Motivation and relationship of the student with the school as factors involved in the perceived learning

Motivation should be seen as a very important factor in the learning process. The motivated student has the inner strength to learn, to discover and capitalize on capabilities, to improve academic performance and to adapt to the demands of the school context. Contextual factors like the psychological sense of school membership may be also especially important to students’ classroom engagement, their motivation and learning success. So with this study we intend to examine how the sense of school belonging and intrinsic motivation influences perceived learning.A structural model reveals that the negative sense of school belonging has a negative impact on intrinsic motivation and on perceived learning. In turn, intrinsic motivation positively and significantly influences perceived learning in the course.

Learning curves: analysing pace and challenge in four successful puzzle games

The pace at which challenges are introduced in a game has long been identified as a key determinant of both the enjoyment and difficulty experienced by game players, and their ability to learn from game play. In order to understand how to best pace challenges in games, there is great value in analysing games already demonstrated as highly engaging. Play-through videos of four puzzle games (Portal, Portal 2 Co-operative mode, Braid and Lemmings), were observed and analysed using metrics derived from a behavioural psychology understanding of how people solve problems. Findings suggest that; 1) the main skills learned in each game are introduced separately, 2) through simple puzzles that require only basic performance of that skill, 3) the player has the opportunity to practice and integrate that skill with previously learned skills, and 4) puzzles increase in complexity until the next new skill is introduced. These data provide practical guidance for designers, support contemporary thinking on the design of learning structures in games, and suggest future directions for empirical research.

An investigation of two real time machine learning techniques that could enhance the adaptability of game AI agents

Developers strive to create innovative Artificial Intelligence (AI) behaviour in their games as a key selling point. Machine Learning is an area of AI that looks at how applications and agents can be programmed to learn their own behaviour without the need to manually design and implement each aspect of it. Machine learning methods have been utilised infrequently within games and are usually trained to learn offline before the game is released to the players. In order to investigate new ways AI could be applied innovatively to games it is wise to explore how machine learning methods could be utilised in real-time as the game is played, so as to allow AI agents to learn directly from the player or their environment. Two machine learning methods were implemented into a simple 2D Fighter test game to allow the agents to fully showcase their learned behaviour as the game is played. The methods chosen were: Q-Learning and an NGram based system. It was found that N-Grams and QLearning could significantly benefit game developers as they facilitate fast, realistic learning at run-time.

Teaching / learning process through the case method in the law degree

This project in teaching innovation and improvement aims to disseminate the case method as one of the most innovative educational instruments inteaching of Law in general, and specifically with regard to Family and Inheritance Law. The methodology used ensures learning through a legal conflict, which must be resolved by the students themselves from different viewpoints as legal agents. This is an activity in teaching innovation, in which students become the protagonists. Participation is voluntary, and the main aim is student motivation. The subject's aim is for students to learn public speaking skills fundamental to the profession while familiarising themselves with judicial practice. Theteacher sets up a legal conflict in order for students to resolve the dispute as legal agents with divergent viewpoints - in other words, as judges, attorneys, lawyers and so on. The project seeks alternatives to traditional teaching methods and is an innovative teaching method aimed at professionally training future lawyers as well as being a model that involves students more in their own learning.

A literary approach to teaching english language in a multicultural classroom

Literature is not generally considered as a coherent branch of the curriculum in relation to language development in either native or foreign language teaching. As teachers of English in multicultural Indian classrooms, we come across students with varying degrees of competence in English language learning. Although language learning is a natural process for natives, students of other languages put in colossal efforts to learn it. Despite their sincere efforts, they face challenges regarding pronunciation, spelling, and vocabulary. Indian classrooms are a microcosm of the larger society, so teaching English language in a manner that equips the students to face the cutthroat competition has become a necessity and a challenge for English language teachers. English today has become the key determinant for being successful in their careers. The hackneyed and stereotypical methods of teaching are not acceptable now. Teachers are no longer arbitrary dispensers of knowledge, but they are playing the role of a guide and facilitator for the students. Teachers of English are using innovative ideas to make English language teaching and learning interesting and simple. Teachers have started using literary texts and their analyses to explore and ignite the imagination and creative skills of the students. One needs to think and rethink the contribution of literature to intelligent thinking as well as its role in the process of teaching/learning. This article is, therefore, an attempt at exploring the nature of the literary experience in the present-day classrooms and the broader role of literature in life.

