A Trustworthy Automated Short-Answer Scoring System Using a New Dataset and Hybrid Transfer Learning Method

To measure the quality of student learning, teachers must conduct evaluations. One of the most efficient modes of evaluation is the short answer question. However, there can be inconsistencies in teacher-performed manual evaluations due to an excessive number of students, time demands, fatigue, etc. Consequently, teachers require a trustworthy system capable of autonomously and accurately evaluating student answers. Using hybrid transfer learning and student answer dataset, we aim to create a reliable automated short answer scoring system called Hybrid Transfer Learning for Automated Short Answer Scoring (HTL-ASAS). HTL-ASAS combines multiple tokenizers from a pretrained model with the bidirectional encoder representations from transformers. Based on our evaluation of the training model, we determined that HTL-ASAS has a higher evaluation accuracy than models used in previous studies. The accuracy of HTL-ASAS for datasets containing responses to questions pertaining to introductory information technology courses reaches 99.6%. With an accuracy close to one hundred percent, the developed model can undoubtedly serve as the foundation for a trustworthy ASAS system

The Influence of Variance in Learner Answers on Automatic Content Scoring

Automatic content scoring is an important application in the area of automatic educational assessment. Short texts written by learners are scored based on their content while spelling and grammar mistakes are usually ignored. The difficulty of automatically scoring such texts varies according to the variance within the learner answers. In this paper, we first discuss factors that influence variance in learner answers, so that practitioners can better estimate if automatic scoring might be applicable to their usage scenario. We then compare the two main paradigms in content scoring: (i) similarity-based and (ii) instance-based methods, and discuss how well they can deal with each of the variance-inducing factors described before

Defining and Assessing Critical Thinking: toward an automatic analysis of HiEd students’ written texts

