Towards adaptive argumentation learning systems : theoretical and practical considerations in the design of argumentation learning systems

This dissertation addresses four issues of pivotal importance in realizing the promises of adaptive argumentation learning systems: (1) User interface: How can argumentation user interfaces be designed to effectively structure and support problem solving, peer interaction, and learning? (2) Software architecture: How can software architectures of adaptive argumentation learning systems be designed to be employable across different argumentation domains and application scenarios in a flexible and cost-effective manner? (3) Diagnostics: How can user behavior be analyzed, automatically and accurately, to drive automated adaptations and help generation? (4) Adaptation: How can strategies for automated adaptation and support be designed to promote problem solving, peer interaction, and learning in an optimal fashion? Regarding issue (1), this dissertation investigates argument diagrams and structured discussion interfaces, two areas of focal interest in argumentation learning research during the past decades. The foundation for such structuring approaches is given by theories of learning and teaching with knowledge representations (theory of representational guidance) and collaboration scripts (script theory of guidance in computer-supported collaborative learning). This dissertation brings these two strands of research together and presents a computer-based learning environment that combines both approaches to support students in conducting high-quality discussions of controversial texts. An empirical study confirms that this combined approach has positive impact on the quality of discussions, thus, underpins the theoretical basis of the approach. Regarding issue (2), this dissertation presents a software framework for enhancing argumentation systems with adaptive support mechanisms. Adaptive support functionality of past argumentation systems has been tailored to particular domains and application scenarios. A novel software framework is presented that abstracts from the specific demands of different domains and application scenarios to provide a more general approach. The approach comprises an extensive configuration subsystem that allows the flexible definition of intelligent software agents, that is, software components able to reason and act autonomously to help students engage in fruitful learning activities. A graphical authoring tool has been conceptualized and implemented to simplify the process of defining and administering software agents beyond what has been achieved with the provided framework system. Among other things, the authoring tool allows, for the first time, specifying relevant patterns in argument diagrams using a graphical language. Empirical results indicate the high potential of the authoring approach but also challenges for future research. Regarding issue (3), the dissertation investigates two alternative approaches to automatically analyzing argumentation learning activities: the knowledge-driven and the data-driven analysis method. The knowledge-driven approach utilizes a pattern search component to identify relevant structures in argument diagrams based on declarative pattern specifications. The capabilities and appropriateness of this approach are demonstrated through three exemplary applications, for which pedagogically relevant patterns have been defined and implemented within the component. The approach proves particularly useful for patterns of limited complexity in scenarios with sufficient expert knowledge available. The data-driven approach is based on machine learning techniques, which have been employed to induce computational classifiers for important aspects of graphical online discussions, such as off-topic contributions, reasoned claims, and question-answer interactions. Validation results indicate that this approach can be realistically used even for complex classification tasks involving natural language. This research constitutes the first investigation on the use of machine learning techniques to analyze diagram-based educational discussions. The dissertation concludes with discussing the four addressed research challenges in the broader context of existing theories and empirical results. The pros and cons of different options in the design of argumentation learning systems are juxtaposed; areas for future research are identified. This final part of the dissertation gives researchers and practitioners a synopsis of the current state of the art in the design of argumentation learning systems and its theoretical and empirical underpinning. Special attention is paid to issue (4), with an in-depth discussion of existing adaptation approaches and corresponding empirical results.Diese