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

Multiple Retrieval Models and Regression Models for Prior Art Search

This paper presents the system called PATATRAS (PATent and Article Tracking, Retrieval and AnalysiS) realized for the IP track of CLEF 2009. Our approach presents three main characteristics: 1. The usage of multiple retrieval models (KL, Okapi) and term index definitions (lemma, phrase, concept) for the three languages considered in the present track (English, French, German) producing ten different sets of ranked results. 2. The merging of the different results based on multiple regression models using an additional validation set created from the patent collection. 3. The exploitation of patent metadata and of the citation structures for creating restricted initial working sets of patents and for producing a final re-ranking regression model. As we exploit specific metadata of the patent documents and the citation relations only at the creation of initial working sets and during the final post ranking step, our architecture remains generic and easy to extend

Challenges for chemical information extraction and text retrieval

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Multilingual interactive experiments with Flickr

This paper presents a proposal for iCLEF 2006, the interactive track of the CLEF cross-language evaluation campaign. In the past, iCLEF has addressed applications such as information retrieval and question answering. However, for 2006 the focus has turned to text-based image retrieval from Flickr. We describe Flickr, the challenges this kind of collection presents to cross-language researchers, and suggest initial iCLEF tasks

Searching Data: A Review of Observational Data Retrieval Practices in Selected Disciplines

A cross-disciplinary examination of the user behaviours involved in seeking and evaluating data is surprisingly absent from the research data discussion. This review explores the data retrieval literature to identify commonalities in how users search for and evaluate observational research data. Two analytical frameworks rooted in information retrieval and science technology studies are used to identify key similarities in practices as a first step toward developing a model describing data retrieval

Report of MIRACLE team for the Ad-Hoc track in CLEF 2006

This paper presents the 2006 MIRACLE’s team approach to the AdHoc Information Retrieval track. The experiments for this campaign keep on testing our IR approach. First, a baseline set of runs is obtained, including standard components: stemming, transforming, filtering, entities detection and extracting, and others. Then, a extended set of runs is obtained using several types of combinations of these baseline runs. The improvements introduced for this campaign have been a few ones: we have used an entity recognition and indexing prototype tool into our tokenizing scheme, and we have run more combining experiments for the robust multilingual case than in previous campaigns. However, no significative improvements have been achieved. For the this campaign, runs were submitted for the following languages and tracks: - Monolingual: Bulgarian, French, Hungarian, and Portuguese. - Bilingual: English to Bulgarian, French, Hungarian, and Portuguese; Spanish to French and Portuguese; and French to Portuguese. - Robust monolingual: German, English, Spanish, French, Italian, and Dutch. - Robust bilingual: English to German, Italian to Spanish, and French to Dutch. - Robust multilingual: English to robust monolingual languages. We still need to work harder to improve some aspects of our processing scheme, being the most important, to our knowledge, the entities recognition and normalization

What do faculties specializing in brain and neural sciences think about, and how do they approach, brain-friendly teaching-learning in Iran?

Objective: to investigate the perspectives and experiences of the faculties specializing in brain and neural sciences regarding brain-friendly teaching-learning in Iran. Methods: 17 faculties from 5 universities were selected by purposive sampling (2018). In-depth semi-structured interviews with directed content analysis were used. Results: 31 sub-subcategories, 10 subcategories, and 4 categories were formed according to the “General teaching model”. “Mentorship” was a newly added category. Conclusions: A neuro-educational approach that consider the roles of the learner’s brain uniqueness, executive function facilitation, and the valence system are important to learning. Such learning can be facilitated through cognitive load considerations, repetition, deep questioning, visualization, feedback, and reflection. The contextualized, problem-oriented, social, multi-sensory, experiential, spaced learning, and brain-friendly evaluation must be considered. Mentorship is important for coaching and emotional facilitation