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Voluntary use of automated writing evaluation by content course students

Published online by Cambridge University Press:  31 March 2021

Aysel Saricaoglu
Affiliation:
TED University, Turkey (saricaogluaysel@gmail.com)
Zeynep Bilki
Affiliation:
TED University, Turkey (zeynep.bilki@tedu.edu.tr)
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Abstract

Automated writing evaluation (AWE) technologies are common supplementary tools for helping students improve their language accuracy using automated feedback. In most existing studies, AWE has been implemented as a class activity or an assignment requirement in English or academic writing classes. The potential of AWE as a voluntary language learning tool is unknown. This study reports on the voluntary use of Criterion by English as a foreign language students in two content courses for two assignments. We investigated (a) to what extent students used Criterion and (b) to what extent their revisions based on automated feedback increased the accuracy of their writing from the first submitted draft to the last in both assignments. We analyzed students’ performance summary reports from Criterion using descriptive statistics and non-parametric statistical tests. The findings showed that not all students used Criterion or resubmitted a revised draft. However, the findings also showed that engagement with automated feedback significantly reduced users’ errors from the first draft to the last in 11 error categories in total for the two assignments.

Type
Research Article
Information
ReCALL , Volume 33 , Issue 3 , September 2021 , pp. 265 - 277
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of European Association for Computer Assisted Language Learning

1. Introduction

One of the major effects of globalization on education is the increasing number of universities in non-English speaking countries that adopt English as the medium of instruction (EMI) (Dang, Nguyen & Le, Reference Dang, Nguyen and Le2013). In EMI contexts, in addition to learning content knowledge, students are expected to develop their language competences (Wilkinson, Reference Wilkinson, Doiz, Lasagabaster and Sierra2013). However, the content staff in EMI programs “rarely see their role as one of developing the students’ language ability” (Wilkinson, Reference Wilkinson, Doiz, Lasagabaster and Sierra2013: 16). Because instructors often employ a content-oriented focus in EMI, students’ language needs may not be equally addressed. Such an educational landscape highlights the need for students to confront the challenge of continuous language improvement on their own. Fortunately, today’s technologies such as automated writing evaluation (AWE) have the potential to assist them with this challenge.

AWE tools evaluate students’ written language in seconds and offer specific and individualized feedback (Chapelle & Voss, Reference Chapelle and Voss2016; Chun, Reference Chun2016; Wang, Shang & Briody, Reference Wang, Shang and Briody2013). The benefits of automated feedback from AWE tools for students, including writing longer essays, obtaining higher machine scores, making fewer language errors, and improving the rhetorical quality of writing, are readily acknowledged in the literature (Feng, Reference Feng2015; Wang, Reference Wang2013; Wang et al., Reference Wang, Shang and Briody2013). Most existing AWE studies were conducted in English or writing classes where the use of an AWE tool was a requirement for a certain assignment and was led by the instructor (e.g. Chen & Cheng, Reference Chen and Cheng2008; Lavolette, Polio & Kahng, Reference Lavolette, Polio and Kahng2015; Saricaoglu, Reference Saricaoglu2019). Today, more AWE tools are offered to students as self-led supplementary aids, but the potential of AWE for such use is still unknown, which is the concern of the current study. Addressing this gap, we aimed to explore English as a foreign language (EFL) students’ voluntary use of Criterion, an AWE tool developed by Educational Testing Service. More specifically, we are concerned with students’ engagement with automated feedback from Criterion for their content course assignments and their error reduction rates as a result of making use of automated feedback.

1.1 Students’ engagement with automated feedback

Researchers examining students’ engagement with AWE feedback have generally looked at details of the revision process, such as whether students revised their written drafts based on automated feedback, how much time they spent revising a draft, how many resubmissions they made, whether their revisions were correct or incorrect, what type of revisions they made (i.e. correction, substitution, addition, or deletion), or whether students used any strategies (i.e. cognitive or metacognitive) in the revision process (e.g. Lavolette et al., Reference Lavolette, Polio and Kahng2015; Zhang & Hyland, Reference Zhang and Hyland2018).

