the dean said, “What are you doing with technology in your department?” And I said, “Well, we have a really nice cassette language lab.” And he said, “No, no, no. I mean, what are you doing with computers?” This was in 1991, I believe. I said, “We’re doing quite a bit. Each professor is using word processing. We have Word Perfect 5.1, you know. We’re up to date and so our secretary doesn't have to type out the manuscripts from handwritten copies.” He said, “No, that’s not what I’m talking about at all. I’m talking about, how are you using it in teaching?” I said, “Well, we type up handouts and then we mimeograph those.” He said, “No, I want you to look into what you can really do with computers and language learning.” It turns out he came from Auburn University and George Mitrevski there in Russian had developed a Russian HyperTutor series, hypercard stacks, for learning Russian that were quite impressive and he was familiar with that coming from Auburn University. And so he really encouraged me to look into that. And just at that time, we had somebody from our Technology Center at VCU who was offering training sessions using Macintosh computers–I'd never seen a Macintosh computer much less used one–but I was curious. So I went over and she showed me Quicktime video. I thought, “Wow!” Even though it was tiny–it was the size of a postage stamp and pretty jerky in terms of playback–but you could already envision the potential of that kind of multimedia for language learning. It immediately seemed to me that this is something we need to look into. So I wrote a grant to get, in fact, Macintosh computers and was successful and wrote another grant the second year and got more computers. And it turned out nobody in the department was quite interested in doing this add-on to their already heavy workload in terms of teaching. So it fell to me to investigate what we could do and, as I said, I was familiar with George Mitrevski’s work and actually corresponded with him and got to know him and started writing hypercard programs of my own and doing workshops for my colleagues. And so that was kind of the beginning, you know, I was very interested at that time in tutorial CALL, in using computers to have students work with the vocabulary and the grammar outside of class. And you could do even back then nice things in hypercard with audio and video that were really motivating to students, and there was a potential there of having authentic voices in the target language other than the instructor. To me, that's always been really important for language learning.
H: What an interesting progression, and isn’t it amazing how being in the right place at the right time and having the right sort of support–or in your case, a dean coming over and kind of pointing the way and saying, “Go and investigate there”–it’s fantastic, isn’t it, this serendipity? Do you still teach German and/or French?
RGJ: French, these days, because we have more folks in French. We have fewer people in German. But yeah, I teach mostly German. I teach courses in intercultural communication as well, but mostly German.
H: Right. And so you've been at Virginia for almost 40 years. Amazing. And so your collaboration, your involvement with Language Learning and Technology, the journal, how did that come about?
H: You mentioned AI and of course the special issue, and I just had a conversation with Jim Ranalli and Volker Hegelheimer, the editors of that special issue on automated writing evaluation. And in your column, you take a slightly broader view. Not to put you on the spot here, because it's such a difficult question, but when we look at the role, and especially the role in the coming years, looking at the medium-term, so not 50 years into the future and not five months but sort of five years from now, when you ask people in our field, you sort of go and fall into three camps: there are those who kind of don't know much about it and don't want to know much about it, there are those who say, well, you know, it's like any other technology that we've ever had, we've been told for so long that artificial intelligence will change everything but we've been told that about other technologies and it hasn't really happened, and then there are those who say, well, actually there's something that is in some ways fundamentally different about this collection of technologies all together labeled as artificial intelligence and it's developing in an exponential way and so we can't quite yet foresee the ramifications of it and it could be much more extensive and intensive than many people expect. Are you in any of those three camps? What is your thought on the development of AI?
RBJ: Well, I’m in the camp that thinks we can’t afford to ignore and that we have to look at what our students are doing with AI-based tools. For example, machine translation is now very sophisticated based on deep learning algorithms that take advantage of the huge collection of data that Google and other companies have collected and analyze that for patterns. And you know, this is something that performance there has to be improved as the AI systems have become more sophisticated. That’s true not just for machine translation, that’s true in other areas as well. And I think, you know, I have colleagues–and I imagine there are similar situations at other institutions–I have language colleagues that say to their students, don't use Google. You're not allowed to use Google Translate. Well, for one thing, how are they going to prevent students from doing that at home? And for the second, you know, you don’t want language learning to just be an artificial classroom experience. You want the students to really get interested, get motivated to learn a language as a lifelong skill. That's more of a challenge in the United States than it is maybe some other countries, in European countries, where the need to learn a second language is evident to anybody in everyday life. It’s not the case for a lot of US students. And so you know I think that what you need to do as a language teacher is encourage students to use any and all possible tools that will help them learn language and improve their language proficiency. And certainly there are ways to use Google Translate that are not effective pedagogically, if students just copy and paste assignments. But in that column, that most recent column that I wrote about intelligent writing systems, I looked at a number of articles that have been written in the last few years of people, of language teachers who have been very creative in how they have integrated machine translation and mostly Google Translate, teaching in a way that expands the students’ horizon in terms of language learning and makes them aware of the fact that this is not just an artificial exercise, it’s not just an academic requirement to learn a language, but this is a real language skill and that in real life there are tools that will help me improve my language ability and to maintain my language proficiency beyond the classroom. And I think there are other areas where AI can play a similar role, for example, in chatbots. I gave a talk