Dissertationsschrift behandelt die folgenden vier Fragestellungen, welche bei der Realisierung adaptiver Argumentationssysteme von zentraler Bedeutung sind: (1) Benutzerschnittstelle: Wie müssen Benutzerschnittstellen beschaffen sein, um Problemlöse-, Kooperations- und Lernprozesse effektiv zu strukturieren und zu unterstützen? (2) Softwarearchitektur: Wie können die Funktionalitäten eines adaptiven Argumentationslernsystems in eine Softwarearchitektur abgebildet werden, welche flexibel und mit angemessenem Aufwand in verschiedenen Bereichen und Szenarien einsetzbar ist? (3) Diagnostik: Wie kann Benutzerverhalten automatisch und mit hoher Genauigkeit analysiert werden, um automatisierte Anpassungen und Hilfestellungen effektiv zu steuern? (4) Adaption: Wie sollten automatisierte Anpassungen und Hilfestellungen ausgestaltet werden, um Problemlöse-, Kooperations- und Lernprozesse optimal zu unterstützen? Hinsichtlich Fragestellung (1) untersucht diese Arbeit Argumentationsdiagramme und strukturierte Onlinediskussionen, zwei Schwerpunkte der Forschung zu Lernsystemen für Argumentation der vergangenen Jahre. Die Grundlage solcher Strukturierungsansätze bilden Theorien zum Lehren und Lernen mit Wissensrepräsentationen (theory of representational guidance) und Kooperationsskripten (script theory of guidance in computer-supported collaborative learning). Diese Arbeit führt beide Forschungsstränge in einer neuartigen Lernumgebung zusammen, die beide Ansätze vereint, um Lernende beim Diskutieren kontroverser Texte zu unterstützen. Eine empirische Untersuchung zeigt, dass sich dieser kombinierte Ansatz positiv auf die Diskussionsqualität auswirkt und bekräftigt damit die zu Grunde liegenden theoretischen Annahmen. Hinsichtlich Fragestellung (2) stellt diese Arbeit ein Software-Rahmensystem zur Bereitstellung adaptiver Unterstützungsmechanismen in Argumentationssystemen vor. Das Rahmensystem abstrahiert von domänen- und anwendungsspezifischen Besonderheiten und stellt damit einen generelleren Ansatz im Vergleich zu früheren Systemen dar. Der Ansatz umfasst ein umfangreiches Konfigurationssystem zur Definition intelligenter Softwareagenten, d. h. Softwarekomponenten, die eigeständig schlussfolgern und handeln, um Lernprozesse zu unterstützen. Um das Definieren und Administrieren von Softwareagenten über das bereitgestellte Rahmensystem hinaus zu vereinfachen, wurde ein grafisches Autorenwerkzeug konzipiert und entwickelt. Unter anderem erlaubt dieses erstmals, relevante Muster in Argumentationsdiagrammen ohne Programmierung mittels einer grafischen Sprache zu spezifizieren. Empirische Befunde zeigen neben dem hohen Potential des Ansatzes auch die Notwendigkeit weiterführender Forschung. Hinsichtlich Fragestellung (3) untersucht diese Arbeit zwei alternative Ansätze zur automatisierten Analyse von Lernaktivitäten im Bereich Argumentation: die wissensbasierte und die datenbasierte Analysemethodik. Der wissensbasierte Ansatz wurde mittels einer Softwarekomponente zur Mustersuche in Argumentationsdiagrammen umgesetzt, welche auf Grundlage deklarativer Musterbeschreibungen arbeitet. Die Möglichkeiten und Eignung des Ansatzes werden anhand von drei Beispielszenarien demonstriert, für die verschiedenartige, pädagogisch relevante Muster innerhalb der entwickelten Softwarekomponente definiert wurden. Der Ansatz erweist sich insbesondere als nützlich für Muster eingeschränkter Komplexität in Szenarien, für die Expertenwissen in ausreichendem Umfang verfügbar ist. Der datenbasierte Ansatz wurde mittels maschineller Lernverfahren umgesetzt. Mit deren Hilfe wurden Klassifikationsroutinen zur Analyse zentraler Aspekte von Onlinediskussionen, wie beispielsweise themenfremde Beiträge, begründete Aussagen und Frage-Antwort-Interaktionen, algorithmisch hergeleitet. Validierungsergebnisse zeigen, dass sich dieser Ansatz selbst für komplexe Klassifikationsprobleme eignet, welche die Berücksichtigung natürlicher Sprache erfordern. Dies ist die erste Arbeit zum Einsatz maschineller Lernverfahren zur Analyse von diagrammbasierten Lerndiskussionen. Die Arbeit schließt mit einer Diskussion des aktuellen Forschungsstands hinsichtlich der vier Fragestellungen im breiteren Kontext existierender Theorien und empirischer Befunde. Die Vor- und Nachteile verschiedener Optionen für die Gestaltung von Lernsystemen für Argumentation werden gegenübergestellt und zukünftige Forschungsfelder vorgeschlagen. Dieser letzte Teil der Arbeit bietet Forschern und Anwendern einen umfassenden Überblick des aktuellen Forschungsstands bezüglich des Designs computerbasierter Argumentationslernsysteme und den zugrunde liegenden lehr- und lerntheoretischen Erkenntnissen. Insbesondere wird auf Fragestellung (4) vertiefend eingegangen und bisherige Adaptionsansätze einschließlich entsprechender empirischer Befunde erörtert