Zhang and Hyland (Reference Zhang and Hyland2018) describe a “highly engaged learner” as the one who makes efforts in the revision task to improve the quality of their writing based on the automated feedback received from Pigai, an AWE tool working in a similar way to commonly known AWE systems such as Criterion. The student who had a high level of engagement with Pigai resubmitted her essay to the AWE system 13 times and increased her score as a result from 79 to 90. Another characteristic of the highly engaged learner was that she did not complete the revisions immediately but over two weeks, having some gaps between her resubmissions. She was also emotionally invested in improving her essay and was motivated to see her score increase after revisions. On the other hand, Zhang and Hyland (Reference Zhang and Hyland2018) describe a “moderately engaged learner” as the one who does not make enough effort in the revision task to improve their writing. For example, the student who had a moderate level of engagement with automated feedback resubmitted his essay to Pigai only once and did not address several of the errors identified automatically. He also reported to be overwhelmed by the feedback that he received.

In the investigation of students’ engagement with automated feedback, factors that influence students’ AWE use are also important. The findings of Grimes and Warschauer (Reference Grimes and Warschauer2010) suggest that the amount of revision using an AWE tool increases in time as teachers allow more time for writing assignments. In the first year of their MY Access! implementation, only 12% of student essays were revised. In the third year, this percentage increased to 53. Lavolette et al. (Reference Lavolette, Polio and Kahng2015) investigated the effects of three factors on students’ revisions using Criterion: feedback category, the accuracy of error identification, and the timing of the feedback. Their findings showed that students made related revisions for the ill-formed verb, proofread, and subject–verb agreement errors about 85% of the time and about 50% of the time for the missing comma, preposition, and spelling errors, but they ignored capitalization errors 41% of the time. They revised incorrectly identified errors about half the time. Whether the feedback was immediate or delayed did not have a significant effect on their revisions. In a study on AWE feedback on students’ written scientific arguments, Zhu et al. (Reference Zhu, Lee, Wang, Liu, Belur and Pallant2017) found that 77% of the students revised their writing after receiving automated feedback. They also found that those whose initial AWE scores were higher were more likely to revise their writing. According to Ranalli (Reference Ranalli2018), how specific or generic the feedback is determines students’ abilities to correct their errors, with specific feedback resulting in more successful corrections. He found course level not to have a significant effect on students’ error correction abilities when using Criterion. In a survey study, Li et al. (Reference Li, Meng, Tian, Zhang, Ni and Xiao2019) found that learners’ intention to use Pigai was influenced by perceived usefulness, attitude towards using, computer self-efficacy, and perceived ease of use.

Research on AWE has also highlighted the critical role of teacher and/or peer feedback that follows automated feedback. In Lai’s (Reference Lai2010) study, students adopted peer feedback more often than automated feedback from MY Access! as they found automated feedback to be too general and indirect, whereas peer feedback was direct and specific. Chen and Cheng (Reference Chen and Cheng2008) compared three ways of integrating MY Access! into EFL college-level writing courses. In the first integration, students had to achieve a minimum score of 4 out of 6 from the system. They also received written teacher feedback and in-class peer feedback. In the second integration, students used the tool as much as they needed without a requirement of a certain score. They also received written teacher feedback but no peer feedback. In the third integration, students used the program autonomously with the instructor being less involved. They did not receive any teacher feedback, only online peer feedback. Chen and Cheng (Reference Chen and Cheng2008) found that AWE feedback was perceived more positively by the students when it was followed by teacher and peer feedback. In fact, using the AWE tool without human facilitation made students frustrated. These findings are supported by Wilson and Czik (Reference Wilson and Czik2016) who explored students’ writing motivation in two conditions: automated feedback from PEG Writing, developed by Measurement Incorporated, combined with teacher feedback condition and only electronic teacher feedback condition. Teacher feedback combined with automated feedback was found to increase students’ motivation and to lead to a higher number of essay drafts, but no statistically significant gains were observed in writing quality between the two conditions.