at EuroCALL last month on chatbots and chatbots are becoming increasingly sophisticated and I think are familiar through a very fundamental way, through intelligent personal assistants like Siri and Google Assistant and Alexa, and those are getting very sophisticated backends in terms of AI systems that will allow them to do so much more than used to be the case in terms of interfacing with the user. Now these systems are not designed to be conversation partners for language learners. They’re designed to be transactional agents, where the user asks a question and in as brief a way as possible, the personal assistant gives a response to that inquiry. But there are systems that are being experimented with in language learning that take those basic systems like, for example, Alexa and there are add-ons that you can put in more specific language learning skills that are called Skills in Alexa that expand the capabilities of that particular personal assistant. And one of the things that I'm very interested in increasingly is student motivation and student autonomy. And I think, you know, one of the things that if chatbots can do what intelligent CALL has done for a long time, mainly build a personal profile of the learner that keeps track of conversations… And Siri and Alexa don't do that by default. There’s very little information that they save, which is because there are privacy issues of course. But if you have a profile that is built up so that the personal assistant remembers previous conversations, learns about what the learner interested in, what hobbies, what sports teams, what movies and so forth, then you can imagine, you know, conversations taking place with the learner outside of academic environment and maybe beyond the school experience. And I think this is part of where we're going with informal language learning which has exploded. If you look at research in the field of CALL, there's so much research now in the digital wilds, as it’s called, informal language learning, particularly for English but for the other languages as well. And I think AI-based systems like chatbots are going to figure into that because you have systems like LaMDA from Google or GPT-3 from OpenAI. This summer, one of the Google Engineers claimed that, based on conversations that he had with the LaMDA chatbot, he said that LaMDA is sentient. Which is ridiculous, but what that shows us in terms of language learning potential, if you have a human being who thinks that conversation with that AI system was so real that he had the impression he was talking to a real human being or at least a thinking human being, a sentient being, what does that mean for language learning? It doesn't matter if it’s sentient or not. But what it does mean is that there’s a lot [of] language taking place, a lot of give and take in that conversation and I think that's one of the things that we need to look at in AI is what are the developing opportunities for conversation partners, for these artificial partners to go beyond what the capabilities are today which are fairly limited.
H: And it's worth pointing out that the poor Google engineer of course got fired over his assertions. So be careful what you [say] about AI in language education.
RBJ: Another thing about AI systems that I’ll just mention briefly, somebody else who was fired 2 years ago from Google got into trouble because she was talking about issues of equity and fairness in AI systems. And that's something we need to be concerned with, the fact that, for example, the data collected by these huge systems like LaMDA or GPT-3 include a lot of hateful speech that’s on the internet. The internet’s full of junk and trash. Lots of good stuff, too, but you know these intelligent systems aren't intelligent enough to be able to filter out and then you can have a blacklist, it just doesn't work well. So you have the question of fairness and equity in terms of representation, of underrepresented populations for example dialect speakers, for one, of languages beyond English that don't get much coverage by AI systems. So there are a lot of concerns and terms of privacy and fairness and equity, I think, in AI development today.
H: We have a long and challenging and very interesting road ahead of us when it comes to all that. Plenty of columns for you to write in the future as a result. So that’s a nice segue to circle back to the journal. Any concluding thoughts about maybe publishing in general but specifically journals like Language Learning and Technology and your involvement, where do you see us going?
RBJ: Well, you know one of the reasons that my columns get cited is not necessarily because they’re good. It's because they're open access. And I think we're seeing more and more interest in Open Access in all academic fields. But I think in terms of, for example, language and technology journals, that's something I think that we're seeing increasing interest in that direction
and I think, you know, one of the things that I mentioned earlier that I think it's a good development for LLT are the special issues. One of the problems LLT has had is that there’s so much being written today and so much good stuff being written that it’s been difficult for us to publish the number of articles that we've accepted. And so that's why we've gone to this ongoing system now LLT publishes articles as they’re made ready, as they’re edited and ready for publication, and I think that's important because we have a fast-moving field and so we want articles that deal with new and recent technologies to appear in as timely a way as possible.
and I think that that's something that's really important for journals.
H: And can we look forward to many more columns from you in the years to come?
RBJ: Well, so far I continue to enjoy working on the columns, and it’s something that I look forward to. We’ll see.
H: And we look forward to reading them every time that they come out. Well, it's been wonderful talking to you. Thank you so much.
Liudmila Klimanova talks about her LLT article, “The Evolution of Identity Research in CALL: From Scripted Chatrooms to Engaged Construction of the Digital Self,” which was the 2021 recipient of the Dorothy Chun Award for Best Paper.
A conversation with Liudmila Klimanova: The 2021 recipient of the Dorothy Chun Award for Best Paper in LLT (Transcript)
Host [H]: Hello, you lovely language and terrific technology people, and welcome to the next episode of the Language Learning and Technology podcast. I'm your host, Hayo Reinders, and today, we have another very special guest, Liudmila Klimanova. Welcome!
Liudmila Klimanova [LK]: Hello and welcome, everyone who is watching us, and thank you, Hayo, for inviting me.
H: Of course, my pleasure. Lovely to talk to you. Of course, you are very famous as an award-winning author for Language Learning and Technology. So this is an opportunity for everyone to kind of see the face that goes with the writing and just hear a little bit more about who you are and also to hear a little bit more about your work and your article. So tell us a little bit, you are at University of Arizona, is that right?
