Implementing virtual collaborative inquiry practises in a middle-school context

The aim of the present study was to investigate the challenges that relate to the implementation of virtual inquiry practises in middle school. The case was a school course in which a group of Finnish students (N = 14) and teachers (N = 7) completed group inquiries through virtual collaboration, using a web-based learning environment. The task was to accomplish a cross-disciplinary inquiry into cultural issues. The students worked mainly at home and took much responsibility for their course achievements. The investigators analysed the pedagogical design of the course and the content of the participants' interaction patterns in the web-based environment, using qualitative content analysis and social network analysis. The findings suggest that the students succeeded in producing distinctive cultural products, and both the students and the teachers adopted novel roles during the inquiry. The web-based learning environment was used more as a coordination tool for organizing the collaborative work than as a forum for epistemic inquiry. The tension between the school curriculum and the inquiry practises was manifest in the participants' discussions of the assessment criteria of the course.

Kehittämistutkimus: Tieto- ja viestintätekniikkaa kemian opetukseen

The main aim of the present study was to develop information and communication technology (ICT) based chemistry education. The goals for the study were to support meaningful chemistry learning, research-based teaching and diffusion of ICT innovations. These goals were used as guidelines that form the theoretical framework for this study. This Doctoral Dissertation is based on eight-stage research project that included three design researches. These three design researches were scrutinized as separate case studies in which the different cases were formed according to different design teams: i) one researcher was in charge of the design and teachers were involved in the research process, ii) a research group was in charge of the design and students were involved in the research process, and iii) the design was done by student teams, the research was done collaboratively, and the design process was coordinated by a researcher. The research projects were conducted using mixed method approach, which enabled a comprehensive view on education design. In addition, the three central areas of design research: problem analysis, design solution and design process were included in the research, which was guided by the main research questions formed according to these central areas: 1) design solution: what kind of elements are included in ICT-based learning environments that support meaningful chemistry learning and diffusion of innovation, 2) problem analysis: what kind of new possibilities the designed learning environments offer for the support of meaningful chemistry learning, and 3) design process: what kind of opportunities and challenges does collaboration bring to the design of ICT-based learning environments? The main research questions were answered according to the analysis of the survey and observation data, six designed learning environments and ten design narratives from the three case studies. Altogether 139 chemistry teachers and teacher students were involved in the design processes. The data was mainly analysed by methods of qualitative content analysis. The first main result from the study give new information on the meaningful chemistry learning and the elements of ICT-based learning environment that support the diffusion of innovation, which can help in the development of future ICT-education design. When the designed learning environment was examined in the context of chemistry education, it was evident that an ICT-based chemistry learning environment supporting the meaningful learning of chemistry motivates the students and makes the teacher s work easier. In addition, it should enable the simultaneous fulfilment of several pedagogical goals and activate higher-level cognitive processes. The learning environment supporting the diffusion of ICT innovation is suitable for Finnish school environment, based on open source code, and easy to use with quality chemistry content. According to the second main result, new information was acquired about the possibilities of ICT-based learning environments in supporting meaningful chemistry learning. This will help in setting the goals for future ICT education. After the analysis of design solutions and their evaluations, it can be said that ICT enables the recognition of all elements that define learning environments (i.e. didactic, physical, technological and social elements). The research particularly demonstrates the significance of ICT in supporting students motivation and higher-level cognitive processes as well as versatile visualization resources for chemistry that ICT makes possible. In addition, research-based teaching method supports well the diffusion of studied innovation on individual level. The third main result brought out new information on the significance of collaboration in design research, which guides the design of ICT education development. According to the analysis of design narratives, it can be said that collaboration is important in the execution of scientifically reliable design research. It enables comprehensive requirement analysis and multifaceted development, which improves the reliability and validity of the research. At the same time, it sets reliability challenges by complicating documenting and coordination, for example. In addition, a new method for design research was developed. Its aim is to support the execution of complicated collaborative design projects. To increase the reliability and validity of the research, a model theory was used. It enables time-pound documenting and visualization of design decisions that clarify the process. This improves the reliability of the research. The validity of the research is improved by requirement definition through models. This way learning environments that meet the design goals can be constructed. The designed method can be used in education development from comprehensive to higher level. It can be used to recognize the needs of different interest groups and individuals with regard to processes, technology and substance knowledge as well as interfaces and relations between them. The developed method has also commercial potential. It is used to design learning environments for national and international market.