1.2 Automated feedback and error reductions

Previous studies on the effects of AWE feedback on the accuracy of students’ writing have reported mixed but generally positive findings. Based on the increase in the automated holistic scores that students received from their first essays to their fourth, El Ebyary and Windeatt (Reference El Ebyary and Windeatt2010) concluded that the accuracy of students’ writing improved as a result of making use of automated feedback from Criterion. Wang et al. (Reference Wang, Shang and Briody2013) compared the accuracy of students’ writing before and after they used CorrectEnglish. Their findings revealed significantly fewer errors in run-on sentences, sentence fragments, capitalization errors, missing articles, and punctuation in the post-test. Lavolette et al. (Reference Lavolette, Polio and Kahng2015) compared the essays of students who received immediate (40 minutes after submission) and delayed (three weeks after submission) feedback from Criterion and observed no significant difference between the feedback groups. There was also no difference between the groups in accuracy over time. Liao (Reference Liao2016), who investigated whether using Criterion improved the accuracy of students’ writing in revised texts and in new texts, found that AWE feedback had a positive effect on the reduction of errors both in text revision and in text composition. Students’ accuracy significantly improved in four error categories: fragments, subject–verb disagreement, run-on sentences, and ill-formed verbs. Li, Feng and Saricaoglu (Reference Li, Feng and Saricaoglu2017) examined the short-term (from the first draft to the last draft of the same paper) and the long-term (from the first draft of a paper to the first draft of a subsequent paper) effects of Criterion feedback on ESL students’ development of grammatical accuracy. The findings regarding the short-term effects showed that students reduced errors in eight error categories: word choice, verb form, articles, pronouns, run-on sentences, fragments, sentence structure, and subject–verb agreement. In the long term, there was significant error reduction in only one error category: run-on sentences.

Our study adds to the AWE literature reviewed above by focusing on the voluntary use of Criterion by content course students. It investigates students’ engagement with Criterion as a voluntary language learning tool and the improvement in the accuracy of their written assignments as a result of using automated feedback. The study was guided by the following research questions:

  1. 1. To what extent do students voluntarily use Criterion for their content course assignments?

  2. 2. To what extent does students’ voluntary use of Criterion increase the accuracy of their writing?

2. Methodology

This study was conducted as an action research project since it emerged as a context-specific effort to find a solution to an educational problem: students’ lack of language support in content courses. Action research, involving both action and research activities, intervenes in social processes in particular contexts, such as classrooms, schools, or organizations, for positive change (Burns, Reference Burns, Burns and Richards2009). It starts with the identification of a problem or question, continues with the planning and implementation of an action, and ends with observation and reflection on the outcome and future plans (Lewin, Reference Lewin1946). We had prior knowledge about and experiences of Criterion and brought our belief to the inquiry process that students would benefit from using Criterion for their content course assignments. We took a quantitative approach to investigate students’ voluntary use of Criterion and the improvement in the accuracy of their writing as a result of engaging with automated feedback.

2.1 Context and participants

This study was conducted at a private Turkish university with English as the medium of instruction. Volunteer sampling was used to recruit the participants. All faculty members were sent an email introducing the study, and the members who believed that their students would benefit from using Criterion for their course assignments were invited to the study. Faculty members were informed that this implementation would not create any extra workload for them, and that all the work, including training the students, creating course assignments on Criterion, and tracking students’ use of the program, would be handled by the researchers.

The number of Criterion seats were limited to 200 student accounts. Thus, the researchers met face to face with the first six faculty members who expressed an interest and provided them with more details about the study. Three of the faculty members were teaching two sections of the same course, and one faculty member was teaching three sections of the same course. Table 1 presents the distribution of courses. In total, students from five different courses and 10 classes taught by six faculty members were involved in the study: Materials Science (three sections), Research Project, Introduction to Education (two sections), Community Service (two sections), and Introduction to Sociology (two sections). Courses, assignment genres, and the number of assignments across courses are presented in Table 1.

Table 1. Courses, assignment genres, and assignment numbers

The medium of instruction in all classes was English, and excellence in English was one of the priorities of the university. All faculty members were native speakers of Turkish, but they were all proficient speakers of English. The faculty members teaching the Materials Science, Introduction to Sociology, Introduction to Education, and Community Service courses were assistant professors. The faculty member teaching the Research Project course was a full professor.

