2015
DOI: 10.1558/wap.v7i2-3.26376
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Using the Developmental Path of Cause to Bridge the Gap between AWE Scores and Writing Teachers’ Evaluations

Abstract: Supported by artificial intelligence (AI), the most advanced Automatic Writing Evaluation (AWE) systems have gained increasing attention for their ability to provide immediate scoring and formative feedback, yet teachers have been hesitant to implement them into their classes because correlations between the grades they assign and the AWE scores have generally been low. This begs the question of where improvements in evaluation may need to be made, and what approaches are available to carry out this improvemen… Show more

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Cited by 11 publications
(3 citation statements)
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“…However, they stressed that not all writing genres may be appropriate for AES and that it would be impractical to use in most small classrooms, due to the need to calibrate the system with a large number of pre-scored assessments. The benefits of using algorithms that find patterns in text responses, however, has been found to lead to encouraging more revisions by students (Ma & Slater, 2015) and to move away from merely measuring student knowledge and abilities by multiple choice tests (Nehm, Ha, & Mayfield, 2012). Continuing issues persist, however, in the quality of feedback provided by AES (Dikli, 2010), with Barker (2011) finding that the more detailed the feedback provided was, the more likely students were to question their grades, and a question was raised over the benefits of this feedback for beginning language students (Aluthman, 2016).…”
Section: Assessment and Evaluationmentioning
confidence: 99%
“…However, they stressed that not all writing genres may be appropriate for AES and that it would be impractical to use in most small classrooms, due to the need to calibrate the system with a large number of pre-scored assessments. The benefits of using algorithms that find patterns in text responses, however, has been found to lead to encouraging more revisions by students (Ma & Slater, 2015) and to move away from merely measuring student knowledge and abilities by multiple choice tests (Nehm, Ha, & Mayfield, 2012). Continuing issues persist, however, in the quality of feedback provided by AES (Dikli, 2010), with Barker (2011) finding that the more detailed the feedback provided was, the more likely students were to question their grades, and a question was raised over the benefits of this feedback for beginning language students (Aluthman, 2016).…”
Section: Assessment and Evaluationmentioning
confidence: 99%
“…Automated Essay Scoring (AES) systems are the most common AI-powered assessments and can be applied across various disciplines, but most of the research has focused on its application to undergraduate courses (Zawacki-Richter et al, 2019). There are a variety of methods of developing AES systems, such as statistical modelling, natural language processing (NLP) and Latent Semantic Analysis (LSA), and the algorithms can be used to identify patterns in text responses and prompt students to revise their responses (Ma & Slater, 2015). This, in turn, could allow educators to consider a broader range of assessment methods than only using multiple-choice tests to assess students' knowledge and abilities.…”
Section: Teacher-facing Ai Applicationsmentioning
confidence: 99%
“…Despite two studies reporting no statistically significant change in academic performance, both Topal et al (2021) and Yang & Shulruf (2019) observed increased learning interest and confidence among students. While AI can be an invaluable tool in the assessment and feedback process, it is not without its limitations, particularly in comprehending intricate subject matter, contextual nuances, and the distinctive qualities of individual students' work [4] [5] . Moreover, there is a lack of standardized criteria for evaluating the appropriateness of grading systems across diverse contexts [6] .…”
Section: Introductionmentioning
confidence: 99%