A Computational Linguistic Analysis of Learners Discourse in Computer-Mediated Group Learning Environments

Communication, collaboration and the social co-construction of knowledge are now considered critical 21st century skills and have taken a principal role in recent theoretical and technological developments in education research. The overall objective of this dissertation was to investigate collaborative learning to gain insight on why some groups are more successful than others. In such discussions, group members naturally assume different roles. These roles emerge through participants’ interactions without any prior instruction or assignment. Different combinations of these roles can produce characteristically different group outcomes, being either less or more productive towards collective goals. However, there has been little research on how to automatically identify these roles and fuse the quality of the process of collaborative interactions with the learning outcome. A major goal of this dissertation is to develop a group communication analysis (GCA) framework, a novel methodology that applies automated computational linguistic techniques to the sequential interactions of online group communication. The GCA involves computing six distinct measures of participant discourse interaction and behavioral patterns and then clustering participants based on their profiles across these measures. The GCA was applied to several large collaborative learning datasets, and identified roles that exhibit distinct patterns in behavioral engagement style (i.e., active or passive, leading or following), contribution characteristics (i.e., providing new information or echoing given material), and social orientation. Through bootstrapping and replication analysis, the roles were found to generalize both within and across different collaborative interaction datasets, indicating that these roles are robust constructs. A multilevel analysis shows that the social roles are predictive of success, both for individual team members and for the overall group. Furthermore, the presence of specific roles within a team produce characteristically different outcomes; leading to specific hypotheses as to optimal group composition. Ideally, the developed analytical tools and findings of this dissertation will contribute to our understanding of how individuals learn together as a group and thereby advance the learning and discourse sciences. More broadly, GCA provides a framework to explore the intra- and inter-personal patterns indicative of the participants’ roles and the sociocognitive processes related to successful collaboration

Automated detection of reflection in texts. A machine learning based approach

Promoting reflective thinking is an important educational goal. A common educational practice is to provide opportunities for learners to express their reflective thoughts in writing. The analysis of such text with regard to reflection is mainly a manual task that employs the principles of content analysis. Considering the amount of text produced by online learning systems, tools that automatically analyse text with regard to reflection would greatly benefit research and practice. Previous research has explored the potential of dictionary-based approaches that automatically map keywords to categories associated with reflection. Other automated methods use manually constructed rules to gauge insight from text. Machine learning has shown potential for classifying text with regard to reflection-related constructs. However, not much is known of whether machine learning can be used to reliably analyse text with regard to the categories of reflective writing models. This thesis investigates the reliability of machine learning algorithms to detect reflective thinking in text. In particular, it studies whether text segments from student writings can be analysed automatically to detect the presence (or absence) of reflective writing model categories. A synthesis of the models of reflective writing is performed to determine the categories frequently used to analyse reflective writing. For each of these categories, several machine learning algorithms are evaluated with regard to their ability to reliably detect reflective writing categories. The evaluation finds that many of the categories can be predicted reliably. The automated method, however, does not achieve the same level of reliability as humans do