LK: That’s correct. So my name is Liudmila Klimanova and I’m a professor at the University of Arizona. And I specialize in Second Language Acquisition with a particular focus on computer-assisted language learning (CALL). So this is my area of research, and I have been doing CALL for a long time, I would say maybe fifteen years, if not more than that. And my specific area within CALL is… I started as a SCMC, computer-mediated communication specialist and later I developed interest in identity research, in particularly the area of digital spaces, in the area of CALL, computer-assisted language learning. And i have been working with identity research for quite a while now.
H: Right. Nice. And of course, your identity research is what won you the Dorothy Chun award. And for those of the readers of the journal who are not aware, this is an award that was… I think you won the first one. It was initiated last year, is that correct?
LK: I believe so. This is 2021 Dorothy Chun Best Journal Article in Language Learning and Technology Award. And I'm very honored because last year in particular was very rich in very high-quality articles in the journal. And my article on identity research was selected and was awarded, and this is a special recognition for me.
H: Yeah, indeed, and it's a great article. I had read it before and I've reread it just in preparation for our conversation. And for those of our listeners and viewers who haven't read it, of course you should go and do that right away. But essentially–correct me if I'm wrong–you’re essentially giving an overview of the development of identity research in computer-assisted language learning and also giving the readers some pointers as to where the field might be heading. So I'm particularly interested in that final piece because in the article you talk about the development from the 2000s onwards. You have a communicative turn and then social turn and then later on this little multilingual and critical turn. And I think you kind of position that as leading up to about 2020. So where are we now and where are we going next? What's the next turn in the road?
LK: Well, let me start just by saying that what I'm describing in the article is my past in identity research. I started off many years ago just looking at identity from the very basic approach of labeling and classifying students into groups based on their first language, their level of proficiency, their background. And right now, over the years, my perspective on identity in digital spaces has evolved tremendously. And of course my insight has been influenced by new theories and new approaches in second language acquisition and teaching that have also been developing quite intensively over the past two decades. But more recently, I think we have been paying more attention to multilingualism, big questions of power and equity and equality. And this has been a conversation for a long time in language acquisition, in language planning research, but as a field, computer-assisted language learning, I think we have been trying to look more closely at how we teach instead of paying attention to what the environment provides for our learners, and how our students interact in digital spaces outside the classroom, and how that experience influences the way they learn languages and they use languages. And I think the usage is now more important than before. So the thinking, and going back now to your question, identity research is blooming, so to speak, so we are getting more and more interesting studies, more theoretical frameworks, and more things become obvious as a necessary focus of research because of the way technology has transformed us as human beings. We use technology in a fundamentally different way these days. We use technology to communicate professionally and personally. Because of technology, we are able to engage in translegal communication, where sometimes we don't know the language but technology provides the necessary support for communication to take place. And that wealth and richness of noneducational online communication has informed the ways we approach communication, digital communication, in instructional settings as well. So we need to pay attention to what our students do outside the classroom environment, what types of technologies they use, but also how technology frames our communication, and how technology can facilitate or liberate language learners as well as… it can also restrict some of their use as well as the opportunities for them to express themselves freely in digital spaces. So these are in general, in broad strokes, this is what we’re thinking more about when we talk about identity research in CALL. But of course, the multilingual approaches in applied linguistics have influenced tremendously how we approach communication in digital spaces. The way how languages are now available in digital spaces, how we navigate through instant translations, how some language decisions are made for us by the platforms, by geolocation devices where sometimes languages are proposed to us as opposed to us choosing the languages that we want to use. All these factors influence the way we position ourselves in non-digital communication. So maybe in a nutshell, broadly speaking, I think this is where we are right now and where research is going when it comes to identity, self-presentation, self-performance in digital spaces in instructional as well as more noneducational settings.
H: So what’s next for you? What are you working on at the moment? And where is your research heading?
LK: Well, in line with what I just said, I’m working right now on the new notion of group identity.
And it is influenced by the fact that more and more these days we have been interacting in groups. And these groups are not accidental groups but groups that meet in the same configuration over time. And it’s interesting to see how language use transforms over time within each group. I’m mostly working different types of Zoom interactions where we have multilingual speakers interacting over a particular constructional task or a project. But it’s interesting to think about identity as not necessarily belonging to one individual in digital space, but identity as a dynamic way, transforming into a group belonging. And this is something that I find particularly fascinating, to see how language within a group changes and evolves as participants find some new way of communicating ideas to the group and how those ways get picked up by other group participants. And they develop one unified code that transforms into their group identity. This is my new research I’m working on right now and I'm very excited about some of the findings.
H: Sounds very interesting. See if you can win another award with that.
LK: Thank you.
H: For the benefit of any graduate students, PhD students watching or listening hear
any words of wisdom that you might like to pass on to them?
LK: Graduate students, of course I want to invite students who are still looking for topics to research for their dissertation projects to consider identity as a very rich, interesting topic that needs more attention, more research attention. We also need to pay more attention to the way how technology has transformed the ways we communicate. Before, I think, when the field of CALL, computer-assisted language learning, was still in its beginning stages, we were borrowing a lot of theory from applied linguistics. But given the amount of digital communication takes place today, I feel that as a field that we now are ready to supply theoretical assumptions and provide applied linguists with more data that they cannot see outside the digital space. And in that regard, doing more research in digital communication, in computer-assisted language learning is what we need to do. We need more graduate students to undertake such research and develop new theories so we can inform applied linguistics of all of the new ways how we communicate.
