Support of the collaborative inquiry learning process: influence of support on task and team regulation

Regulation of the learning process is an important condition for efficient and effective learning. In collaborative learning, students have to regulate their collaborative activities (team regulation) next to the regulation of their own learning process focused on the task at hand (task regulation). In this study, we investigate how support of collaborative inquiry learning can influence the use of regulative activities of students. Furthermore, we explore the possible relations between task regulation, team regulation and learning results. This study involves tenth-grade students who worked in pairs in a collaborative inquiry learning environment that was based on a computer simulation, Collisions, developed in the program SimQuest. Students of the same team worked on two different computers and communicated through chat. Chat logs of students from three different conditions are compared. Students in the first condition did not receive any support at all (Control condition). In the second condition, students received an instruction in effective communication, the RIDE rules (RIDE condition). In the third condition, students were, in addition to receiving the RIDE rules instruction, supported by the Collaborative Hypothesis Tool (CHT), which helped the students with formulating hypotheses together (CHT condition). The results show that students overall used more team regulation than task regulation. In the RIDE condition and the CHT condition, students regulated their team activities most often. Moreover, in the CHT condition the regulation of team activities was positively related to the learning results. We can conclude that different measures of support can enhance the use of team regulative activities, which in turn can lead to better learning results

Epilogue for the IJSME Special Issue: Metacognition for science and mathematics learning in technology-infused learning environments

Epilogue for the IJSME Special Issue: Metacognition for science and mathematics learning in technology-infused learning environment

Do Metacognitive Strategies Improve Student Achievement in Secondary Science Classrooms?

Increasing prevalence of high-stakes testing calls for focus on value-added teaching and learning practices. Following is an inquiry regarding metacognitive teaching and learning practices as it pertains to secondary science classrooms. Research shows that the orchestration and inclusion of metacognitive strategies in the science classroom improve achievement under the following preconditions: (1) are pervasively embedded in the educational structure; (2) are part of appropriately rigorous and relevant curriculum; (3) are supported by ‘metacognitive friendly’ teaching strategies; (4) are explicitly practiced by students and teachers; and (5) enable students to take responsibility for their own learning

Techniques for Enhancing Reflection and Learning in an Online Course

The authors designed new content for an online research skills course, to provide instruction and expert modeling of the process for determining bias when evaluating information sources. They also introduced a specific metacognitive strategy (self-questioning) to enhance student self-awareness. Students were encouraged to complete a self-regulated learning survey to raise their awareness of metacognitive strategies. The instructional content, an Adobe Captivate movie, described a cognitive strategy for identifying bias, MAPit, and included activities and questions throughout for students to assess their understanding. Instruction was followed by an online quiz that provided practice in applying the MAPit strategy. Metacognitive prompts within the quiz encouraged students to reflect on and assess their learning. The final course assignment (Capstone) also included application questions, with a reminder about the MAPit strategy. A review of performance on both assignments showed improvement after this intervention. When compared to a later offering of the same course where a more efficient approach to encouraging student self-questioning was applied, the improvement was sustained. This approach can be effectively implemented in a large enrollment online course

Classification of Metacognitive Into Two Catagories to Support the Learning Process

: Learning outcomes are the patterns of actions, values, understanding, attitudes, appreciation and skills. Learning outcomes are related to the metacognitive of student where the elements contained in metacognitive is cognitive. The relationship between cognitive and metacognitive which is the foundation of cognitive is metacognitive. There are two components such as knowledge of metacognitive and regulation of metacognitive. In the learning process, cognitive factors are not the only one that can support, but also a metacognitive factor is a very influential factor for the success of the learning process. Thus, it is very important to do with a deeper analysis about metacognitive by identifying me-tacognitive level to support the learning process. Identification of metacognitive is performed by using Naïve Bayes Classifier algorithm (NBC) which NBC is one of an algorithm that is used for classification algorithm for data mining. In these studies, it is obtained that the accuracy scored 88,0597% when tested using NBC

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

Assessing a Collaborative Online Environment for Music Composition

The current pilot study tested the effectiveness of an e-learning environment built to enable students to compose music collaboratively. The participants interacted online by using synchronous and asynchronous resources to develop a project in which they composed a new music piece in collaboration. After the learning sessions, individual semi-structured interviews with the participants were conducted to analyze the participants\u2019 perspectives regarding the e-learning environment\u2019s functionality, the resources of the e-learning platform, and their overall experience with the e-learning process. Qualitative analyses of forum discussions with respect to metacognitive dimensions, and semi-structured interview transcriptions were performed. The findings showed that the participants successfully completed the composition task in the virtual environment, and that they demonstrated the use of metacognitive processes. Moreover, four themes were apparent in the semi-structured interview transcriptions: Teamwork, the platform, face-to-face/online differences, and strengths/weaknesses. Overall, the participants exhibited an awareness of the potential of the online tools, and the task performed. The results are discussed in consideration of metacognitive processes, and the following aspects that rendered virtual activity effective for learning: The learning environment, the platform, the technological resources, the level of challenge, and the nature of the activity. The possible implications of the findings for research on online collaborative composition are also considered

The role of pedagogical tools in active learning: a case for sense-making

Evidence from the research literature indicates that both audience response systems (ARS) and guided inquiry worksheets (GIW) can lead to greater student engagement, learning, and equity in the STEM classroom. We compare the use of these two tools in large enrollment STEM courses delivered in different contexts, one in biology and one in engineering. The instructors studied utilized each of the active learning tools differently. In the biology course, ARS questions were used mainly to check in with students and assess if they were correctly interpreting and understanding worksheet questions. The engineering course presented ARS questions that afforded students the opportunity to apply learned concepts to new scenarios towards improving students conceptual understanding. In the biology course, the GIWs were primarily used in stand-alone activities, and most of the information necessary for students to answer the questions was contained within the worksheet in a context that aligned with a disciplinary model. In the engineering course, the instructor intended for students to reference their lecture notes and rely on their conceptual knowledge of fundamental principles from the previous ARS class session in order to successfully answer the GIW questions. However, while their specific implementation structures and practices differed, both instructors used these tools to build towards the same basic disciplinary thinking and sense-making processes of conceptual reasoning, quantitative reasoning, and metacognitive thinking.Comment: 20 pages, 5 figure