Professional Practice in Learning and Development: How to Design and Deliver Plans for the Workplace

Introduction The world is changing! It is volatile, uncertain, complex and ambiguous. As cliché as it may sound the evidence of such dynamism in the external environment is growing. Business-as-usual is more of the exception than the norm. Organizational change is the rule; be it to accommodate and adapt to change, or instigate and lead change. A constantly changing environment is a situation that all organizations have to live with. What makes some organizations however, able to thrive better than others? Many scholars and practitioners believe that this is due to the ability to learn. Therefore, this book on developing Learning and Development (L&D) professionals is timely as it explores and discusses trends and practices that impact organizations, the workforce and L&D professionals. Being able to learn and develop effectively is the cornerstone of motivation as it helps to address people’s need to be competent and to be autonomous (Deci & Ryan, 2002; Loon & Casimir, 2008; Ryan & Deci, 2000). L&D stimulates and empowers people to perform. Organizations that are better at learning at all levels; the individual, group and organizational level, will always have a better chance of surviving and performing. Given the new reality of a dynamic external environment and constant change, L&D professionals now play an even more important role in their organizations than ever before. However, L&D professionals themselves are not immune to the turbulent changes as their practices are also impacted. Therefore, the challenges that L&D professionals face are two-pronged. Firstly, in relation to helping and supporting their organization and its workforce in adapting to the change, whilst, secondly developing themselves effectively and efficiently so that they are able to be one-step ahead of the workforce that they are meant to help develop. These challenges are recognised by the CIPD, as they recently launched their new L&D qualification that has served as an inspiration for this book. L&D plays a crucial role at both strategic (e.g. organizational capability) and operational (e.g. delivery of training) levels. L&D professionals have moved from being reactive (e.g. following up action after performance appraisals) to being more proactive (e.g. shaping capability). L&D is increasingly viewed as a driver for organizational performance. The CIPD (2014) suggest that L&D is increasingly expected to not only take more responsibility but also accountability for building both individual and organizational knowledge and capability, and to nurture an organizational culture that prizes learning and development. This book is for L&D professionals. Nonetheless, it is also suited for those studying Human Resource Development HRD at intermediate level. The term ‘Human Resource Development’ (HRD) is more common in academia, and is largely synonymous with L&D (Stewart & Sambrook, 2012) Stewart (1998) defined HRD as ‘the practice of HRD is constituted by the deliberate, purposive and active interventions in the natural learning process. Such interventions can take many forms, most capable of categorising as education or training or development’ (p. 9). In fact, many parts of this book (e.g. Chapters 5 and 7) are appropriate for anyone who is involved in training and development. This may include a variety of individuals within the L&D community, such as line managers, professional trainers, training solutions vendors, instructional designers, external consultants and mentors (Mayo, 2004). The CIPD (2014) goes further as they argue that the role of L&D is broad and plays a significant role in Organizational Development (OD) and Talent Management (TM), as well as in Human Resource Management (HRM) in general. OD, TM, HRM and L&D are symbiotic in enabling the ‘people management function’ to provide organizations with the capabilities that they need.

Vocational training and knowledge development: a deeper understanding

Construction management research literature has identified the importance of understanding the practical realities of skills and training provision and the role of reflective practice in the development of knowledge. This paper examines vocational training of experienced site staff in the development of their knowledge through SVQ training to investigate the primary factors for successful learning in site-based construction staff with a supervisory/management role. Using semi-structured interviews the impact of vocational training on individual candidates and other sitebased staff are investigated. The paper explores, through the reflections of 26 SVQ candidates (20 SVQ3 and 6 SVQ4), a deeper understanding of how site supervisors and site managers learn through the SVQ process and develop tacit knowledge through formal reflection. Reflective practice develops practical wisdom (Phronesis). The investigation explains aspects of practical wisdom and how knowledge, practice and skills are developed through vocational training. There is a clear perception by those completing the qualification that it has enabled them to perform their job better identifying numerous examples relating to problem solving, critical thinking, making decisions and leadership. It has been found that Phronesis is evident on a day-to-day basis on site activities developed through reflective practice in personal development. The reflective practice in developing knowledge also builds, within individuals, a better understanding of themselves and their capabilities through the learning achieved in the SVQ. Future work is identified around analysing the role of the assessor in facilitating Phronesis in the SVQ context.