Out of 199 students in 10 classes, 114 (female = 81, male = 33) volunteered to participate in this study. Table 2 displays the number of volunteers from each class. The language proficiency level of students when they start their departmental programs is upper-intermediate (B2) according to the Common European Framework of Reference for Languages (Council of Europe, 2001). They either submit an international English language proficiency exam score (84 TOEFL IBT or 6.5 IELTS Academic) or pass the English language proficiency exam administered by the university with a minimum score of 80, which is again an equivalent of 84 TOEFL IBT or 6.5 IELTS Academic.

Table 2. Courses, years, number of students, and number of volunteers

2.2 AWE tool

Criterion was chosen as the AWE tool in this study as researchers had prior classroom experience with it. Criterion is an online application that offers students several writing-specific features, including planning, holistic scores, trait-specific feedback on different types of errors, unlimited revision and resubmission opportunities, as well as peer and instructor feedback opportunities. Different from other AWE applications such as Grammarly, which not only allows the user to upload a completed text for instant evaluation but also provides corrective feedback during the process of text creation, Criterion provides instant feedback on a written text once the completed text is submitted to the system. One advantage that Criterion has over other AWE applications is that it logs user data and generates reports (e.g. score analysis reports or detailed or summary performance reports) or trait feedback analysis, including number of words or sentences and numbers of errors in specific error categories that could be used for instructional or research purposes.

Criterion generates feedback in five categories: organization and development, grammar, usage, mechanics, and style. Because Criterion gives organization and development feedback for the essay genre, we disabled this feedback category, and students only received feedback on grammar, usage, mechanics, and style. Table 1A in the supplementary material presents Criterion’s error categories and specific types of errors in each category. Criterion does not display student errors from different categories all together on a written text. In order to see feedback from each error category, the user needs to click on a specific type of error from a specific error category, as displayed in Figure 1. The user then needs to move the cursor to the highlighted error in order to see the feedback on that specific error, as seen in Figure 2.

Figure 1.Criterion’s display of identified errors

Figure 2.Criterion’s feedback on a highlighted ill-formed verb error

Although Criterion has a rich library of essay topics, we used the customized topics since students wrote in different genres such as lab reports, research articles, reflections, journals, projects proposals, and movie reviews, the topics of which were determined by the faculty members.

2.3 Procedures

Classes of the six faculty members were visited to introduce the study to the students, train them, and recruit volunteers. Before class visits, student accounts were created on Criterion for all students taking the selected courses. During the training, students were informed that they all had access to Criterion and were provided with their student account details. Students were trained in how to log in to the system, how to open an assignment, how to submit a text for evaluation, how to see the automated feedback, how to revise language errors, and how to resubmit the text. They were delivered a guidebook including details of how to use Criterion. After a 15-minute training, they were informed about the study and were invited as participants. It was explicitly stated that they would be able to use Criterion for their class assignments whether or not they volunteered to share their AWE data. Consent forms were delivered and signed by those volunteering.

During the semester, content course assignments were created on Criterion by the researchers. Students were informed via email that the tool was ready for their use. Although faculty members were not asked to do anything specific for this project, some faculty members expressed several times during our conversations that they orally encouraged their students to use Criterion. Faculty members teaching the Materials Science, Research Project, and Introduction to Education courses stated that they offered frequent oral encouragement in their courses to facilitate their students’ Criterion use. Faculty members teaching the Introduction to Sociology classes decided to offer their students two bonus points in each assignment for using Criterion and reducing their errors. For this reason, these two faculty members were given a report of their students’ Criterion use including students’ number of submissions and error counts after each submission.

2.4 Data collection and analysis

Although five courses were involved in the study, we collected data from only two courses. We excluded the Materials Science course from the data collection process because students worked in groups for each assignment. It would not be possible to track individual students’ use of the tool. We also excluded the Community Service course as there was only a very small number of volunteers. We did not collect data from the Research Project course as there were no volunteers. We collected data only from the Introduction to Education (IE) and Introduction to Sociology (IS) courses. Both were first year courses with a similar number of volunteers. While IE students used Criterion for nine assignments, IS students used it for only two assignments. Therefore, we used data from only the first two assignments in both courses.

