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Semantics-based automated essay evaluation
ZUPANC, KAJA (Author), Bosnić, Zoran (Mentor) More about this mentor... This link opens in a new window

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Abstract
Automated essay evaluation (AEE) is a widely used practical solution for replacing time-consuming manual grading of student essays. Automated systems are used in combination with human graders in different high-stake assessments, as well as in classrooms. During the last 50 years, since the beginning of the development of the field, many challenges have arisen in the field, including seeking ways to evaluate the semantic content, providing automated feedback, determining reliability of grades, making the field more "exposed", and others. In this dissertation we address several of these challenges and propose novel solutions for semantic based essay evaluation. Most of the AEE research has been conducted by commercial organizations that protect their investments by releasing proprietary systems where details are not publicly available. We provide comparison (as detailed as possible) of 20 state-of-the-art approaches for automated essay evaluation and we propose a new automated essay evaluation system named SAGE (Semantic Automated Grader for Essays) with all the technological details revealed to the scientific community. Lack of consideration of text semantics is one of the main weaknesses of the existing state-of-the-art systems. We address the evaluation of essay semantics from perspectives of essay coherence and semantic error detection. Coherence describes the flow of information in an essay and allows us to evaluate the connections between the discourse. We propose two groups of coherence attributes: coherence attributes obtained in a highly dimensional semantic space and coherence attributes obtained from a sentence-similarity networks. Furthermore, we propose the Automated Error Detection (AED) system and evaluate the essay semantics from the perspective of essay consistency. The system detects semantic errors using information extraction and logic reasoning and is able to provide semantic feedback for the writer. The proposed system SAGE achieves significantly higher grading accuracy compared with other state-of-the-art automated essay evaluation systems. In the last part of the dissertation we address the question of reliability of grades. Despite the unified grading rules, human graders introduce bias into scores. Consequently, a grading model has to implement a grading logic that may be a mixture of grading logics from various graders. We propose an approach for separating a set of essays into subsets that represent different graders, which uses an explanation methodology and clustering. The results show that learning from the ensemble of separated models significantly improves the average prediction accuracy on artificial and real-world datasets.

Language:English
Keywords:automated scoring, essay evaluation, natural language processing, semantic attributes, coherence, semantic feedback
Work type:Doctoral dissertation (mb31)
Organization:FRI - Faculty of computer and information science
Year:2018
Views:239
Downloads:105
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Secondary language

Language:Slovenian
Title:Semantično usmerjeno avtomatsko ocenjevanje esejev
Abstract:
Avtomatsko ocenjevanje esejev predstavlja praktično rešitev za številne težave, povezane s časovno zahtevnim ročnim ocenjevanjem. Avtomatizirani sistemi se uporabljajo v kombinaciji s človeškimi ocenjevalci pri številnih standardiziranih testih, vse več pa tudi v učilnicah. V zadnjih 50 letih, od začetka razvoja področja, so se pojavili številni izzivi, vključno z iskanjem pristopov za ocenjevanje semantične vsebine, zagotavljanjem avtomatskih povratnih informacij, določanjem zanesljivosti ocen, težnjo po dostopnosti podrobnosti delovanja sistemov in s tem odprtosti področja, in drugi. V pričujoči disertaciji obravnavamo te izzive in predlagamo nove rešitve za semantično usmerjeno avtomatsko ocenjevanje esejev. Eden od glavnih problemov sistemov za avtomatsko ocenjevanje esejev je problem ocenjevanja semantične pravilnosti besedila. V disertaciji obravnavamo ocenjevanje semantike besedila z različnimi pristopi: ocenjevanje koherence esejev in zaznavanje semantičnih napak. Koherenca opisuje pretok informacij v eseju in nam omogoča, da ocenimo povezanost besedila. Predlagamo dve skupini atributov za ocenjevanje koherence: atributi, pridobljeni v visoko dimenzionalnem semantičnem prostoru, in atributi, pridobljeni iz omrežij stavčne podobnosti. Poleg tega predlagamo sistem za avtomatsko odkrivanje napak, ki nam pomaga oceniti semantiko eseja z vidika doslednosti. Sistem zaznava semantične napake z uporabo ekstrakcije informacij in logičnega sklepanja ter zagotavlja povratno semantično informacijo. Predlagani sistem SAGE (Semantic Automated Grader for Essays) dosega višjo napovedno točnost v primerjavi z drugimi sodobnimi sistemi za avtomatsko ocenjevanje esejev. V zadnjem delu disertacije se posvečamo vprašanju zanesljivosti ocen. Kljub poenotenim kriterijem za človeške ocenjevalce, ocenjevalci vnašajo pristranskost v rezultate. Zato mora napovedni model uporabiti napovedno logiko, ki je lahko mešanica ocenjevalne logike različnih ocenjevalcev. Predlagamo pristop za ločevanje množice esejev v podmnožice, ki predstavljajo različne ocejevalce, kjer uporabimo metodologijo razlage napovedi in gručenje. Rezultati kažejo, da učenje na ansamblu ločenih modelov bistveno izboljša povprečno točnost napovedi na umetnih in realnih podatkovnih množicah.

Keywords:avtomatsko ocenjevanje, evalvacija esejev, procesiranje naravnega jezika, semantični atributi, skladnost, semantična povratna informacija

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