Datasets, features, learning, and models in visual recognition

Visual recognition is a fundamental research topic in computer vision. This dissertation explores datasets, features, learning, and models used for visual recognition. In order to train visual models and evaluate different recognition algorithms, this dissertation develops an approach to collect object image datasets on web pages using an analysis of text around the image and of image appearance. This method exploits established online knowledge resources (Wikipedia pages for text; Flickr and Caltech data sets for images). The resources provide rich text and object appearance information. This dissertation describes results on two datasets. The first is Berg’s collection of 10 animal categories; on this dataset, we significantly outperform previous approaches. On an additional set of 5 categories, experimental results show the effectiveness of the method. Images are represented as features for visual recognition. This dissertation introduces a text-based image feature and demonstrates that it consistently improves performance on hard object classification problems. The feature is built using an auxiliary dataset of images annotated with tags, downloaded from the Internet. Image tags are noisy. The method obtains the text features of an unannotated image from the tags of its k-nearest neighbors in this auxiliary collection. A visual classifier presented with an object viewed under novel circumstances (say, a new viewing direction) must rely on its visual examples. This text feature may not change, because the auxiliary dataset likely contains a similar picture. While the tags associated with images are noisy, they are more stable when appearance changes. The performance of this feature is tested using PASCAL VOC 2006 and 2007 datasets. This feature performs well; it consistently improves the performance of visual object classifiers, and is particularly effective when the training dataset is small. With more and more collected training data, computational cost becomes a bottleneck, especially when training sophisticated classifiers such as kernelized SVM. This dissertation proposes a fast training algorithm called Stochastic Intersection Kernel Machine (SIKMA). This proposed training method will be useful for many vision problems, as it can produce a kernel classifier that is more accurate than a linear classifier, and can be trained on tens of thousands of examples in two minutes. It processes training examples one by one in a sequence, so memory cost is no longer the bottleneck to process large scale datasets. This dissertation applies this approach to train classifiers of Flickr groups with many group training examples. The resulting Flickr group prediction scores can be used to measure image similarity between two images. Experimental results on the Corel dataset and a PASCAL VOC dataset show the learned Flickr features perform better on image matching, retrieval, and classification than conventional visual features. Visual models are usually trained to best separate positive and negative training examples. However, when recognizing a large number of object categories, there may not be enough training examples for most objects, due to the intrinsic long-tailed distribution of objects in the real world. This dissertation proposes an approach to use comparative object similarity. The key insight is that, given a set of object categories which are similar and a set of categories which are dissimilar, a good object model should respond more strongly to examples from similar categories than to examples from dissimilar categories. This dissertation develops a regularized kernel machine algorithm to use this category dependent similarity regularization. Experiments on hundreds of categories show that our method can make significant improvement for categories with few or even no positive examples.

Managing Succession and Knowledge Transfer in Family Businesses: Lessons from a Comparative Research

The most natural mode of family firm succession is the intergenerational ownership transfer. Statistical evidence, however, suggests that in most cases the succession process fails. There can be several reasons as a lot of personal, emotional and structural factors can act as an inhibitor to succession. The effectiveness of the implementation of any succession strategy is strongly dependent on the efficiency of intergenerational knowledge transfer, which is related to the parties’ absorptive capacity and willingness to learn. The paper is based on the experiences learned from the INSIST project. In the framework of the project different aspects of family business succession have been investigated in three participating countries (Hungary, Poland and the United Kingdom). The aim of the paper is to identify the patterns of management, succession, knowledge transfer and learning in family businesses. Issues will be examined in detail such as the succession strategies of companies investigated and the efforts family businesses and their managers make in order to harmonize family goals (such as emotional stability, harmony, and reputation) with business- related objectives (e.g. survival, growth or profitability).

