The relationship between class participation and law students' learning, engagement and stress: Do demographics matter?

Student participation in the classroom has long been regarded as an important means of increasing student engagement and enhancing learning outcomes by promoting active learning. However, the approach to class participation common in U.S. law schools, commonly referred to as the Socratic method, has been criticised for its negative impacts on student wellbeing. A multiplicity of American studies have identified that participating in law class discussions can be alienating, intimidating and stressful for some law students, and may be especially so for women, and students from minority backgrounds. Using data from the Law School Student Assessment Survey (LSSAS), conducted at UNSW Law School in 2012, this Chapter provides preliminary insights into whether assessable class participation (ACP) at an Australian law school is similarly alienating and stressful for students, including the groups identified in the American literature. In addition, we compare the responses of undergraduate Bachelor of Laws (LLB) and graduate Juris Doctor (JD) students. The LSSAS findings indicate that most respondents recognise the potential learning and social benefits associated with class participation in legal education, but remain divided over their willingness to participate. Further, in alignment with general trends identified in American studies, LLB students, women, international students, and non-native English speakers perceive they contribute less frequently to class discussions than JD students, males, domestic students, and native English speakers, respectively. Importantly, the LSSAS indicates students are more likely to be anxious about contributing to class discussions if they are LLB students (compared to their JD counterparts), and if English is not their first language (compared to native English speakers). There were no significant differences in students’ self-reported anxiety levels based on gender, which diverges from the findings of American research.

Older Australians: Structural barriers to learning in later life

Learning in older age is associated with a wide range of benefits including increases in skills, social interactions, self-satisfaction, coping ability, enjoyment, and resilience to age-related changes in the brain. It is also recognized as being a fundamental component of active ageing and if active ageing objectives are to be met for the growing ageing population, barriers to learning for this group need to be fully understood so that they can be properly addressed. This paper reports on findings from a study aimed at determining the degree that structural factors deter older people aged 55 years and older from engaging in learning activities relative to other factors, based on survey (n=421) and interview (n=40) data. Quantitative and qualitative analyses revealed that factors related to educational institutions as well as infrastructure were commonly cited as barriers to participation in learning. The implications of these and other findings are discussed.

Making sense of a trial maths intervention program for teachers of students with disability in Australia: Interim report

The Disability Standards for Education (2005) and the Australian Curriculum, Assessment and Reporting Authority relevant standards underscore the right of students with disability to access the curriculum on the same basis as students without disability. Students with disability are entitled to rigorous, relevant and engaging learning opportunities drawn from the Australian curriculum content. Taking this context into account, this paper provides a work-in-progress report on a two-year mathematics intervention project conducted in 12 special schools (Preparatory-Year 12) in Queensland, Australia. The project aims to build the capacity of teachers to teach mathematics to their students and to identify and make sense of the intervention program’s impact. It combines two approaches—appreciative inquiry and action research to monitor schools’ change processes. The interim findings demonstrated that teachers were concerned about their students’ underachievement in mathematics and that the multi-sensory forms of teaching advocated in the program increased student engagement and performance.

