Pub Date : 2022-06-01Epub Date: 2022-04-04DOI: 10.1007/s11524-021-00601-7
Nevo Itzhak, Tomer Shahar, Robert Moskovich, Yuval Shahar
The effect of socio-economic factors, ethnicity, and other factors, on the morbidity and mortality of COVID-19 at the sub-population-level, rather than at the individual level, and their temporal dynamics, is only partially understood. Fifty-three county-level features were collected between 4/2020 and 11/2020 from 3,071 US counties from publicly available data of various American government and news websites: ethnicity, socio-economic factors, educational attainment, mask usage, population density, age distribution, COVID-19 morbidity and mortality, presidential election results, and ICU beds. We trained machine learning models that predict COVID-19 mortality and morbidity using county-level features and then performed a SHAP value game theoretic importance analysis of the predictive features for each model. The classifiers produced an AUROC of 0.863 for morbidity prediction and an AUROC of 0.812 for mortality prediction. A SHAP value-based analysis indicated that poverty rate, obesity rate, mean commute time, and mask usage statistics significantly affected morbidity rates, while ethnicity, median income, poverty rate, and education levels heavily influenced mortality rates. Surprisingly, the correlation between several of these factors and COVID-19 morbidity and mortality gradually shifted and even reversed during the study period; our analysis suggests that this phenomenon was probably due to COVID-19 being initially associated with more urbanized areas and, then, from 9/2020, with less urbanized ones. Thus, socio-economic features such as ethnicity, education, and economic disparity are the major factors for predicting county-level COVID-19 mortality rates. Between counties, low variance factors (e.g., age) are not meaningful predictors. The inversion of some correlations over time can be explained by COVID-19 spreading from urban to rural areas.
{"title":"The Impact of US County-Level Factors on COVID-19 Morbidity and Mortality.","authors":"Nevo Itzhak, Tomer Shahar, Robert Moskovich, Yuval Shahar","doi":"10.1007/s11524-021-00601-7","DOIUrl":"10.1007/s11524-021-00601-7","url":null,"abstract":"<p><p>The effect of socio-economic factors, ethnicity, and other factors, on the morbidity and mortality of COVID-19 at the sub-population-level, rather than at the individual level, and their temporal dynamics, is only partially understood. Fifty-three county-level features were collected between 4/2020 and 11/2020 from 3,071 US counties from publicly available data of various American government and news websites: ethnicity, socio-economic factors, educational attainment, mask usage, population density, age distribution, COVID-19 morbidity and mortality, presidential election results, and ICU beds. We trained machine learning models that predict COVID-19 mortality and morbidity using county-level features and then performed a SHAP value game theoretic importance analysis of the predictive features for each model. The classifiers produced an AUROC of 0.863 for morbidity prediction and an AUROC of 0.812 for mortality prediction. A SHAP value-based analysis indicated that poverty rate, obesity rate, mean commute time, and mask usage statistics significantly affected morbidity rates, while ethnicity, median income, poverty rate, and education levels heavily influenced mortality rates. Surprisingly, the correlation between several of these factors and COVID-19 morbidity and mortality gradually shifted and even reversed during the study period; our analysis suggests that this phenomenon was probably due to COVID-19 being initially associated with more urbanized areas and, then, from 9/2020, with less urbanized ones. Thus, socio-economic features such as ethnicity, education, and economic disparity are the major factors for predicting county-level COVID-19 mortality rates. Between counties, low variance factors (e.g., age) are not meaningful predictors. The inversion of some correlations over time can be explained by COVID-19 spreading from urban to rural areas.</p>","PeriodicalId":47046,"journal":{"name":"Recall","volume":"13 1","pages":"562-570"},"PeriodicalIF":4.3,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8979577/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78981265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-23DOI: 10.1017/S0958344022000076
M. Cappellini, Yu-Yin Hsu
Abstract Drawing on existing research with a holistic stance toward multimodal meaning-making, this paper takes an analytic approach to integrating eye-tracking data to study the perception and use of multimodality by teachers and learners. To illustrate this approach, we analyse two webconference tutoring sessions from a telecollaborative project involving pre-service teachers and learners of Mandarin Chinese. The tutoring sessions were recorded and transcribed multimodally, and our analysis of two types of conversational side sequences shows that the integration of eye-tracking data into an ecological approach provides richer results. Specifically, our proposed approach provided a window on the participants’ cognitive management of graphic and visual affordances during interaction and uncovered episodes of joint attention.