H: Couldn't agree with you more. Dear listeners, dear viewers, you heard it here directly from an award-winning author. This is an area that requires more attention, more people to contribute to it. So what are you waiting for? Liudmila, it’s been lovely talking to you. Thank you so much for your time.
LK: Thank you.
Jim Ranalli and Volker Hegelheimer, long-time contributors and guest editors of Language Learning & Technology (LLT), talk about the June 2022 Special Issue on Automated Writing Evaluation.
A conversation with Jim Ranalli and Volker Hegelheimer (Transcript)
Host [H]: Hello, everybody, and welcome, you lovely language and terrific technology people. My name is Hayo, and welcome to the second episode of the Language Learning and Technology podcast. Today, we have two very special guests, Jim Ranalli and Volker Hegelheimer.
Welcome to you both.
Jim Ranalli [JR]: Thank you. It’s great to be here.
H: Right. I understand that you’re both at Iowa State University. Jim, can you just very briefly tell us about your background and your research interests?
JR: Sure, I’m a newly-minted associate professor and I work in the TESOL Applied Linguistics MA program and the Applied Linguistics and Technology PhD program. I also teach undergraduate courses in the Linguistics major. In terms of my research interests, I'm really interested in academic writing and technology and the theoretical lens of self-regulation. My work takes place mostly at the intersection of these three things.
H: Yeah, that interest in technology. I’ve figured that one out, given that you’ve just guest edited the special issue for Language Learning and Technology. Volker, I think you are at the same Institution, is that right?
Volker Hegelheimer [VH]: That is correct. Yes, I'm also at Iowa State University. I’ve been here since 1998 so this is my one and only institution that I've worked for. And I currently also serve as the department chair so my research interests have taken a bit of a backseat to administering a large department. But I am interested in the use of technology in the classroom and trying to see how it can be effectively employed.
H: Wonderful, and great to be able to see you both. This is one of the nice things about doing the podcast for the journal, especially for our listeners and those who are watching this. You know, we see all these famous names like yours and now we have a face to go along with that so that's awesome. The reason why we have you on the podcast today is especially because you have a special issue of Language Learning and Technology coming out, or by the time this goes live, maybe this just came out. Maybe one of you, tell us very briefly, what is the subject of the special issue?
JR: Do you want to take that one, Volker?
VH: Sure, and this dates back to over two years ago when I got an email from the editors of LLT asking me if I was interested in hosting or being the guest editor for a special issue on automated writing evaluation (AWE). They had seen earlier work that I had done in 2016 and there hadn’t been a special issue on that topic since then. And I decided to ask Jim to help with the co-editing and together we took this on, and it's been two years in the making and everything pretty much kept on track.
H: Yeah, these things take a long time, don't they? Just for the benefit of some of our listeners who may not be familiar with the terminology, just very briefly, what is automatic writing evaluation?
JR: So I think it refers to basically technologies that can analyze machine-readable text and provide some kind of feedback on that text to the writers of that text.
H: And so from your own experience and the literature, if you had to summarize briefly, what are some of the main benefits for both learners and teachers of this technology?
VH: I can take a stab at that, just to get us started. I think the benefit of this technology really is to free up mental and intellectual resources. You know, when you have a technology that can provide feedback that doesn't have to rely on a human, as a teacher or student, if there is anything you can automate and you can do it well, that frees up resources that can then be directed at things that technology doesn't do such a good job at providing feedback for.
H: Yeah, you make a good point. It's a benefit for both the learner and the teacher, right? Because the teacher may have to spend less time maybe offering corrections or what have you and therefore can focus on other things. And the same goes for the learner, who perhaps doesn't have to process as much information so that’s very, very helpful. Now you mentioned earlier, Volker, that this is kind of a follow-up to an earlier special issue that I think… did both of you edit the 2016 issue or one of you? I think I saw one of your names there.
VH: Yeah, this is the one that I did with Ahmet Dursun and Zhi Li. So the three of us edited that. It was a CALICO Journal special issue.
H: Oh, right. Okay, so 2016, 2022. I mean, 6 years, it's not nothing but it's also not a tremendously long period of time. So why a new special issue on this topic? What have been some of the main developments, the main changes?
JR: I mean, technology obviously moves fast, and one of the changes that we've seen is in the focuses of the AWE systems that are coming out now. So you have systems in addition to those that analyzed text for things like grammatical correctness. You also have systems that can analyze adherence to genre conventions, and systems that can analyze students’ enactment of writing strategies, and more content-oriented focuses like students’ use of evidence and argumentation, which is the focus of one of the articles that we've got in the special issue. So that kind of focus of AWE system has sort of broadened out a little bit.
H: Because it used to be mostly grammar correction, spelling correction, and you're saying now it's really expanded to be a lot more useful in a lot more aspects of writing, is that correct?
JR: That's right.
VH: In addition to that, we've all seen the arrival of multiple additional players over the last five, six, seven years. You know, where in the past, there were a few prime AWE systems that were implemented and used. We now have systems that are more ubiquitous in terms of accessibility. So we have systems that are no longer only accessible after you’ve paid some money, so we have some free versions. So I think there's been a breadth of additional resources that are making a difference here.
