Talip Gönülal

Hey Alexa, what is the future of language learning?

May 2023

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Hey Alexa, what is the future of language learning?

Recent years have witnessed an unprecedented level of improvements in human-computer interaction (HCI), and IPAs are great examples of this. IPAs, with different names (e.g., conversational agents, voice-controlled agents, virtual personal assistants or smart speakers) and on different devices (e.g., smartphones, tablets and smart home devices), have recently become part of daily life for many people. The most well-known IPAs include Apple Siri, Google Assistant, Microsoft Cortana, Amazon Alexa and Samsung Bixby.

Although the IPAs were initially deployed on smartphones and tablets, they have recently shifted to homebuyers with their own hardware devices such as Apple HomePod, Amazon Echo and Google Home. As an effective example of HCI, IPAs make use of a set of technologies including speech recognition, speech synthesis, natural language processing and dialog management, which are all supported by AI. Through voice interaction with users, IPAs are able to fulfil a wide range of daily tasks, including scheduling meetings, setting alarms and reminders, getting weather data, booking hotels, reading news, paying bills, streaming music and many more, on behalf of users. According to the latest market analysis report by Korad, Rake and Kumar (2020), the IPA market is estimated to grow at the rate of 37.7% over the 2020-2027 period. This means that more users will start using IPAs in the near future.

Apart from the usability of IPAs for daily routines, they have lately drawn scholarly attention in educational contexts, especially in the field of L2 learning, probably because IPAs can simulate some levels of real-life conversations in the target language with the users. A slowly growing number of L2 studies (Chen et al., 2020; Dizon, 2017, 2020; Moussali & Cardoso, 2016, 2020; Tai & Chen, 2020; Underwood, 2017) have reported that IPAs and similar technologies could be highly effective in providing more personalized and learner-centered language learning materials to language learners both inside and outside the classroom.

To read more on this, please see my recent article:

Gonulal, T. (2021). Investigating EFL learners’ humorous interactions with an intelligent personal assistant. Interactive Learning Environments, p-p. [link]

How effective are podcasts and vodcasts in improving L2 listening skills?


In line with the pervasiveness of digital devices, many new digital avenues for L2 listening are now available for language learners and educators. Podcasts and vodcasts are just two of them. Podcasts are the audio files that are distributed over the Internet through subscription. Vodcasts are the podcasts with video content instead of audio. As of January 2020, there were more than 850,000 podcasts and 30 million episodes available around the globe, and these numbers are increasing daily (Winn, 2020). Apart from the increasing number of podcasts and vodcasts, the themes and topics addressed in them vary far and wide. Further, podcasts and vodcasts can provide authentic, contemporary, culturally-rich, and easily accessible materials, which makes them a highly useful and practical language learning resource. L2 learners can listen to a variety of podcasts and vodcasts of their own choice in their own time outside the classroom. Furthermore, podcasting and vodcasting technology provides L2 learners with rich and authentic listening input which is highly needed in EFL contexts. Additionally, L2 learners, depending on their needs and levels, can obtain the utmost use out of podcast- and vodcast-based listening practice by controlling the listening input with pause, replay and slow-down options (Alm, 2013).

To read more on this topic, please see my recent article :

Gonulal, T. (2020). Improving listening skills with extensive listening using podcasts and vodcasts. International Journal of Contemporary Educational Research, 7(1), 311-320. [link]

Learning Languages InforMALLy: The Effectiveness of Mobile-Assisted Language Learning (MALL)



Recent years have witnessed an upsurge of interest in mobile-assisted language learning (MALL). MALL can take language learning out of the classroom and enable anytime, anywhere language learning (Kukulska-Hulme, 2009; Kukulska-Hulme & Shield, 2008). Although there is a considerable body of research examining the potentials of MALL in language learning (Burston, 2015), relatively little research to date has focused on MALL end-users’ experiences. In a recent study, I’ve attempted to investigate language learners’ experiences in using MALL apps for informal language learning purposes as well as their opinions about the effectiveness of MALL. I recruited the participants through online networks, websites, listservs and communities. Results showed that participants varied in their experiences of MALL use: 30% with less than one-month MALL experience, 24% one month to three months, 21% four to six months, 14% seven months to one year, and 11% more than one year. Not surprisingly, smartphones were the most commonly used mobile devices, followed by tablets. In addition, Duolingo appeared to be the most popular MALL app preferred by language learners. Majority of the participants found MALL apps highly effective, especially in learning vocabulary and basic features of the target language. Further, most participants reported that they began to use MALL apps simply because they offered more flexibility and spontaneity when it came to learning languages. However, several participants stated that although MALL could be a valuable learning aid, it could not fully replace the traditional, face-to-face, formal language learning. Further, one particular issue that most participants had was to stay committed to the language learning process since the level of motivation tended to decrease over the time.

