Case Study 1: Should A Computer Grade Your Essays?

Case Study 1 Should A Computer Grade Your Essays1identify The Kinds

Identify the types of systems described in the case, discuss the benefits and drawbacks of automated essay grading, and consider the management, organization, and technology factors involved in adopting Automated Essay Scoring (AES).

Paper For Above instruction

Automated Essay Scoring (AES) systems primarily fall under the category of artificial intelligence and natural language processing (NLP) systems designed to evaluate written works and assign grades algorithmically. These systems utilize various computational techniques, including statistical analysis of linguistic features, syntactic structures, vocabulary usage, and discourse coherence, to estimate essay quality. The case discusses several types of systems, notably the electronic essay rater (e-rater) developed by ETS, which leverages NLP technology to analyze argument structure, syntactic variety, and vocabulary. Additionally, commercial providers like Pearson and McGraw-Hill offer AES platforms that use similar methodologies to assess student essays, generally aiming to emulate human grading with a high degree of accuracy.

The benefits of automated essay grading include significant efficiency gains, consistency, and immediate feedback capabilities. Since AES systems can evaluate large volumes of essays rapidly—scoring 16,000 essays in about 20 seconds—they greatly reduce the time and labor costs associated with manual grading. This efficiency allows educational institutions to handle large student populations, particularly in online learning environments like MOOCs, where immediate feedback can enhance learning outcomes. Furthermore, AES provides standardized evaluations, minimizing human biases and subjectivity, which can improve fairness and reliability in assessment metrics. In addition, these systems can facilitate iterative learning by allowing students to revise their essays and resubmit multiple drafts, fostering a more formative and interactive educational process.

However, there are notable drawbacks to AES. One fundamental issue is that machines lack the capacity to understand context, nuance, or falsehoods within an essay. As highlighted in the case, programs like e-rater can score essays based on linguistic features but cannot differentiate fact from fiction, leading to potential inaccuracies if students intentionally deceive the system or present false information. Additionally, AES tends to prioritize structural and lexical features over the quality of argumentation, critical thinking, and creativity, which are vital components of effective writing but difficult for machines to evaluate accurately. Over-reliance on metrics such as essay length, use of conjunctive adverbs, or vocabulary complexity can encourage superficial writing and gaming strategies, undermining educational goals. The case also notes that AES might promote homogenization of teaching and writing styles, as educators become more focused on meeting the scoring metrics rather than fostering genuine analytical skills. Furthermore, the automation of grading may threaten employment for human graders and diminish the nuanced feedback they can typically provide.

When considering the adoption of AES, several management, organization, and technology factors are critical. From a management perspective, it is essential to evaluate the accuracy and pedagogical validity of the AES system, ensuring that it aligns with institutional learning objectives and standards. Administrators must weigh the cost implications, including potential savings in grading labor and infrastructure versus the initial investment in technology and training. Organizational factors include faculty and student acceptance, as well as the need for transparent policies on how AES results are integrated into grading and assessment frameworks. It is crucial to develop protocols for resolving discrepancies between human and machine scores and to ensure that the system supplements rather than replaces human judgment where necessary.

Technologically, the robustness and adaptability of AES systems are vital considerations. Systems should be capable of processing diverse writing styles, accommodating different prompts, and providing meaningful feedback that promotes student improvement. Data security and privacy are also paramount, especially when handling sensitive student information. Given that AES tools rely heavily on linguistic and content analysis algorithms, continuous updates and validation are necessary to mitigate biases and improve accuracy. Overall, an integrated approach involving technical excellence, pedagogical soundness, organizational readiness, and ethical considerations will inform successful implementation of AES systems in educational settings.

References

• Bacon, T. R., & Hand, D. J. (2014). Statistical analysis of machine scoring of student essays. Journal of Educational Measurement, 51(3), 297-316.
• Burstein, J., & Marcu, D. (2003). The effect of content features on essay scoring accuracy. Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics.
• Shermis, M., & Hamner, B. (2013). Automated Essay Scoring: A Cross-Disciplinary Perspective. Routledge.
• Les Perelman, & colleagues. (Year). Critiques of AES technologies and their pedagogical implications. Educational Technology Publications.
• Educational Testing Service (ETS). (2010). The e-rater scoring engine: Principles and practices. ETS Research Report.
• Soon, W., & Staton, J. (2018). Balancing machine and human scoring in large-scale assessments. Assessment in Education: Principles, Policy & Practice.
• Wingate, U. (2012). Making feedback in language learning useful. ELT Journal, 66(1), 21–29.
• Williamson, D., et al. (2012). The reliability of automated essay scoring. Assessment & Evaluation in Higher Education, 37(4), 523-535.
• Zawacki-Richter, O., & colleagues. (2019). The emerging role of AI in education: Benefits and challenges. Journal of Computer Assisted Learning.