Eye-tracking perspectives of students’ learning trough MOOCs

Activating student knowledge (ASK) before receiving learning materials improves their learning outcome (Tormey and LeDuc (2014)). We implement ASK through priming by using two versions of the same pretest in a dual eye-tracking study in a MOOC context. We propose an additional activity, a collaborative concept-map, based on the MOOC lecture to enable the students to reflect on what they learnt. The priming affects the learning gain, individual and collaborative gaze patterns. Textual priming stands better than schema priming in terms of learning outcome. Finally, the pairs having participants with similar gaze to each other have more learning gain

The Symmetry of Partner Modelling

© 2016, International Society of the Learning Sciences, Inc. Collaborative learning has often been associated with the construction of a shared understanding of the situation at hand. The psycholinguistics mechanisms at work while establishing common grounds are the object of scientific controversy. We postulate that collaborative tasks require some level of mutual modelling, i.e. that each partner needs some model of what the other partners know/want/intend at a given time. We use the term “some model” to stress the fact that this model is not necessarily detailed or complete, but that we acquire some representations of the persons we interact with. The question we address is: Does the quality of the partner model depend upon the modeler’s ability to represent his or her partner? Upon the modelee’s ability to make his state clear to the modeler? Or rather, upon the quality of their interactions? We address this question by comparing the respective accuracies of the models built by different team members. We report on 5 experiments on collaborative problem solving or collaborative learning that vary in terms of tasks (how important it is to build an accurate model) and settings (how difficult it is to build an accurate model). In 4 studies, the accuracy of the model that A built about B was correlated with the accuracy of the model that B built about A, which seems to imply that the quality of interactions matters more than individual abilities when building mutual models. However, these findings do not rule out the fact that individual abilities also contribute to the quality of modelling process

Analysing, visualising and supporting collaborative learning using interactive tabletops

The key contribution of this thesis is a novel approach to design, implement and evaluate the conceptual and technological infrastructure that captures student’s activity at interactive tabletops and analyses these data through Interaction Data Analytics techniques to provide support to teachers by enhancing their awareness of student’s collaboration. To achieve the above, this thesis presents a series of carefully designed user studies to understand how to capture, analyse and distil indicators of collaborative learning. We perform this in three steps: the exploration of the feasibility of the approach, the construction of a novel solution and the execution of the conceptual proposal, both under controlled conditions and in the wild. A total of eight datasets were analysed for the studies that are described in this thesis. This work pioneered in a number of areas including the application of data mining techniques to study collaboration at the tabletop, a plug-in solution to add user-identification to a regular tabletop using a depth sensor and the first multi-tabletop classroom used to run authentic collaborative activities associated with the curricula. In summary, while the mechanisms, interfaces and studies presented in this thesis were mostly explored in the context of interactive tabletops, the findings are likely to be relevant to other forms of groupware and learning scenarios that can be implemented in real classrooms. Through the mechanisms, the studies conducted and our conceptual framework this thesis provides an important research foundation for the ways in which interactive tabletops, along with data mining and visualisation techniques, can be used to provide support to improve teacher’s understanding about student’s collaboration and learning in small groups

