Avtomatsko ocenjevanje odgovorov na vprašanja odprtega tipa : magistrsko delo

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Title:	Avtomatsko ocenjevanje odgovorov na vprašanja odprtega tipa : magistrsko delo
Authors:	ID Kac, Klemen (Author) ID Bošković, Borko (Mentor) More about this mentor... ID Majninger, Sandi (Comentor)
Files:	MAG_Kac_Klemen_2020.pdf (1,06 MB) MD5: DC6FEE4539BBE64BCE5ABB37F8D5151B PID: 20.500.12556/dkum/3a142379-9827-4b1c-b6de-2af4e42b8e8d
Language:	Slovenian
Work type:	Master's thesis/paper
Typology:	2.09 - Master's Thesis
Organization:	FERI - Faculty of Electrical Engineering and Computer Science
Abstract:	V magistrskem delu smo implementirali algoritem, ki avtomatsko oceni odgovore na vprašanja odprtega tipa. Odgovor študenta primerjamo s pričakovanim na podlagi različnih tehnik leksikalne in semantične analize. Algoritem temelji na poravnavi besed med pričakovanim in vnesenim odgovorom ter računanjem razdalje med vektorji besednih vložitev iz obeh odgovorov. Za implementacijo smo uporabili programski jezik Python.
Keywords:	avtomatsko ocenjevanje, ocenjevanje odgovorov, obdelava naravnega jezika, vprašanja odprtega tipa
Place of publishing:	Maribor
Place of performance:	Maribor
Publisher:	[K. Kac]
Year of publishing:	2020
Number of pages:	IX, 59 str.
PID:	20.500.12556/DKUM-77208
UDC:	004.434:81\'322.2(043.2)
COBISS.SI-ID:	38103299
NUK URN:	URN:SI:UM:DK:KFGLNVVH
Publication date in DKUM:	03.11.2020
Views:	1215
Downloads:	136
Metadata:
Categories:	KTFMB - FERI
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License:	CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International

Link:	http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:	The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:	21.08.2020

Language:	English
Title:	Automatic assessment of free-text answers
Abstract:	In this Master’s thesis we implemented an algorithm that automatically evaluates answers to open-ended questions. We compare the student’s answer with the expected one based on different techniques of lexical and semantic analysis. The algorithm is based on the alignment of words between student’s answer and the expected one and calculation of distance between word embedding vectors from both answers. We used Python programming language for implementation.
Keywords:	automatic assessment, free-text answers, natural language processing, open-ended answers

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