Applying outcomes of lifelong learning to organisational achievement

The focus of this doctoral research study is making the most what a person knows and can do, as an outcome from their lifelong learning, so as to better contribute to organisational achievement. This has been motivated by a perceived gap in the extensive literature linking knowledge with organisational achievement. Whereas there is a rich body of literature addressing the meta-philosophies giving rise to the emergence of learning organisations there is, as yet, scant attention paid to the detail of planning and implementing action which would reveal individual/organisational opportunities of mutual advantage and motivate, and sustain, participation at the day-to-day level of the individual. It is in this space that this dissertation seeks to contribute by offering a mechanism for bringing the, hindsight informed, response “but that’s obvious” into the abiding explicit realm at the level of the individual. In moving beyond the obvious which is prone to be overlooked, the emphasis on “better” in the introductory sentence, is very deliberately made and has a link to awakening latent individual, and hence organisational, capabilities that would otherwise languish. The evolved LCM Model – a purposeful integration valuing the outcomes from lifelong learning (the L) with nurturing a culture supporting this outcome (the C) and with responsiveness to potentially diverse motivations (the M) – is a reflective device for bringing otherwise tacit, and latent, logic into the explicit realm of action. In the course of the development of the model, a number of supplementary models included in this dissertation have evolved from the research. They form a suite of devices which inform action and lead to making the most of what an individual knows and can do within the formal requirements of a job and within the informal influences of a frequently invisible community of practice. The initial inquiry drew upon the views and experiences of water industry engineering personnel and training facilitators associated with the contract cleaning and waste management industries. However, the major research occurred as an Emergency Management Australia (EMA) project with the Country Fire Authority (CFA) as the host organisation. This EMA/CFA research project explored the influence of making the most of what a CFA volunteer knows and can do upon retention of that volunteer. In its aggregate, across the CFA volunteer body, retention is a critical community safety objective. A qualitative research, ethnographic in character, approach was adopted. Data was collected through interviews, workshops and outcomes from attempts at action research projects. Following an initial thirteen month scoping study including respondents other than from the CFA, the research study moved into an exploration of the efficacy of an indicative model with four contextual foci – i.e. the manner of welcoming new members to the CFA, embracing training, strengthening brigade sustainability and leadership. Interestingly, the research environment which forced a truncated implementation of action research projects was, in itself, an informing experience indicative of inhibitors to making the most of what people know and can do. Competition for interest, time and commitment were factors governing the manner in which CFA respondents could be called upon to explore the efficacy of the model, and were a harbinger of the influences shaping the more general environment of drawing upon what CFA volunteers know and can do. Subsequent to the development of the indicative model, a further 16 month period was utilised in the ethnographic exploration of the relevance of the model within the CFA as the host organisation. As a consequence, the model is a more fully developed tool (framework) to aid reflection, planning and action. Importantly, the later phase of the research study has, through application of the model to specific goals within the CFA, yielded operational insight into its effective use, and in which activity systems have an important place. The model – now confidently styled as the LCM Model – has three elements that when enmeshed strengthen the likelihood of organisational achievement ; and the degree of this meshing, as relevant to the target outcome, determines the strength of outcome. i.e. - • Valuing outcomes from learning: When a person recognises and values (appropriately to achievement by the organisation) what they know and can do, and associated others recognise and value what this person knows and can do, then there is increased likelihood of these outcomes from learning being applied to organisational achievement. • Valuing a culture that is conducive to learning: When a person, and associated others, are further developing and drawing upon what they know and can do within the context of a culture that is conducive to learning, then there is increased likelihood that outcomes from learning will be applied to organisational achievement. • Valuing motivation of the individual: When a person’s motivation to apply what they know and can do is valued by them, and associated others, as appropriate to organisational achievement then there is increased likelihood that appropriately drawing upon outcomes from learning will occur. Activity theory was employed as a device to scope and explore understanding of the issues as they emerged in the course of the research study. Viewing the data through the prism of activity theory led not only to the development of the LCM Model but also to an enhanced understanding of the role of leadership as a foundation for acting upon the model. Both formal and informal leadership were found to be germane in asserting influence on empowering engagement with learning and drawing upon its outcomes. It is apparent that a “leaderful organisation”, as postulated by Raelin (2003), is an environment which supports drawing upon the LCM model; and it may be the case that the act of drawing upon the model will move a narrowly leadership focused organisation toward leaderful attributes. As foreshadowed at the beginning of this synopsis, nurturing individual and organisational capability is the guiding mantra for this dissertation - “Capability embraces competence but is also forward-looking, concerned with the realisation of potential” (Stephenson 1998, p. 3). Although the inquiry focussed upon a need for CFA volunteer retention, it began with a broader investigation as part of the scoping foundation and the expanded usefulness of the LCM Model invites further investigation. The dissertation concludes with the encapsulating sentiment that “You have really got to want to”. With this predisposition in mind, this dissertation contributes to knowledge through the development and discussion of the LCM model as a reflective device informing transformative learning (Mezirow and Associates 1990). A leaderful environment (Raelin 2003) aids transformative learning – accruing to the individual and the organisation - through engendering and maintaining making the most of knowledge and skill – motivating and sustaining “the will”. The outcomes from this research study are a strong assertion that wanting to make the most of what is known and can be done is a hallmark of capability. Accordingly, this dissertation is a contribution to the “how” of strengthening the capability, and the commitment to applying that capability, of an individual and an organisation.

