Computer Self-Efficacy, Cognitive Actions, and Metacognitive Strategies of High School Students While Engaged in Interactive Learning Modules

The purpose of this research was to investigate high school students’ computer self-efficacy, cognitive actions, and metacognitive strategies in a self-regulated learning (SRL) framework while utilizing an interactive learning module. The researcher hypothesized that computer self-efficacy is correlated positively with cognitive actions and metacognitive strategies while the students are engaged with interactive learning modules. This research used a mixed-methods approach to answer the research questions. Two research questions guided this research: (1) How is students’ computer self-efficacy related to cognitive actions and metacognitive strategies while using interactive learning modules?; and (2) How do students plan monitor their cognitive actions, and regulate their monitoring strategies during learning with interactive learning modules?This study utilized self-regulated learning framework that covered self-efficacy, cognitive, and metacognitive components. While self-efficacy was represented by computer self-efficacy, the metacognitive component was represented by planning, monitoring, and regulating strategies. Cognitive actions represent contextual activities while using interactive learning modules. One hundred and thirteen students from two high schools in Northern Utah, USA(i.e., InTech Collegiate High School and Logan High School) participated in this study. Each student worked on three modules: Boolean Logic, Minimum Spanning Tree, and Modeling Using Graphs. Due to the differences in class schedules between both schools, students at InTech Collegiate High School and Logan High School completed the activities within 2 and 4 days, respectively. Three different forms of data were gathered for analysis. These data included questionnaires, screen captured videos, and audio recordings of the interviews. The students completed three questionnaires: demographic, computer self-efficacy, and self-regulated computer-based learning questionnaires.The findings of the study revealed that while computer self-efficacy was not positively correlated with cognitive actions, it was positively correlated with metacognitive strategies. Specifically, the findings revealed a significant positive correlation between computer self-efficacy and planning strategies. Screen-captured video analyses showed that there were different profiles of cognitive actions and metacognitive strategies between high and low computer self-efficacy groups. The findings were confirmed by issues from interview analyses between the groups

High and Low Computer Self-Efficacy Groups and Their Learning Behavior from Self-Regulated Learning Perspective While Engaged in Interactive Learning Modules

The purpose of this research was to investigate high school students’ computer self-efficacy (CSE) and learning behavior in a selfregulated learning (SRL) framework while utilizing an interactive learning module. The researcher hypothesizes that CSE is reflected on cognitive actions and metacognitive strategies while the students are engaged with interactive learning modules. Two research questions guided this research: (1) how is students’ CSE while engaged in interactive learning modules? and (2) how do high and low CSE groups plan and monitor their cognitive action, and regulate their monitoring strategies based on their CSE level? The research used a mixedmethods approach to answer the research questions. This study utilized a SRL framework that covered self-efficacy, cognitive actions, and metacognitive components. While self-efficacy was represented by CSE, metacognitive component was represented by planning, monitoring, and regulating strategies. Cognitive actions represent contextual activities while using interactive learning modules. One hundred students from two high schools, InTech Collegiate and Logan High Schools, completed activities in this study. Each student worked on three modules, namely Boolean Logic, Minimum Spanning Tree, and Modeling Using Graphs. Three different forms of data were gathered for analysis. These data included questionnaires, screen captured videos, and audio recordings of interviews. The findings of this study revealed that the students achieved the highest average score on beginning skills compared to advanced skills and file and software skills for their CSE. Furthermore, screen-captured video analysis showed that there were different profiles of cognitive actions and metacognitive strategies between high and low CSE groups in terms of the strategy changes and duration of their strategies. Issues gathered from interview analysis between these two groups were also elaborated

A Multilevel Analysis of the Effect of Prompting Self-Regulation in Technology-Delivered Instruction

We used a within-subjects design and multilevel modeling in two studies to examine the effect of prompting self-regulation, an intervention designed to improve learning from technology-delivered instruction. The results of two studies indicate trainees who were prompted to self-regulate gradually improved their knowledge and performance over time, relative to the control condition. In addition, Study 2 demonstrated that trainees’ cognitive ability and self-efficacy moderated the effect of the prompts. Prompting self-regulation resulted in stronger learning gains over time for trainees with higher ability or higher self-efficacy. Overall, the two studies demonstrate that prompting self-regulation had a gradual, positive effect on learning, and the strength of the effect increased as trainees progressed through training. The results are consistent with theory suggesting self-regulation is a cyclical process that has a gradual effect on learning and highlight the importance of using a within-subjects design in self-regulation. research

