![]() Sentence fluency - The use of complex and varied sentences to skillfully create a smooth flow of ideas.Word choice - The appropriate use of advanced vocabulary, precision, and application of vocabulary to an essay.Style - The use of strong word choices and varied sentence constructions to establish a unique voice that connects with the audience.Organization - The writer’s overall plan (coherence) and internal weaving together of ideas (cohesion).Development of Ideas - The writer’s presentation of supportive details and information pertinent to support their idea.The six characteristics of effective writing that PEG provides scores on are outlined below (learn more at ). Today, PEG provides scores on six characteristics of writing and uses separate models for three genres: argumentative, informational/explanatory, and narrative. Over time, scoring evolved to provide feedback on unique traits of effective writing, and different scoring algorithms were developed for distinct genres. When PEG was first used operationally, its focus was on predicting scores holistically that is, recovering the overall writing score a human assigned the essay. Teachers can give quick, quantitative ratings on how effectively students used textual evidence as well as how accurate the content of their writing is in relation to a given prompt topic. To balance out the automated PEG feedback, ERB Writing Practice also includes options for users to collect feedback from peers and/or teachers. While that criticism is valid, the 30-40 variables used by PEG represent the traits and skills of good writing, and thus are extremely relevant to budding writers who need feedback to learn how to improve their writing as they practice. Humans can rate the quality of an idea or the strength of an argument in ways that computers cannot, even if such ratings can be idiosyncratic and inconsistent at times. Once the model is trained, the automated scoring system “reads” subsequent essays, quantifies values for them on each variable in the model, and uses the prediction model to score the essay.ĭespite the proven accuracy of automated scoring systems, a common criticism is that the scores such systems produce lack an understanding of the meaning of a student-written essay. ![]() 80s on a scale of 0-1, which is a high level of prediction accuracy-one that is typically higher than correlations between different human raters and themselves. ![]() In most instances, the combination of these variables yields correlations with human raters in the mid. Typical examples of such variables include sentence length, use of higher-level vocabulary, and grammar. The models typically include 30-40 features, or variables, within a set of essays that predict human ratings. PEG and other systems require training essays that have human scores, and these systems use such essays to create scoring (or prediction) models. The foundational concept of automated scoring is that good writing can be predicted. PEG was eventually acquired by ERB’s longtime partner, Measurement Inc., and continues to evolve and find new uses today. ![]() Given that Page’s two principles are still as relevant today as they were then, PEG was given new life in the 1990s scoring essays for NAEP, Praxis, and GRE testing programs when computerization became feasible. The state of computers at the time of Page’s invention did not leave much room for automation, so PEG lay dormant until the mid-1980s. It was invented in the 1960s by Ellis Batten Page, a former high school English teacher, who spent “many long weekends sifting through stacks of papers wishing for some help.” His guiding principles? 1) the more we write, the better writer we become, and 2) computers can grade as reliably as their human counterparts (Page, 2003). PEG, or Project Essay Grade, is the automated scoring system at the core of ERB Writing Practice.
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