Constructing a Community : Myths and Realities of the Open Development Model

The open development model of software production has been characterized as the future model of knowledge production and distributed work. Open development model refers to publicly available source code ensured by an open source license, and the extensive and varied distributed participation of volunteers enabled by the Internet. Contemporary spokesmen of open source communities and academics view open source development as a new form of volunteer work activity characterized by hacker ethic and bazaar governance . The development of the Linux operating system is perhaps the best know example of such an open source project. It started as an effort by a user-developer and grew quickly into a large project with hundreds of user-developer as contributors. However, in hybrids , in which firms participate in open source projects oriented towards end-users, it seems that most users do not write code. The OpenOffice.org project, initiated by Sun Microsystems, in this study represents such a project. In addition, the Finnish public sector ICT decision-making concerning open source use is studied. The purpose is to explore the assumptions, theories and myths related to the open development model by analysing the discursive construction of the OpenOffice.org community: its developers, users and management. The qualitative study aims at shedding light on the dynamics and challenges of community construction and maintenance, and related power relations in hybrid open source, by asking two main research questions: How is the structure and membership constellation of the community, specifically the relation between developers and users linguistically constructed in hybrid open development? What characterizes Internet-mediated virtual communities and how can they be defined? How do they differ from hierarchical forms of knowledge production on one hand and from traditional volunteer communities on the other? The study utilizes sociological, psychological and anthropological concepts of community for understanding the connection between the real and the imaginary in so-called virtual open source communities. Intermediary methodological and analytical concepts are borrowed from discourse and rhetorical theories. A discursive-rhetorical approach is offered as a methodological toolkit for studying texts and writing in Internet communities. The empirical chapters approach the problem of community and its membership from four complementary points of views. The data comprises mailing list discussion, personal interviews, web page writings, email exchanges, field notes and other historical documents. The four viewpoints are: 1) the community as conceived by volunteers 2) the individual contributor s attachment to the project 3) public sector organizations as users of open source 4) the community as articulated by the community manager. I arrive at four conclusions concerning my empirical studies (1-4) and two general conclusions (5-6). 1) Sun Microsystems and OpenOffice.org Groupware volunteers failed in developing necessary and sufficient open code and open dialogue to ensure collaboration thus splitting the Groupware community into volunteers we and the firm them . 2) Instead of separating intrinsic and extrinsic motivations, I find that volunteers unique patterns of motivations are tied to changing objects and personal histories prior and during participation in the OpenOffice.org Lingucomponent project. Rather than seeing volunteers as a unified community, they can be better understood as independent entrepreneurs in search of a collaborative community . The boundaries between work and hobby are blurred and shifting, thus questioning the usefulness of the concept of volunteer . 3) The public sector ICT discourse portrays a dilemma and tension between the freedom to choose, use and develop one s desktop in the spirit of open source on one hand and the striving for better desktop control and maintenance by IT staff and user advocates, on the other. The link between the global OpenOffice.org community and the local end-user practices are weak and mediated by the problematic IT staff-(end)user relationship. 4) Authoring community can be seen as a new hybrid open source community-type of managerial practice. The ambiguous concept of community is a powerful strategic tool for orienting towards multiple real and imaginary audiences as evidenced in the global membership rhetoric. 5) The changing and contradictory discourses of this study show a change in the conceptual system and developer-user relationship of the open development model. This change is characterized as a movement from hacker ethic and bazaar governance to more professionally and strategically regulated community. 6) Community is simultaneously real and imagined, and can be characterized as a runaway community . Discursive-action can be seen as a specific type of online open source engagement. Hierarchies and structures are created through discursive acts. Key words: Open Source Software, open development model, community, motivation, discourse, rhetoric, developer, user, end-user

Active learning in keyword search-based data integration

The problem of scaling up data integration, such that new sources can be quickly utilized as they are discovered, remains elusive: Global schemas for integrated data are difficult to develop and expand, and schema and record matching techniques are limited by the fact that data and metadata are often under-specified and must be disambiguated by data experts. One promising approach is to avoid using a global schema, and instead to develop keyword search-based data integration-where the system lazily discovers associations enabling it to join together matches to keywords, and return ranked results. The user is expected to understand the data domain and provide feedback about answers' quality. The system generalizes such feedback to learn how to correctly integrate data. A major open challenge is that under this model, the user only sees and offers feedback on a few ``top-'' results: This result set must be carefully selected to include answers of high relevance and answers that are highly informative when feedback is given on them. Existing systems merely focus on predicting relevance, by composing the scores of various schema and record matching algorithms. In this paper, we show how to predict the uncertainty associated with a query result's score, as well as how informative feedback is on a given result. We build upon these foundations to develop an active learning approach to keyword search-based data integration, and we validate the effectiveness of our solution over real data from several very different domains.

A Bacterial Foraging Optimization and Learning Automata Based Feature Selection for Motor Imagery EEG Classification

Selection of relevant features is an open problem in Brain-computer interfacing (BCI) research. Sometimes, features extracted from brain signals are high dimensional which in turn affects the accuracy of the classifier. Selection of the most relevant features improves the performance of the classifier and reduces the computational cost of the system. In this study, we have used a combination of Bacterial Foraging Optimization and Learning Automata to determine the best subset of features from a given motor imagery electroencephalography (EEG) based BCI dataset. Here, we have employed Discrete Wavelet Transform to obtain a high dimensional feature set and classified it by Distance Likelihood Ratio Test. Our proposed feature selector produced an accuracy of 80.291% in 216 seconds.

