Awareness of Daylighting on Student Learning in an Educational Facility

This study examines how awareness of the interior architecture of a building, specifically daylighing, affects students academic performance. Extensive research has proven that the use of daylighting in a classroom can significantly enhance students’ academic success. The problem statement and purpose of this study is to determine if student awareness of daylighting in their learning environment affects academic performance compared to students with no knowledge of daylighting. Research and surveys in existing and newly constructed high schools were conducted to verify the results of this study. These design ideas and concepts could influence the architecture and design industry to advocate construction and building requirements that incorporate more sustainable design teaching techniques.

Combining meta-learning and search techniques to select parameters for support vector machines

Support Vector Machines (SVMs) have achieved very good performance on different learning problems. However, the success of SVMs depends on the adequate choice of the values of a number of parameters (e.g., the kernel and regularization parameters). In the current work, we propose the combination of meta-learning and search algorithms to deal with the problem of SVM parameter selection. In this combination, given a new problem to be solved, meta-learning is employed to recommend SVM parameter values based on parameter configurations that have been successfully adopted in previous similar problems. The parameter values returned by meta-learning are then used as initial search points by a search technique, which will further explore the parameter space. In this proposal, we envisioned that the initial solutions provided by meta-learning are located in good regions of the search space (i.e. they are closer to optimum solutions). Hence, the search algorithm would need to evaluate a lower number of candidate solutions when looking for an adequate solution. In this work, we investigate the combination of meta-learning with two search algorithms: Particle Swarm Optimization and Tabu Search. The implemented hybrid algorithms were used to select the values of two SVM parameters in the regression domain. These combinations were compared with the use of the search algorithms without meta-learning. The experimental results on a set of 40 regression problems showed that, on average, the proposed hybrid methods obtained lower error rates when compared to their components applied in isolation.

ParTec and KarnUMa-Digital tools as a help for freshman learning

[EN]Freshman students always present lower success rates than other levels of students. Digital systems is a course usually taught at first year studentsand its success rate is not very high. In this work we introduce three digital tools to improve freshman learning designed for easy use and one of them is a tool for mobile terminals that can be used as a game. The first tool is ParTec and is used to implement and test the partition technique. This technique is used to eliminate redundant states in finite state machines. This is a repetitive task that students do not like to perform. The second tool is called KarnUMa and is used for simplifying logic functions through Karnaugh Maps. Simplifying logical functions is a core task for this course and although students usually perform this task better than other tasks, it can still be improved. The third tool is a version of KarnUMa, designed for mobile devices. All the tools are available online for download and have been a helpful tool for students.

Formazione, apprendimento e nuove prospettive e-learning. Lo sviluppo delle nuove tecnologie didattiche e la creazione di sistemi di apprendimento lungo l’intero arco della vita

The thesis of this paper is based on the assumption that the socio-economic system in which we are living is characterised by three great trends: growing attention to the promotion of human capital; extremely rapid technological progress, based above all on the information and communication technologies (ICT); the establishment of new production and organizational set-ups. These transformation processes pose a concrete challenge to the training sector, which is called to satisfy the demand for new skills that need to be developed and disseminated. Hence the growing interest that the various training sub-systems devote to the issues of lifelong learning and distance learning. In such a context, the so-called e-learning acquires a central role. The first chapter proposes a reference theoretical framework for the transformations that are shaping post-industrial society. It analyzes some key issues such as: how work is changing, the evolution of organizational set-ups and the introduction of learning organization, the advent of the knowledge society and of knowledge companies, the innovation of training processes, and the key role of ICT in the new training and learning systems. The second chapter focuses on the topic of e-learning as an effective training model in response to the need for constant learning that is emerging in the knowledge society. This chapter starts with a reflection on the importance of lifelong learning and introduces the key arguments of this thesis, i.e. distance learning (DL) and the didactic methodology called e-learning. It goes on with an analysis of the various theoretic and technical aspects of e-learning. In particular, it delves into the theme of e-learning as an integrated and constant training environment, characterized by customized programmes and collaborative learning, didactic assistance and constant monitoring of the results. Thus, all the aspects of e-learning are taken into exam: the actors and the new professionals, the virtual communities as learning subjects, the organization of contents in learning objects, the conformity to international standards, the integrated platforms and so on. The third chapter, which concludes the theoretic-interpretative part, starts with a short presentation of the state-of-the-art e-learning international market that aims to understand its peculiarities and its current trends. Finally, we focus on some important regulation aspects related to the strong impulse given by the European Commission first, and by the Italian governments secondly, to the development and diffusion of e-learning. The second part of the thesis (chapters 4, 5 and 6) focus on field research, which aims to define the Italian scenario for e-learning. In particular, we have examined some key topics such as: the challenges of training and the instruments to face such challenges; the new didactic methods and technologies for lifelong learning; the level of diffusion of e-learning in Italy; the relation between classroom training and online training; the main factors of success as well as the most critical aspects of the introduction of e-learning in the various learning environments. As far as the methodological aspects are concerned, we have favoured a qualitative and quantitative analysis. A background analysis has been done to collect the statistical data available on this topic, as well as the research previously carried out in this area. The main source of data is constituted by the results of the Observatory on e-learning of Aitech-Assinform, which covers the 2000s and four areas of implementation (firms, public administration, universities, school): the thesis has reviewed the results of the last three available surveys, offering a comparative interpretation of them. We have then carried out an in-depth empirical examination of two case studies, which have been selected by virtue of the excellence they have achieved and can therefore be considered advanced and emblematic experiences (a large firm and a Graduate School).

