better essay checker

better essay checker

Improving the Accuracy and Effectiveness of Essay Checkers

1. Introduction to Essay Checkers

The difference in the sort of features upon which essay checkers are based results in two differing ways of using these assessment tools. Once a score is received, feedback to the person can only be provided by another person. The medium of the two-way interaction in which the humans communicate is the written essay. Hence, the first use is in providing formative feedback acceptable to the students who were the authors of the essays. The second use is to allow the checker to redefine its hard-and-fast decision when it is apparent that the basis for that decision was mistaken. This function is referred to as the scribe stop functionality of the expert interface shell. Although in the past mastery learning, AI-tutoring, and AI-assessment research had barely intersected, the development of that expert interface shell, organizers of the workshop, and contributors to this special issue are gradually beginning to recognize the importance of the scribe stop capabilities of assessment tools within educational software systems that provide formative feedback.

The increasing digitization of educational materials has led to the desire to automate the grading of student essays. But while there has been extensive work on the use of computerized techniques to grade and provide immediate feedback on grammar, spelling, word usage, and factual information, computer-aided efforts to improve the accuracy and effectiveness of teachers’ final judgments about the overall quality of students’ essays have been significantly less successful. Computers can now easily enable many students to receive at least some feedback on their work, but the lack of inter-rater agreement of teachers about student essay quality, together with the cost of having multiple human graders, typically require the development of a computerized essay grader to rely upon features other than those available in the essay for the first one or two drafts.

2. Challenges and Limitations of Current Essay Checkers

The use of essay checker systems allows the instructor to assign a standardized grade to tasks that have usually been beyond the reach of large scale projects. The system can also be used to more effectively pair assessment with instruction, identifying students who require clarification on a topic, remediation of incorrect ideas, or the opportunity for more challenging work. Given the lengthy process of essay marking, many instructors under-sample student homework, “attempting to provide a well-rounded assessment of several abilities.” This can lead to assessments for which students may not receive immediate feedback and can lengthen the time required to cover a course’s topics.

While current essay checker technology represents a significant improvement over the use of human instructors to review essays, it nevertheless faces a number of challenges and limitations. Some of the major challenges we see in creating and deploying effective and accurate essay checker technology include issues related to the rating of the essay and the validation and scoring of students’ responses. Among other things, such response validation often requires that items are rated as correct or incorrect with a single correct solution, a potentially limiting constraint given the open-ended nature of essay and long answer questions. Essays are expected to contain original content by the student that is relevant to the question or task at hand and be free of material copied from other sources that is not cited using a recognized citation format.

3. Innovations and Technologies for Enhancing Essay Checkers

Checking and essay writing tasks hinge on a set of intrinsic parameters that are not necessarily covered by the current spellers and checking tools: The incomplete acquisition of a culture-dependent language (school years vs. out-of-school education, migration with no correspondence between the foreign host country and the origin conventions, etc.); The use of an L2 and L3 on the multiplicity of semio-linguistic and strategic bases (variances between the mother tongue and foreign tongue we are handling, transfer laws, linguistic transparency, partial command of the linguistic code, reasons for the foreign linguistic choice, frustrating, unhearable, non-pedagogical, ideological, or confidential motivations, etc.); Intertextuality (usage of libraries; borrowing, quoting or assembling different types of texts, whether from various authors or schools of thought; etc.).

Both current stylistic and grammatical tools possess the characteristic of having grown out of a word-based essay corrector, which provided an underlining hint to the user sketching the location of an anomaly, its length, and an adjacent word-based denotation that a possible source of error was to be found close by. The suggestion signaled was then generated on the basis of several thousand lexico-contextual sequences previously input through the respective software engineering phases.

In its broadest sense, an essay checker is any software program able to recognize and indicate some or very specific errors of the lexico-morphological, syntactic, semantic, pragmatic, quantificational, nucleating, or prosodic types on the one hand, and to communicate such language errors to the student writer/essay checker user for direct or indirect self-correction on the other.

Contrary to the previous demand or extrinsic approach to error correction and essay writing, numerous educational, linguistic, and pedagogical research studies have changed goals for error correction to favor an intrinsic approach to each such process. Current technological advances can make good use of the know-how acquired through these pedagogical inquiries and model and theorize on the very specific interfaces inherent to essay writing and correction activities. Such a technology may be encapsulated in what the presenters have called “essay checkers.”

4. Best Practices for Utilizing Essay Checkers

Different essay checkers are likely to be utilized at varying expertise levels, with experts’ needs differing from those of less experienced or trained users. For instance, the countless potential complexities of a grammar point require differing levels of rules and details at which to discuss them with novices, intermediate English language learners, and experts. When experts make decisions about the value of including specific points of advice, halt error identification, or analyze the content of advice to decide whether to follow the advice, they benefit from tools that can provide more than just a list of potential errors and suggestions.

Effective utilization of the essay checkers previously mentioned can help catch many potential errors. For clarity in discussing the function levels involved in these combinations, “essay checker” is used to refer to systems that automatically assess text according to sets of features (levels 4-9), and “user” is used to refer to the person who writes, reviews, and considers revising the essay (levels 1-3). “Automated essay scoring” refers to the practice of assessing features of writing used for scoring (levels 5-8) by using programs that analyze text (automatic assessment up to level 4) combined with human assessments as training data. At this time, no existing essay checker appears to provide automated scoring at the same level employed by standardized tests, which consider about ten different features, with exemplary essays scored by experts assessing writing based on multiple criteria.

3. Utilizing Essay Checkers

5. Conclusion and Future Directions

We have built EST, an error-specific scorer, and have used it to produce and evaluate two new essay checkers. EST and these checkers reveal numerous expert errors and highly frequent n-gram errors in essay writing that have eluded previous oversight. The detection accuracy of the essay checkers promises to be substantially more effective in comparison to previous essay checkers, which have overlooked important determinative characteristics of essay writing in their preoccupation with surface-related, single word features. Our initial error analysis also suggests three fruitful directions for distinguishing among these categories on a more fine-grained basis in the future. We point to better effective rules and methods for misspelled word detection and for stylistic error detection in future essay checker work. At the same time, however, all essay checkers which detect any category of error promise an accompanying substantial raise in writing quality over what it would be without them.

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