Learning to manage : transformative outcomes of competency-based training

Transformative learning theory is a dominant approach to understanding adult learning. The theory addresses the way our perspectives on the world, others and ourselves can be challenged and transformed in our ongoing efforts to make sense of the world. It is a conception of learning that does not focus on the measurable acquisition of knowledge and skills, but looks rather to the dynamics of self-questioning and upheaval as the key to adult learning. In this article, transformative learning theory is used as a lens for studying learning in a competency-based, entry-level management course. Instead of asking which knowledge and skills were developed and how effectively, the research enquired into deeper changes wrought by the learning experiences. The research found that for some learners the course contributed to significant discontent as they discovered that management practices they took to represent the norm fell dramatically short of the model promoted in the training.

Using vignettes to evaluate the outcomes of student learning : data from the evaluation of the new social work degree in England

This paper reports the use of vignettes as a methodology to analyse the extent to which the new social work degree programmes enabled students to develop their analytical and reflective capabilities. Two vignettes, which focused on children and families and adult social care respectively, were developed for the study. Students were asked to respond in writing, from the perspective of a social worker, to a standard set of questions at the beginning (T1) and end of their degree programme (T2). Considering the responses to all questions across the two vignettes, a series of scales was developed to measure the key themes which had been identified by qualitative analysis. These included ‘Attention to process of relationships’ and ‘Social/structural/political awareness’. Responses were also rated as ‘descriptive’, ‘analytic’ or ‘reflective’.

Students from six universities in England participated. From an original sample of 222 students, it was possible to match 79 T1 and T2 responses. Analysis of variance demonstrated statistically significant increases in nine of the 11 themes and increases in ratings for analysis and reflection.

In conclusion, vignettes can be used to produce both qualitative and quantitative data in respect of changes in students’ acquisition of knowledge and skills over time.

Computational studies to understand the role of social learning in team familiarity and its effects on team performance

This paper concerns social learning modes and their effects on team performance. Social learning, such as by observing others' actions and their outcomes, allows members of a team to learn what other members know. Knowing what other members know can reduce task communication and co-ordination overhead, which helps the team to perform faster since members can devote their attention to their tasks. This paper describes agent-based simulation studies using a computational model that implements different social learning modes as parameters that can be controlled in the simulations. The results show that social learning from both direct and indirect observations positively contributes to learning about what others know, but the value of social learning is sensitive to prior familiarity such that minimum thresholds of team familiarity are needed to realise the benefits of social learning. This threshold increases with task complexity. These findings clarify the level of influence that sociality has on social learning and sets up a formal framework by which to conduct studies on how social context influences learning and group performance.

Improving outcomes of preschool language delay in the community : protocol for the Language for Learning randomised controlled trial

Background

Early language delay is a high-prevalence condition of concern to parents and professionals. It may result in lifelong deficits not only in language function, but also in social, emotional/behavioural, academic and economic well-being. Such delays can lead to considerable costs to the individual, the family and to society more widely. The Language for Learning trial tests a population-based intervention in 4 year olds with measured language delay, to determine (1) if it improves language and associated outcomes at ages 5 and 6 years and (2) its cost-effectiveness for families and the health care system.

