Power to the Teachers:An Exploratory Review on Artificial Intelligence in Education

This exploratory review attempted to gather evidence from the literature by shedding light on the emerging phenomenon of conceptualising the impact of artificial intelligence in education. The review utilised the PRISMA framework to review the analysis and synthesis process encompassing the search, screening, coding, and data analysis strategy of 141 items included in the corpus. Key findings extracted from the review incorporate a taxonomy of artificial intelligence applications with associated teaching and learning practice and a framework for helping teachers to develop and self-reflect on the skills and capabilities envisioned for employing artificial intelligence in education. Implications for ethical use and a set of propositions for enacting teaching and learning using artificial intelligence are demarcated. The findings of this review contribute to developing a better understanding of how artificial intelligence may enhance teachers’ roles as catalysts in designing, visualising, and orchestrating AI-enabled teaching and learning, and this will, in turn, help to proliferate AI-systems that render computational representations based on meaningful data-driven inferences of the pedagogy, domain, and learner models

Designing Hybrid Human-AI Orchestration Tools for Individual and Collaborative Activities: A Technology Probe Study

Combining individual and collaborative learning is common, but dynamic combinations (which happen as-the-need arises, rather than in pre-planned ways, and may happen on an individual basis) are rare. This work reports findings from a technology probe study exploring alternative designs for classroom co-orchestration support for dynamically transitioning between individual and collaborative learning. The study involved 1) a technology-probe classroom study in an authentic, AI-supported classroom to understand teachers\u27 and students\u27 needs for co-orchestration support over dynamic transitions; and 2) workshops and interviews with students and teachers to get informed feedback about their lived experiences. 118 students and three teachers from a middle school in the US experienced a pairing policy – student, teacher and, AI-controlled pairing policy – (i.e., identifying students needing help and potential helpers) for switching from individual to a peer tutoring activity. This work aims to answer the following questions: 1) How did students and teachers react to these pairing policies?; and 2) What are students\u27 and teachers\u27 desires for sharing control over the orchestration of dynamic transitions? Findings suggest the need for a form of hybrid control between students, teachers, and AI systems over transitions, as well as for adaptivity and adaptability for different classroom characteristics, teachers, and students\u27 prior knowledge

The TA Framework: Designing Real-time Teaching Augmentation for K-12 Classrooms

Recently, the HCI community has seen increased interest in the design of teaching augmentation (TA): tools that extend and complement teachers' pedagogical abilities during ongoing classroom activities. Examples of TA systems are emerging across multiple disciplines, taking various forms: e.g., ambient displays, wearables, or learning analytics dashboards. However, these diverse examples have not been analyzed together to derive more fundamental insights into the design of teaching augmentation. Addressing this opportunity, we broadly synthesize existing cases to propose the TA framework. Our framework specifies a rich design space in five dimensions, to support the design and analysis of teaching augmentation. We contextualize the framework using existing designs cases, to surface underlying design trade-offs: for example, balancing actionability of presented information with teachers' needs for professional autonomy, or balancing unobtrusiveness with informativeness in the design of TA systems. Applying the TA framework, we identify opportunities for future research and design.Comment: to be published in Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 17 pages, 10 figure

Producing Acoustic-Prosodic Entrainment in a Robotic Learning Companion to Build Learner Rapport

abstract: With advances in automatic speech recognition, spoken dialogue systems are assuming increasingly social roles. There is a growing need for these systems to be socially responsive, capable of building rapport with users. In human-human interactions, rapport is critical to patient-doctor communication, conflict resolution, educational interactions, and social engagement. Rapport between people promotes successful collaboration, motivation, and task success. Dialogue systems which can build rapport with their user may produce similar effects, personalizing interactions to create better outcomes. This dissertation focuses on how dialogue systems can build rapport utilizing acoustic-prosodic entrainment. Acoustic-prosodic entrainment occurs when individuals adapt their acoustic-prosodic features of speech, such as tone of voice or loudness, to one another over the course of a conversation. Correlated with liking and task success, a dialogue system which entrains may enhance rapport. Entrainment, however, is very challenging to model. People entrain on different features in many ways and how to design entrainment to build rapport is unclear. The first goal of this dissertation is to explore how acoustic-prosodic entrainment can be modeled to build rapport. Towards this goal, this work presents a series of studies comparing, evaluating, and iterating on the design of entrainment, motivated and informed by human-human dialogue. These models of entrainment are implemented in the dialogue system of a robotic learning companion. Learning companions are educational agents that engage students socially to increase motivation and facilitate learning. As a learning companion’s ability to be socially responsive increases, so do vital learning outcomes. A second goal of this dissertation is to explore the effects of entrainment on concrete outcomes such as learning in interactions with robotic learning companions. This dissertation results in contributions both technical and theoretical. Technical contributions include a robust and modular dialogue system capable of producing prosodic entrainment and other socially-responsive behavior. One of the first systems of its kind, the results demonstrate that an entraining, social learning companion can positively build rapport and increase learning. This dissertation provides support for exploring phenomena like entrainment to enhance factors such as rapport and learning and provides a platform with which to explore these phenomena in future work.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Introductory programming: a systematic literature review

As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming. This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research

Maximizing the Benefits of Collaborative Learning in the College Classroom

abstract: This study tested the effects of two kinds of cognitive, domain-based preparation tasks on learning outcomes after engaging in a collaborative activity with a partner. The collaborative learning method of interest was termed "preparing-to-interact," and is supported in theory by the Preparation for Future Learning (PFL) paradigm and the Interactive-Constructive-Active-Passive (ICAP) framework. The current work combined these two cognitive-based approaches to design collaborative learning activities that can serve as alternatives to existing methods, which carry limitations and challenges. The "preparing-to-interact" method avoids the need for training students in specific collaboration skills or guiding/scripting their dialogic behaviors, while providing the opportunity for students to acquire the necessary prior knowledge for maximizing their discussions towards learning. The study used a 2x2 experimental design, investigating the factors of Preparation (No Prep and Prep) and Type of Activity (Active and Constructive) on deep and shallow learning. The sample was community college students in introductory psychology classes; the domain tested was "memory," in particular, concepts related to the process of remembering/forgetting information. Results showed that Preparation was a significant factor affecting deep learning, while shallow learning was not affected differently by the interventions. Essentially, equalizing time-on-task and content across all conditions, time spent individually preparing by working on the task alone and then discussing the content with a partner produced deeper learning than engaging in the task jointly for the duration of the learning period. Type of Task was not a significant factor in learning outcomes, however, exploratory analyses showed evidence of Constructive-type behaviors leading to deeper learning of the content. Additionally, a novel method of multilevel analysis (MLA) was used to examine the data to account for the dependency between partners within dyads. This work showed that "preparing-to-interact" is a way to maximize the benefits of collaborative learning. When students are first cognitively prepared, they seem to make the most efficient use of discussion towards learning, engage more deeply in the content during learning, leading to deeper knowledge of the content. Additionally, in using MLA to account for subject nonindependency, this work introduces new questions about the validity of statistical analyses for dyadic data.Dissertation/ThesisPh.D. Educational Psychology 201

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