Lowestoft College - Riding the waves of change: How mobile learning has guided learners through rough seas

Over the past six years Lowestoft College has embraced the revolution in mobile learning by welcoming Web 2.0, social media, cloud computing and Bring Your Own Device (BYOD). This open attitude to new technologies has led to a marked improvement in student achievement rates, has increased staff and student satisfaction and has resulted in a variety of cost savings for senior management during the current economic downturn.

Plasma brain-derived neurotrophic factor levels, learning capacity and cognition in patients with first episode psychosis

Background: Cognitive impairments are seen in first psychotic episode (FEP) patients. The neurobiological underpinnings that might underlie these changes remain unknown. The aim of this study is to investigate whether Brain Derived Neurotrophic Factor (BDNF) levels are associated with cognitive impairment in FEP patients compared with healthy controls. Methods: 45 FEP patients and 45 healthy controls matched by age, gender and educational level were selected from the Basque Country area of Spain. Plasma BDNF levels were assessed in healthy controls and in patients. A battery of cognitive tests was applied to both groups, with the patients being assessed at 6 months after the acute episode and only in those with a clinical response to treatment. Results: Plasma BDNF levels were altered in patients compared with the control group. In FEP patients, we observed a positive association between BDNF levels at six months and five cognitive domains (learning ability,immediate and delayed memory, abstract thinking and processing speed) which persisted after controlling for medications prescribed, drug use, intelligence quotient (IQ) and negative symptoms. In the healthy control group, BDNF levels were not associated with cognitive test scores. Conclusion: Our results suggest that BDNF is associated with the cognitive impairment seen after a FEP. Further investigations of the role of this neurotrophin in the symptoms associated with psychosis onset are warranted.

Contributions to unsupervised and supervised learning with applications in digital image processing

311 p. : il.

Integrated treatment of first episode psychosis with online training (e-learning): study protocol for a randomised controlled trial

Background: The integrated treatment of first episode psychosis has been shown to improve functionality and negative symptoms in previous studies. In this paper, we describe a study of integrated treatment (individual psychoeducation complementary to pharmacotherapy) versus treatment as usual, comparing results at baseline with those at 6-month re-assessment (at the end of the study) for these patients, and online training of professionals to provide this complementary treatment, with the following objectives: 1) to compare the efficacy of individual psychoeducation as add-on treatment versus treatment as usual in improving psychotic and mood symptoms; 2) to compare adherence to medication, functioning, insight, social response, quality of life, and brain-derived neurotrophic factor, between both groups; and 3) to analyse the efficacy of online training of psychotherapists. Methods/design: This is a single-blind randomised clinical trial including patients with first episode psychosis from hospitals across Spain, randomly assigned to either a control group with pharmacotherapy and regular sessions with their psychiatrist (treatment as usual) or an intervention group with integrated care including treatment as usual plus a psychoeducational intervention (14 sessions). Training for professionals involved at each participating centre was provided by the coordinating centre (University Hospital of Alava) through video conferences. Patients are evaluated with an extensive battery of tests assessing clinical and sociodemographic characteristics (Positive and Negative Syndrome Scale, State-Trait Anxiety Inventory, Liebowitz Social Anxiety Scale, Hamilton Rating Scale for Depression, Scale to Assess Unawareness of Mental Disorders, Strauss and Carpenter Prognostic Scale, Global Assessment of Functioning Scale, Morisky Green Adherence Scale, Functioning Assessment Short Test, World Health Organization Quality of Life instrument WHOQOL-BREF (an abbreviated version of the WHOQOL-100), and EuroQoL questionnaire), and brain-derived neurotrophic factor levels are measured in peripheral blood at baseline and at 6 months. The statistical analysis, including bivariate analysis, linear and logistic regression models, will be performed using SPSS. Discussion: This is an innovative study that includes the assessment of an integrated intervention for patients with first episode psychosis provided by professionals who are trained online, potentially making it possible to offer the intervention to more patients.

Feature Selection for Speech Emotion Recognition in Spanish and Basque: On the Use of Machine Learning to Improve Human-Computer Interaction

Study of emotions in human-computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested.