H: Yeah, that's a very good point that has, of course, potentially some major pedagogical consequences which I'd like to come back to a little bit later with a couple of questions for you. One thing that I read–I had a little sneak peek prior to publication of your introduction to the special issue–and you point out that it was hard to get a large number of submissions. I think you have 30 or 35 or so and eventually invited only 13 or 14 and ended up with only four. I had a similar experience last year on a special issue on artificial intelligence or big data, I believe it was. And the other thing you pointed out was that the four papers you ended up with all came from the Chinese context so I wanted to ask you about that. Firstly, why did you think that there was relatively little uptake initially, given that technology is so important and especially this topic could be seen to be particularly important at the moment, and secondly related to that, why does there seem to be so much activity especially in China?
JR: I can take the first part of the question. So I think frankly part of it has to do with the global pandemic. I mean, my own research productivity has kind of taken a hit, and I assume that's the case for quite a lot of other people. So I guess, in that sense, the timing of this special issue wasn’t ideal. But I know that there is a lot of work going on out there in this area so maybe just people weren't able to bring it to fruition and share with us this time.
VH: In terms of the second part of the question, why so many studies come out of China. I think that we see a lot of resource investment in China in terms of English learning, in terms of the development of some of these systems. And it is an ideal training ground so to speak for some of these systems in the Chinese context, where you just have access to a lot more learners and you can really do things, you know, so I think that's one benefit here.
H: Yeah, that was sort of what I was getting to with my slightly leading question. I get the impression and I wanted to get your feedback on this, that in certain parts of the world, there does seem to be a lot more understanding of the importance, going into the future, of not just of the area of your particular special issue but natural language processing in artificial intelligence and everything else that comes out of that, more so than in other parts of the world, and as a result of that, perhaps also considerably larger amounts of funding being available for primary research. That is just an impression that I had when I read your introduction. I was thinking, do you feel that maybe that has something to do with that? Are we investing enough perhaps say in, for example, your institution or the context where you work in these areas?
JR: I don't know. I mean, I could always use more funding.
VH: If the question is are we investing enough, I don't have the numbers ready, but my very personal perception is that there's not a whole lot of funding that goes into this here. Plus, I mean, the context in which we operate deals with English language learners. And if you look at the past 3 years or so with the pandemic, we’ve had a significant decrease in English language learners coming into the US that actually predates the pandemic a bit in terms of how difficult it was or has been for learners to come in so that there's also a much smaller population of learners that are coming into the US. I think that we’ve seen an uptick of that at our university certainly and I think nationwide but that has something to do with this as well. And I think this speaks to Jim’s response to the first part of the question in terms of… you know, there's just not that much out there. We have an intensive English program that at some point had 100-200 students and now it's down to single digits or maybe teens.
H: Exactly, and that’s the case in many contexts around the world. Some of our listeners, especially those who are not deeply familiar with the topic but it’s something that I hear a lot from teachers I speak with, may be concerned about some of the implications of the types of technologies that are being described in the papers in the special issue and in the field at large. The implications of, for example, machine translation, and students using that to not just to do spelling checks and grammar checks but potentially even to generate text that in some cases are almost indistinguishable from authored texts.What would you say to teachers who have question marks around the feasibility, maybe whether it’s even ethical to use these types of technologies in language education?
JR: That's a tough one. I feel… and again this goes back to the reason for the impetus for this special issue. I mean, automated writing evaluation is now a fixture, I think, on the second language writing landscape. So it's not something that we can ignore or pretend doesn't exist even if we were inclined to do so. But Robert Godwin-Jones, who writes the Emerging Technologies column for [LLT] and has a special column on AWE and intelligent writing assistance technologies in our special issue, talks about this and he talks about machine translation, automatic text generation, and basically he says these new technologies, we have to explore ways to integrate these into the classroom and this integration has to be guided by situated practice and established goals and desired outcomes. So basically I think we have to approach these things with an open mind and look for opportunities to harness their pedagogical value rather than holding onto maybe outdated notions of what writing is supposed to be and what role technology is supposed to play.
H: Volker, did you want to add anything there?
VH: I could add a few sentences here. I think Jim is right on target. What we have seen also is a little bit of a shift of AWE tools that have been used for second-language learners. Now these AWE tools are now also being integrated immensely for first language learners, for native speakers of English, for example. Jim is running a large project at Iowa State University that integrates one of those tools not just in second language classrooms but also for all the students. So I think we have to broaden the base of how we view these tools. So it's no longer just a tool for second language learning but it's also a tool for language learning, for becoming more proficient in your first language. So I think that’s an important aspect that we're trying to explore here.
H: Yeah, I think you both make a very good point, and that's something that perhaps is sometimes easy to forget, especially when you're heavily involved in the pedagogical side of things, is that the nature of of writing as you put it, Jim, itself has changed, and just like 20 years ago, people were complaining about the language of SMS messages and texting on phones and how it wasn't proper language and so on. Now perhaps we need to also reconsider what writing is and then also ask, how does that change how pedagogically we support the necessary processes and maybe that involves harnessing, using the technology for the better. And that leads me to my next question which involves the teachers. What sort of support do teachers need, in your view? I know that goes a little bit beyond the scope of the special issue, but to be able to work meaningfully with these new technologies.