Reinforcement learning over encrypted data

Reinforcement learning is a particular paradigm of machine learning that, recently, has proved times and times again to be a very effective and powerful approach. On the other hand, cryptography usually takes the opposite direction. While machine learning aims at analyzing data, cryptography aims at maintaining its privacy by hiding such data. However, the two techniques can be jointly used to create privacy preserving models, able to make inferences on the data without leaking sensitive information. Despite the numerous amount of studies performed on machine learning and cryptography, reinforcement learning in particular has never been applied to such cases before. Being able to successfully make use of reinforcement learning in an encrypted scenario would allow us to create an agent that efficiently controls a system without providing it with full knowledge of the environment it is operating in, leading the way to many possible use cases. Therefore, we have decided to apply the reinforcement learning paradigm to encrypted data. In this project we have applied one of the most well-known reinforcement learning algorithms, called Deep Q-Learning, to simple simulated environments and studied how the encryption affects the training performance of the agent, in order to see if it is still able to learn how to behave even when the input data is no longer readable by humans. The results of this work highlight that the agent is still able to learn with no issues whatsoever in small state spaces with non-secure encryptions, like AES in ECB mode. For fixed environments, it is also able to reach a suboptimal solution even in the presence of secure modes, like AES in CBC mode, showing a significant improvement with respect to a random agent; however, its ability to generalize in stochastic environments or big state spaces suffers greatly.

The space for social media in structured online learning

In this paper, we explore the benefits of using social media in an online educational setting, with a particular focus on the use of Facebook and Twitter by participants in a Massive Open Online Course (MOOC) developed to enable educators to learn about the Carpe Diem learning design process. We define social media as digital social tools and environments located outside of the provision of a formal university-provided Learning Management System. We use data collected via interviews and surveys with the MOOC participants as well as social media postings made by the participants throughout the MOOC to offer insights into how participants’ usage and perception of social media in their online learning experiences differed and why. We identified that, although some participants benefitted from social media by crediting it, for example, with networking and knowledge-sharing opportunities, others objected or refused to engage with social media, perceiving it as a waste of their time. We make recommendations for the usage of social media for educational purposes within MOOCs and formal digital learning environments.

Blended Learning Essentials Project reports

Blended Learning Essentials is a free suite of online courses for the Vocational Education and Training sector to promote effective practice and pedagogy in blended learning. The courses were run and supported from 2016 onwards by a consortium of partners funded by Ufi Charitable Trust. The lead partners were the University of Leeds, the UCL Institute of Education, the Association for Learning Technology (ALT), and FutureLearn. The Blended Learning Essentials (BLE) courses are for anyone working in further education, skills training, vocational education, workplace learning, lifelong learning or adult education, who wants to learn about and implement blended learning. The project reports cover engagement and marketing work undertaken during this project phase to reach the courses’ key audiences and work undertaken during this project phase to develop and promote the pathways to accreditation available to course participants. These reports are shared by ALT as a project partner on behalf of the BLE Project under a CC-BY-NC-ND licence. �

Continual learning for computer vision applications

One of the most visionary goals of Artificial Intelligence is to create a system able to mimic and eventually surpass the intelligence observed in biological systems including, ambitiously, the one observed in humans. The main distinctive strength of humans is their ability to build a deep understanding of the world by learning continuously and drawing from their experiences. This ability, which is found in various degrees in all intelligent biological beings, allows them to adapt and properly react to changes by incrementally expanding and refining their knowledge. Arguably, achieving this ability is one of the main goals of Artificial Intelligence and a cornerstone towards the creation of intelligent artificial agents. Modern Deep Learning approaches allowed researchers and industries to achieve great advancements towards the resolution of many long-standing problems in areas like Computer Vision and Natural Language Processing. However, while this current age of renewed interest in AI allowed for the creation of extremely useful applications, a concerningly limited effort is being directed towards the design of systems able to learn continuously. The biggest problem that hinders an AI system from learning incrementally is the catastrophic forgetting phenomenon. This phenomenon, which was discovered in the 90s, naturally occurs in Deep Learning architectures where classic learning paradigms are applied when learning incrementally from a stream of experiences. This dissertation revolves around the Continual Learning field, a sub-field of Machine Learning research that has recently made a comeback following the renewed interest in Deep Learning approaches. This work will focus on a comprehensive view of continual learning by considering algorithmic, benchmarking, and applicative aspects of this field. This dissertation will also touch on community aspects such as the design and creation of research tools aimed at supporting Continual Learning research, and the theoretical and practical aspects concerning public competitions in this field.

Computational methods applied to undeciphered scripts from the Aegean and Cyprus: Linear A and Cypro-Minoan

The study of ancient, undeciphered scripts presents unique challenges, that depend both on the nature of the problem and on the peculiarities of each writing system. In this thesis, I present two computational approaches that are tailored to two different tasks and writing systems. The first of these methods is aimed at the decipherment of the Linear A afraction signs, in order to discover their numerical values. This is achieved with a combination of constraint programming, ad-hoc metrics and paleographic considerations. The second main contribution of this thesis regards the creation of an unsupervised deep learning model which uses drawings of signs from ancient writing system to learn to distinguish different graphemes in the vector space. This system, which is based on techniques used in the field of computer vision, is adapted to the study of ancient writing systems by incorporating information about sequences in the model, mirroring what is often done in natural language processing. In order to develop this model, the Cypriot Greek Syllabary is used as a target, since this is a deciphered writing system. Finally, this unsupervised model is adapted to the undeciphered Cypro-Minoan and it is used to answer open questions about this script. In particular, by reconstructing multiple allographs that are not agreed upon by paleographers, it supports the idea that Cypro-Minoan is a single script and not a collection of three script like it was proposed in the literature. These results on two different tasks shows that computational methods can be applied to undeciphered scripts, despite the relatively low amount of available data, paving the way for further advancement in paleography using these methods.

