“Get it off my stack”: Teachers’ tools for grading papers

Baker, Nicki Litherland

doi:10.1016/j.asw.2013.11.005

Cited by 10 publications

(5 citation statements)

References 33 publications

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“…Despite the significant impact of the traditional written feedback approach, paradoxical findings in the literature reveal that teachers struggle to keep up with the workload of providing formative written feedback on student assignment (Graham, Hebert, and Harris 2015). What is more is that students also fail to acquire proficient writing skills as they ignore or misunderstand the formative written feedback (Baker 2014;McGrath and Atkinson-Leadbeater 2016). Besides, it has been found that students with lower levels of language proficiency can feel overwhelmed by large amounts of written feedback (Lee 2014).…”

Section: From Traditional Written To Digital Audio Feedbackmentioning

confidence: 99%

“…However, giving written feedback to students in large online classes is probably the most time-consuming task for a writing instructor (Hill, 2019). According to Baker (2014), many students become dissatisfied and fail to acquire proficient academic writing skills as they ignore or misunderstand the formative written feedback. They also lack adequate personalized oral feedback on their academic writing projects.…”

Section: Introductionmentioning

confidence: 99%

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Kaizena: A Tool for Individualized Audio Feedback on Academic Writing

Mohand Cherif,

Al Aufi

2023

ESPEAP

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Providing effective feedback through audio technology to promote academic writing has yet to be fully explored in higher education institutions today. This paper reports on an investigation of how the final level foundation students have perceived the impact of Kaizena as a digital audio feedback tool in the context of a higher education institution in Oman named the Middle East College (MEC). The Kaizena platform was more specifically evaluated in terms of its effectiveness as to foundation students’ provision of personalized audio feedback concerning their academic writing. The paper adopted an interpretive epistemological stance with quantitative and qualitative inquiries to seek EFL students’ perceptions. A set of questionnaires was distributed to 60 students at English foundation levels to collect their satisfaction on the tool in question while a focus group of 6 EFL students taking up an academic writing course were interviewed to elicit their deep analytical responses towards the investigated modality of feedback. Overall, the data-driven results revealed positive responses from the participants, assuring that Kaizena is an effective tool to be used to ultimately enhance their academic writing skills. Implications on the usefulness of the Kaizena and its potential applicability and dissemination in a wider context of HEIs worldwide are discussed.

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Section: From Traditional Written To Digital Audio Feedbackmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Kaizena: A Tool for Individualized Audio Feedback on Academic Writing

Mohand Cherif,

Al Aufi

2023

ESPEAP

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“…Balances must be struck among instruction, formative evaluation, summative assessment, standardized test preparation, daily classroom management issues, and a myriad of other school‐related responsibilities. Each writing task assigned must be thoroughly evaluated, and writing evaluation takes copious amounts of time (Baker, ). Providing in‐depth feedback both written and orally is an important component of formative writing assessment.…”

Section: National Surveys Examining Secondary Student Writingmentioning

confidence: 99%

Writing Instruction for Adolescents in the Shadow of the Common Core State Standards

Sundeen¹

2015

J Adolescent & Adult Lit

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Current research on instructional practices for secondary student writing reveals little emphasis on compositions requiring analysis, interpretation, and argumentation. Indeed, written expression in English language arts classes is generally confined to writing assignments of very limited length. Additionally, recent efforts for educational reform have led the United States to adopt national academic standards. This article examines the current focus on composition instruction for secondary students as girded by recent influences of the Common Core State Standards and within the context of four teacher surveys that investigated secondary instructional practices for writing. Questions are addressed regarding what and how often students write, influences of standardized testing, instruction for struggling writers, and teacher preparation. Opportunities are described for improving secondary writing instruction while building on the potential effects of the Common Core.

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“…Despite the generally acknowledged importance of individualized teacher feedback in the teaching of writing, its real-life implementation is rather a strenuous act for teachers in many institutions of various educational levels due to issues of excessive workload on behalf of teachers resulting from the high number of students they have to deal with each semester, which is still an unsolved problem (Ritten, 2012) that is also criticized by Baker (2014). The ideal conditions in terms of teacher feedback in writing are also considered to be unattainable in practical classroom conditions by Hyland and Hyland (2006).…”

Section: Introductionmentioning

confidence: 99%

Home-Grown Automated Essay Scoring in the Literature Classroom: A Solution for Managing the Crowd?

Uzun

2018

CONT ED TECHNOLOGY

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Managing crowded classes in terms of classroom assessment is a difficult task due to the amount of time which needs to be devoted to providing feedback to student products. In this respect, the present study aimed to develop an automated essay scoring environment as a potential means to overcome this problem. Secondarily, the study aimed to test if automatically-given scores would correlate with the scores given by a human rater. A quantitative research design employing a machine learning approach was preferred to meet the aims of the study. The data set to be used for machine learning consisted of 160 scored literary analysis essays written in an English Literature course, each essay analyzing a theme in a given literary work. To train the automated scoring model, LightSide software was used. First, textual features were extracted and filtered. Then, Logistic Regression, SMO, SVO, Logistic Tree and Naïve Bayes text classification algorithms were tested by using 10-Fold Cross-Validation to reach the most accurate model. To see if the scores given by the computer correlated with the scores given by the human rater, Spearman's Rank Order Correlation Coefficient was calculated. The results showed that none of the algorithms were sufficiently accurate in terms of the scores of the essays within the data set. It was also seen that the scores given by the computer were not significantly correlated with the scores given by the human rater. The findings implied that the size of the data collected in an authentic classroom environment was too small for classification algorithms in terms of automated essay scoring for classroom assessment.

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“Get it off my stack”: Teachers’ tools for grading papers

Cited by 10 publications

References 33 publications

Kaizena: A Tool for Individualized Audio Feedback on Academic Writing

Kaizena: A Tool for Individualized Audio Feedback on Academic Writing

Writing Instruction for Adolescents in the Shadow of the Common Core State Standards

Home-Grown Automated Essay Scoring in the Literature Classroom: A Solution for Managing the Crowd?

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