Other relevant papers:

Gonulal, T. (2019). The development and validation of an attitude towards MALL instrument. Educational Technology Research and Development67(3), 733-748. [link]

Gonulal, T. (2019). The use of Instagram as a mobile-assisted language learning tool. Contemporary Educational Technology, 10(3), 309-323. [link]

How SLAtistically knowledgeable are we?


Finally, our paper on the state of statistical knowledge in the field of SLA is out! Here is the abstract:

“Despite the prevalence of quantitative approaches in applied linguistics (AL) and second language acquisition (SLA) research (Gass, 2009), evidence indicates a need for improvement in analyzing and reporting SLA data (e.g., Larson-Hall & Plonsky, 2015). However, to improve quantitative research, researchers must possess the statistical knowledge necessary to conduct quality research. This study assesses AL and SLA researchers’ knowledge of key statistical concepts on a statistical knowledge test. One hundred and ninety-eight AL and SLA researchers from North America and Europe responded to 26 discipline-specific questions designed to measure participants’ ability to (a) understand basic statistical concepts and procedures, (b) interpret statistical analyses, and (c) critically evaluate statistical information. Results indicate that participants generally understood basic descriptive statistics, but performance on items requiring more advanced statistical knowledge was lower. Quantitative research orientation, number of statistics courses taken, and frequent use of statistics textbooks had positive influences on researchers’ statistical knowledge.”

Here is the full paper:

Loewen, S., Gonulal, T., Isbell, D. R., Ballard, L., Crowther, D., Lim, J., Maloney, J., & Tigchelaar, M. (2019). How knowledgeable are applied linguistics and SLA researchers about basic statistics? Data from North America and Europe. Studies in Second Language Acquisition. [link]

Other relevant papers:

Gonulal, T. (2020). Statistical knowledge and training in second language acquisition: The case of doctoral students. ITL – International Journal of Applied Linguistics, 171(1), 62-89. [link]

Gonulal, T. (2019). Missing data management practices in L2 research: The good, the bad and the ugly. Erzincan University Education Faculty Journal, 21(1), 56-73. [link]

Gonulal, T. (2018). An investigation of the predictors of statistical literacy in second language acquisition. Eurasian Journal of Applied Linguistics, 4(1), 49-70. [link]

Gonulal, T., Loewen, S.., & Plonsky, L. (2017). The development of statistical literacy in applied linguistics graduate students. ITL – International Journal of Applied Linguistics, 168(1), 4-32[pdf]


A new research method for SLA researchers: Scoping Review!

scoping review.png

The field of SLA and applied linguistics has witnessed a rapid growth in the number of studies employing research synthesis and meta-analysis over the years—but most of the time, these different methods have been used interchangeably.  One reason for this confusion might be the availability of many relevant yet different terminologies (e.g., systematic review, research synthesis, meta-analysis, methodological synthesis; just to name a few). A new research method slowly finding its way in L2 research is ‘scoping review’. A scoping review shares so many similarities with research synthesis but obviously there are certain differences. Mays, Roberts and Popay (2001) argue that scoping review is useful for “mapping rapidly the key concepts underpinning a research area and the main sources and types of evidence available, and can be undertaken as stand-alone projects in their own right, especially where an area is complex or has not been reviewed comprehensively before (p. 194). Similarly, Arksey and O’Malley (2005) list four common reasons to employ scoping review: (1) to examine the extent, range and nature of research activity; (2) to determine the value of undertaking a full systematic review; (3) to summarize and disseminate research findings; (4)to identify research gaps in the existing literature.

To read up on an example scoping review study in L2 research, please see Gurzynski-Weiss, and Plonsky’s (2017) chapter entitled “Look who’s interacting: A scoping review of research involving non-teacher/non-peer interlocutors.”


A version of Erdős Number in SLA?


Probably, a few, if not many, of you have heard of the name Paul Erdős. He was a Hungarian-born, phenomenal mathematician who loved only numbers. His research interests centered mostly around number theory and combinatorics. He was also one of the most prolific mathematicians of the 20th century. He has more than 1,500 mathematical papers! It is simply amazing! What is even more interesting is that he did a lot of joint research, collaborating more than 500 authors, in a field where collaboration is said to be very scarce. His immense joint research has given rise to the Erdős number project. Basically, an Erdős number indicates the collaborative distance between an author and Erdős. So, those who directly co-authored with Erdős have Erdős number 1. And those who co-authored a paper with someone with Erdős number 1 (but not with Erdős himself) have Erdős number 2, and so forth. Erdős’s Erdős number is 0. I think this is a great way to honor Erdős and to boost the collaborative work in the field at the same time. Given that, I think it would be interesting to have a similar project in the field of second language acquisition and applied linguistics. For instance, how does Gass number sound? (Professor Susan Gass is one of the leading L2 scholars, with a lot of collaborative work). My Gass number is 2 for now. What is your Gass number?