Ecological Literacy And Beyond: Problem-based Learning For Future Professionals.

Ecological science contributes to solving a broad range of environmental problems. However, lack of ecological literacy in practice often limits application of this knowledge. In this paper, we highlight a critical but often overlooked demand on ecological literacy: to enable professionals of various careers to apply scientific knowledge when faced with environmental problems. Current university courses on ecology often fail to persuade students that ecological science provides important tools for environmental problem solving. We propose problem-based learning to improve the understanding of ecological science and its usefulness for real-world environmental issues that professionals in careers as diverse as engineering, public health, architecture, social sciences, or management will address. Courses should set clear learning objectives for cognitive skills they expect students to acquire. Thus, professionals in different fields will be enabled to improve environmental decision-making processes and to participate effectively in multidisciplinary work groups charged with tackling environmental issues.

Delivery Of A Urology Online Course Using Moodle Versus Didactic Lectures Methods.

To subjectively and objectively compare an accessible interactive electronic library using Moodle with lectures for urology teaching of medical students. Forty consecutive fourth-year medical students and one urology teacher were exposed to two teaching methods (4 weeks each) in the form of problem-based learning: - lectures and - student-centered group discussion based on Moodle (modular object-oriented dynamic learning environment) full time online delivered (24/7) with video surgeries, electronic urology cases and additional basic principles of the disease process. All 40 students completed the study. While 30% were moderately dissatisfied with their current knowledge base, online learning course delivery using Moodle was considered superior to the lectures by 86% of the students. The study found the following observations: (1) the increment in learning grades ranged from 7.0 to 9.7 for students in the online Moodle course compared to 4.0-9.6 to didactic lectures; (2) the self-reported student involvement in the online course was characterized as large by over 60%; (3) the teacher-student interaction was described as very frequent (50%) and moderately frequent (50%); and (4) more inquiries and requisitions by students as well as peer assisting were observed from the students using the Moodle platform. The Moodle platform is feasible and effective, enthusing medical students to learn, improving immersion in the urology clinical rotation and encouraging the spontaneous peer assisted learning. Future studies should expand objective evaluations of knowledge acquisition and retention.

Formação empirica na educação superior : um estudo de caso na Faculdade de Educação Fisica da Unicamp

Universidade Estadual de Campinas. Faculdade de Educação Física

Using Bayesian networks with rule extraction to infer the risk of weed infestation in a corn-crop

This paper describes the modeling of a weed infestation risk inference system that implements a collaborative inference scheme based on rules extracted from two Bayesian network classifiers. The first Bayesian classifier infers a categorical variable value for the weed-crop competitiveness using as input categorical variables for the total density of weeds and corresponding proportions of narrow and broad-leaved weeds. The inferred categorical variable values for the weed-crop competitiveness along with three other categorical variables extracted from estimated maps for the weed seed production and weed coverage are then used as input for a second Bayesian network classifier to infer categorical variables values for the risk of infestation. Weed biomass and yield loss data samples are used to learn the probability relationship among the nodes of the first and second Bayesian classifiers in a supervised fashion, respectively. For comparison purposes, two types of Bayesian network structures are considered, namely an expert-based Bayesian classifier and a naive Bayes classifier. The inference system focused on the knowledge interpretation by translating a Bayesian classifier into a set of classification rules. The results obtained for the risk inference in a corn-crop field are presented and discussed. (C) 2009 Elsevier Ltd. All rights reserved.

Type 1 and Type 2 cytokines: from basic science to fungal infections

At the present time, it is clear that Th1 responses afford protection against the fungi; however, the development, maintenance and function of the protective immune responses are complex mechanisms and are influenced by multiple factors. The route of infection has been shown to affect initial cytokine production and, consequently, the induction of protective Th1 responses. The ability of different isolates of the same fungal agent to induce and sustain a protective response has also been emphasized. Protective immune responses have been shown to vary in genetically different mouse strains after infection. In addition, these protective responses, such as cellular influx and cytokine production, also vary within the same animal depending on the tissue infected. The functional dominance of certain cytokines over others in influencing development and maintenance of protective responses has been discussed. Certain cytokines may act differently in hosts lacking important components of their innate or immune repertoire. It is evident from these presentations that a more comprehensive understanding of the protective mechanisms against different fungal agents is emerging. However, there is still much to learn before cytokine modulatory therapy can be used effectively without risk in the human host.