Naslov
Exploring Pre-scoring Clustering for Short Answer Grading

Autori
Petricioli, Lucija ; Skračić, Kristian ; Petrović, Juraj ; Pale, Predrag

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
2023 46th International Convention on Information, Communication and Electronic Technology (MIPRO) / - Rijeka : Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO, 2023, 1782-1786

Skup
46th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO 2023)

Mjesto i datum
Opatija, Hrvatska, 22.05.2023. - 26.05.2023

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
automatic short answer grading ; ASAG ; semiautomated short answer scoring ; short answer grading ; short text ; short answer ; automatic grading ; natural language processing

Sažetak
Automatic short answer grading is a topic that has gained significant popularity recently, especially due to developments in natural language processing. While automated grading in computer supported assessment tasks traditionally imposed significant restrictions on the answer format (e.g., multiple choice questions), automated short answer grading could enable assessment scalability with very few answer format limitations and thereby increase the assessment tasks’ validity. Here, ‘short answer’ refers to a text of up to, approximately, 10 sentences. However, automatic solutions require a lot of pre-graded material. In this paper, several pre-trained machine learning models were utilized to explore pre-scoring clustering for short answer grading of text in Croatian. The aim of this approach is to shorten the process of manual short answer grading by clustering similar answers, facilitating the development of automatic grading solutions. The described approach was evaluated on a dataset containing graduate students’ answers in Croatian to six questions related to cyber security topics. The obtained results are promising and show how increases in cluster purity, normalized mutual information, Rand index, and adjusted Rand index measures can be achieved by finetuning a pre-trained model.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

POVEZANOST RADA