{"title":"Multimodality in webconference-based language tutoring: An ecological approach integrating eye tracking","authors":"M. Cappellini, Yu-Yin Hsu","doi":"10.1017/S0958344022000076","DOIUrl":"https://doi.org/10.1017/S0958344022000076","url":null,"abstract":"Abstract Drawing on existing research with a holistic stance toward multimodal meaning-making, this paper takes an analytic approach to integrating eye-tracking data to study the perception and use of multimodality by teachers and learners. To illustrate this approach, we analyse two webconference tutoring sessions from a telecollaborative project involving pre-service teachers and learners of Mandarin Chinese. The tutoring sessions were recorded and transcribed multimodally, and our analysis of two types of conversational side sequences shows that the integration of eye-tracking data into an ecological approach provides richer results. Specifically, our proposed approach provided a window on the participants’ cognitive management of graphic and visual affordances during interaction and uncovered episodes of joint attention.","PeriodicalId":47046,"journal":{"name":"Recall","volume":"34 1","pages":"255 - 273"},"PeriodicalIF":4.5,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43489169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-16DOI: 10.1017/S0958344022000088
Chen Chi, H. Chen, Wen-Ta Tseng, Yeu-Ting Liu
Abstract Video materials require learners to manage concurrent verbal and pictorial processing. To facilitate second language (L2) learners’ video comprehension, the amount of presented information should thus be compatible with human beings’ finite cognitive capacity. In light of this, the current study explored whether a reduction in multimodal comprehension scaffolding would lead to better L2 comprehension gain when viewing captioned videos and, if so, which type of reduction (verbal vs. nonverbal) is more beneficial. A total of 62 L2 learners of English were randomly assigned to one of the following viewing conditions: (1) full captions + animation, (2) full captions + static key frames, (3) partial captions + animation, and (4) partial captions + static key frames. They then completed a comprehension test and cognitive load questionnaire. The results showed that while viewing the video with reduced nonverbal visual information (static key frames), the participants had well-rounded performance in all aspects of comprehension. However, their local comprehension (extraction of details) was particularly enhanced after viewing a key-framed video with full captions. Notably, this gain in local comprehension was not as manifest after viewing animated video content with full captions. The qualitative data also revealed that although animation may provide a perceptually stimulating viewing experience, its transient feature most likely taxed the participants’ attention, thus impacting their comprehension outcomes. These findings underscore the benefit of a reduction in nonverbal input and the interplay between verbal and nonverbal input. The findings are discussed in relation to the use of verbal and nonverbal input for different pedagogical purposes.
