Presentation, expectations, and experience: Sources of student perceptions of automated writing evaluation

Roscoe, Rod D.; Wilson, Joshua; Johnson, Adam C.; Mayra, Christopher R.

doi:10.1016/j.chb.2016.12.076

Cited by 65 publications

(38 citation statements)

References 42 publications

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“…Moreover, the satisfaction of having a high rate of evaluation speed expressed by these students also showed the feature of this program in saving time in evaluating their academic writing (Chou, et al, 2016;Cotos, 2011;Roscoe, et al, 2017) --------------------------------------------------------------------------------------------------------- -Premise Journal Vol 7 No 1, April 2018, e-ISSN: 2442-482x, p-ISSN: 2089 Copyright@2018 by PJEE them in revising their academic writing in a shorter time. Indirectly, these students also revealed that the free-service given by Grammarly can save their cost in proofreading their academic writing (Chou, et al, 2016).…”

Section: Discussionmentioning

confidence: 85%

“…It uses an artificial intelligent developed by computational linguistics to rate and to score the writing submitted to the program (Ferster, et al, 2012;Wilson, 2016;Wilson, & Andrada, 2016) by analyzing the writing on lexical, syntactic, discourse, and grammar levels (Chen & Cheng, 2008;Chou, Moslehpour, & Yang, 2016) and provide diagnostic feedback and correction for the user (Chen & Cheng, 2008). Thus, the user can preview their evaluation result by looking at the feedback and correction given by the system and can start to revise the writing based on the evaluation given by themselves (Chen & Cheng, 2008;Ferster, et al, 2012) and save their time in checking and evaluating the writings (Chou, et al, 2016;Cotos, 2011;Roscoe, Wilson, Johnson, & Mayra, 2017). Equipped with the diagnostic feature, AWE program can be considered as an effective tool in evaluating writing.…”

Section: Pjee -------------------------------------------------------mentioning

confidence: 99%

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Utilizing Grammarly in Evaluating Academic Writing: A Narrative Research on Efl Students’ Experience

Nova¹

2018

PJEE

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With the development of technology, any writer now can easily check their academic writing with automated writing evaluation program. Though, the utilization of this program may bring both benefits and drawbacks. Thus, a consideration of its strengths and weaknesses is needed. To fill the need, this study aimed to identify the strengths and weaknesses of Grammarly program as an automated writing evaluation program in evaluating academic writing. Using a narrative inquiry in exploring three Indonesian postgraduate students’ experiences by conducting interview and documentation, the result showed that this program has provided useful color-coded feedback with explanation and example, ease of account access, high rate of evaluation speed, and free service for evaluating academic writing. However, some caveats were also found in this program utilization, such as several misleading feedbacks, weaknesses on detecting the type of English and reference list, and lack of context and content evaluation experienced, which became the weaknesses of this program. Further investigation on the efficiency of the feedback given by Grammarly in improving students’ writing quality is needed.

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Section: Discussionmentioning

confidence: 85%

Section: Pjee -------------------------------------------------------mentioning

confidence: 99%

Utilizing Grammarly in Evaluating Academic Writing: A Narrative Research on Efl Students’ Experience

Nova¹

2018

PJEE

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“…While evaluating learners' compositions might be time-consuming, Chen, Chiu, and Liao (2009) indicated that "with the help of the help of these new computer programs, writing teachers can reduce the time spent correcting and commenting on students' compositions" (p. 3). Besides, AWE increases more writing practices and assist writing instruction (Roscoe et al, 2017). As pointed out by Vojak, Kline, Cope, McCarthey, & Kalantzis (2011), AWE provides not only comprehensive summative scores but also feedbacks that are beyond mechanic levels and contain writing components such as content and development, organization, and so on.…”

Section: Automated Writing Evaluationmentioning

confidence: 99%

An AWE-Based Diagnosis of L2 English Learners’ Written Errors

Lei¹

2020

ELT

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While Automated Writing Evaluation (AWE) can perform an error diagnosis (Chen & Cheng, 2008), previous studies used to exclude it from the process of error analysis. This study aimed to examine the reactions of Grammarly Premium towards a group of night school students’ English writings at a Taiwanese technical university. The participants of the research produced 175 essays. The researcher checked the data against the AWE program. 1042 errors were detected and classified into 40 types. The 40 types of errors were at three hierarchical levels: a word and phrase level, a sentence level, and a discourse level. This study suggested future studies to view AWE’s functions in a new perspective and find it a space in the process of error diagnosis.

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“…Rogers (1986) noted that one component of the industrial age that continued to flourish during the information age was automation. Automation is a challenging term to define, but most agree that it is the use of machines “to execute or help execute physical operations, computational commands or tasks” (Nof, 2009, p. 43), such as automated nurse rostering (Mihaylov, Smet, Van Den Noortgate, & Vanden Berghe, 2016), automated writing evaluation (e.g., Roscoe, Wilson, Johnson, & Mayra, 2017), virtual research assistants (e.g., Hasler, Tuchman, & Friedman, 2013), automated bots (e.g., Clément & Guitton, 2015; Edwards, Beattie, Edwards, & Spence, 2016), and automated health systems (e.g., Farzanfar, Frishkopf, Friedman, & Ludena, 2007). Rogers observed that during the information age automation was materializing in new arenas, including communication.…”

Section: Do Some People Avoid Automated Communication Technologies? Vmentioning

confidence: 99%

I want to talk to a real person: theorising avoidance in the acceptance and use of automated technologies

et al. 2017

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Automated communication systems are increasingly common in mobile and ehealth contexts. Yet, there is reason to believe that some high risk segments of the population might be prone to avoid automated systems even though they are often designed to reach these groups. To facilitate research in this area, avoidance of automated communication (AAC) is theorized – and a measurement instrument validated – across two studies. In study 1, an AAC scale was found to be unidimensional and internally reliable as well as negatively correlated with comfort, perceptions, and intentions to use technology. Moreover, individuals with social phobia had lower AAC scores which was consistent with the idea that they preferred non-human interaction facilitated by automated communication. In study 2, confirmatory factor analysis supported the unidimensional structure of the measure and the instrument once again proved to be reliable. Individuals with lower AAC had greater intentions to utilize automated communication, EHRs, and an automated virtual nurse program. AAC is a disposition that predicts significant variance in intentions and comfort with various automated communication technologies. Avoidance increases with age but may be mitigated by systems that allow participants to opt-out or immediately interact with a live person.

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Presentation, expectations, and experience: Sources of student perceptions of automated writing evaluation

Cited by 65 publications

References 42 publications

Utilizing Grammarly in Evaluating Academic Writing: A Narrative Research on Efl Students’ Experience

Utilizing Grammarly in Evaluating Academic Writing: A Narrative Research on Efl Students’ Experience

An AWE-Based Diagnosis of L2 English Learners’ Written Errors

I want to talk to a real person: theorising avoidance in the acceptance and use of automated technologies

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