L'obiettivo principale di questa tesi di dottorato è testare, attraverso due studi empirici, l'affidabilità di un metodo volto a valutare automaticamente le manifestazioni del Pensiero Critico (CT) nei testi scritti da studenti universitari. Gli studi empirici si sono basati su una review critica della letteratura volta a proporre una nuova classificazione per sistematizzare le diverse definizioni di CT e i relativi approcci teorici. La review esamina anche la relazione tra le diverse definizioni di CT e i relativi metodi di valutazione. Dai risultati emerge la necessità di concentrarsi su misure aperte per la valutazione del CT e di sviluppare strumenti automatici basati su tecniche di elaborazione del linguaggio naturale (NLP) per superare i limiti attuali delle misure aperte, come l’attendibilità e i costi di scoring. Sulla base di una rubrica sviluppata e implementata dal gruppo di ricerca del Centro di Didattica Museale – Università di Roma Tre (CDM) per la valutazione e l'analisi dei livelli di CT all'interno di risposte aperte (Poce, 2017), è stato progettato un prototipo per la misurazione automatica di alcuni indicatori di CT. Il primo studio empirico condotto su un gruppo di 66 docenti universitari mostra livelli di affidabilità soddisfacenti della rubrica di valutazione, mentre la valutazione effettuata dal prototipo non era sufficientemente attendibile. I risultati di questa sperimentazione sono stati utilizzati per capire come e in quali condizioni il modello funziona meglio. La seconda indagine empirica era volta a capire quali indicatori del linguaggio naturale sono maggiormente associati a sei sottodimensioni del CT, valutate da esperti in saggi scritti in lingua italiana. Lo studio ha utilizzato un corpus di 103 saggi pre-post di studenti universitari di laurea magistrale che hanno frequentato il corso di "Pedagogia sperimentale e valutazione scolastica". All'interno del corso, sono state proposte due attività per stimolare il CT degli studenti: la valutazione delle risorse educative aperte (OER) (obbligatoria e online) e la progettazione delle OER (facoltativa e in modalità blended). I saggi sono stati valutati sia da valutatori esperti, considerando sei sotto-dimensioni del CT, sia da un algoritmo che misura automaticamente diversi tipi di indicatori del linguaggio naturale. Abbiamo riscontrato un'affidabilità interna positiva e un accordo tra valutatori medio-alto. I livelli di CT degli studenti sono migliorati in modo significativo nel post-test. Tre indicatori del linguaggio naturale sono 5 correlati in modo significativo con il punteggio totale di CT: la lunghezza del corpus, la complessità della sintassi e la funzione di peso tf-idf (term frequency–inverse document frequency). I risultati raccolti durante questo dottorato hanno implicazioni sia teoriche che pratiche per la ricerca e la valutazione del CT. Da un punto di vista teorico, questa tesi mostra sovrapposizioni inesplorate tra diverse tradizioni, prospettive e metodi di studio del CT. Questi punti di contatto potrebbero costituire la base per un approccio interdisciplinare e la costruzione di una comprensione condivisa di CT. I metodi di valutazione automatica possono supportare l’uso di misure aperte per la valutazione del CT, specialmente nell'insegnamento online. Possono infatti facilitare i docenti e i ricercatori nell'affrontare la crescente presenza di dati linguistici prodotti all'interno di piattaforme educative (es. Learning Management Systems). A tal fine, è fondamentale sviluppare metodi automatici per la valutazione di grandi quantità di dati che sarebbe impossibile analizzare manualmente, fornendo agli insegnanti e ai valutatori un supporto per il monitoraggio e la valutazione delle competenze dimostrate online dagli studenti.The main goal of this PhD thesis is to test, through two empirical studies, the reliability of a method aimed at automatically assessing Critical Thinking (CT) manifestations in Higher Education students’ written texts. The empirical studies were based on a critical review aimed at proposing a new classification for systematising different CT definitions and their related theoretical approaches. The review also investigates the relationship between the different adopted CT definitions and CT assessment methods. The review highlights the need to focus on open-ended measures for CT assessment and to develop automatic tools based on Natural Language Processing (NLP) technique to overcome current limitations of open-ended measures, such as reliability and costs. Based on a rubric developed and implemented by the Center for Museum Studies – Roma Tre University (CDM) research group for the evaluation and analysis of CT levels within open-ended answers (Poce, 2017), a NLP prototype for the automatic measurement of CT indicators was designed. The first empirical study was carried out on a group of 66 university teachers. The study showed satisfactory reliability levels of the CT evaluation rubric, while the evaluation carried out by the prototype was not yet sufficiently reliable. The results were used to understand how and under what conditions the model works better. The second empirical investigation was aimed at understanding which NLP features are more associated with six CT sub-dimensions as assessed by human raters in essays written in the Italian language. The study used a corpus of 103 students’ pre-post essays who attended a Master's Degree module in “Experimental Education and School Assessment” to assess students' CT levels. Within the module, we proposed two activities to stimulate students' CT: Open Educational Resources (OERs) assessment (mandatory and online) and OERs design (optional and blended). The essays were assessed both by expert evaluators, considering six CT sub-dimensions, and by an algorithm that automatically calculates different kinds of NLP features. The study shows a positive internal reliability and a medium to high inter-coder agreement in expert evaluation. Students' CT levels improved significantly in the post-test. Three NLP indicators significantly correlate with CT total score: the Corpus Length, the Syntax Complexity, and an adapted measure of Term Frequency- Inverse Document Frequency. The results collected during this PhD have both theoretical and practical implications for CT research and assessment. From a theoretical perspective, this thesis shows unexplored similarities among different CT traditions, perspectives, and study methods. These similarities could be exploited to open up an interdisciplinary dialogue among experts and build up a shared understanding of CT. Automatic assessment methods can enhance the use of open-ended measures for CT assessment, especially in online teaching. Indeed, they can support teachers and researchers to deal with the growing presence of linguistic data produced within educational 4 platforms. To this end, it is pivotal to develop automatic methods for the evaluation of large amounts of data which would be impossible to analyse manually, providing teachers an

Analyzing short-answer questions and their automatic scoring - studies on semantic relations in reading comprehension and the reduction of human annotation effort