{"title":"Efficacy of different presentation modes for L2 video comprehension: Full versus partial display of verbal and nonverbal input","authors":"Chen Chi, H. Chen, Wen-Ta Tseng, Yeu-Ting Liu","doi":"10.1017/S0958344022000088","DOIUrl":"https://doi.org/10.1017/S0958344022000088","url":null,"abstract":"Abstract Video materials require learners to manage concurrent verbal and pictorial processing. To facilitate second language (L2) learners’ video comprehension, the amount of presented information should thus be compatible with human beings’ finite cognitive capacity. In light of this, the current study explored whether a reduction in multimodal comprehension scaffolding would lead to better L2 comprehension gain when viewing captioned videos and, if so, which type of reduction (verbal vs. nonverbal) is more beneficial. A total of 62 L2 learners of English were randomly assigned to one of the following viewing conditions: (1) full captions + animation, (2) full captions + static key frames, (3) partial captions + animation, and (4) partial captions + static key frames. They then completed a comprehension test and cognitive load questionnaire. The results showed that while viewing the video with reduced nonverbal visual information (static key frames), the participants had well-rounded performance in all aspects of comprehension. However, their local comprehension (extraction of details) was particularly enhanced after viewing a key-framed video with full captions. Notably, this gain in local comprehension was not as manifest after viewing animated video content with full captions. The qualitative data also revealed that although animation may provide a perceptually stimulating viewing experience, its transient feature most likely taxed the participants’ attention, thus impacting their comprehension outcomes. These findings underscore the benefit of a reduction in nonverbal input and the interplay between verbal and nonverbal input. The findings are discussed in relation to the use of verbal and nonverbal input for different pedagogical purposes.","PeriodicalId":47046,"journal":{"name":"Recall","volume":"35 1","pages":"105 - 121"},"PeriodicalIF":4.5,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46499136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-28DOI: 10.1017/S0958344022000027
Olov Engwall, José Lopes, Ronald Cumbal, Gustav Berndtson, Ruben Lindström, Patrik Ekman, Eric Hartmanis, Emelie Jin, Ella Johnston, Gara Tahir, Michael Mekonnen
Abstract This article focuses on designing and evaluating conversation practice in a second language (L2) with a robot that employs human spoken and non-verbal interaction strategies. Based on an analysis of previous work and semi-structured interviews with L2 learners and teachers, recommendations for robot-led conversation practice for adult learners at intermediate level are first defined, focused on language learning, on the social context, on the conversational structure and on verbal and visual aspects of the robot moderation. Guided by these recommendations, an experiment is set up, in which 12 pairs of L2 learners of Swedish interact with a robot in short social conversations. These robot–learner interactions are evaluated through post-session interviews with the learners, teachers’ ratings of the robot’s behaviour and analyses of the video-recorded conversations, resulting in a set of guidelines for robot-led conversation practice: (1) societal and personal topics increase the practice’s meaningfulness for learners; (2) strategies and methods for providing corrective feedback during conversation practice need to be explored further; (3) learners should be encouraged to support each other if the robot has difficulties adapting to their linguistic level; (4) the robot should establish a social relationship by contributing with its own story, remembering the participants’ input, and making use of non-verbal communication signals; and (5) improvements are required regarding naturalness and intelligibility of text-to-speech synthesis, in particular its speed, if it is to be used for conversations with L2 learners.
{"title":"Learner and teacher perspectives on robot-led L2 conversation practice","authors":"Olov Engwall, José Lopes, Ronald Cumbal, Gustav Berndtson, Ruben Lindström, Patrik Ekman, Eric Hartmanis, Emelie Jin, Ella Johnston, Gara Tahir, Michael Mekonnen","doi":"10.1017/S0958344022000027","DOIUrl":"https://doi.org/10.1017/S0958344022000027","url":null,"abstract":"Abstract This article focuses on designing and evaluating conversation practice in a second language (L2) with a robot that employs human spoken and non-verbal interaction strategies. Based on an analysis of previous work and semi-structured interviews with L2 learners and teachers, recommendations for robot-led conversation practice for adult learners at intermediate level are first defined, focused on language learning, on the social context, on the conversational structure and on verbal and visual aspects of the robot moderation. Guided by these recommendations, an experiment is set up, in which 12 pairs of L2 learners of Swedish interact with a robot in short social conversations. These robot–learner interactions are evaluated through post-session interviews with the learners, teachers’ ratings of the robot’s behaviour and analyses of the video-recorded conversations, resulting in a set of guidelines for robot-led conversation practice: (1) societal and personal topics increase the practice’s meaningfulness for learners; (2) strategies and methods for providing corrective feedback during conversation practice need to be explored further; (3) learners should be encouraged to support each other if the robot has difficulties adapting to their linguistic level; (4) the robot should establish a social relationship by contributing with its own story, remembering the participants’ input, and making use of non-verbal communication signals; and (5) improvements are required regarding naturalness and intelligibility of text-to-speech synthesis, in particular its speed, if it is to be used for conversations with L2 learners.","PeriodicalId":47046,"journal":{"name":"Recall","volume":"34 1","pages":"344 - 359"},"PeriodicalIF":4.5,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48588653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-18DOI: 10.1017/S0958344022000040
