A framework to analyze argumentative knowledge construction in computer-supported collaborative learning

Computer-supported collaborative learning (CSCL) is often based on written argumentative discourse of learners, who discuss their perspectives on a problem with the goal to acquire knowledge. Lately, CSCL research focuses on the facilitation of specific processes of argumentative knowledge construction, e.g., with computer-supported collaboration scripts. In order to refine process-oriented instructional support, such as scripts, we need to measure the influence of scripts on specific processes of argumentative knowledge construction. In this article, we propose a multi-dimensional approach to analyze argumentative knowledge construction in CSCL from sampling and segmentation of the discourse corpora to the analysis of four process dimensions (participation, epistemic, argumentative, social mode)

Analytic frameworks for assessing dialogic argumentation in online learning environments

Over the last decade, researchers have developed sophisticated online learning environments to support students engaging in argumentation. This review first considers the range of functionalities incorporated within these online environments. The review then presents five categories of analytic frameworks focusing on (1) formal argumentation structure, (2) normative quality, (3) nature and function of contributions within the dialog, (4) epistemic nature of reasoning, and (5) patterns and trajectories of participant interaction. Example analytic frameworks from each category are presented in detail rich enough to illustrate their nature and structure. This rich detail is intended to facilitate researchers’ identification of possible frameworks to draw upon in developing or adopting analytic methods for their own work. Each framework is applied to a shared segment of student dialog to facilitate this illustration and comparison process. Synthetic discussions of each category consider the frameworks in light of the underlying theoretical perspectives on argumentation, pedagogical goals, and online environmental structures. Ultimately the review underscores the diversity of perspectives represented in this research, the importance of clearly specifying theoretical and environmental commitments throughout the process of developing or adopting an analytic framework, and the role of analytic frameworks in the future development of online learning environments for argumentation

Retrieval-, Distributed-, and Interleaved Practice in the Classroom:A Systematic Review

Three of the most effective learning strategies identified are retrieval practice, distributed practice, and interleaved practice, also referred to as desirable difficulties. However, it is yet unknown to what extent these three practices foster learning in primary and secondary education classrooms (as opposed to the laboratory and/or tertiary education classrooms, where most research is conducted) and whether these strategies affect different students differently. To address these gaps, we conducted a systematic review. Initial and detailed screening of 869 documents found in a threefold search resulted in a pool of 29 journal articles published from 2006 through June 2020. Seventy-five effect sizes nested in 47 experiments nested in 29 documents were included in the review. Retrieval- and interleaved practice appeared to benefit students’ learning outcomes quite consistently; distributed practice less so. Furthermore, only cognitive Student*Task characteristics (i.e., features of the student’s cognition regarding the task, such as initial success) appeared to be significant moderators. We conclude that future research further conceptualising and operationalising initial effort is required, as is a differentiated approach to implementing desirable difficulties

Effects of differently sequenced classroom scripts on transformative and regulative processes in inquiry learning

Kooperatives Forschendes Lernen hat sich empirisch als ein effektiver Instruktionsansatz für die Förderung des naturwissenschaftlichen Denkens bewährt. Obwohl Forschung zur Orchestrierung von Sozialformen im Unterricht zeigt, dass diese einen wichtigen Einfluss auf die Qualität von Lernprozessen, wie Kommunikations- und Interaktionsprozessen, und damit auf die Lernergebnisse von Gruppe und einzelnen Lernenden hat, wurde im Bereich des Forschenden Lernens die Verteilung und Abfolge von individuellen und kooperativen Lernaktivitäten bislang jedoch kaum untersucht. Basierend auf Erkenntnissen zu Scaffolding, Fading, Productive Failure und dem ICAP-Rahmenmodell wird in der vorliegenden Arbeit der Einfluss zweier Unterrichtsskripts auf die transformativen und regulativen Prozesse des forschenden Lernens bei Individuen und Gruppen untersucht. Das eine Unterrichtsskript sieht die Abfolge „Plenum-Kleingruppe-Individuum“ vor (PKI-Skript), das andere wechselt vom Plenum über die individuelle Ebene zur Kleingruppenebene (PIK-Skript). Transformationsprozesse beziehen sich dabei auf wissensgenerierende Prozesse, während regulative Prozesse meta-kognitive Prozesse darstellen. Deskriptiv zeigten sich unterschieden zwischen den beiden Bedingungen: Lernende mit dem PKI-Skript zeigten mehr und intensivere individuelle transformative Prozesse, z.B. während bei der Datenauswertung und beim wissenschaftlichen Schlussfolgern. Lernende mit dem PIK-Skript zeigten hingegen mehr transformative und regulative Prozessen auf der Gruppenebene. Lernende, die mit diesem Skript arbeiteten, zeigten mehr und intensivere Grounding-Aktivitäten, die das gemeinsame Verständnis und das Entstehen eines Common Ground förderten. Dementsprechend zeigten sich hier auch häufiger intensivere transformative Prozesse auf der Gruppenebene.Collaborative inquiry learning has been empirically proven to be an effective instructional approach to foster students’ scientific literacy. However, there is little research on the coordination of individual and collaborative activities during inquiry learning which could shape the quality of communication and interaction, and consequentially, individual and group learning outcomes. Research has indicated that classroom orchestration (i.e., distribution and sequencing of activities) could have profound effect on learning processes and outcomes. Premised on theories of scaffolding, fading, productive failure and the ICAP (interactive, constructive, active and passive) framework on different activity types, this study investigates the effects of two differently sequenced classroom scripts on the individual and group transformative and regulative processes in inquiry learning. Transformative processes refers to processes that yield knowledge and regulative processes are meta-cognitive processes. Descriptive statistics suggest that the Plenary-Small Group-Individual (PSI) script transition facilitated better individual engagement in transformative processes such as generating of evidence and the drawing of conclusions, whereas the Plenary-Individual-Small Group (PIS) script condition fostered better transformative and regulative processes for the group. Establishing shared understanding and forging common grounds through grounding and high-level grounding was more prevalent in this script condition, which also accounted for more occurrences of high-level transformative processes at the group level