Short-answer questions are a wide-spread exercise type in many educational areas. Answers given by learners to such questions are scored by teachers based on their content alone ignoring their linguistic correctness as far as possible. They typically have a length of up to a few sentences. Manual scoring is a time-consuming task, so that automatic scoring of short-answer questions using natural language processing techniques has become an important task. This thesis focuses on two aspects of short-answer questions and their scoring: First, we concentrate on a reading comprehension scenario for learners of German as a foreign language, where students answer questions about a reading text. Within this scenario, we examine the multiple relations between reading texts, learner answers and teacher-specified target answers. Second, we investigate how to reduce human scoring workload by both fully automatic and computer-assisted scoring. The latter is a scenario where scoring is not done entirely automatically, but where a teacher receives scoring support, for example, by means of clustering similar answers together. Addressing the first aspect, we conduct a series of corpus annotation studies which highlight the relations between pairs of learner answers and target answers, as well as between both types of answers and the reading text they refer to. We annotate sentences from the reading text that were potentially used by learners or teachers for constructing answers and observe that, unsurprisingly, most correct answers can easily be linked to the text; incorrect answers often link to the text as well, but are often backed up by a part of the text not relevant to answer the question. Based on these findings, we create a new baseline scoring model which considers for correctness whether learners looked for an answer in the right place or not. After identifying those links into the text, we label the relation between learner answers and target answers as well as between reading texts and answers by annotating entailment relations. In contrast to the widespread assumption that scoring can be fully mapped to the task of recognizing textual entailment, we find the two tasks to be only closely related and not completely equivalent. Correct answers do often, but not always, entail the target answer, as well as part of the related text, and incorrect answers do most of the time not stand in an entailment relation to the target answer, but often have some overlap with the text. This close relatedness allows us to use gold-standard entailment information to improve the performance of automatic scoring. We also use links between learner answers and both reading texts and target answers in a statistical alignment-based scoring approach using methods from machine translation and reach a performance comparable to an existing knowledge-based alignment approach. Our investigations into how human scoring effort can be reduced when learner answers are manually scored by teachers are based on two methods: active learning and clustering. In the active learning approach, we score particularly informative items first, i.e., items from which a classifier can learn most, identifying them using uncertainty-based sample selection. In this way, we reach a higher performance with a given number of annotation steps compared to randomly selected answers. In the second research strand, we use clustering methods to group similar answers together, such that groups of answers can be scored in one scoring step. In doing so, the number of necessary labeling steps can be substantially reduced. When comparing clustering-based scoring to classical supervised machine learning setups, where the human annotations are used to train a classifier, supervised machine learning is still in the lead in terms of performance, whereas clusters provide the advantage of structured output. However, we are able to close part of the performance gap by means of supervised feature selection and semi-supervised clustering. In an additional study, we investigate the automatic processing of learner language with respect to the performance of part-of-speech (POS) tagging tools. We manually annotate a German reading comprehension corpus both with spelling normalization and POS information and find that the performance of automatic POS tagging can be improved by spell-checking the data using the reading text as additional evidence for lexical material intended in a learner answer.Short-Answer-Fragen sind ein weit verbreiteter Aufgabentyp in vielen Bildungsbereichen. Die Antworten, die Lerner zu solchen Aufgaben geben, werden von Lehrenden allein auf Grundlage ihres Inhalts bewertet; linguistische Korrektheit wird soweit möglich ignoriert. Diese Doktorarbeit legt ihren Schwerpunkt auf zwei Aspekte im Zusammenhang mit Short- Answer-Fragen und ihrer Bewertung: Zum einen betrachten wir ein Leseverständnisszenario, bei dem Studenten Fragen zu Lesetexten beantworten. Dabei untersuchen wir insbesondere die verschiedenen Beziehungen, die es zwischen Lesetexten, Lernerantworten und vom Lehrer erstellten Musterantworten gibt. Zum anderen untersuchen wir, wie der menschliche Bewertungsaufwand durch voll-automatisches und computergestütztes Bewerten reduziert werden kann. Bei letzterem handelt es sich um ein Szenario, in dem Lehrer bei der Bewertung unterstützt werden, z.B. indem ähnliche Antworten automatisch gruppiert werden. Zur Untersuchung des ersten Aspekts unternehmen wir eine Reihe von Korpusannotationsstudien, die sowohl die Beziehungen zwischen Lerner- und Musterantworten beleuchten, als auch die Beziehung zwischen diesen Antworten und dem Lesetext, auf den sie sich beziehen. Wir annotieren Sätze aus dem Lesetext, die vermutlich bei der Formulierung einer Antwort benutzt wurden und machen die zu erwartende Beobachtung, dass die meisten korrekten Antworten problemlos mit bestimmten Textpassagen in Verbindung gebracht werden können. Inkorrekte Antworten haben ebenfalls oft eine Verbindung zu bestimmten Textpassagen, die aber oft für die jeweilige Frage nicht relevant sind. Auf Grundlage dieser Erkenntnisse entwerfen wir ein neues Baseline-Bewertungsmodell, das für die Korrektheit einer Antwort nur in Betracht zieht, ob der Lerner die Antwort an der richtigen Stelle im Lesetext gesucht hat oder nicht. Nachdem wir diese Verbindungen in den Text identifiziert haben, annotieren wir die Relation zwischen Lerner- und Musterantworten und zwischen Texten und Antworten mit Entailment- Relationen. Im Gegensatz zur der weitverbreiteten Annahme, dass das Bewerten von Short- Answer-Fragen und das Erkennen von Textual-Entailment-Relationen zwischen Lerner und Musterantworten sich direkt entsprechen, finden wir heraus, dass die beiden Aufgaben nur nahe verwandt aber nicht vollständig äquivalent sind. Korrekte Antworten entailen meistens, aber nicht immer, die Musterantwort und auch den entsprechenden Satz im Lesetext. Inkorrekte Antworten stehen meist in keiner Entailmentrelation mit der Musterantwort, haben aber oft zumindest teilweisen Overlap mit dem Text. Diese nahe Verwandtschaft erlaubt es uns, Goldstandard-Entailmentinformation zu benutzen, um die Performanz beim automatischen Bewerten zu verbessern. Wir benutzen die annotierten Verbindungen zwischen Lesetexten und Antworten auch in einem Scoringansatz, der auf statistischem Alignment basiert und Methoden aus dem Bereich der maschinellen Übersetzung nutzt. Dabei erreichen wir eine Scoringgenauigkeit, die mit Ansätzen, die ein existierendes wissensbasiertes Alignment nutzen, vergleichbar ist. Unsere Untersuchungen, wie der Bewertungsaufwand beim Menschen verringert werden kann, wenn Antworten vom Lehrer manuell bewertet werden, basieren auf zwei Methoden: Active Learning und Clustering. Beim Active-Learning-Ansatz werden besonders informative Antworten vorrangig zur Bewertung ausgewählt, d.h. solche Antworten, von denen ein Klassifikator besonders viel lernen kann. Wir identifizieren solche Antworten durch Uncertainty-Sampling- Methoden und erreichen dadurch mit einer gegebenen Anzahl von Annotationsschritten eine höhere Klassifikationsgenauigkeit als mit zufällig ausgewählten Antworten. In unserem zweiten Forschungszweig nutzen wir Clusteringmethoden um ähnliche Antworten zu gruppieren, so dass Gruppen von Antworten in einem Annotationsschritt bewertet werden können. Dadurch kann die Anzahl der insgesamt nötigen Bewertungsschritte drastisch reduziert werden. Beim Vergleich zwischen clusteringbasierten Bewertungsverfahren und klassischem überwachten maschinellen Lernen, bei dem menschliche Annotationen dazu genutzt werden, einen Klassifikator zu trainieren, erbringen überwachte maschinelle Lernverfahren immer noch eine höhere Bewertungsgenauigkeit. Demgegenüber bringen Cluster den Vorteil eines strukturierten Outputs mit sich. Wir sind jedoch in der Lage, einen Teil diese Genauigkeitslücke zu schließen, in dem wir überwachte Featureauswahl und halbüberwachtes Clustering anwenden. In einer zusätzlichen Studie untersuchen wir die automatische Verarbeitung von Lernersprache im Hinblick auf die Performanz vonWerkzeugen für dasWortarten-Tagging. Wir annotieren ein deutsches Leseverstehenskorpus manuell sowohl mit Normalisierungsinformation in Bezug auf Rechtschreibung als auch mit Wortartinformation. Als Ergebnis der Studie finden wir, dass die Performanz bei der automatischen Wortartenzuweisung durch Rechtschreibkorrektur verbessert werden kann, insbesondere wenn wir den Lesetext als zusätzliche Evidenz dafür verwenden, welche Wörter der Leser in einer Antwort vermutlich benutzen wollte