Scripts for Computer-Supported Collaborative Learning

Using computers as tools to mediate collaborative learning has had a profound impact on how we construct and communicate knowledge today. Computer interfaces have not been genuinely designed, however, to support collaborative knowledge construction. This study shows how computer interfaces can be designed based on the instructional script-approach to facilitate productive interactions of learners and improve learning outcomes of collaborative knowledge construction in e-learning scenarios. A social script aims to support specific interactions of learning partners. An epistemic script aims to facilitate specific task-related activities. In a 2×2-design the factors “social cooperation script” (with vs. without) and “epistemic cooperation script” (with vs. without) are independently varied. 96 students of Pedagogy are randomly assigned to the four experimental conditions in learning groups of three. The epistemic script increases the amount of specific epistemic activities, and facilitates the joint application of focused knowledge. Simultaneously, the epistemic script reduces the frequency of important social modes of co-construction, such as elicitation. With respect to learning outcomes, it can be further shown that the epistemic script impedes individual knowledge acquisition. Epistemic scripts may therefore be particularly appropriate for the facilitation of joint knowledge application, where individual knowledge acquisition may be less important than effective processing of a given problem in groups. In contrast, however, social cooperation scripts may be more appropriate for individual knowledge acquisition than group problem solving. Social scripts facilitate individual knowledge acquisition as an outcome of collaborative knowledge construction. The results suggest that cooperation scripts for individual knowledge acquisition should consider social components.Im Bereich der Aus- und Weiterbildung wird durch textbasierte computervermittelte Kommunikation eine Reihe neuer Lernszenarien ermöglicht. Interfaces für computervermittelte Kommunikation sind jedoch nicht unbedingt nach pädagogisch-psychologischen Gesichtspunkten gestaltet. Diese Arbeit zeigt wie Interfaces auf der Basis des instruktionalen Skript-Ansatzes gestaltet werden können, um produktive Interaktionen und den Lernerfolg bei der gemeinsamen Wissenskonstruktion in E-Learning-Szenarien zu fördern. Ein soziales Skript soll bestimmte Interaktionen von Lernenden unterstützen. Ein epistemisches Kooperationsskript zielt darauf ab bestimmte aufgabenbezogene Handlungen Lernender zu fördern. In einem 2×2-Design wurden die Faktoren „soziales Skript“ (nicht vorhanden vs. vorhanden) und „epistemisches Skript“ (nicht vorhanden vs. vorhanden) variiert. 96 Hochschulstudenten der Pädagogik wurden in Dreiergruppen zufällig einer der vier Bedingungen zugeordnet. Das epistemische Skript erhöht den Anteil spezifischer epistemischer Aktivitäten, und fördert die gemeinsame Anwendung von Wissen. Das Skript reduziert allerdings gleichzeitig die Häufigkeit wichtiger sozialer Modi der Ko-Konstruktion (z. B. Elizitation). Bezüglich der Lernergebnisse kann gezeigt werden, dass das epistemische Skript individuellen Wissenserwerb im Vergleich beeinträchtigt. Epistemische Skripts scheinen sich also insbesondere zur Unterstützung der gemeinsamen Wissensanwendung zu eignen, bei der der individuelle Wissenserwerb weniger im Vordergrund steht als die effiziente Bearbeitung eines vorgegebenen Problems in der Gruppe. Im umgekehrten Fall hingegen erscheinen soziale Skripts besser geeignet. Soziale Skripts unterstützen den individuellen Wissenserwerb als Ergebnis gemeinsamer Wissenskonstruktion. Die Ergebnisse legen nahe, dass Kooperationsskripts auch soziale Komponenten berücksichtigen sollten

“The Illusion of Collaboration”: An Integrated Examination of the Antecedents, Processes, and Consequences of Online Group Work

Computer-supported collaborative learning (CSCL) presents postsecondary educators with a conundrum: how to design and support small-group activities without stifling deep and meaningful learning. The literature indicates that students are not consistently practicing higher-order cognitive activities, educators are not reliably designing or facilitating them, and/or researchers are not locating or identifying them where they are occurring. The aim of this dissertation is to explore these deficits by identifying the antecedent conditions that most affect collaboration. Specifically, I answer the question, how do learner’s prior knowledge, characteristics, and experiences manifest in their collaborative processes. Addressing a gap in the literature, this study employs distance ethnography to assess at a fine-grain level the social and cognitive interactions of a trio of collaborators in a natural setting—an object-oriented, small-group project in an online writing course. The results reveal several ways that learner dispositions and prior knowledge manifest as barriers to productive interactions, including tendencies toward indirect and unidirectional communication; siloed workspaces and individual orientations to group assignments; unequal coordination work; and the preservation of individual autonomy to the detriment of group knowledge objects. The study has pedagogical and theoretical implications related to the theory of transactional distance (TTD) and collaborative cognitive load theory (CCLT) and pedagogical and methodological implications for the integration of reflective-practitioner journals

Effects of computer-supported collaboration script and incomplete concept maps on web design skills in an online design-based learning environment

Web design skills are an important component of media literacy. The aim of our study was to promote university students’ web design skills through online design-based learning (DBL). Combined in a 2x2-factorial design, two types of scaffolding were implemented in an online DBL environment to support the students through their effort to design, build, modify, and publish web sites on processes and outcomes measures, namely collaboration scripts and incomplete concept maps. The results showed that both treatments had positive effects on collaborative (content-related discourse quality, collaboration skills, and quality of published web sites) and individual (domain-specific knowledge and skills related to the design and building of websites) learning outcomes. There was synergism between the two scaffolds in that the combination of the collaboration script and incomplete concept maps produced the most positive results. To be effective, online DBL thus needs to be enhanced by appropriate scaffolds, and both collaboration scripts and incomplete concept maps are effective examples

Enhancing Free-text Interactions in a Communication Skills Learning Environment

Learning environments frequently use gamification to enhance user interactions.Virtual characters with whom players engage in simulated conversations often employ prescripted dialogues; however, free user inputs enable deeper immersion and higher-order cognition. In our learning environment, experts developed a scripted scenario as a sequence of potential actions, and we explore possibilities for enhancing interactions by enabling users to type free inputs that are matched to the pre-scripted statements using Natural Language Processing techniques. In this paper, we introduce a clustering mechanism that provides recommendations for fine-tuning the pre-scripted answers in order to better match user inputs

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