Self-supervised information retrieval: a novel approach based on Deep Metric Learning and Neural Language Models

Most of the existing open-source search engines, utilize keyword or tf-idf based techniques to find relevant documents and web pages relative to an input query. Although these methods, with the help of a page rank or knowledge graphs, proved to be effective in some cases, they often fail to retrieve relevant instances for more complicated queries that would require a semantic understanding to be exploited. In this Thesis, a self-supervised information retrieval system based on transformers is employed to build a semantic search engine over the library of Gruppo Maggioli company. Semantic search or search with meaning can refer to an understanding of the query, instead of simply finding words matches and, in general, it represents knowledge in a way suitable for retrieval. We chose to investigate a new self-supervised strategy to handle the training of unlabeled data based on the creation of pairs of ’artificial’ queries and the respective positive passages. We claim that by removing the reliance on labeled data, we may use the large volume of unlabeled material on the web without being limited to languages or domains where labeled data is abundant.

Edge-aware Graph Attention Networks: Joint Reasoning On Text and Knowledge Graphs for Biomedical Question Answering

Much of the real-world dataset, including textual data, can be represented using graph structures. The use of graphs to represent textual data has many advantages, mainly related to maintaining a more significant amount of information, such as the relationships between words and their types. In recent years, many neural network architectures have been proposed to deal with tasks on graphs. Many of them consider only node features, ignoring or not giving the proper relevance to relationships between them. However, in many node classification tasks, they play a fundamental role. This thesis aims to analyze the main GNNs, evaluate their advantages and disadvantages, propose an innovative solution considered as an extension of GAT, and apply them to a case study in the biomedical field. We propose the reference GNNs, implemented with methodologies later analyzed, and then applied to a question answering system in the biomedical field as a replacement for the pre-existing GNN. We attempt to obtain better results by using models that can accept as input both node and edge features. As shown later, our proposed models can beat the original solution and define the state-of-the-art for the task under analysis.

Methods for integrating machine learning and constrained optimization

In the framework of industrial problems, the application of Constrained Optimization is known to have overall very good modeling capability and performance and stands as one of the most powerful, explored, and exploited tool to address prescriptive tasks. The number of applications is huge, ranging from logistics to transportation, packing, production, telecommunication, scheduling, and much more. The main reason behind this success is to be found in the remarkable effort put in the last decades by the OR community to develop realistic models and devise exact or approximate methods to solve the largest variety of constrained or combinatorial optimization problems, together with the spread of computational power and easily accessible OR software and resources. On the other hand, the technological advancements lead to a data wealth never seen before and increasingly push towards methods able to extract useful knowledge from them; among the data-driven methods, Machine Learning techniques appear to be one of the most promising, thanks to its successes in domains like Image Recognition, Natural Language Processes and playing games, but also the amount of research involved. The purpose of the present research is to study how Machine Learning and Constrained Optimization can be used together to achieve systems able to leverage the strengths of both methods: this would open the way to exploiting decades of research on resolution techniques for COPs and constructing models able to adapt and learn from available data. In the first part of this work, we survey the existing techniques and classify them according to the type, method, or scope of the integration; subsequently, we introduce a novel and general algorithm devised to inject knowledge into learning models through constraints, Moving Target. In the last part of the thesis, two applications stemming from real-world projects and done in collaboration with Optit will be presented.