Shona Whyte
May is here and with it comes a new issue of ReCALL for 2022. This time, we have seven papers covering three broad topics: the first involves automatic L2 proficiency assessment, the second, mobile-assisted language learning (MALL), and the third, L2 vocabulary acquisition, particularly the role of audiovisual materials. Several papers draw on Paivio’s (1971) dual processing theory, according to which “knowledge representation in verbal and visual modes may facilitate processing and therefore aid understanding and retention of knowledge more effectively than representations depending on a single mode” (Sato, Lai & Burden). There is also some overlap between the MALL and vocabulary studies, since the MALL meta-analysis by Burston and Giannakou reveals that by far the most common MALL learning objective is, in fact, lexical learning. Similarly, Lin’s paper on a new web-based app focusing on formulaic expressions in YouTube videos sits at the intersection of MALL and learning of vocabulary and phraseology. As you will read, the studies include a range of methodologies and research designs, from meta-analysis (Burston & Giannakou; Yu & Trainin), to survey (Puebla, Fievet, Tsopanidi & Clahsen), experimental study (Dziemianko; Sato et al.) and computer modelling (Gaillat et al.), through to research and development (Lin). Our first paper shows that collaborative research between universities in Paris and Galway is making headway in the complex area of automatic L2 proficiency assessment by developing AI systems to analyse learners’ writing samples and assign them to appropriate CEFR proficiency levels. The research by Thomas Gaillat, Andrew Simpkin, Nicolas Ballier, Bernardo Stearns, Annanda Sousa, Manon Bouyé and Manel Zarrouk focuses on machine learning from a large corpus of Cambridge and Education First essays, using linguistic microsystems constructed around L2 functions such as modals of obligation, expressions of time, and proforms, for instance, in addition to more traditional measures of complexity involving lexis, syntax, semantics, and discourse features. After training on some 12,500 English L2 texts written by around 1,500 L1 French and Spanish examinees, which had been assigned to one of the six CEFR levels by human raters, the AI system reached 82% accuracy in identifying writers’ proficiency levels. It also identified specific microsystems associated with learners at level A (nominals, modals of obligation, duration, quantification), level B (quantifiers and determiners), and level C (proforms and should/will). External validation for the model was less successful, however: only 51% of texts from the ASAG corpus (a different set of graded short answers) were correctly identified using logistic regression, rising to 59% with a more sophisticated elastic net method. Moving on to the MALL papers, Jack Burston and Konstantinos Giannakou report on an extensive meta-analysis of a large number of studies published over the past quarter century
{"title":"Editorial","authors":"Shona Whyte","doi":"10.1017/S0958344022000040","DOIUrl":"https://doi.org/10.1017/S0958344022000040","url":null,"abstract":"May is here and with it comes a new issue of ReCALL for 2022. This time, we have seven papers covering three broad topics: the first involves automatic L2 proficiency assessment, the second, mobile-assisted language learning (MALL), and the third, L2 vocabulary acquisition, particularly the role of audiovisual materials. Several papers draw on Paivio’s (1971) dual processing theory, according to which “knowledge representation in verbal and visual modes may facilitate processing and therefore aid understanding and retention of knowledge more effectively than representations depending on a single mode” (Sato, Lai & Burden). There is also some overlap between the MALL and vocabulary studies, since the MALL meta-analysis by Burston and Giannakou reveals that by far the most common MALL learning objective is, in fact, lexical learning. Similarly, Lin’s paper on a new web-based app focusing on formulaic expressions in YouTube videos sits at the intersection of MALL and learning of vocabulary and phraseology. As you will read, the studies include a range of methodologies and research designs, from meta-analysis (Burston & Giannakou; Yu & Trainin), to survey (Puebla, Fievet, Tsopanidi & Clahsen), experimental study (Dziemianko; Sato et al.) and computer modelling (Gaillat et al.), through to research and development (Lin). Our first paper shows that collaborative research between universities in Paris and Galway is making headway in the complex area of automatic L2 proficiency assessment by developing AI systems to analyse learners’ writing samples and assign them to appropriate CEFR proficiency levels. The research by Thomas Gaillat, Andrew Simpkin, Nicolas Ballier, Bernardo Stearns, Annanda Sousa, Manon Bouyé and Manel Zarrouk focuses on machine learning from a large corpus of Cambridge and Education First essays, using linguistic microsystems constructed around L2 functions such as modals of obligation, expressions of time, and proforms, for instance, in addition to more traditional measures of complexity involving lexis, syntax, semantics, and discourse features. After training on some 12,500 English L2 texts written by around 1,500 L1 French and Spanish examinees, which had been assigned to one of the six