JR: Teachers obviously have a very important role because there's research that shows that teachers’ attitudes and practices regarding AWE influence students’ attitudes and practices. So first of all addressing the kind of concerns that we were discussing in response to the earlier question, you know, more traditional views of the role of technology and the idea that AWE makes students lazy or undermines learning. We need to address those, and then also getting them to see the possibilities, to understand how use of AWE may contribute to second language development. I think that the empirical evidence is still out on whether you know that can actually be a factor in language learning. But I think a prevailing attitude is AWE, to the extent it focuses on grammatical and usage concerns, is just a proofreading tool. It's something that can only contribute to the polishing of the current text. Whereas in applied linguistics, we've been waiting a long time for these sorts of technologies to come along. And one of the reasons why is because of the prospect that it can actually help students improve their language proficiency. So the transfer of learning from the current writing task to subsequent writing tasks. So again, getting teachers to understand that there's a wider perspective here and that they can be partners in exploring the potential of this new and ever-expanding area of writing systems technology.
[Unfortunately, this is where Volker Hegelheimer’s internet connection gave up…]
H: Nice one. Jim, I have one final question for you. You’ve got four articles plus your own introduction plus Robert Godwin-Jones’ column in the special issue. The four articles, I read them with gusto, they were very interesting, and they led me to this completing question. Based on those contributions and your own work, where do you see the field going next, especially in terms of research in say the next few years?
JR: While we do have kind of a narrow cross-section of research in this special issue for the reasons that we were discussing earlier, but as I said, I think there is a lot of work going on out there and I'm excited about seeing this work eventually published. And I'm talking about work… you know, a lot of the research has focused on tertiary settings, so the use of AWE in college and university classrooms. I'm really interested to see how it's being used at the secondary level and maybe professional contexts as well and also systems that analyze languages other than English, I think is an important area.
H: Yeah, that’s a big gap, right? And I like what you say about the secondary level because if we are hoping that these technologies can be used more developmentally rather than just as a corrective tool then it's all the more important that learners learn how to work with these technologies in a productive, meaningful way, and the earlier you can start with that, the better, right?
JR: That’s right.
H: Before we wrap up, is there anything that either of you want to say about the special issue or about any of the particular papers that were included?
JR: Just that we're very excited. I don't mean to take anything away from the quality of the work of our four contributing sets of authors. I think it's a nice cross-section of work, and we're very grateful to them for their contributions and for working with us through the peer-review process to bring this special issue to the fore. So I hope everybody enjoys it.
H: I hope it gets as wide an audience as it deserves and hopefully this podcast will help with that. Jim and Volker, thank you so much for joining us.
JR: Thank you, Hayo.
Language Learning Technology (LLT) journal Editors-in-Chief, Dorothy Chun and Trude Heift, discuss how the journal works, its open access philosophy and tips for submitting manuscripts for review.
Links mentioned during the interview:
The Affordances and Challenges of Open-access Journals: The Case of an Applied Linguistics Journal. [Chapter 8]
About the Language Learning & Technology Journal (Transcript)
Host [H]: Welcome, all you lovely language and terrific technology people, to a very, very special first episode of Language Learning and Technology, our first ever podcast with two very, very special guests, Dorothy Chun and Trude Heift. Dorothy and Trude, welcome.
Dorothy Chun [DC]: Hello.
Trude Heift [TH]: Hello.
H: How are you both doing? You're good?
TH: Good. Thank you.
DC: Doing very well. Thanks.
H: Excellent, excellent. So today we're going to learn a little bit about this wonderful journal that you both edit. We're going to learn a little bit about the history, its aspirations. But before we do all of that, Dorothy, I'm gonna start with you. Just very briefly, where did your interest in technology and language education come from?
DC: Well, you know, I've actually always liked gadgets. I loved the IBM Selectric typewriter when it first came out. My PhD is actually in historical Germanic linguistics.
DC: But I knew early on that I really wanted to do more applied linguistics. So my dissertation was about intonation in German, Chinese and English. And I did lots of recordings of learners of Chinese and learners of German. And I had a trusty little cassette tape recorder, I would have to play, stop, rewind, play, stop, rewind, trying to listen to the tones and the intonation in the pitches. And I realized, I need better technology for this. So that was my first need that got me interested in using technology for language learning. And then I had to write this dissertation. And I know I'm dating myself, but I wrote it on an Apple II computer. And I needed 40 floppy disks for my dissertation. And I again thought, there has got to be a better way than this. So from the very beginning, I've always wanted to learn about the newest technologies to make my life easier, to make my career in applied linguistics easier.
H: Wow, that's a lovely story. My supervisor in Holland, when I was a master's student there, told me that she used a computer back in the 70s. I suppose that you still had to use kind of paper cards that you inserted into them. I never actually used one of them. She was telling me that she was riding home on her bicycle–I mean, this is Holland, after all–and that a big gust of wind came and threw all of her computer cards and everything out over the road and she lost her dissertation there. So yeah, good reason. All right. Trude, how about you?
TH: I actually ended up in computational linguistics for my master's degree already and then found a wonderful supervisor with whom I did my PhD focusing on computational linguistics and applied linguistics by designing programs for language learning.
TH: And when you talk about years ago, so my supervisor, he graduated with his PhD in the early 80s. Naturally, computational linguistics involves parsing, and he told me that in the 80s, they would submit a sentence in the evening and then could pick up the parse the next morning. That tells you how computer speed and volume have increased over the decades.
H: Yes, indeed. Yeah. And it's having a massive impact on so many and so many levels, hasn't it? Right. Okay, Dorothy. I'm sure our readers are interested to hear a little bit about the background of this journal. Where did it come from?