Supporting engagement in active video watching using quality nudges and visualisations

Learning by watching videos has been a popular method in e-learning. However, developing and maintaining constructive engagement is a crucial challenge in video-based learning (VBL). AVW-Space is an online VBL platform that enhances student engagement by providing note- taking and peer-reviewing. Previous studies with AVW-Space showed higher learning outcomes for students who write high-quality comments. Furthermore, an earlier study on AVW-Space suggested that visualising the student progress could help learners monitor and regulate their learning. Thus, this research aimed to increase engagement in AVW-Space by offering 1) personalised prompts, named Quality nudges, to encourage writing better comments and 2) visualisations of the student model to facilitate monitoring and controlling learning. I conducted a series of studies to investigate the effectiveness of Quality nudges and visualisations on the students’ engagement and learning. Firstly, I automated the assessment of comments quality using machine learning approaches. Then, I developed Quality nudges which encourage students to write better comments by triggering critical thinking and self-reflection. Next, I conducted a study in the context of presentation skills to analyse the effectiveness of the Quality nudges. The results showed that Quality nudges improved the quality of comments and increased learning consequently. After adding new visual learning analytics to AVW- Space, I investigated the effectiveness of the visualisations by conducting another study in the context of presentation skills. The results showed that the visualisations enhanced constructive engagement and learning even further. I also investigated the generalisability of nudges and visualisation for another transferable skill by making Quality nudges and visualisations customisable and conducting a study in the context of communication skills. Although the results showed that students used visualisations and nudges for communication skills differently from the participants in the study on presentation skills, findings indicated these interventions were still effective in increasing the quality of comments and enhancing constructive behaviour and learning. This research contributes to the development of intelligent learning environments which provide personalised interventions to encourage constructive commenting behaviours during video-based learning. The interventions proposed in this research can be applied to other domains which involve critical thinking and self-reflection. Another contribution of this research is providing visual learning analytics for students in VBL platforms to increase learning awareness and engagement. The nudges and visualisations proposed in this research could be applied to any other video-based learning platform that allows commenting