Missing Impossible: A review of missing data management in L2 research


Missing data occur in almost any research we conduct, but a more important point is whether and how we should deal with missing data. Most commonly, researchers simply ignore the missingness or use the classic methods of listwise deletion or mean substitution. However, in many cases neither of these produces good results. The two important steps in missing data management are to determine the type and amount of missing data. In particular, the type of the missingness allows us to determine how to handle the missing data. However, there are limited discipline-specific sources on handling missing data in the fields of SLA and AL. Hopefully, I am going to soon provide a picture of the current practices for missing data management in L2 research. Stay tuned for the details of this study!

Factor Analysis for L2 Research

FA figure

In the field of SLA, L2 researchers have begun to employ more advanced and sophisticated statistical methods over the years. Factor analysis (FA) is a such method. FA is a series of complex structure-analyzing procedures . Although it is mostly used with survey data, it can also be used for hypothesis testing, instrument development, or summarizing patterns of interrelationships.There are two broad types of FA: confirmatory factor analysis (CFA) and exploratory factor analysis (EFA). CFA is generally preferred when researchers have a specific expectation regarding the number and nature of factors whereas EFA is, as its names suggests, exploratory in nature. Along with the differences between CFA and EFA, there is some ambiguity in the terminology used within EFA. EFA is often used as umbrella term covering both principal components analysis (PCA) and EFA, although there are two schools of thought on the differences between PCA and EFA. In addition to the terminological ambiguity, there are several decision points that researchers need to consider  when conducting FA such as the decisions on the appropriateness of FA (factorability of data), the factor extraction model used, the factor retention criteria, the factor rotation methods, and the interpretation of the factor solution. Because of its complexity and availability of many options in each step, the state of the art of FA in SLA is less than optimal. Fortunately, a number of discipline-specific advanced stats sources have been introduced in recent years. Let’s factor analyze!

Further readings:

Loewen, S., & Gonulal, T. (2015). Exploratory factor analysis and principal components analysis. In Plonsky, L. (Ed), Advancing quantitative methods in second language research. New York: Routledge. [pdf]

Plonsky, L., & Gonulal, T. (2015). Methodological synthesis in quantitative L2 research: A review of reviews and a case study of exploratory factor analysis. Language Learning, 65, (S1), 9-36. [pdf]

Bandalos D. L., & Boehm-Kaufman M. R. (2009). Four common misconceptions in exploratory factor analysis. In C. E. Lance & R. J. Vandenberg (Ed.), Statistical and methodological myths and urban legends: Doctrine, verity and fable in the organizational and social sciences. New York: Routledge.

Michigan Stats University


The field of second language acquisition (SLA) doesn’t have a long history as a discipline but the use of quantitative research methods and statistics in the field has increased considerably since the field’s inception. Although qualitative-oriented research is slowly increasing, most SLA research today relies on stats in some form or another. Recently, the status quo of quantitative research methods has received some scholarly and editorial attention. Now, more researchers are interested in the current state of statistical literacy in the field. I am so happy that the Second Language Studies program here at Michigan Stats(!) University is playing a pioneering role in this area of research. My advisor, Dr. Shawn Loewen, I and some other stats aficionados in the program are investigating the development and assessment of statistical literacy among SLA researchers. In a few years from now, we might see a couple of studies addressing this research topic in SLA journals.

Rasch Hour!

Rasch analysis map task

Rasch hour time, dear fellow slatisticians! Yes, we need to Rasch! Rasch analysis is a statistical method used for estimating the probability of success of particular persons on particular items. These probabilities are expressed in logits. The Rasch models can be applied to a wide range of data types. The Rasch family consists of the simple Rasch model (can only be used to analyze dichotomous data such as right or wrong), the rating scale model (handles data with rating scales such as likert-scale items), the partial credit model (in a writing or speaking test) and the many-facet Rasch model (suitable for analyzing multiple aspects of testing such as the test location, the experience of raters and so on). Some of the modern software packages used for Rasch models are Winsteps, Facets, RUMM, and Conquest. Probably one of the most useful output from a Rasch analysis is item/person map (aka Wright Map). This map (see the figure above) provides a vertical histogram describing the representation of persons and items on the same measurement scale. The Rasch models are slowly finding their way into L2 research. I am planning to use a Rasch model for my dissertation. I will update this post as I learn something new about the Rasch family…stay tuned!

Further reading:

Knoch, U., & McNamara, T. (2015). Rasch analysis. In L. Plonsky (Ed.), Advancing quantitative methods in second language research (pp. 275-304). New York: Routledge.

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