CEFR levels by human raters, the AI system reached 82% accuracy in identifying writers’ proficiency levels. It also identified specific microsystems associated with learners at level A (nominals, modals of obligation, duration, quantification), level B (quantifiers and determiners), and level C (proforms and should/will). External validation for the model was less successful, however: only 51% of texts from the ASAG corpus (a different set of graded short answers) were correctly identified using logistic regression, rising to 59% with a more sophisticated elastic net method. Moving on to the MALL papers, Jack Burston and Konstantinos Giannakou report on an extensive meta-analysis of a large number of studies published over the past quarter century","PeriodicalId":47046,"journal":{"name":"Recall","volume":"34 1","pages":"127 - 129"},"PeriodicalIF":4.5,"publicationDate":"2022-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41850673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-13DOI: 10.1017/S0958344022000039
HyeJi Yang, Heyoung Kim, J. Lee, Dongkwang Shin
Abstract With the growth of chatbots, concerns about implementing artificial intelligence (AI) chatbots in educational settings have consistently arisen, especially for the purpose of language learning. This study introduced a task-based voice chatbot called “Ellie”, newly developed by the researchers, and examined the appropriateness of its task design and performance as an English conversation partner and students’ perceptions on using it in EFL class. Korean EFL learners (N = 314) aged 10–15 years performed three speaking tasks with Ellie in their school classroom. The participants took 9.63 turns per session on average using the first 1,000-word band, indicating that the chatbot highly encouraged students to engage in conversation, which rarely occurs in general EFL classes in Korea. The high task success rates (88.3%) showed the design appropriateness of both L2 tasks and operational intents in terms of users’ successful understanding and completeness of the given chatbot tasks. The participants’ responses to the survey not only supported the positive potential of the chatbot in EFL settings but also revealed limitations to be resolved. Future suggestions for advancing and implementing AI chatbots in EFL classrooms are discussed.
{"title":"Implementation of an AI chatbot as an English conversation partner in EFL speaking classes","authors":"HyeJi Yang, Heyoung Kim, J. Lee, Dongkwang Shin","doi":"10.1017/S0958344022000039","DOIUrl":"https://doi.org/10.1017/S0958344022000039","url":null,"abstract":"Abstract With the growth of chatbots, concerns about implementing artificial intelligence (AI) chatbots in educational settings have consistently arisen, especially for the purpose of language learning. This study introduced a task-based voice chatbot called “Ellie”, newly developed by the researchers, and examined the appropriateness of its task design and performance as an English conversation partner and students’ perceptions on using it in EFL class. Korean EFL learners (N = 314) aged 10–15 years performed three speaking tasks with Ellie in their school classroom. The participants took 9.63 turns per session on average using the first 1,000-word band, indicating that the chatbot highly encouraged students to engage in conversation, which rarely occurs in general EFL classes in Korea. The high task success rates (88.3%) showed the design appropriateness of both L2 tasks and operational intents in terms of users’ successful understanding and completeness of the given chatbot tasks. The participants’ responses to the survey not only supported the positive potential of the chatbot in EFL settings but also revealed limitations to be resolved. Future suggestions for advancing and implementing AI chatbots in EFL classrooms are discussed.","PeriodicalId":47046,"journal":{"name":"Recall","volume":"34 1","pages":"327 - 343"},"PeriodicalIF":4.5,"publicationDate":"2022-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48262499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-14DOI: 10.1017/S0958344022000015
Marwa F. Hafour
Abstract Owing to the anytime-anywhere-anyhow nature of mobile learning, together with the ubiquity of affordably priced mobile phones, learning has become a mobigital practice, as termed by Şad and Göktaş (2014). Consequently, language teaching/learning is gradually shifting from computer-assisted language learning to mobile-assisted language learning (MALL). In response, the current study examined the impact of MALL training on preservice and in-service EFL teachers’ perceptions and use of mobile technology (MT). For this purpose, two groups of preservice (N = 33) and in-service (N = 31) EFL teachers were randomly selected and exposed to MALL training. The pretest-posttest experimental mixed-methods design was used as a framework for collecting and analyzing both quantitative and qualitative data (using closed- and open-ended-question surveys). Quantitative results revealed that both preservice and in-service teachers had similar perceptions of MT before and after training. The only exception is that, after training, in-service teachers were more interested in MT than preservice teachers. However, both groups demonstrated an overall (and subfactor) improvement in their perceptions after MT training, except for their perceived ease of use. In-service teachers’ use also improved after training and, due to the yielded positive correlation, their perceptions were a significant predictor of use. Qualitative findings showed that in-service teachers used MT more in listening and speaking (for synchronous communication) than in reading and writing, selecting social media and translation apps as the least useful ones. Moreover, they regarded technical and digital literacy problems as the ones most challenging to the use of MT.