Fostering complex learning-task performance through scripting student use of computer supported representational tools

This study investigated whether scripting student use of computer supported representational tools fostered students’ collaborative performance of a complex business-economics problem. Scripting the problem-solving process sequenced and made its phase-related part-task demands explicit, namely (1) determining core concepts, (2) proposing multiple solutions, and (3) coming to a final solution. The representational tools facilitated students in constructing specific representations of the domain (i.e., conceptual, causal, or mathematical) and were each suited for carrying out the part-task demands of a specific phase. Student groups in four experimental conditions had to carry out all part-tasks in a predefined order, but differed in the representational tool(s) they received during their collaborative problem-solving process. In three mismatch conditions, student groups received either a conceptual, causal, or simulation representational tool which supported them in only carrying out one of the three part-tasks. In the match condition, student groups received the three representational tools in the specified order, each matching the part-task demands of a specific problem phase. The results revealed that student groups in the match condition constructed more task-appropriate representations and had more elaborated and meaningful discussions about the domain. As a consequence, those student groups performed better on the complex learning-task. However, similar results were obtained by student groups who only received a representational tool for constructing causal representations for all part-tasks

Fostering complex learning-task performance through scripting student use of computer supported representational tools

Slof, B., Erkens, G., Kirschner, P. A., Janssen, J., & Phielix, C. (2010). Fostering complex learning-task performance through scripting student use of computer supported representational tools. Computers & Education, 55(4), 1707-1720.This study investigated whether scripting student use of computer supported representational tools fostered students’ collaborative performance of a complex business-economics problem. Scripting the problem-solving process sequenced and made its phase-related part-task demands explicit, namely (1) determining core concepts, (2) proposing multiple solutions, and (3) coming to a final solution. The representational tools facilitated students in constructing specific representations of the domain (i.e., conceptual, causal, or mathematical) and were each suited for carrying out the part-task demands of a specific phase. Student groups in four experimental conditions had to carry out all part-tasks in a predefined order, but differed in the representational tool(s) they received during their collaborative problem-solving process. In three mismatch conditions, student groups received either a conceptual, causal, or simulation representational tool which supported them in only carrying out one of the three part-tasks. In the match condition, student groups received the three representational tools in the specified order, each matching the part-task demands of a specific problem phase. The results revealed that student groups in the match condition constructed more task-appropriate representations and had more elaborated and meaningful discussions about the domain. As a consequence, those student groups performed better on the complex learning-task. However, similar results were obtained by student groups who only received a representational tool for constructing causal representations for all part-tasks

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

Utilizing Online Activity Data to Improve Face-to-Face Collaborative Learning in Technology-Enhanced Learning Environments

