Learning to lead: The development and testing of a model of leadership learning

Drawing upon an action learning perspective, we hypothesized that a leader’s learning of project leadership skills would be related to facilitative leadership, team reflexivity, and team performance. Secondly, we proposed that new and experienced leaders would differ in the amount they learn from their current and recent experience as project managers, and in the strength of the relationship between their self-reported learning, facilitative leadership, and team reflexivity. We conducted a 1-year longitudinal study of 50 R&D teams, led by 25 new and 25 experienced leaders, with 313 team members and 22 project customers, collecting both quantitative and qualitative data. We found evidence of a significant impact of the leader’s learning on subsequent facilitative leadership and team performance 8 and 12 months later, suggesting a lag between learning leadership skills and translating these skills into leadership behavior. The findings contribute to an understanding of how leaders consolidate their learned experience into facilitative leadership behavior.

Do-Rank: DCG optimization for learning-to-rank in tag-based item recommendation systems

Discounted Cumulative Gain (DCG) is a well-known ranking evaluation measure for models built with multiple relevance graded data. By handling tagging data used in recommendation systems as an ordinal relevance set of {negative,null,positive}, we propose to build a DCG based recommendation model. We present an efficient and novel learning-to-rank method by optimizing DCG for a recommendation model using the tagging data interpretation scheme. Evaluating the proposed method on real-world datasets, we demonstrate that the method is scalable and outperforms the benchmarking methods by generating a quality top-N item recommendation list.

Characteristics of on-road driving by persons with central vision loss: Learning to drive with a bioptic telescope

There is limited research on the driving performance and safety of bioptic drivers and even less regarding the driving skills that are most challenging for those learning to drive with bioptic telescopes. This research consisted of case studies of five trainee bioptic drivers whose driving skills were compared with those of a group of licensed bioptic drivers (n = 23) while they drove along city, suburban, and controlled-access highways in an instrumented dual-brake vehicle. A certified driver rehabilitation specialist was positioned in the front passenger seat to monitor safety and two backseat evaluators independently rated driving using a standardized scoring system. Other aspects of performance were assessed through vehicle instrumentation and video recordings. Results demonstrate that while sign recognition, lane keeping, steering steadiness, gap judgments and speed choices were significantly worse in trainees, some driving behaviors and skills, including pedestrian detection and traffic light recognition were not significantly different to those of the licensed drivers. These data provide useful insights into the skill challenges encountered by a small sample of trainee bioptic drivers which, while not generalizable because of the small sample size, provide valuable insights beyond that of previous studies and can be used as a basis to guide training strategies.

Horsing around – Using equine facilitated learning to support the development of social-emotional competence of students at risk of school failure

The social-emotional issues some students experience can place them at risk of school failure. Traditional methods of support can be ineffective or not sustainable and new alternative approaches need to be attempted to support social-emotional competency, school engagement and success for students at risk. This paper discusses preliminary outcomes of an equine facilitated learning (EFL) programme specifically designed to focus on using horses to improve the resilience and social-emotional competency in students perceived as ‘at risk’ of school failure. This qualitative exploratory study used interviews and observations over a six month period to listen to the voices of the students themselves about their experiences of EFL. Initial findings from the pilot study suggest that EFL programmes can be a novel and motivating way to promote resilience training and social-emotional development of students at risk of failure and, in turn, improve their level of engagement and connection with school environments.

Dorothea (Dola) Guttmann learning to drive; Montreal Portraits Women

Born April 27, 1922 in Tilsit, died June 8, 2003 Zurich

The social tasks of learning to become a physical education teacher: Considering the HPE subject department as a community of practice

Initial teacher education (ITE) students participate in various workplaces within schools and in doing so, form understandings about the numerous, and at times competing, expectations of teachers’ work. Through these experiences they form understandings about themselves as health and physical education (HPE) teachers. This paper examines the ways communities of practice within HPE subject department offices function as sites of workplace learning for student teachers. In particular this research focused on how ITE students negotiate tacit and contradictory expectations as well as social tasks during the practicum and the ways in which their understandings are mediated through participation in the workspace. Qualitative methods of survey and semi-structured interview were used to collect data on a cohort of student teachers during and following their major (10 week) practicum experience. Analysis was informed by theories of communities of practice (Wenger, 1998), workplace learning (Billett, 2001), and social task systems (Doyle, 1977). It was evident that considerable effort, attention, and energy was expended on various interrelated social tasks aimed at building positive relationships with their supervisor and other HPE teachers at the school. The social dynamics were highly nuanced and required a game-like approach. In our view the complexity that student teachers must negotiate in striving for an excellent evaluation warrants specific attention in physical education teacher education (PETE) programs. This study raises questions regarding our responsibilities in sending student teachers into contexts that might even be described as toxic. We offer some suggestions for how PETE might better support students going into practicum contexts that might be regarded as problematic workplaces.

Learning to write: Analysing writing samples as part of considering how children become writers

This chapter draws on a large data set of children's work samples collected as part of a five-year school reform project in a community of high poverty. One component of the data set from this project is a corpus of more than 2000 writing samples collected from students across eight grade levels (Prep to year 7) annually, across four years of the project (2009-2013). This paper utilises a selection of these texts to consider insights available to teachers and schools through a simple process of collecting and assessing writing samples produced by children over time. The focus is on what samples of writing might enable us to know and understand about learning and teaching this important dimension of literacy in current classrooms.

People and Business Development (PBD): Learning to deliver learning effectively

In October 2010, PBD introduced its eQual Learning VLE (virtual learning environment) to provide an online knowledge resource for its students. During the project, the company learnt many lessons about how to deliver learning effectively. In the course of a year researching VLE platforms, looking for material, and remapping NVQ courses for new QCF qualifications, the company realised that it was more important to deliver engaging and relevant content, rather than boasting the most innovative technology.

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.

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.

From subspace learning to distance learning: A geometrical optimization approach

In this paper, we adopt a differential-geometry viewpoint to tackle the problem of learning a distance online. As this problem can be cast into the estimation of a fixed-rank positive semidefinite (PSD) matrix, we develop algorithms that exploits the rich geometry structure of the set of fixed-rank PSD matrices. We propose a method which separately updates the subspace of the matrix and its projection onto that subspace. A proper weighting of the two iterations enables to continuously interpolate between the problem of learning a subspace and learning a distance when the subspace is fixed. © 2009 IEEE.