DC: Well, Language Learning and Technology was founded in 1997 by Mark Warschauer. He was actually a graduate student at the time, but he had the foresight and the courage to begin this completely open access, fully online journal. And it was the first fully online journal for CALL. It has remained completely open access and fully online. It has been supported by three different National Language Resource Centers. One that has been the ongoing center is the one at the University of Hawaii. And they have been a sponsor for the 25+ years that LLT has been in existence. The other two centers are one at MSU, Michigan State, and most recently, University of Texas at Austin. The bulk of the funding from these Centers is really for a graduate student who is the editorial assistant or the managing editor, whatever you want to call that person, and for a webmaster. One of the, I think, key features of our journal is that authors always retain copyright of their work. So unlike the commercial journals where the journal owns the copyright, our authors own their own copyright. Since 2020, though, we've switched to a Creative Commons license for all the work published in LLT. If you want more of the gory details, Trude and I wrote a chapter for Carl Blyth and Joshua Thoms' open access book. The title of that book is Open Education and Second Language Learning and Teaching. We will provide a link for that book in which our chapter appears on the LLT website. And if there's a way to link it to this podcast, we will also do that.
H: That's great. Great. You've mentioned open access, and the journal being the first in our field to provide open access. You've also mentioned the Creative Commons. Trude, can you tell us a little bit more about this because not all listeners may know what these terms exactly entail.
TH: When you look at open access, a journal like that has huge advantages over articles being published by a journal. So one of them is, for instance, broad dissemination, meaning that the works that are published are not only accessible by developed countries but also by developing countries, and therefore you have a huge distribution without having them require huge subscription fees. For the journal itself, there are advantages such as you can track readership, quantitatively and geographically. You can actually see where your readers come from. Naturally like with any online environment, you have ease of hyperlinks, content that can be linked within an article. You have an unlimited virtual space. And LLT lately has implemented a rollout model, meaning that unlike a print journal, where you have to wait until the next issue comes out, LLT now publishes articles as they are processed without waiting two years for an article to come out. And that naturally is a huge advantage for authors. So we just started that in January and are basically still working on a backlog. But we are catching up. And these articles are now appearing as they get processed.
DC: I just wanted to add one point about open access. There are journals today that will say, okay, these articles are open access, but they require APCs, or author processing charges. And that is very different from our journal, which has no author charges. So it's free to the authors, and it's free to the readers. And that's different from some of the models of publishers that will charge these APCs, allow the readers to read it for free, but the authors are actually paying a fee.
H: Exactly, which potentially excludes a large number of people from publishing their work in widely read journals like LLT. So that's a really important point about this journal. Dorothy, there are different types of submissions that authors can make to the journal, different types of articles that get published. Can you just briefly talk us through what they are?
DC: Yes, well, the bulk of the items are, of course, the research articles, and they are first processed by our managing editor, who at the moment is Skyler Smela. These submissions are then vetted by the two editors-in-chief, namely Trude and myself. And then if they pass the initial review and we think they're worthy of an external review, we assign them to one of the eight associate editors, and our associate editors at present are Philip Hubbard, Meei-Ling Liaw, Lara Lomicka Anderson, yourself–Hayo Reinders, Jonathon Reinhardt, Jim Ranalli, Shannon Sauro, and Nina Vyatkina. So in addition to these regular articles, we also publish special issues on cutting-edge topics. And these special issues are guest-edited by experts on these particular topics. They are also fully vetted research articles. In addition to the research articles, we have book and multimedia reviews, currently edited by Ruslan Suvorov. We also have an emerging technologies column, and this is edited by Bob Godwin-Jones. His articles historically have been the most popular and the most downloaded of all of the pieces in LLT, and they deal with the cutting-edge technologies. And he also reports on emerging research on these technologies. Obviously, they're not fully expanded research articles, but he gives a flavor of what is to come and the important things to be thinking about. We also have forums. There's a forum on language teaching and technology. There's also a newish forum on language teacher education and technology. The former is edited by Greg Kessler, and the one on teacher education is edited by Mimi Li. These are shorter, more teacher-focused articles. And so those are the main types of pieces that we publish in LLT.
H: Brilliant. Okay, that's very helpful. So I guess the next question is the one that some of our listeners have been desperately waiting for. And we're going to ask you, Trude, how do I get my work published in this journal? What is the secret advice you can give me as editors?
TH: Well, there are a few tips I can give potential authors. Frst of all, I think what applies to every journal is you gotta select an appropriate journal, meaning we sometimes get submissions that do not include technology. They naturally get rejected because our journal is all about language learning and technology. With regards to writing the actual article, draft an introduction by focusing on: What is the problem? What is the question I want to answer? Then sometimes we get articles with very lengthy literature reviews. Well, provide a focused literature review that basically focuses on: What do we already know? What is your contribution? And then list or define your succinct research questions. What does this article actually want to answer? And they should be really precise and, at the same time, comprehensive. Then comes the methodology. Describe it in detail. Who are the participants? How were the data collected? And then describe the methods. Provide details of the materials, instruments and interventions, and especially how learning was measured. Because in the end, we are interested in getting a contribution that empirically measures how these learning outcomes were achieved. Then we report on the results. Provide the actual data, what was in the pretest, post tests and so on–you may include it as an appendix–and then you report on the results. And naturally, very important, you have to contextualize those results with regards to previous findings. How do the results you obtained actually fit in the existing literature? Is there something new we found? Or is it in contradiction to previous results? Focus on that one, and then conclude with your contribution. And the very last step is you would write an abstract. We often get submissions, and they might be rejected, or at least authors will be asked to have it proofread by somebody familiar with academic prose. Because naturally, we don't want to send out an article to reviewers which is not polished because reviewers really don't appreciate that. And stick to the word limits we are providing on our submission guidelines. Again, a reviewer doesn't want to read an article with 15,000 words if the word limit is 8,500. And that's basically all what authors should pay attention to. We have a few slides also on our website which outline this in some detail and which authors could consult.