Deep learning for computer vision: a comparison between convolutional neural networks and hierarchical temporal memories on object recognition tasks

In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general. However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of "intelligence". The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them. CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems. HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain. In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.

Different Learning Curves for Axillary Brachial Plexus Block: Ultrasound Guidance versus Nerve Stimulation

Little is known about the learning of the skills needed to perform ultrasound- or nerve stimulator-guided peripheral nerve blocks. The aim of this study was to compare the learning curves of residents trained in ultrasound guidance versus residents trained in nerve stimulation for axillary brachial plexus block. Ten residents with no previous experience with using ultrasound received ultrasound training and another ten residents with no previous experience with using nerve stimulation received nerve stimulation training. The novices' learning curves were generated by retrospective data analysis out of our electronic anaesthesia database. Individual success rates were pooled, and the institutional learning curve was calculated using a bootstrapping technique in combination with a Monte Carlo simulation procedure. The skills required to perform successful ultrasound-guided axillary brachial plexus block can be learnt faster and lead to a higher final success rate compared to nerve stimulator-guided axillary brachial plexus block.

An empirical examination of the integration of causal attribution and social learning theories in achievement situations

Using path analysis, the present investigation sought to clarify possible operational linkages among constructs from social learning and attribution theories within the context of a self-esteem system. Subjects were 300 undergraduate university students who completed a measure of self-esteem and indicated expectancies for success and minimal goal levels for an experimental task. After completing the task and receiving feedback about their performance, subjects completed causal attribution and self-esteem questionnaires. Results revealed gender differences in the degree and strength of the proposed relations, but not in the mean levels of the variables studied. Results suggested that the integration of social learning and attribution theories within a single conceptual model provides a better understanding of students' behaviors and self-esteem in achievement situations.

Reshaping Rural Extension: Learning for Sustainability (LforS): An Integrative and Learning-Based Advisory Approach for Rural Extension with Small-Scale Farmers

This publication offers concrete suggestions for implementing an integrative and learning-oriented approach to agricultural extension with the goal of fostering sustainable development. It targets governmental and non-governmental organisations, development agencies, and extension staff working in the field of rural development. The book looks into the conditions and trends that influence extension today, and outlines new challenges and necessary adaptations. It offers a basic reflection on the goals, the criteria for success and the form of a state-of-the-art approach to extension. The core of the book consists of a presentation of Learning for Sustainability (LforS), an example of an integrative, learning-oriented approach that is based on three crucial elements: stakeholder dialogue, knowledge management, and organizational development. Awareness raising and capacity building, social mobilization, and monitoring & evaluation are additional building blocks. The structure and organisation of the LforS approach as well as a selection of appropriate methods and tools are presented. The authors also address key aspects of developing and managing a learning-oriented extension approach. The book illustrates how LforS can be implemented by presenting two case studies, one from Madagascar and one from Mongolia. It addresses conceptual questions and at the same time it is practice-oriented. In contrast to other extension approaches, LforS does not limit its focus to production-related aspects and the development of value chains: it also addresses livelihood issues in a broad sense. With its focus on learning processes LforS seeks to create a better understanding of the links between different spheres and different levels of decision-making; it also seeks to foster integration of the different actors’ perspectives.

In the Loop: One Librarian's Experiences Teaching Within First-Year Learning Communities

Unique as snowflakes, learning communities are formed in countless ways. Some are designed specifically for first-year students, while others offer combined or clustered upper-level courses. Most involve at least two linked courses, and some add residential and social components. Many address core general education and basic skills requirements. Learning communities differ in design, yet they are similar in striving to enhance students' academic and social growth. First-year learning communities foster experiences that have been linked to academic success and retention. They also offer unique opportunities for librarians interested in collaborating with departmental faculty and enhancing teaching skills. This article will explore one librarian's experiences teaching within three first-year learning communities at Buffalo State College.