{"title":"The effects of MALL training on preservice and in-service EFL teachers’ perceptions and use of mobile technology","authors":"Marwa F. Hafour","doi":"10.1017/S0958344022000015","DOIUrl":"https://doi.org/10.1017/S0958344022000015","url":null,"abstract":"Abstract Owing to the anytime-anywhere-anyhow nature of mobile learning, together with the ubiquity of affordably priced mobile phones, learning has become a mobigital practice, as termed by Şad and Göktaş (2014). Consequently, language teaching/learning is gradually shifting from computer-assisted language learning to mobile-assisted language learning (MALL). In response, the current study examined the impact of MALL training on preservice and in-service EFL teachers’ perceptions and use of mobile technology (MT). For this purpose, two groups of preservice (N = 33) and in-service (N = 31) EFL teachers were randomly selected and exposed to MALL training. The pretest-posttest experimental mixed-methods design was used as a framework for collecting and analyzing both quantitative and qualitative data (using closed- and open-ended-question surveys). Quantitative results revealed that both preservice and in-service teachers had similar perceptions of MT before and after training. The only exception is that, after training, in-service teachers were more interested in MT than preservice teachers. However, both groups demonstrated an overall (and subfactor) improvement in their perceptions after MT training, except for their perceived ease of use. In-service teachers’ use also improved after training and, due to the yielded positive correlation, their perceptions were a significant predictor of use. Qualitative findings showed that in-service teachers used MT more in listening and speaking (for synchronous communication) than in reading and writing, selecting social media and translation apps as the least useful ones. Moreover, they regarded technical and digital literacy problems as the ones most challenging to the use of MT.","PeriodicalId":47046,"journal":{"name":"Recall","volume":"34 1","pages":"274 - 290"},"PeriodicalIF":4.5,"publicationDate":"2022-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48517098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-14DOI: 10.1017/S0958344021000331
H. Jin, Yasin Karatay, Fatemeh Bordbarjavidi, Junghun Yang, Timothy Kochem, A. A. Muhammad, V. Hegelheimer
Abstract Participation in online courses has become essential for training language professionals in under-resourced contexts with skills in computer-assisted language learning (CALL) (Godwin-Jones, 2014). Most online CALL courses use asynchronous computer-mediated communication (ACMC) to facilitate meaningful learning for participants. Although participants’ sustained engagement with ACMC is the target, global realities of participants interfere with their participation levels. This article investigates participants’ engagement profiles in asynchronous online discussions in an 8-week CALL-based global online course developed and implemented by a team at Iowa State University. Using a case study approach, nine focal participants’ engagement profiles have been analyzed in terms of identifying patterns of engagement in the discussion posts and their relation to the types of discussion prompts. Then, social network analysis (SNA) and thematic analysis were employed to investigate patterns of interaction among the participants in the replies. The results indicated that engagement patterns observed in discussion posts overall aligned with the primary goals of prompt types. SNA further identified two participants as social mediators to connect participants with each other. These findings are significant in that they suggest the effectiveness of using ACMC to promote co-construction of knowledge for a global audience. This article also provides implications regarding the design of discussion prompts to help maximize participant engagement with course content.
