Artificial Intelligence-Based Tools in Research Writing : Current Trends and Future Potentials

Questions begin to arise around to what extent research writing and training can be supported electronically. The emergence of Artificial Intelligence (AI) technologies have triggered a tremendous interest among educational technologists. One area that has received much attention is AI-based tools, developed to assist researchers in the writing process. AI-powered writing tools aim to not only ease the process of research writing but also to enhance the quality of critical analysis particularly in the aspect of literature review and language style. Despite the trends in adopting AI components within research writing, there are still-limited efforts to examine its implementation, strengths and weaknesses. This chapter reviews some of the key studies concerning AI in research writing as well as an extensive review of existing tools by covering their AI-based features, affordances and constraints. The chapter also includes a discussion on the future potentials of AI implementation with regard to research writing. This chapter would serve as a good reference to uncover the hidden potentials of AI-based tools in assisting students to produce high-quality research writing

A systematic review of the literature on the effectiveness of cognitive based instruction for adult learning.

Master of Education in Educational Psychology. University of KwaZulu-Natal, Edgewood 2015.Instructional research in adult learning has evolved over the years with increasing interest in the shift from behavioural to more cognitive models of instruction. Researchers and instructional designers have been drawn towards learners‟ cognitive structures and mental processes in learning environments in a bid to create effective instructional methods. Substantive research has been conducted on individual models of instruction, but current research on cognitive models of instruction across a range of disciplines in higher education was necessary. As more models of instruction emerge, an evaluation of their effectiveness is crucial to ensure successful learning. This study assessed the effectiveness of cognitive-based instruction for adult learning. A systematic review of the literature was conducted to locate current relevant studies that presented cognitive-based models of instruction applied to adult learning populations. A search strategy was used to search for relevant literature through databases, journals and reference lists. Inclusionary criteria yielded 31 qualitative and quantitative studies conducted in Africa, Asia, America, Australia and Europe; published between 2000 and 2014. A pooled sample size of over 32,033 male and female adult learners participated in the included studies. Models represented in the selected studies included problem-based learning, cognitive apprenticeship, adaptive instruction and intelligent tutoring systems respectively. The Quality assessment procedure resulted in 12 studies that indicated minimal strength in methodological rigour. Data was extracted with the use of data extraction sheets and presented in graphs and tables. Thematic and textual narrative syntheses were used to analyse the data and the systematic procedure was documented and presented in tables and flowcharts. Results indicated that cognitive-based instruction is most effective when a combination of valid cognitive tools and methods are used in tandem with adult learners‟ cognitive learning styles in appropriate learning environments

Assessing Quantitative Reasoning in a Ninth Grade Science Class Using Interdisciplinary Data Story Assignments

In a data-driven world, it is necessary that students graduate from high school quantitatively literate, with the ability to interpret quantities within a context to make informed decisions for their lives. A critical component of science learning is developing the ability to make sense of data, critically evaluate it, and effectively communicate scientific ideas. The purpose of this study is two-fold: 1) to investigate how 9th grade students in an Earth Science class use quantitative reasoning (QR) skills when constructing evidence-based scientific explanations during Data Story assignments and 2) to provide teachers with supports to incorporate Data Stories into their curriculum. A Data Story is an interdisciplinary, scaffolded written argumentation assignment that requires students to analyze authentic, real-world scientific data and draw their own conclusions. In doing so, students integrate several discrete skills to synthesize an argument that is supported by evidence. Quantitative and qualitative results were used to investigate affordances and challenges students face when constructing a Data Story, what QR skills they use in the process, and what aspects of QR are challenging for them. Two evidence-based learning progressions provided the foundation for the development of two rubrics to score the student Data Stories quantitatively. Four student interviews analyzed using Grounded Theory provided qualitative insight into the role of QR in evidence-based explanations. Results suggest students enjoyed the Data Story assignments, which exposed them to a range of graph-types and data literacy skills. However, students seemed to struggle to develop appropriate evidence to support a claim in the Claim-Evidence- Reasoning (CER) framework and may need additional supports in this area. Further analysis with the QR Rubric and student interviews revealed some aspects of QR that may be hindering science learning and the development of evidence-based reasoning including: 1) not reasoning about variables in the context of a dataset 2) looking only for a correlation or difference and 3) not using quantitative language. These are aspects teachers should consider when implementing Data Story assignments in their own classrooms as a way to enhance students’ abilities in developing appropriate evidence to support a claim

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