학위논문 (박사)-- 서울대학교 대학원 : 융합과학기술대학원 융합과학부(디지털정보융합전공), 2019. 2. Rhee, Wonjong .We live in a flood of information and face more and more complex problems that are difficult to be solved by a single individual. Collaboration with others is necessary to solve these problems. In educational practice, this leads to more attention on collaborative learning. Collaborative learning is a problem-solving process where students learn and work together with other peers to accomplish shared tasks. Through this group-based learning, students can develop collaborative problem-solving skills and improve the core competencies such as communication skills. However, there are many issues for collaborative learning to succeed, especially in a face-to-face learning environment. For example, group formation, the first step to design successful collaborative learning, requires a lot of time and effort. In addition, it is difficult for a small number of instructors to manage a large number of student groups when trying to monitor and support their learning process. These issues can amount hindrance to the effectiveness of face-to-face collaborative learning. The purpose of this dissertation is to enhance the effectiveness of face-to-face collaborative learning with online activity data. First, online activity data is explored to find whether it can capture relevant student characteristics for group formation. If meaningful characteristics can be captured from the data, the entire group formation process can be performed more efficiently because the task can be automated. Second, learning analytics dashboards are implemented to provide adaptive support during a class. The dashboards system would monitor each group's collaboration status by utilizing online activity data that is collected during class in real-time, and provide adaptive feedback according to the status. Lastly, a predictive model is built to detect at-risk groups by utilizing the online activity data. The model is trained based on various features that represent important learning behaviors of a collaboration group. The results reveal that online activity data can be utilized to address some of the issues we have in face-to-face collaborative learning. Student characteristics captured from the online activity data determined important group characteristics that significantly influenced group achievement. This indicates that student groups can be formed efficiently by utilizing the online activity data. In addition, the adaptive support provided by learning analytics dashboards significantly improved group process as well as achievement. Because the data allowed the dashboards system to monitor current learning status, appropriate feedback could be provided accordingly. This led to an improvement of both learning process and outcome. Finally, the predictive model could detect at-risk groups with high accuracy during the class. The random forest algorithm revealed important learning behaviors of a collaboration group that instructors should pay more attention to. The findings indicate that the online activity data can be utilized to address practical issues of face-to-face collaborative learning and to improve the group-based learning where the data is available. Based on the investigation results, this dissertation makes contributions to learning analytics research and face-to-face collaborative learning in technology-enhanced learning environments. First, it can provide a concrete case study and a guide for future research that may take a learning analytics approach and utilize student activity data. Second, it adds a research endeavor to address challenges in face-to-face collaborative learning, which can lead to substantial enhancement of learning in educational practice. Third, it suggests interdisciplinary problem-solving approaches that can be applied to the real classroom context where online activity data is increasingly available with advanced technologies.Abstract i Chapter 1. Introduction １ 1.1. Motivation １ 1.2. Research questions ４ 1.3. Organization ６ Chapter 2. Background ８ 2.1. Learning analytics ８ 2.2. Collaborative learning ２２ 2.3. Technology-enhanced learning environment ２７ Chapter 3. Heterogeneous group formation with online activity data ３５ 3.1. Student characteristics for heterogeneous group formation ３６ 3.2. Method ４１ 3.3. Results ５１ 3.4. Discussion ５９ 3.5. Summary ６４ Chapter 4. Real-time dashboard for adaptive feedback in face-to-face CSCL ６７ 4.1. Theoretical background ７０ 4.2. Dashboard characteristics ８１ 4.3. Evaluation of the dashboard ９４ 4.4. Discussion １０７ 4.5. Summary １１４ Chapter 5. Real-time detection of at-risk groups in face-to-face CSCL １１８ 5.1. Important learning behaviors of group in collaborative argumentation １１８ 5.2. Method １２０ 5.3. Model performance and influential features １２５ 5.4. Discussion １２９ 5.5. Summary １３２ Chapter 6. Conclusion １３４ Bibliography １４０Docto

Applying science of learning in education: Infusing psychological science into the curriculum

The field of specialization known as the science of learning is not, in fact, one field. Science of learning is a term that serves as an umbrella for many lines of research, theory, and application. A term with an even wider reach is Learning Sciences (Sawyer, 2006). The present book represents a sliver, albeit a substantial one, of the scholarship on the science of learning and its application in educational settings (Science of Instruction, Mayer 2011). Although much, but not all, of what is presented in this book is focused on learning in college and university settings, teachers of all academic levels may find the recommendations made by chapter authors of service. The overarching theme of this book is on the interplay between the science of learning, the science of instruction, and the science of assessment (Mayer, 2011). The science of learning is a systematic and empirical approach to understanding how people learn. More formally, Mayer (2011) defined the science of learning as the “scientific study of how people learn” (p. 3). The science of instruction (Mayer 2011), informed in part by the science of learning, is also on display throughout the book. Mayer defined the science of instruction as the “scientific study of how to help people learn” (p. 3). Finally, the assessment of student learning (e.g., learning, remembering, transferring knowledge) during and after instruction helps us determine the effectiveness of our instructional methods. Mayer defined the science of assessment as the “scientific study of how to determine what people know” (p.3). Most of the research and applications presented in this book are completed within a science of learning framework. Researchers first conducted research to understand how people learn in certain controlled contexts (i.e., in the laboratory) and then they, or others, began to consider how these understandings could be applied in educational settings. Work on the cognitive load theory of learning, which is discussed in depth in several chapters of this book (e.g., Chew; Lee and Kalyuga; Mayer; Renkl), provides an excellent example that documents how science of learning has led to valuable work on the science of instruction. Most of the work described in this book is based on theory and research in cognitive psychology. We might have selected other topics (and, thus, other authors) that have their research base in behavior analysis, computational modeling and computer science, neuroscience, etc. We made the selections we did because the work of our authors ties together nicely and seemed to us to have direct applicability in academic settings