A modular architecture for systematic text categorisation

This work examines and attempts to overcome issues caused by the lack of formal standardisation when defining text categorisation techniques and detailing how they might be appropriately integrated with each other. Despite text categorisation’s long history the concept of automation is relatively new, coinciding with the evolution of computing technology and subsequent increase in quantity and availability of electronic textual data. Nevertheless insufficient descriptions of the diverse algorithms discovered have lead to an acknowledged ambiguity when trying to accurately replicate methods, which has made reliable comparative evaluations impossible. Existing interpretations of general data mining and text categorisation methodologies are analysed in the first half of the thesis and common elements are extracted to create a distinct set of significant stages. Their possible interactions are logically determined and a unique universal architecture is generated that encapsulates all complexities and highlights the critical components. A variety of text related algorithms are also comprehensively surveyed and grouped according to which stage they belong in order to demonstrate how they can be mapped. The second part reviews several open-source data mining applications, placing an emphasis on their ability to handle the proposed architecture, potential for expansion and text processing capabilities. Finding these inflexible and too elaborate to be readily adapted, designs for a novel framework are introduced that focus on rapid prototyping through lightweight customisations and reusable atomic components. Being a consequence of inadequacies with existing options, a rudimentary implementation is realised along with a selection of text categorisation modules. Finally a series of experiments are conducted that validate the feasibility of the outlined methodology and importance of its composition, whilst also establishing the practicality of the framework for research purposes. The simplicity of experiments and results gathered clearly indicate the potential benefits that can be gained when a formalised approach is utilised

Promoting Andean children's learning of science through cultural and digital tools

Conference Theme: To see the world and a grain of sand: Learning across levels of space, time, and scaleIn Peru, there is a large achievement gap in rural schools. In order to overcome this problem, the study aims to design environments that enhance science learning through the integration of ICT with cultural artifacts, respecting the Andean culture and empower rural children to pursue lifelong learning. This investigation employs the Cultural-Historical Activity Theory (CHAT) framework, and the Design-Based Research (DBR) methodology using an iterative process of design, implementation and evaluation of the innovative practice.published_or_final_versio

The implementation of dialogue-based pedagogy to improve written argumentation amongst secondary school students in Malaysia

The purpose of this study is to find solutions on how to improve secondary school students’ persuasive argumentative English essay writing. The participants of this study are groups of ESL students aged 13 and 17 who live and study in a sub-urban area in Malaysia. All students and teachers converse amongst themselves using the Malay language on a daily basis while English language is merely used during classroom interaction time. Not only do they have very little opportunity to communicate using English language in their daily lives and for academic purposes, they also have limited opportunity to learn how to argue persuasively in their English classroom. Thus, they have difficulties in writing two-sided argumentative essays in English. The teaching-to-the-test culture has taken its toll on students’ writing performance when writing argumentative essays. In order to help students to score well in examination, teachers often overlook the need to teach critical thinking skills for the English subject. They focus solely on writing narrative essays as these essays require less critical thinking skill from the students. The Design-Based Research is employed to solve this problem of writing persuasive argumentative essays. Based on the pre-intervention essays written by the participants, it is believed that their difficulties are because of two major factors; insufficient English language skills and no exposure to persuasive argumentation skills. The initial design framework asserts that students should improve their persuasive argumentative essay writing if they are initially exposed to face-to-face group argumentation. However, the findings from the exploratory study revealed that face-to-face group argumentation is unmanageable in the context studied. Hence, an online learning intervention was considered to support secondary school students to improve their written argument. It was developed underpinned by design principles based on Exploratory Talk to achieve persuasive argumentation. The prototype online intervention was tested and developed through a series of iterations. Findings from Iteration 1 show that only a small number of students manage to write two-sided essays because most of them have an extreme attitude when writing about an issue and display a lack of positive transfer from group to individual argumentation. Prior to Iteration 2, the prototype intervention was adapted to tackle the extreme attitude and negative transfer issues by highlighting five elements: face-to-face classroom practice, focus more on three main ground rules, argument game, role of teachers during group argumentation and the use of argument map during the post-intervention essay writing. The findings demonstrate that all students in the second iteration wrote argumentative essays which are more persuasive. The final design framework developed in this study suggests a design framework that could be used by future researchers and ESL teachers at secondary school level who are interested in improving students’ persuasive argumentative essays