H: Very nice. And we'll make sure to include that in the show notes, and people can can look that up. I think that's a very helpful set of guidelines. Now, despite the fact that a lot of authors do follow those guidelines, more or less, Dorothy, we have, as a journal, a really quite low acceptance rate. So leaving aside issues with somebody submitting an article that is really outside of the scope of the journal and so on, what are some of the reasons for that low acceptance rate?
DC: Well, one of the key requirements of LLT is that articles have to include empirical research that focuses on learning outcomes. And we have this in our guidelines for contributors, but not everyone reads this or takes it to heart because we receive many articles that present the results of surveys or questionnaires, for example, questionnaires about teacher or learner attitudes about using a particular technology, did they think it helps them to learn, and that is actually not empirical data that focuses on learning outcomes. And so that is one of the most common reasons that we reject articles and do not even send them for external review. We also do not accept descriptions of, you know, using technology in the classroom, or if they don't include learning outcomes. So it's not enough to describe this great online course that you just did during the pandemic or whatever it was. But if you don't focus on measuring actual learning outcomes, that is another reason that we would reject it initially and not even send it for external review. Trude mentioned that sometimes the literature review is too long and unfocused. Well, sometimes it's often too short and it doesn't contain CALL references to CALL articles. You'd be surprised how many articles we get where authors have not cited relevant literature from CALL journals. Another reason for rejecting articles often in the initial phase is that they don't have a solid research methodology. Some don't have a control group, for example, or they haven't controlled for relevant variables. And this is something that, in this day and age, is a little bit surprising. Also, in this day and age, you know, we're not so much trying to compare using technology with not using technology, we've gone way beyond that, to the point where we are looking for what are the particular affordances of a given technology for learning. And we're not trying to compare using paper and pencil versus using VR, for example. That's just a sort of gross example. Another reason that I often will reject a submission outright is that they focus so heavily on the statistics, pages and pages of this statistic and that statistic, but they haven't really defined the instruments and they haven't really analyzed what these statistics are telling us about the kind of learning that took place. And then finally, well, two more things. One is that, you know, because of all these statistics, they are not analyzing deeply enough. And so maybe that was redundant, maybe I just said that. Then the final item is that some submissions lack a discussion of the pedagogical implications. So they're so heavily focused on the statistics that they fail to help the readers understand. Okay, this is what these statistics have told us about learning. And this is how we're going to implement this in our teaching practices, in our classroom instruction, or out of classroom instruction for that matter.
H: Yeah, that's a good final point, perhaps to emphasize. That's also something that I know I myself and colleagues on the editorial board look for is the answer to the question: So what? What is this article actually trying to say? What does it mean? What are the implications or interpretations that can be drawn from this? Has it been put into a broader context and so on? And I think that's worth emphasizing to the listeners. I think between the two of you, you've given some really good guidelines. Now just looking towards the future. Trude, what is on the horizon for the journal? Any exciting new developments that we need to know about?
TH: Well, LLT never stops thinking. And this is just due to the wonderful team we have. Hardly a week goes by without getting suggestions from someone, be it an associate editor, be it our managing editor or our sponsors. We are really pleased about our new rollout model, which we have now in place. But I think what we are looking at currently is how to improve our presence on social media. How can we put LLT out there even more than it is already at this point? So one of these is, for instance, the wonderful podcast you're doing with us because this is one way of reaching out, and in another way, than just providing a paragraph on what we are doing. The podcasts and also maybe some interviews with current authors publishing their work in LLT, or we have our wonderful special issues editors who report on a particular topic by including articles. So it would be nice to actually include some videos there where we interview our special issues editors and give us a little bit of insight on where that particular topic is currently at. So these are some ideas. Naturally, mostly we are all volunteers and our time is limited. We try and move forward as we can. But with a big team like LLT, it is certainly possible to always innovate, what I think we have been doing in the past.
H: Wonderful, yes. And I think this is a good place as any to invite our listeners and the readers of the journal to also send us their ideas. You know, where would you like to see us be more present is, you know, is it on Facebook or Twitter or Instagram? Or, you know, where do you consume your academic information? And how can we play a role in that? And also let us know what you think about this podcast? Is it helpful? Is it interesting? Should we do something else? We'd love to hear from you. Thank you both so much for making the time to join this podcast, Dorothy and Trude, and most importantly, thank you both so much for your hard work because as you've said, and you didn't say it about yourself, so I will. A lot of the work that we do, a lot of the work that you do, is volunteer work, and it's really thanks to contributions like the ones that you make that we can have this wonderful community, this resource for teachers and researchers and students around the world. So thank you both very much for joining and for your hard work.
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