Rich environments for active learning: a definition

Rich Environments for Active Learning, or REALs, are comprehensive instructional systems that evolve from and are consistent with constructivist philosophies and theories. To embody a constructivist view of learning, REALs: promote study and investigation within authentic contexts; encourage the growth of student responsibility, initiative, decision making, and intentional learning; cultivate collaboration among students and teachers; utilize dynamic, interdisciplinary, generative learning activities that promote higher-order thinking processes to help students develop rich and complex knowledge structures; and assess student progress in content and learning-to-learn within authentic contexts using realistic tasks and performances. REALs provide learning activities that engage students in a continuous collaborative process of building and reshaping understanding as a natural consequence of their experiences and interactions within learning environments that authentically reflect the world around them. In this way, REALs are a response to educational practices that promote the development of inert knowledge, such as conventional teacher-to-student knowledge-transfer activities. In this article, we describe and organize the shared elements of REALs, including the theoretical foundations and instructional strategies to provide a common ground for discussion. We compare existing assumptions underlying education with new assumptions that promote problem-solving and higher-level thinking. Next, we examine the theoretical foundation that supports these new assumptions. Finally, we describe how REALs promote these new assumptions within a constructivist framework, defining each REAL attribute and providing supporting examples of REAL strategies in action

Effects of a Professional Learning Experience on Middle School Teachers’ Perceptions, Experiences, and Implementation of Curricular-Embedded Self-Regulated Learning and Motivation

During the middle school transitional years, students face potential obstacles that can affect their achievement and motivation. It is essential that they learn how to self-regulate their learning and motivation so that they can persevere in an increasingly challenging academic environment. Teachers may support students directly or indirectly by embedding various practices and strategies into extant curricula. This two-phase qualitative case study provided four purposively selected teachers with a professional learning (PL) experience on self-regulated learning (SRL) and self-regulated motivation (SRM). The purpose of this study was to understand the perceptions and experiences of middle school teachers regarding the training and implementation of curricular-embedded SRL with the goal of developing a PL framework aimed at training and supporting teachers with curricular-embedded SRL practices and strategies. The study was guided by one research question: How does SRL-focused PL affect teachers’ perceptions, experiences, and implementation of curricular-embedded SRL practices and strategies in the middle school classroom? ATLAS.ti was used to analyze the interview data and the reflective journals. Results were mixed, but they showed more perceived successes than challenges regarding the framework and implementation of practices and strategies, a variety of SRL practices and strategies employed during the implementation phase, and a thorough understanding of theoretical knowledge, which was demonstrated through reflections and implementation. This information can serve as a foundation to develop a PL framework for training and supporting teachers as they embed and implement SRL practices and strategies in the middle school classroom

EXAMINING A HIGH-IMPACT, FIRST-SEMESTER SEMINAR CLASS ON ONLINE UNDERGRADUATE STUDENT SELF-REGULATION, SELF-DIRECTION, ONLINE LEARNING SELF-EFFICACY, AND PERSISTENCE

EXAMINING A HIGH-IMPACT, FIRST-SEMESTER SEMINAR CLASS ON ONLINE UNDERGRADUATE STUDENT SELF-REGULATION, SELF-DIRECTION, ONLINE LEARNING SELF-EFFICACY, AND PERSISTENC

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

A Meta-Analysis of Self-Regulated Learning Interventions and Learning Outcomes in Higher Education E-Learning Environments

Through a systematic review of the literature, 36 empirical studies regarding self-regulated learning (SRL) interventions and learning outcomes in higher education e-learning environments were identified and meta-analyzed using15 years of data. Frequently studied interventions included providing SRL scaffolding, SRL training, or SRL training and scaffolding either as a precursor or as part of the learning environment or both. Scaffolding interventions were embedded as part of the learning environment and designed to guide learners to perform cognitive and metacognitive strategies such as task analysis, goal setting, and reflection during a learning activity. Training interventions, by contrast, involved instruction in the use of SRL strategies prior to beginning a learning activity, course or program. In some studies, both training and scaffolding SRL interventions were implemented. Information about the types of SRLinterventions including the means of measuring learning outcomes (more or less complex), instructional design characteristics and learning outcomes data for calculating effect sizes were extracted for the purposes of conducting this meta-analysis

An Exploratory Study of Students’ Perceptions of Learning Management System Utilisation and Learning Community

Blackboard Learn is one of the learning management systems (LMSs), which is used in teaching to manage user learning interventions and assist in the planning, distribution and evaluation of a specific learning process. The purpose of this study was to investigate how the functionalities of Blackboard Learn were used in online courses and how students perceived the benefits of using them. Also, the study was to investigate how students’ perceptions of teaching, cognitive and social presences within the Community of Inquiry and perceived benefits of using Blackboard Learn were related to their learning efforts. The results revealed that students who consider Blackboard tools more beneficial on their learning are most likely to have higher perceptions of teaching presence. Moreover, students’ learning efforts were increased primarily by students’ perceptions on perceived benefits of using Blackboard and secondarily by students’ perceptions of social presences. In conclusion, utilising LMS tools effectively in online courses can benefit students’ course work and would motivate them to put more efforts on their learning