Port community learning needs:analysis and design

The port industry is facing a dramatic wave of changes that have transformed the structure of the industry. Modern seaports are increasingly shifting from a “hardware-based” approach towards “knowhow intensive” configuration. In this context knowledge resources, learning processes and training initiatives increasingly represent key elements to guarantee the quality of service supplied and hence the competitiveness of modern seaport communities. This paper describes the learning needs analysis conducted amongst key port community actors in three ports in the south east of Ireland during 2005 in the context of the I-Sea.Net project. It goes on to describe the learning requirements report and the training design carried out based on this analysis.

Learning higher-level features with convolutional restricted Boltzmann machines for sentiment analysis

In recent years, learning word vector representations has attracted much interest in Natural Language Processing. Word representations or embeddings learned using unsupervised methods help addressing the problem of traditional bag-of-word approaches which fail to capture contextual semantics. In this paper we go beyond the vector representations at the word level and propose a novel framework that learns higher-level feature representations of n-grams, phrases and sentences using a deep neural network built from stacked Convolutional Restricted Boltzmann Machines (CRBMs). These representations have been shown to map syntactically and semantically related n-grams to closeby locations in the hidden feature space. We have experimented to additionally incorporate these higher-level features into supervised classifier training for two sentiment analysis tasks: subjectivity classification and sentiment classification. Our results have demonstrated the success of our proposed framework with 4% improvement in accuracy observed for subjectivity classification and improved the results achieved for sentiment classification over models trained without our higher level features.

The effectiveness of workplace coaching:a meta-analysis of learning and performance outcomes from coaching

This study presents a meta-analysis synthesizing the existing research on the effectiveness of workplace coaching. We exclusively explore workplace coaching provided by internal or external coaches and therefore exclude cases of manager-subordinate and peer coaching. We propose a framework of potential outcomes from coaching in organizations, which we examine meta-analytically (k = 17). Our analyses indicated that coaching had positive effects on organizational outcomes overall (δ = 0.36), and on specific forms of outcome criteria (skill-based δ = 0.28; affective δ = 0.51; individual-level results δ = 1.24). We also examined moderation by a number of coaching practice factors (use of multisource feedback; type of coach; coaching format; longevity of coaching). Our analyses of practice moderators indicated a significant moderation of effect size for type of coach (with effects being stronger for internal coaches compared to external coaches) and use of multisource feedback (with the use of multisource feedback resulting in smaller positive effects). We found no moderation of effect size by coaching format (comparing face-to-face, with blended face-to-face and e-coaching) or duration of coaching (number of sessions or longevity of intervention). The effect sizes give support to the potential utility of coaching in organizations. Implications for coaching research and practice are discussed.

The effectiveness of workplace coaching:A meta-analysis of learning and performance outcomes; scale development; theoretical model of individual differences and longitudinal study

The extant literature on workplace coaching is characterised by a lack of theoretical and empirical understanding regarding the effectiveness of coaching as a learning and development tool; the types of outcomes one can expect from coaching; the tools that can be used to measure coaching outcomes; the underlying processes that explain why and how coaching works and the factors that may impact on coaching effectiveness. This thesis sought to address these substantial gaps in the literature with three linked studies. Firstly, a meta-analysis of workplace coaching effectiveness (k = 17), synthesizing the existing research was presented. A framework of coaching outcomes was developed and utilised to code the studies. Analysis indicated that coaching had positive effects on all outcomes. Next, the framework of outcomes was utilised as the deductive start-point to the development of the scale measuring perceived coaching effectiveness. Utilising a multi-stage approach (n = 201), the analysis indicated that perceived coaching effectiveness may be organised into a six factor structure: career clarity; team performance; work well-being; performance; planning and organizing and personal effectiveness and adaptability. The final study was a longitudinal field experiment to test a theoretical model of individual differences and coaching effectiveness developed in this thesis. An organizational sample of 84 employees each participated in a coaching intervention, completed self-report surveys, and had their job performance rated by peers, direct reports and supervisors (a total of 352 employees provided data on participant performance). The results demonstrate that compared to a control group, the coaching intervention generated a number of positive outcomes. The analysis indicated that coachees’ enthusiasm, intellect and orderliness influenced the impact of coaching on outcomes. Mediation analysis suggested that mastery goal orientation, performance goal orientation and approach motivation in the form of behavioural activation system (BAS) drive, were significant mediators between personality and outcomes. Overall, the findings of this thesis make an original contribution to the understanding of the types of outcomes that can be expected from coaching, and the magnitude of impact coaching has on outcomes. The thesis also provides a tool for reliably measuring coaching effectiveness and a theoretical model to understand the influence of coachee individual differences on coaching outcomes.

Learning to live with Parkinson’s disease in the family unit:an interpretative phenomenological analysis of well-being

We investigated family members’ lived experience of Parkinson’s disease (PD) aiming to investigate opportunities for well-being. A lifeworld-led approach to healthcare was adopted. Interpretative phenomenological analysis was used to explore in-depth interviews with people living with PD and their partners. The analysis generated four themes: It’s more than just an illness revealed the existential challenge of diagnosis; Like a bird with a broken wing emphasizing the need to adapt to increasing immobility through embodied agency; Being together with PD exploring the kinship within couples and belonging experienced through support groups; and Carpe diem! illuminated the significance of time and fractured future orientation created by diagnosis. Findings were interpreted using an existential-phenomenological theory of well-being. We highlighted how partners shared the impact of PD in their own ontological challenges. Further research with different types of families and in different situations is required to identify services required to facilitate the process of learning to live with PD. Care and support for the family unit needs to provide emotional support to manage threats to identity and agency alongside problem-solving for bodily changes. Adopting a lifeworld-led healthcare approach would increase opportunities for well-being within the PD illness journey.

Learning user and product distributed representations using a sequence model for sentiment analysis

In product reviews, it is observed that the distribution of polarity ratings over reviews written by different users or evaluated based on different products are often skewed in the real world. As such, incorporating user and product information would be helpful for the task of sentiment classification of reviews. However, existing approaches ignored the temporal nature of reviews posted by the same user or evaluated on the same product. We argue that the temporal relations of reviews might be potentially useful for learning user and product embedding and thus propose employing a sequence model to embed these temporal relations into user and product representations so as to improve the performance of document-level sentiment analysis. Specifically, we first learn a distributed representation of each review by a one-dimensional convolutional neural network. Then, taking these representations as pretrained vectors, we use a recurrent neural network with gated recurrent units to learn distributed representations of users and products. Finally, we feed the user, product and review representations into a machine learning classifier for sentiment classification. Our approach has been evaluated on three large-scale review datasets from the IMDB and Yelp. Experimental results show that: (1) sequence modeling for the purposes of distributed user and product representation learning can improve the performance of document-level sentiment classification; (2) the proposed approach achieves state-of-The-Art results on these benchmark datasets.