A Data Dropout Compensation Algorithm Based on the Iterative Learning Control Methodology for Discrete-Time Systems

This paper deals with the convergence of a remote iterative learning control system subject to data dropouts. The system is composed by a set of discrete-time multiple input-multiple output linear models, each one with its corresponding actuator device and its sensor. Each actuator applies the input signals vector to its corresponding model at the sampling instants and the sensor measures the output signals vector. The iterative learning law is processed in a controller located far away of the models so the control signals vector has to be transmitted from the controller to the actuators through transmission channels. Such a law uses the measurements of each model to generate the input vector to be applied to its subsequent model so the measurements of the models have to be transmitted from the sensors to the controller. All transmissions are subject to failures which are described as a binary sequence taking value 1 or 0. A compensation dropout technique is used to replace the lost data in the transmission processes. The convergence to zero of the errors between the output signals vector and a reference one is achieved as the number of models tends to infinity.

Learning to perceive in the sensor motor approach: Piaget's theory of equilibration interpreted dynamically

Learning to perceive is faced with a classical paradox: if understanding is required for perception, how can we learn to perceive something new, something we do not yet understand? According to the sensorimotor approach, perception involves mastery of regular sensorimotor co-variations that depend on the agent and the environment, also known as the "laws" of sensorimotor contingencies (SMCs). In this sense, perception involves enacting relevant sensorimotor skills in each situation. It is important for this proposal that such skills can be learned and refined with experience and yet up to this date, the sensorimotor approach has had no explicit theory of perceptual learning. The situation is made more complex if we acknowledge the open-ended nature of human learning. In this paper we propose Piaget's theory of equilibration as a potential candidate to fulfill this role. This theory highlights the importance of intrinsic sensorimotor norms, in terms of the closure of sensorimotor schemes. It also explains how the equilibration of a sensorimotor organization faced with novelty or breakdowns proceeds by re-shaping pre-existing structures in coupling with dynamical regularities of the world. This way learning to perceive is guided by the equilibration of emerging forms of skillful coping with the world. We demonstrate the compatibility between Piaget's theory and the sensorimotor approach by providing a dynamical formalization of equilibration to give an explicit micro-genetic account of sensorimotor learning and, by extension, of how we learn to perceive. This allows us to draw important lessons in the form of general principles for open-ended sensorimotor learning, including the need for an intrinsic normative evaluation by the agent itself. We also explore implications of our micro-genetic account at the personal level.

Learning Multirobot Hose Transportation and Deployment by Distributed Round-Robin Q-Learning

Multi-Agent Reinforcement Learning (MARL) algorithms face two main difficulties: the curse of dimensionality, and environment non-stationarity due to the independent learning processes carried out by the agents concurrently. In this paper we formalize and prove the convergence of a Distributed Round Robin Q-learning (D-RR-QL) algorithm for cooperative systems. The computational complexity of this algorithm increases linearly with the number of agents. Moreover, it eliminates environment non sta tionarity by carrying a round-robin scheduling of the action selection and execution. That this learning scheme allows the implementation of Modular State-Action Vetoes (MSAV) in cooperative multi-agent systems, which speeds up learning convergence in over-constrained systems by vetoing state-action pairs which lead to undesired termination states (UTS) in the relevant state-action subspace. Each agent's local state-action value function learning is an independent process, including the MSAV policies. Coordination of locally optimal policies to obtain the global optimal joint policy is achieved by a greedy selection procedure using message passing. We show that D-RR-QL improves over state-of-the-art approaches, such as Distributed Q-Learning, Team Q-Learning and Coordinated Reinforcement Learning in a paradigmatic Linked Multi-Component Robotic System (L-MCRS) control problem: the hose transportation task. L-MCRS are over-constrained systems with many UTS induced by the interaction of the passive linking element and the active mobile robots.

Recent origin of a hominoid-specific splice form of neuropsin, a gene involved in learning and memory

Neuropsin is a secreted-type serine protease involved in learning and memory. The type II splice form of neuropsin is abundantly expressed in the human brain but not in the mouse brain. We sequenced the type II-spliced region of neuropsin gene in humans and representative nonhuman primate species. Our comparative sequence analysis showed that only the hominoid species (humans and apes) have the intact open reading frame of the type II splice form, indicating that the type II neuropsin originated recently in the primate lineage about 18 MYA. Expression analysis using RT-PCR detected abundant expression of the type II form in the frontal lobe of the adult human brain, but no expression was detected in the brains of lesser apes and Old World monkeys, indicating that the type II form of neuropsin only became functional in recent time, and it might contribute to the progressive change of cognitive abilities during primate evolution.