{"title":"Exploring global online course participants’ interactions: Value of high-level engagement","authors":"H. Jin, Yasin Karatay, Fatemeh Bordbarjavidi, Junghun Yang, Timothy Kochem, A. A. Muhammad, V. Hegelheimer","doi":"10.1017/S0958344021000331","DOIUrl":"https://doi.org/10.1017/S0958344021000331","url":null,"abstract":"Abstract Participation in online courses has become essential for training language professionals in under-resourced contexts with skills in computer-assisted language learning (CALL) (Godwin-Jones, 2014). Most online CALL courses use asynchronous computer-mediated communication (ACMC) to facilitate meaningful learning for participants. Although participants’ sustained engagement with ACMC is the target, global realities of participants interfere with their participation levels. This article investigates participants’ engagement profiles in asynchronous online discussions in an 8-week CALL-based global online course developed and implemented by a team at Iowa State University. Using a case study approach, nine focal participants’ engagement profiles have been analyzed in terms of identifying patterns of engagement in the discussion posts and their relation to the types of discussion prompts. Then, social network analysis (SNA) and thematic analysis were employed to investigate patterns of interaction among the participants in the replies. The results indicated that engagement patterns observed in discussion posts overall aligned with the primary goals of prompt types. SNA further identified two participants as social mediators to connect participants with each other. These findings are significant in that they suggest the effectiveness of using ACMC to promote co-construction of knowledge for a global audience. This article also provides implications regarding the design of discussion prompts to help maximize participant engagement with course content.","PeriodicalId":47046,"journal":{"name":"Recall","volume":"34 1","pages":"291 - 308"},"PeriodicalIF":4.5,"publicationDate":"2022-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47421174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-13DOI: 10.1017/S0958344021000343
Rui Li
Abstract This study aims to synthesize research trends of blended language learning studies over the past two decades, from 2000 to 2019. Data were collected from the Web of Science, and a total of 60 SSCI-indexed journal articles were retrieved for bibliometric synthesis. Drawing on the revised technology-based learning model, participants, learning strategies, research methods, research foci, adopted technologies, and application effectiveness, advantages, and challenges were addressed. The findings demonstrated that publications were increasing rapidly, and that most articles were published in computer-assisted language learning, educational technology, and applied linguistic journals. The most common target language was English as a foreign language, and the most common learners were college students. In most studies, technologies were mainly used for the purposes of practice or exercises. Mixed, quantitative, and qualitative methods were frequently adopted, with a particular eye on the experiment design, questionnaires, and other specific methods in the second decade. Productive language skills, along with autonomy, satisfaction, and motivation, were major research foci. Language management systems and computer and web-based applications were frequently adopted technologies. Findings of application effectiveness, advantages, and challenges were summarized.
