Performance Analysis of Students by Evaluating Their Examination Answer Scripts by Using Soft Computing Techniques

Bhanuprakash, C.; Nijagunarya, Y. S.; Jayaram, M. A.

doi:10.1007/978-981-13-5802-9_50

Cited by 2 publications

(1 citation statement)

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“…The most common uses of automatic scoring and feedback are in three areas: 1) programming problems through mechanisms such as assisting the student with the coding [86,87], analyzing coding patterns [88,89], automatic grading [90,91,92,93,94], and customized feedback [86,88,87]; 2) short essays [95]; and 3) extended essays [96]. Programming problems are the easiest to use as input for this technology, as they appear in a structured language that computers can understand [97].…”

Section: Discussionmentioning

confidence: 99%

A Systematic Review of the Effects of Automatic Scoring and Automatic Feedback in Educational Settings

Hahn

Navarro

de-la-Fuente-Valentín

et al. 2021

IEEE Access

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Automatic scoring and feedback tools have become critical components of online learning proliferation. These tools range from multiple-choice questions to grading essays using machine learning (ML). Learning environments such as massive open online courses (MOOCs) would not be possible without them. The usage of this mechanism has brought many exciting areas of study, from the design of questions to the ML grading tools' precision and accuracy. This paper analyzes the findings of 125 studies published in journals and proceedings between 2016 and 2020 on the usages of automatic scoring and feedback as a learning tool. This analysis gives an overview of the trends, challenges, and open questions in this research area. The results indicate that automatic scoring and feedback have many advantages. The most important benefits include enabling scaling the number of students without adding a proportional number of instructors, improving the student experience by reducing the time between submission grading and feedback, and removing bias in scoring.On the other hand, these technologies have some drawbacks. The main problem is creating a disincentive to develop innovative answers that do not match the expected one or have not been considered when preparing the problem. Another drawback is potentially training the student to answer the question instead of learning the concepts. With this, given the exitance of a correct answer, such an answer could be leaked to the internet, making it easier for students to avoid solving the problem. Overall, each of these drawbacks presents an opportunity to look at ways to improve technologies to use these tools to provide a better learning experience to students.

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