In order to explore students’ Criterion use and error responses, we collected expanded performance summary reports from Criterion for each volunteer and each assignment. The summary reports included information regarding an individual student’s resubmission numbers and number of errors in specific error categories in each submitted draft. We transferred the submission numbers and error counts to an Excel sheet for statistical analyses. To address the effect of text length on the number of errors students made, we normalized the raw error counts per 1,000 words. Because data were not normally distributed, we used the Wilcoxon signed-rank test (the non-parametric dependent samples t-test) to compare error reduction rates from students’ first submitted drafts to the last submitted drafts within the same assignments.

3. Results and discussion

3.1 Students’ voluntary use of Criterion for content course assignments

To answer the first research question, we examined the number of Criterion users (i.e. number of students who made at least one submission to Criterion) in each assignment and their number of resubmissions (i.e. how many times they resubmitted the same draft to Criterion after revising their language errors based on the automated feedback) in each assignment. Table 3 presents the descriptive findings.

Table 3. Number of Criterion users and resubmissions across classes and assignments

Note. N = number of volunteers.

In the first assignment, 46% of IE students and 71% of IS students used Criterion. In both courses, some students did not submit any revised drafts (three IE students and five IS students). In the second assignment, fewer IE students (32%) used Criterion than in the first assignment, but they made more resubmissions (M = 3.36) in the second assignment than in the first assignment (M = 2.79). On the other hand, there was no decrease in the number of Criterion users from the IS course in the second assignment, and there was a slight increase in the number of their resubmissions (M = 3.10 in the first assignment and M = 3.57 in the second assignment). Again, not all students revised and resubmitted their drafts in the second assignment. Four students from both courses made only one submission and did not submit a revised draft.

In contrast to previous AWE implementations where AWE use was a class activity or an assignment requirement, the decision of whether or not to use Criterion was made by the students in this study. As a result, the participation rate of students in AWE use was much lower than the participation rate of students in those earlier studies (both for the number of Criterion users and the number of resubmissions). When AWE is used during the class or when a teacher asks students to use it (e.g. Lavolette et al., Reference Lavolette, Polio and Kahng2015; Li et al., Reference Li, Feng and Saricaoglu2017; Saricaoglu, Reference Saricaoglu2019; Wang et al., Reference Wang, Shang and Briody2013), almost all students use the AWE tool to meet the requirement. Explicit integration of AWE technology in the learning process in such implementations may better promote its use by students, as Koh (Reference Koh, Liu, Wang and Ryan2016) argues.

Students’ lower rates of AWE use in this study might also be related to the lack of teacher involvement. In one study, Chen and Cheng (Reference Chen and Cheng2008) revealed that without teacher facilitation, the autonomous use of MY Access! was frustrating for students. In another study, Lai, Yeung and Hu (Reference Lai, Yeung and Hu2016) pointed out that students needed greater support from their teachers in contrast to the minimal responsibility that teachers assumed in promoting out-of-class language learning with technology. In our study, although the teachers facilitated students’ use of Criterion through oral encouragement or extra points, students did not receive any explicit teacher and/or researcher support, for instance, in terms of how to interpret the feedback, how to address the feedback, whether to revise all identified errors or not, and so on, which may have negatively impacted the extent to which they used Criterion.

We noticed that more IS students used Criterion in both assignments than IE students. Also, a higher percentage of IS students resubmitted their drafts in the second assignment than IE students. These findings could be related to how course instructors promoted using Criterion. While the IE instructor explained that they provided frequent oral encouragement to facilitate their students’ Criterion use, IS instructors offered their students two bonus points for using Criterion in each assignment. Bonus points might have been more encouraging to the students. However, since no qualitative follow-up data were collected as to the reasons why some students did not use Criterion or why they did not resubmit a draft, it is hard to interpret the findings.