Simplified implementation of space vector PWM strategies for a three level inverter

Space vector based PWM strategies for three-level inverters have a broader choice of switching sequences to generate the required reference vector than triangle comparison based PWM techniques. However, space vector based PWM involves numerous steps which are computationally intensive. A simplified algorithm is proposed here, which is shown to reduce the computation time significantly. The developed algorithm is used to implement synchronous and asynchronous conventional space vector PWM, synchronized modified space vector PWM and an asynchronous advanced bus-clamping PWM technique on a low-cost dsPIC digital controller. Experimental results are presented for a comparative evaluation of the performance of different PWM methods.

NESTED SPARSE BAYESIAN LEARNING FOR BLOCK-SPARSE SIGNALS WITH INTRA-BLOCK CORRELATION

In this work, we address the recovery of block sparse vectors with intra-block correlation, i.e., the recovery of vectors in which the correlated nonzero entries are constrained to lie in a few clusters, from noisy underdetermined linear measurements. Among Bayesian sparse recovery techniques, the cluster Sparse Bayesian Learning (SBL) is an efficient tool for block-sparse vector recovery, with intra-block correlation. However, this technique uses a heuristic method to estimate the intra-block correlation. In this paper, we propose the Nested SBL (NSBL) algorithm, which we derive using a novel Bayesian formulation that facilitates the use of the monotonically convergent nested Expectation Maximization (EM) and a Kalman filtering based learning framework. Unlike the cluster-SBL algorithm, this formulation leads to closed-form EMupdates for estimating the correlation coefficient. We demonstrate the efficacy of the proposed NSBL algorithm using Monte Carlo simulations.

Priestley College: How do you successfully develop your new learning space? ...ask your students, of course!

When Priestley College began to plan the redevelopment of its learning resource centre, it continued the culture of student involvement that exists within the College by asking students to help plan and create the new development. This case study describes how the Jisc infoKit on 'Planning and Designing Technology-Rich Learning Spaces' was used as the starting point for ideas and planning, and how the finished development was the recognisable result of students' ideas and plans.

Headjogs: Highlighting success through innovation

Headjogs is a well established hair academy within the Essex and East of London region. This work-based learning provider is run by Stephen Daly, Director, and Debbie Scott. Around six months ago, Debbie participated in the e-Guides programme, where she gained the expertise to implement innovative technology into the curriculum. Since then they have received Association of Learning Providers (ALP) Learner Innovation Grant funding with which they plan to reinforce their innovative approach to hairdressing.

How do you change the learning landscape?

Analysis of the Changing the Learning Landscape strategic conversations with senior managers, staff and students in 58 institutions was undertaken in order to identify trends and direction of change in relation to technology-enhanced learning (TEL) and the student experience.

Designing Spaces for Effective Learning: A guide to 21st century learning space design

Increasing investment in estate and learning technologies, combined with the need for more cost-effective space utilisation, is making it increasingly important for senior managers to keep abreast of new thinking about the design of technology-rich learning spaces. Designing Spaces for Effective Learning, one of a series of guides, was launched at the JISC Conference 2006 which helped to meet this need. A visually-rich publication, it was designed to promote better understanding of what makes an effective design for the 21 century and to summarise the key points to consider when approaching a refurbishment or new-build project. The publication takes the reader on a ’walk through’ an educational institution, exploring the relationship between learning technologies and innovative examples of physical space design at each stage of the journey. Discussion of the key points is illustrated by ten case studies from further and higher education, and floor plans from AMA Alexi Marmot Associates, architects and space planners, which provide up-to-date guidelines on the integration of technologies into teaching and learning accommodation.

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.

Corporate document mining for technology intelligence: An analysis of needs, utilization and possibilities

This paper focuses on document data, one of the most significant sources for technology intelligence. To help organisations use their knowledge in documents effectively, this research aims to identify what organizations really want from documents and what might be possible to obtain from them. The research involves a literature review, a series of in-depth/on-site interviews and a descriptive analysis of document mining applications. The output of the research includes: a document mining framework; an analysis of the current condition of document mining in technology-based organisations together with their future requirements; and guidelines for introducing document mining into an organisation along with a discussion on the practical issues that are faced by users. Copyright © 2011 Inderscience Enterprises Ltd.

