Effects of Metacognitive Monitoring on Academic Achievement in an Ill-Structured Problem-Solving Environment

Higher education courses are increasingly moving online while educational approaches are concurrently shifting their focus toward student-centered approaches to learning. These approaches promote critical thinking by asking students to solve a range of ill-structured problems that exist in the real world. Researchers have found that student-centered online learning environments require students to have self-regulated learning skills, including metacognitive skills to regulate their own learning processes. Much of the research suggests that externally supporting students while they are learning online, either directly or indirectly, helps them to succeed academically. However, few empirical studies have investigated what levels of support are most effective for promoting students\u27 self-regulated learning behaviors. Additionally, these studies reported conflicting results – some found maximum support to be most effective while others found no significant difference. The purpose of this study was to investigate the effectiveness of different levels of support for self-regulated learning during a complex learning activity to solve an ill-structured problem-solving situation in an online learning environment. In addition, the role of students\u27 self-efficacy on their academic achievement was examined. A total of 101 undergraduate students from three international studies courses offered at a large urban Southeastern public university in the United States participated in the study. The students were randomly assigned to treatment (minimum support, maximum support) and control groups. Students\u27 academic achievement scores were measured using a conceptual knowledge test created by the professor teaching the courses. O\u27Neil\u27s (1997) Trait Self-Regulation Questionnaire measured students\u27 self-efficacy. Analysis of Co-Variance (ANCOVA) was conducted to analyze the data. The ANCOVA results indicated significant improvement of the academic achievement of the minimum support group versus both the maximum support and control groups. Additionally, self-efficacy as a co-variable did not significantly impact students\u27 achievement scores in any of the groups. The overall results indicated that it is important to consider the level of self-regulated learning support when designing online learning environments promoting students\u27 critical thinking skills. Promoting students\u27 self-regulated learning skills is vital when designing online higher education courses

Using Technology to Encourage Self-Directed Learning: The Collaborative Lecture Annotation System

The rapidly-developing 21st century world of work and knowledge calls for self-directed lifelong (SDL) learners. While higher education must embrace the types of pedagogies that foster SDL skills in graduates, the pace of change in education can be glacial. This paper describes a social annotation technology, the Collaborative Lecture Annotation System (CLAS), that can be used to leverage existing teaching and learning practices for acquisition of 21st Century SDL skills. CLAS was designed to build upon the artifacts of traditional didactic modes of teaching, create enriched opportunities for student engagement with peers and learning materials, and offer learners greater control and ownership of their individual learning strategies. Adoption of CLAS creates educational experiences that promote and foster SDL skills: motivation, self-management and self-monitoring. In addition, CLAS incorporates a suite of learning analytics for learners to evaluate their progress, and allow instructors to monitor the development of SDL skills and identify the need for learning support and guidance. CLAS stands as an example of a simple tool that can bridge the gap between traditional transmissive pedagogy and the creation of authentic and collaborative learning spaces

Digital Assistants for Self-Regulated Learning: Towards a State-Of-The-Art Overview

We observe a continuous shift from face-to-face to hybrid or online education. Today, learners are exposed to a high level of autonomy and, at the same time, have less contact with peers and teachers. In this environment, the ability to self-regulate one’s learning is becoming more relevant to achieve positive learning results and academic success. However, the application of self-regulated learning is not trivial. A potential solution for this challenge comes in the form of digital assistants like chatbots or pedagogical agents that provide structure for the learners. Existing research on digital assistants for self-regulated learning is scattered across several fields. In this research-in-progress paper, we present preliminary results of a systematic literature review (SLR) study providing an overview of the state-of- the-art of digital assistants supporting SRL. Our results show that future research in this domain should focus on affect, behavioral, and context regulation and more longitudinal studies are required

Theoretical and Conceptual Approaches to Co-Regulation: A Theoretical Review

During the last two decades, interpersonal regulation in natural and digital learning environments has gained importance. Ever since the first conceptual and methodological precisions regarding collaborative learning were made, educational psychology has focused its interest on analyzing collective regulation of motivation, cognition, and behavior. Despite the fact that the body of research on co-regulation has grown, emerging epistemological frameworks evidence a lack of conceptual and theoretical clarity. In response to this situation, the authors propose a conceptual approach in order to address interpersonal regulation in four aspects: first, they describe three learning theories which have been used to study co-regulation. Second, the authors recommend a conceptual delimitation of terms regarding the learning theories on social regulation. Third, they highlight diffuse boundaries between theoretical approaches and terms used in the literature on co-regulation. Finally, the authors suggest some challenges the researchers in this field face

Towards a Framework for Metacognition in Game-Based Learning

\u3cp\u3eGame-based learning can motivate learners and help them to acquire new knowledge in an active way. However, it is not always clear for learners how to learn effectively and efficiently within game-based learning environments. As metacognition comprises the knowledge and skills that learners employ to plan, monitor, regulate, and evaluate their learning, it plays a key role in improving their learning in general. Thus, if we want learners to become better at learning through game-based learning, we need to investigate how metacognition can be integrated into the design of game-based learning environments. In this paper we introduce a framework that aids designers and researchers to formally specify the design of game-based learning environments encouraging metacognition. With a more formal specification of the metacognitive objectives and the way the training design and game design aims to achieve these goals, we can learn more through analysing and comparing different approaches. The framework consists of design dimensions regarding metacognitive outcomes, metacognitive training, and metacognitive game design. Each design dimension represents two opposing directions for the design of a game-based learning environment that are likely to affect the encouragement of metacognitive awareness within learners. As such, we introduce a formalised method to design, evaluate and compare games addressing metacognition, thus enabling both researchers and designers to create more effective games for learning in the future.\u3c/p\u3

Towards a framework for metacognition in game-based learning

Game-based learning can motivate learners and help them to acquire new knowledge in an active way. However, it is not always clear for learners how to learn effectively and efficiently within game-based learning environments. As metacognition comprises the knowledge and skills that learners employ to plan, monitor, regulate, and evaluate their learning, it plays a key role in improving their learning in general. Thus, if we want learners to become better at learning through game-based learning, we need to investigate how metacognition can be integrated into the design of game-based learning environments. In this paper we introduce a framework that aids designers and researchers to formally specify the design of game-based learning environments encouraging metacognition. With a more formal specification of the metacognitive objectives and the way the training design and game design aims to achieve these goals, we can learn more through analysing and comparing different approaches. The framework consists of design dimensions regarding metacognitive outcomes, metacognitive training, and metacognitive game design. Each design dimension represents two opposing directions for the design of a game-based learning environment that are likely to affect the encouragement of metacognitive awareness within learners. As such, we introduce a formalised method to design, evaluate and compare games addressing metacognition, thus enabling both researchers and designers to create more effective games for learning in the future.</p

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

Regulation of collaboration in project-based learning mediated by CSCL scripting reflection

Many studies attempt to effectively support student regulation of collaboration using CSCL tools to enrich learning outcomes. However, few studies are aimed at facilitating development of students' internal scripts for regulation of collaboration. This study focuses on developing and evaluating a computer-mediated learning environment for project-based learning to facilitate student internal scripts for regulation by designing external scripts for effective reflection. Forty- eight first-year university students participated in this study as part of their curriculum. Our analyses of their internal scripts before and after PBL participation revealed that significantly more students who encountered an unfamiliar situation during collaboration constructed new regulation scripts. Moreover, in case studies, we found that students augmented their scripts for socially shared regulation when recognizing socio-cognitive challenges, whereas they augmented co-regulation and self-regulation scripts when recognizing socio-emotional challenges.Peer reviewe