Does the Presence of a Learning Disability Elicit a Stigmatization?

Aims: To determine whether or not a Learning Disability(LD) label leads to stigmatization. Study Design: This research used a 2(sex of participant) x 2(LD label)x 2 (Sex of stimulus person) factorial design. Place and Duration of Study: Bucknell University, between October 2010 and April 2011. Methodology: Sample: We included 200 participants (137 women and 63 men, ranging in age from 18 – 75 years, M = 26.41. Participants rated the stimulus individual on 27 personality traits, 8 Life success measures, and the Big-5 personality dimensions. Also, participants completed a Social Desirability measure. Results: A MANOVA revealed a main effect for the Learning Disability description, F(6, 185) = 6.41 p< .0001, eta2 = .17,for the Big-5 personality dimensions, Emotional Stability, F(1, 185) = 13.39, p < .001, eta2 = .066, and Openness to Experiences F(1,185) = 7.12, p< .008, eta2 = .036.Stimulus individuals described as having a learning disability were perceived as being less emotionally stable and more open to experiences than those described as not having a learning disability. Another MANOVA revealed a main effect for having a disability or not, F(8, 183) = 4.29, p< .0001, eta2 = .158, for the Life Success items, Attractiveness, F(1, 198) = 16.63, p< .0001, eta2 = .080, and Future Success,F(1, 198) = 4.57, p< .034, eta2 = .023. Stimulus individuals described as having a learning disability were perceived as being less attractive and with less potential for success than those described as not having a learning disability. Conclusion: The results of this research provide evidence that a bias exists toward those who have learning disabilities. The mere presence of an LD label had the ability to cause a differential perception of those with LDs and those without LDs.

Effect of Classroom Gender Composition on Students' Development of Self-Regulated Learning Competencies

Success in any field depends on a complex interplay among environmental and personal factors. A key set of personal factors for success in academic settings are those associated with self-regulated learners (SRL). Self-regulated learners choose their own goals, select and organize their learning strategies, and self-monitor their effectiveness. Behaviors and attitudes consistent with self-regulated learning also contribute to self-confidence, which may be important for members of underrepresented groups such as women in engineering. This exploratory study, drawing on the concept of "critical mass", examines the relationship between the personal factors that identify a self-regulated learner and the environmental factors related to gender composition of engineering classrooms. Results indicate that a relatively student gender-balanced classroom and gender match between students and their instructors provide for the development of many adaptive SRL behaviors and attitudes.

Learning to Live in a Therapeutic Community: A study of elderly inpatients.

Fifty-two elderly mental patients in a state hospital were transferred to a new milieu ward. In order to evaluate patient success in the unit, three outcome categories were defined nine months after the unit opened: discharge to the community, adjustment to the setting, and return to the previous ward. Despite the unit's emphasis on performance criteria for success, staff evaluations of the patients' personality rather than the patients' achievement of the behavioural criteria, accounted for success in the setting.

AAC&U's Integrative Liberal Learning and the CAS Standards: Advising for a 21st Century Liberal Education

The Association of American Colleges and Universities presented and promoted integrative liberal learning as a collaborative goal that all institutions of higher education must strive to achieve. The similarities between the goals of integrative liberal learning and the Standards for Academic Advising by the Council for the Advancement of Standards in Higher Education are discussed with emphasis placed on the critical role that academic advising plays in support of an integrative liberal learning education, and in turn, future success for all students.

Engaged Learning: Enabling Self-Authorship and Effective Practice

There is now broad consensus that higher education must extend beyond content-based knowledge to encompass intellectual and practical skills, personal and social responsibility, and integrative learning. The college learning outcomes needed for success in 21st century life include critical thinking, a coherent sense of self, intercultural maturity, civic engagement, and the capacity for mutual relationships. Yet, research suggests that college students are struggling to achieve these outcomes in part because skills needed to succeed in college are not those needed to succeed upon graduation. One reason for this gap is that these college learning outcomes require complex developmental capacities or “self-authorship” that higher education is not currently designed to promote.

A Socioeconomic Approach to the Development of E-Learning

A multitude of products, systems, approaches, views and notions characterize the field of e-learning. This article attempts to disentangle the field by using economic and sociological theories, theories of marketing management and strategy as well as practical experience gained by the author while working with leading edge suppliers of e-learning. On this basis, a distinction between knowledge creation e-learning and knowledge transfer e-learning is made. The various views are divided into four different ideal-typical paradigms, each with its own characteristics and limitations. Selecting the right paradigm to use in the development of an e-learning strategy may prove crucial to success. Implications for the development of an e-learning strategy in businesses and educational institutions are outlined.