Perceptual learning, roving and the unsupervised bias

Perceptual learning improves perception through training. Perceptual learning improves with most stimulus types but fails when . certain stimulus types are mixed during training (roving). This result is surprising because classical supervised and unsupervised neural network models can cope easily with roving conditions. What makes humans so inferior compared to these models? As experimental and conceptual work has shown, human perceptual learning is neither supervised nor unsupervised but reward-based learning. Reward-based learning suffers from the so-called unsupervised bias, i.e., to prevent synaptic " drift" , the . average reward has to be exactly estimated. However, this is impossible when two or more stimulus types with different rewards are presented during training (and the reward is estimated by a running average). For this reason, we propose no learning occurs in roving conditions. However, roving hinders perceptual learning only for combinations of similar stimulus types but not for dissimilar ones. In this latter case, we propose that a critic can estimate the reward for each stimulus type separately. One implication of our analysis is that the critic cannot be located in the visual system. © 2011 Elsevier Ltd.

The energy required to produce materials: constraints on energy-intensity improvements, parameters of demand.

In this paper, we review the energy requirements to make materials on a global scale by focusing on the five construction materials that dominate energy used in material production: steel, cement, paper, plastics and aluminium. We then estimate the possibility of reducing absolute material production energy by half, while doubling production from the present to 2050. The goal therefore is a 75 per cent reduction in energy intensity. Four technology-based strategies are investigated, regardless of cost: (i) widespread application of best available technology (BAT), (ii) BAT to cutting-edge technologies, (iii) aggressive recycling and finally, and (iv) significant improvements in recycling technologies. Taken together, these aggressive strategies could produce impressive gains, of the order of a 50-56 per cent reduction in energy intensity, but this is still short of our goal of a 75 per cent reduction. Ultimately, we face fundamental thermodynamic as well as practical constraints on our ability to improve the energy intensity of material production. A strategy to reduce demand by providing material services with less material (called 'material efficiency') is outlined as an approach to solving this dilemma.

Reinforcement learning using a continuous time actor-critic framework with spiking neurons.

Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD) learning of Doya (2000) to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity.

Statistics anxiety, trait anxiety, learning behavior, and academic performance

The present study investigated the relationship between statistics anxiety, individual characteristics (e.g., trait anxiety and learning strategies), and academic performance. Students enrolled in a statistics course in psychology (N=147) filled in a questionnaire on statistics anxiety, trait anxiety, interest in statistics, mathematical selfconcept, learning strategies, and procrastination. Additionally, their performance in the examination was recorded. The structural equation model showed that statistics anxiety held a crucial role as the strongest direct predictor of performance. Students with higher statistics anxiety achieved less in the examination and showed higher procrastination scores. Statistics anxiety was related indirectly to spending less effort and time on learning. Trait anxiety was related positively to statistics anxiety and, counterintuitively, to academic performance. This result can be explained by the heterogeneity of the measure of trait anxiety. The part of trait anxiety that is unrelated to the specific part of statistics anxiety correlated positively with performance.

不同学习策略对改变学生物理要领错误的影响

In the field of misconceptions research, previous research was focused mainly on the effect of naive concepts on the learning of scientific concept. In this study, from the viewpoint of declarative and procedural knowledge, conceptual errors on Newtonian mechanics were studied comparatively between high-performance and low-performance students. Furthermore, the effects of self-explain learning strategies and reflective learning on the change of subjects' conceptual errors were explored. The result of experiments indicated: 1. There was significant difference in the number of conceptual errors of declarative and procedural knowledge between high-performance students and low-performance students. And Low-performance students made more conceptual errors of procedural knowledge than that of declarative knowledge. For high-performance students, there was no distinct difference between these two kinds of errors. 2. In the distribution of conceptual errors, most errors of declarative knowledge were mainly focused on the understanding of concepts of friction and acceleration. The errors of procedure knowledge most errors concentrated on the judgment of vector direction and the conceptual understanding. 3. Compared with high-performance students, the representation of conceptual declarative knowledge of low-performance students is less complex, more concrete and context bound. 4. The comparative analysis of problem-solving strategies showed: high-performance students preferred to apply analytic strategy, solving problems based on physical concepts and principles; low-performance students preferred to use context strategy, solving problem according to the literal meaning of problems, subjective and groundless presumption and wrong concepts and principles. 5. Self-explain strategies can help students correct their conceptual errors effectively. Reflective learning could help students to correct the concept errors in some degree, but the distinct effect was not observed.