3.2 Increase in the accuracy of students’ writing

To answer the second research question, we compared students’ first and last submitted drafts from the same assignment for error counts. Table 4 presents the descriptive findings of students’ error reduction rates in Assignment 1. The highest reduction rate of IE students belongs to usage errors (43%) followed by grammar errors (41%) and mechanics errors (28%). The lowest error reduction rate was in the style category (21%). IS students reduced their grammar errors (59%) the most in Assignment 1, followed by usage errors (41%) and mechanics errors (30%). They also had the lowest error reduction rate in the style category (16%).

Table 4. Error reduction rates in Assignment 1 across classes

Note. N = text number; # = normalized error counts.

a Error reduction rates show the percentage of reduced errors in the last submission made by the students in an error category compared with the number of errors in the first submission.

Table 5 presents the descriptive findings of students’ error reduction rates from the first submission to the last submission in Assignment 2. Both groups had the highest reduction rate (59%) in grammar errors (75% for IE students and 37% for IS students), followed by usage errors (45% for IE students and 30% for IS students), and mechanics errors (27% for IE students and 20% for IS students). Again, style errors had the lowest reduction rate (8% for both groups).

Table 5. Error reduction rates in Assignment 2 across classes

Note. N = text number; # = normalized error counts.

a Error reduction rates show the percentage of reduced errors in the last submission made by the students in an error category compared with the number of errors in the first submission.

Whether there was a significant improvement in the accuracy of students’ drafts in one assignment was measured using the Wilcoxon signed-rank test. Error counts in the first submission were compared with the error counts in the last submission of the same assignment. According to the findings presented in Table 2A in the supplementary material, in Assignment 1, IE students made significant error reductions in Subject–Verb Agreement (z = –2.56, p = .011, with a large effect size, r = –.68), Possessive (z = –2.04, p = .041, with a large effect size, r = –.55), Missing Article (z = –2.56, p = .011, with a large effect size, r = –.68), and Missing Comma (z = –2.03, p = .042, with a medium effect size, r = –.54). In Assignment 2, their error rates significantly decreased for Subject–Verb Agreement (z = –2.03, p = .042, with a large effect size, r = –.72) and Missing Comma (z = –2.38, p = .017, with a large effect size, r = –.84).

According to the findings presented in Table 3A in the supplementary material, in Assignment 1, IS students’ error reductions from the first submission to the last were significant in the following error types: Garbled Sentences (z = –2.33, p = .020, with a large effect size, r = –.52), Subject–Verb Agreement (z = –2.99, p = .003, with a large effect size, r = –.67), Possessive (z = –1.93, p = .054, with a medium effect size, r = –.43), Proofread This! (z = –2.70, p = .007, with a large effect size, r = –.60), Missing Article (z = –2.48, p = .004, with a large effect size, r = –.55), Confused Words (z = –2.85, p = .004, with a large effect size, r = –.64), Wrong Form of Word (z = –2.27, p = .023, with a large effect size, r = .51), Missing Comma (z = –2.27, p = .023, with a large effect size, r = –.51), and Extra Comma (z = –2.39, p = .017, with a large effect size, r = –.53). In Assignment 2, students significantly reduced their Subject–Verb Agreement errors (z = –2.40, p = .016, with a large effect size, r = –.54), Missing Article errors (z = –2.91, p = .004, with a large effect size, r = –.65), Spelling errors (z = –2.03, p = .042, with a medium effect size, r = –.45), Missing Initial Capital Letter in a Sentence errors (z = –1.93, p = .054, with a medium effect size, r = –.43), and Missing Comma errors (z = –2.25, p = .024, with a large effect size, r = –.50).

