The effect of embedded instruction on solving information problems

In higher education students are often faced with information problems: tasks or assignments that require them to identify information needs, locate corresponding information sources, extract and organize relevant information from each source, and synthesize information from a variety of sources. Explicit and intensive instruction is necessary, because solving information problems is a complex cognitive skill. In this study instruction for Information Problem Solving (IPS) was embedded in a competence and web-based course for distance education students about research methodology in the field of Psychology. Eight of the sixteen students following this course received a version of the course with embedded IPS instruction. The other half received a variant of the course without extra IPS instruction. The analysis of the thinking aloud protocols revealed that after the course students in the experimental condition regulate the IPS process more often than students in the control condition. They also judged the information found more often

Survey on Combinatorial Register Allocation and Instruction Scheduling

Register allocation (mapping variables to processor registers or memory) and instruction scheduling (reordering instructions to increase instruction-level parallelism) are essential tasks for generating efficient assembly code in a compiler. In the last three decades, combinatorial optimization has emerged as an alternative to traditional, heuristic algorithms for these two tasks. Combinatorial optimization approaches can deliver optimal solutions according to a model, can precisely capture trade-offs between conflicting decisions, and are more flexible at the expense of increased compilation time. This paper provides an exhaustive literature review and a classification of combinatorial optimization approaches to register allocation and instruction scheduling, with a focus on the techniques that are most applied in this context: integer programming, constraint programming, partitioned Boolean quadratic programming, and enumeration. Researchers in compilers and combinatorial optimization can benefit from identifying developments, trends, and challenges in the area; compiler practitioners may discern opportunities and grasp the potential benefit of applying combinatorial optimization

How can I learn more when I collaborate in a virtual group?

Learning in virtual groups has been a process studied and analysed long from multiple perspectives. However, the literature is scarce when we look for models to explain information problem solving skills in online collaboration. A descriptive model of cognitive skills involved in individual information problem solving while using internet information can be found in recent research. The purpose of this study was to find out what information problem solving skills (IPS) students apply when working collaboratively online, and secondly, to analyse what differentiates students who do well on their knowledge tests after collaboration, in relation to these IPS skills. We conducted a research with more than 40 students in 10 virtual groups to analyse the correlation between learning and IPS skills applied by students during an online task that lasted more than 4 weeks. Students completed a weekly self-report with actions related to IPS skills and time devoted to the collaborative task. Findings show that students applied more frequently the skill to check the communication (30%), secondly, read de information (22%), in the third place exchange information (20%), followed by write the information (15%), analyze the information (8%), and finally, search for information (5%). However, only three skills correlate with learning: information exchange, analysis of information and checking communication. Two of them (exchange and check) are collaborative skills and one of them (analysis) is an information problem-solving skill. The conclusions of this study may provide guidelines for instructors and students on ways to improve learning in online collaborative group work

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

Scaffolding Problem Solving with Embedded Examples to Promote Deep Learning

This study compared the relative utility of an intelligent tutoring system that uses procedure-based hints to a version that uses worked-out examples. The system, Andes, taught college level physics. In order to test which strategy produced better gains in competence, two versions of Andes were used: one offered participants graded hints and the other offered annotated, worked-out examples in response to their help requests. We found that providing examples was at least as effective as the hintsequences and was more efficient in terms of the number of problems it took to obtain the same level of mastery

Embedded librarianship and problem-based learning in undergraduate mathematics courses

A pedagogical approach of problem-based learning with embedded librarianship in several undergraduate mathematics courses is implemented in this educational research. The students are assigned to work on several projects on various applications of mathematical topics in daily life and submit written reports. An embedded librarian collaborates together with the instructor and the students to improve the students' information literacy. Initial reaction and anecdotal evidence show that the students' information literacy and academic performance have improved throughout the semesters.Comment: 4 pages, 2 tables, International Congress of Women Mathematicians Presentation Book, Ewha Womans University, Seoul, South Korea, pp. 117-120, 201

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