Temporary teams learning and performance: the case of 3D printing technology in orthopaedic surgery

This dissertation contributes to the scholarly debate on temporary teams by exploring team interactions and boundaries.The fundamental challenge in temporary teams originates from temporary participation in the teams. First, as participants join the team for a short period of time, there is not enough time to build trust, share understanding, and have effective interactions. Consequently, team outputs and practices built on team interactions become vulnerable. Secondly, as team participants move on and off the teams, teams’ boundaries become blurred over time. It leads to uncertainty among team participants and leaders about who is/is not identified as a team member causing collective disagreement within the team. Focusing on the above mentioned challenges, we conducted this research in healthcare organisations since the use of temporary teams in healthcare and hospital setting is prevalent. In particular, we focused on orthopaedic teams that provide personalised treatments for patients using 3D printing technology. Qualitative and quantitative data were collected using interviews, observations, questionnaires and archival data at Rizzoli Orthopaedic Institute, Bologna, Italy. This study provides the following research outputs. The first is a conceptual study that explores temporary teams’ literature using bibliometric analysis and systematic literature review to highlight research gaps. The second paper qualitatively studies temporary relationships within the teams by collecting data using group interviews and observations. The results highlighted the role of short-term dyadic relationships as a ground to share and transfer knowledge at the team level. Moreover, hierarchical structure of the teams facilitates knowledge sharing by supporting dyadic relationships within and beyond the team meetings. The third paper investigates impact of blurred boundaries on temporary teams’ performance. Using quantitative data collected through questionnaires and archival data, we concluded that boundary blurring in terms of fluidity, overlap and dispersion differently impacts team performance at high and low levels of task complexity.

Computational creativity: an interdisciplinary approach to sequential learning and creative generations

Creativity seems mysterious; when we experience a creative spark, it is difficult to explain how we got that idea, and we often recall notions like ``inspiration" and ``intuition" when we try to explain the phenomenon. The fact that we are clueless about how a creative idea manifests itself does not necessarily imply that a scientific explanation cannot exist. We are unaware of how we perform certain tasks, such as biking or language understanding, but we have more and more computational techniques that can replicate and hopefully explain such activities. We should understand that every creative act is a fruit of experience, society, and culture. Nothing comes from nothing. Novel ideas are never utterly new; they stem from representations that are already in mind. Creativity involves establishing new relations between pieces of information we had already: then, the greater the knowledge, the greater the possibility of finding uncommon connections, and the more the potential to be creative. In this vein, a beneficial approach to a better understanding of creativity must include computational or mechanistic accounts of such inner procedures and the formation of the knowledge that enables such connections. That is the aim of Computational Creativity: to develop computational systems for emulating and studying creativity. Hence, this dissertation focuses on these two related research areas: discussing computational mechanisms to generate creative artifacts and describing some implicit cognitive processes that can form the basis for creative thoughts.

Collaborative e-learning and Learning Objects

The Learning Object (OA) is any digital resource that can be reused to support learning with specific functions and objectives. The OA specifications are commonly offered in SCORM model without considering activities in groups. This deficiency was overcome by the solution presented in this paper. This work specified OA for e-learning activities in groups based on SCORM model. This solution allows the creation of dynamic objects which include content and software resources for the collaborative learning processes. That results in a generalization of the OA definition, and in a contribution with e-learning specifications.

On Building a Memory Evolutive System for Application to Learning and Cognition Modeling

We address here aspects of the implementation of a memory evolutive system (MES), based on the model proposed by A. Ehresmann and J. Vanbremeersch (2007), by means of a simulated network of spiking neurons with time dependent plasticity. We point out the advantages and challenges of applying category theory for the representation of cognition, by using the MES architecture. Then we discuss the issues concerning the minimum requirements that an artificial neural network (ANN) should fulfill in order that it would be capable of expressing the categories and mappings between them, underlying the MES. We conclude that a pulsed ANN based on Izhikevich`s formal neuron with STDP (spike time-dependent plasticity) has sufficient dynamical properties to achieve these requirements, provided it can cope with the topological requirements. Finally, we present some perspectives of future research concerning the proposed ANN topology.