In summary, students from both classes significantly reduced their errors in the following specific error types: Garbled Sentences, Subject–Verb Agreement, Possessive, Missing Article, Confused Words, Wrong Form of Word, Proofread This!, Missing Comma, Extra Comma, Spelling, and Missing Initial Capital Letter in a Sentence. These findings are consistent with findings from earlier research on AWE, which have already reported the positive impact of automated feedback on the accuracy of students’ writing (e.g. Li, Link & Hegelheimer, Reference Li, Link and Hegelheimer2015; Liao, Reference Liao2016; Wang et al., Reference Wang, Shang and Briody2013). The findings are also in line with theoretical perspectives from second language acquisition. From the interactionist perspective, feedback draws the learner’s attention to language forms during L2 interaction, which has a major role in language learning (Gass & Mackey, Reference Gass and Mackey2006; Long Reference Long, Ritchie and Bhatia1996; Polio, Reference Polio2012). “Written correction is a form of feedback that gives learners an indication of their errors” (Polio, Reference Polio2012: 383). Applying the interactionist perspective to AWE, automated feedback indicates students’ errors during L2 interaction that occurs between the students and an AWE tool, thus draws their attention to language forms and positively affects their learning.

While the number of students’ errors in certain categories significantly decreased from their first submission to the last within the same assignment, such an effect is described as a short-term effect in the AWE literature (Li et al., Reference Li, Feng and Saricaoglu2017). The long-term effects of automated feedback on students’ error reductions (i.e. from their first submitted draft of the first assignment to their first submitted draft of the last assignment) were not investigated in this study as we only examined the first two assignments from both groups and not the same students within one group used Criterion for the two assignments. Given that previous research has yielded limited or no benefits of AWE feedback in the long term (Lavolette et al., Reference Lavolette, Polio and Kahng2015; Saricaoglu, Reference Saricaoglu2019), we would not expect long-term benefits in voluntary use of AWE either. However, with an interactionist perspective, we consider short-term changes as evidence of learning (Mackey & Polio, Reference Mackey, Polio, Mackey and Polio2009; Norris & Ortega, Reference Norris, Ortega, Doughty and Long2003; Polio, Reference Polio2012). Moreover, whether the benefits of corrective feedback are long term or short term, as stated by Ferris, Liu, Sinha and Senna (Reference Ferris, Liu, Sinha and Senna2013), teachers continue responding to students’ language errors, thus the important question is how to provide it in the best way. Today, with automated feedback supporting teacher or peer feedback in writing practices, the same question also applies to automated feedback practices. To this end, development of effective strategies for using automated feedback seems to be one important goal for writing instructors and researchers.

4. Conclusion

English remains a challenge for content course students because the focus is generally on the subject matter rather than language (Leki, Reference Leki, Hyland and Hyland2006). Existing technologies such as AWE can create language learning opportunities for students in such contexts (Plonsky & Ziegler, Reference Plonsky and Ziegler2016). In this study, we attempted to explore the potential of AWE as a voluntary language learning tool for content course assignments. We specifically examined EFL students’ voluntary use of Criterion for content course assignments and the improvement in the accuracy of their writing as a result of making use of automated feedback. Using Criterion was not an assignment requirement, but an individual decision of students. Our results showed that not all students used Criterion or attempted to revise their drafts using automated feedback. However, for those who submitted revised drafts, engagement with automated feedback significantly reduced their language errors from the first draft to the last in 11 error categories in total for the two assignments.

Findings of this study should be considered in light of several limitations. First, the present study did not control possible confounds that might have influenced students’ voluntary use of AWE, such as their error correction backgrounds, motivation levels, assignment assessment criteria, and so on. No teacher data were obtained regarding how much feedback they gave to students’ English language in their written assignments, and whether the quality of their language was one of the assignment evaluation criteria or not. What importance students gave to their English language in their assignments would possibly be influenced by what importance their teachers gave to language, which as a result could affect students’ AWE use. Researchers of future studies might usefully include measuring such possible factors.

Second, no qualitative data were collected in this study. Without qualitative data that can shed light on students’ engagement with Criterion, several questions remain unanswered. Why did some students make only one submission in an assignment and did not attempt to correct their errors? We cannot know if this was because they did not understand the feedback or because they did not know how to correct their errors. Did the provision of oral encouragement or bonus points have a role in students’ engagement with Criterion? We cannot know the extent to which oral encouragement or bonus points could have served as a factor underlying students’ Criterion use and revision of errors. The lack of qualitative follow-up data creates a limitation in understanding such matters. Researchers of future studies will need to include a qualitative inquiry into students’ AWE use to avoid such limitations.