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Pub Date : 2022-01-01DOI: 10.1017/s0958344021000306
A. Boulton
Nobody expected an international lockdown in 2020, much less for it to continue into 2021; I certainly did not expect to be writing of this again for January 2022. As many of us have got used to teaching from home, so research in the classroom has diminished, giving way to online or hybrid formats that have generated considerable food for thought, as well as research output. Much of this, however, has been ad hoc, comparing previous course formats against current constraints, with computers and other technology largely given over to this online condition rather than pushing the boundaries of research in CALL. In terms of submissions to ReCALL, the results have been three-fold: (a) a somewhat higher rejection rate; (b) a slightly slower time from submission to acceptance, as reviewers have been more difficult to find; (c) a delay in accepted papers being assigned to an issue – although they are of course available online as soon as they are ready (see FirstView articles). This last point has yet to really be felt, but to pre-empt future delays we have decided to increase the number of papers in each issue. Assuming the rate of submissions returns to pre-COVID levels, this will allow us to catch up with the backlog and make the situation a little more comfortable for special issues in the future. This year, Cambridge University Press instigated a new prize for the “best” ReCALL paper published in the preceding year (issues 32.3 to 33.2), although “best” is of course a delicate question. The editors drew up a shortlist of two papers from each issue to be voted on by the full editorial board, the prize this year being awarded to Christine Appel and Joan-Tomàs Pujolà for their paper in issue 33.2 titled “Designing Speaking Interaction in LMOOCs: An eTandem Approach” – congratulations to them! And indeed to all the other contributors who make ReCALL one of the top journals in its field. On this topic, the annual JCR impact factor for ReCALL is encouraging, increasing by 58% from 1.842 in 2019 to 2.917 in 2020, with a consequent rise to 21st place among all linguistics journals. That said, other journals are also increasing their IF, suggesting that more research overall has been published during COVID; certainly more people are downloading papers from ReCALL. And, as always, we need to be careful with overinterpreting any bibliometrics. In other news, many of you will have seen the call for papers for a ReCALL special issue on Replication in CALL to be guest edited by Cornelia Tschichold (Swansea University, UK). The deadline for submission of full papers is the 15th May 2022, and please do get in touch with her if you have any questions. The issue itself is due out as ReCALL 35.2 in May 2023. Also, we discovered that a special issue of ReCALL from 1998 titled Language Processing in CALL was missing from the journal homepage; our thanks to Cornelia Tschichold for noticing this, the editors of the issue for agreeing to its publication (Mathias Schulze,
{"title":"Editorial","authors":"A. Boulton","doi":"10.1017/s0958344021000306","DOIUrl":"https://doi.org/10.1017/s0958344021000306","url":null,"abstract":"Nobody expected an international lockdown in 2020, much less for it to continue into 2021; I certainly did not expect to be writing of this again for January 2022. As many of us have got used to teaching from home, so research in the classroom has diminished, giving way to online or hybrid formats that have generated considerable food for thought, as well as research output. Much of this, however, has been ad hoc, comparing previous course formats against current constraints, with computers and other technology largely given over to this online condition rather than pushing the boundaries of research in CALL. In terms of submissions to ReCALL, the results have been three-fold: (a) a somewhat higher rejection rate; (b) a slightly slower time from submission to acceptance, as reviewers have been more difficult to find; (c) a delay in accepted papers being assigned to an issue – although they are of course available online as soon as they are ready (see FirstView articles). This last point has yet to really be felt, but to pre-empt future delays we have decided to increase the number of papers in each issue. Assuming the rate of submissions returns to pre-COVID levels, this will allow us to catch up with the backlog and make the situation a little more comfortable for special issues in the future. This year, Cambridge University Press instigated a new prize for the “best” ReCALL paper published in the preceding year (issues 32.3 to 33.2), although “best” is of course a delicate question. The editors drew up a shortlist of two papers from each issue to be voted on by the full editorial board, the prize this year being awarded to Christine Appel and Joan-Tomàs Pujolà for their paper in issue 33.2 titled “Designing Speaking Interaction in LMOOCs: An eTandem Approach” – congratulations to them! And indeed to all the other contributors who make ReCALL one of the top journals in its field. On this topic, the annual JCR impact factor for ReCALL is encouraging, increasing by 58% from 1.842 in 2019 to 2.917 in 2020, with a consequent rise to 21st place among all linguistics journals. That said, other journals are also increasing their IF, suggesting that more research overall has been published during COVID; certainly more people are downloading papers from ReCALL. And, as always, we need to be careful with overinterpreting any bibliometrics. In other news, many of you will have seen the call for papers for a ReCALL special issue on Replication in CALL to be guest edited by Cornelia Tschichold (Swansea University, UK). The deadline for submission of full papers is the 15th May 2022, and please do get in touch with her if you have any questions. The issue itself is due out as ReCALL 35.2 in May 2023. Also, we discovered that a special issue of ReCALL from 1998 titled Language Processing in CALL was missing from the journal homepage; our thanks to Cornelia Tschichold for noticing this, the editors of the issue for agreeing to its publication (Mathias Schulze, ","PeriodicalId":47046,"journal":{"name":"Recall","volume":"34 1","pages":"1 - 3"},"PeriodicalIF":4.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49384373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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