Third, in this study, automated feedback on organization was disabled as our focus was on language errors and improvement across drafts. Because the organization feedback was disabled, we also disabled the scoring function of the tool. Thus, we do not know if there would be differences in students’ Criterion scores across drafts. Zhu et al. (Reference Zhu, Lee, Wang, Liu, Belur and Pallant2017) showed that students who corrected their errors using automated feedback received significantly higher final scores than those who did not and found an association between each revision and an average increase of 0.55 on the scores. Referring to students being “score-obsessed,” Zhang and Hyland (Reference Zhang and Hyland2018: 96) highlight the motivating effect of seeing an improvement in the scores for students. Score increases themselves also serve as a type of feedback to students confirming their error corrections and their language improvement. Thus, a useful area for future research would be to confirm the improvement in students’ accuracy with the increase in automated scores along with its motivating effect for more revisions.

Another limitation of the study is that we did not explore how accurate Criterion’s error identification was, even though our second research question focused on the increase in accuracy across students’ drafts. The capacity of automated systems’ error identification compared to humans’ error identification is a controversy that is still debated in the AWE literature (e.g. Hoang & Kunnan, Reference Hoang and Kunnan2016; Koltovskaia, Reference Koltovskaia2020). Studies by Dikli and Bleyle (Reference Dikli and Bleyle2014), Lavolette et al. (Reference Lavolette, Polio and Kahng2015), and Ranalli, Link and Chukharev-Hudilainen (Reference Ranalli, Link and Chukharev-Hudilainen2017) have shown that Criterion’s error identification accuracy level is much lower than the determined thresholds of 80% or 90% (Burstein, Chodorow & Leacock, Reference Burstein, Chodorow, Leacock, Riedl and Hill2003; Quinlan, Higgins & Wolff, Reference Quinlan, Higgins and Wolff2009). Given that Criterion operates at low error identification capacity, the lack of human raters and the lack of human-generated feedback add to the limitations of our study. Yet because students used Criterion in their content courses where they did not receive human feedback on their language errors, we believe that receiving Criterion feedback was more beneficial than receiving no feedback at all.

Finally, the analysis of the quality of the learners’ texts in this study was limited to the number of errors detected by Criterion across drafts. Evaluating text quality from the perspective of the teachers would also have been helpful given that the study was based on a small corpus of texts. A qualitative or quantitative exploration of whether teachers thought Criterion users submitted noticeably better assignments in terms of language would have provided a better understanding of the effect of automated feedback. Future research should benefit from additional measures of text quality when investigating the effect of automated feedback in voluntary use of AWE.

Supplementary material

To view supplementary material referred to in this article, please visit https://doi.org/10.1017/S0958344021000021

Acknowledgements

This study was supported by an institutional research grant from TED University. We would like to thank Burak Şenel who worked as a research assistant in the project. We presented this study at EuroCALL 2018 Conference, and we thank the audience for their valuable comments.

Ethical statement

The authors reported no potential conflict of interest.

About the authors

Aysel Saricaoglu is an assistant professor in English language and literature at Social Sciences University of Ankara, Turkey. Her research involves academic writing, automated writing evaluation, syntactic complexity, and technology-enhanced language learning. She has disseminated her work in journals such as Studies in Second Language Acquisition, Assessing Writing, Computer Assisted Language Learning, and ReCALL.

Zeynep Bilki is an assistant professor in the English language education program at TED University, Turkey. Her research interests include second language reading and writing, technology-enhanced foreign language learning, and language teacher education. She has been an English language educator in Turkey and USA.

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Figure 0

Table 1. Courses, assignment genres, and assignment numbers

Figure 1

Table 2. Courses, years, number of students, and number of volunteers

Figure 2

Figure 1. Criterion’s display of identified errors

Figure 3

Figure 2. Criterion’s feedback on a highlighted ill-formed verb error

Figure 4

Table 3. Number of Criterion users and resubmissions across classes and assignments

Figure 5

Table 4. Error reduction rates in Assignment 1 across classes

Figure 6

Table 5. Error reduction rates in Assignment 2 across classes

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