Methods/Design

A large-scale randomised trial of a year-long intervention targeting preschoolers with language delay, nested within a well-documented, prospective, population-based cohort of 1464 children in Melbourne, Australia. All children received a 1.25-1.5 hour formal language assessment at their 4th birthday. The 200 children with expressive and/or receptive language scores more than 1.25 standard deviations below the mean were randomised into intervention or ‘usual care’ control arms. The 20-session intervention program comprises 18 one-hour home-based therapeutic sessions in three 6-week blocks, an outcome assessment, and a final feed-back/forward planning session. The therapy utilises a ‘step up-step down’ therapeutic approach depending on the child’s language profile, severity and progress, with standardised, manualised activities covering the four language development domains of: vocabulary and grammar; narrative skills; comprehension monitoring; and phonological awareness/pre-literacy skills. Blinded follow-up assessments at ages 5 and 6 years measure the primary outcome of receptive and expressive language, and secondary outcomes of vocabulary, narrative, and phonological skills.

Discussion

A key strength of this robust study is the implementation of a therapeutic framework that provides a standardised yet tailored approach for each child, with a focus on specific language domains known to be associated with later language and literacy. The trial responds to identified evidence gaps, has outcomes of direct relevance to families and the community, includes a well-developed economic analysis, and has the potential to improve long-term consequences of early language delay within a public health framework.

Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset

For years, we have relied on population surveys to keep track of regional public health statistics, including the prevalence of non-communicable diseases. Because of the cost and limitations of such surveys, we often do not have the up-to-date data on health outcomes of a region. In this paper, we examined the feasibility of inferring regional health outcomes from socio-demographic data that are widely available and timely updated through national censuses and community surveys. Using data for 50 American states (excluding Washington DC) from 2007 to 2012, we constructed a machine-learning model to predict the prevalence of six non-communicable disease (NCD) outcomes (four NCDs and two major clinical risk factors), based on population socio-demographic characteristics from the American Community Survey. We found that regional prevalence estimates for non-communicable diseases can be reasonably predicted. The predictions were highly correlated with the observed data, in both the states included in the derivation model (median correlation 0.88) and those excluded from the development for use as a completely separated validation sample (median correlation 0.85), demonstrating that the model had sufficient external validity to make good predictions, based on demographics alone, for areas not included in the model development. This highlights both the utility of this sophisticated approach to model development, and the vital importance of simple socio-demographic characteristics as both indicators and determinants of chronic disease.

Bias and learning in temporal binding: intervals between actions and outcomes are compressed by prior bias

It has consistently been shown that agents judge the intervals between their actions and outcomes as compressed in time, an effect named intentional binding. In the present work, we investigated whether this effect is result of prior bias volunteers have about the timing of the consequences of their actions, or if it is due to learning that occurs during the experimental session. Volunteers made temporal estimates of the interval between their action and target onset (Action conditions), or between two events (No-Action conditions). Our results show that temporal estimates become shorter throughout each experimental block in both conditions. Moreover, we found that observers judged intervals between action and outcomes as shorter even in very early trials of each block. To quantify the decrease of temporal judgments in experimental blocks, exponential functions were fitted to participants’ temporal judgments. The fitted parameters suggest that observers had different prior biases as to intervals between events in which action was involved. These findings suggest that prior bias might play a more important role in this effect than calibration-type learning processes.

Response threshold models, stochastic learning automata and ant colony optimization-based decentralized self-coordination algorithms for heterogeneous multi-tasks distribution in multi-robot systems

In recent decades, there has been an increasing interest in systems comprised of several autonomous mobile robots, and as a result, there has been a substantial amount of development in the eld of Articial Intelligence, especially in Robotics. There are several studies in the literature by some researchers from the scientic community that focus on the creation of intelligent machines and devices capable to imitate the functions and movements of living beings. Multi-Robot Systems (MRS) can often deal with tasks that are dicult, if not impossible, to be accomplished by a single robot. In the context of MRS, one of the main challenges is the need to control, coordinate and synchronize the operation of multiple robots to perform a specic task. This requires the development of new strategies and methods which allow us to obtain the desired system behavior in a formal and concise way. This PhD thesis aims to study the coordination of multi-robot systems, in particular, addresses the problem of the distribution of heterogeneous multi-tasks. The main interest in these systems is to understand how from simple rules inspired by the division of labor in social insects, a group of robots can perform tasks in an organized and coordinated way. We are mainly interested on truly distributed or decentralized solutions in which the robots themselves, autonomously and in an individual manner, select a particular task so that all tasks are optimally distributed. In general, to perform the multi-tasks distribution among a team of robots, they have to synchronize their actions and exchange information. Under this approach we can speak of multi-tasks selection instead of multi-tasks assignment, which means, that the agents or robots select the tasks instead of being assigned a task by a central controller. The key element in these algorithms is the estimation ix of the stimuli and the adaptive update of the thresholds. This means that each robot performs this estimate locally depending on the load or the number of pending tasks to be performed. In addition, it is very interesting the evaluation of the results in function in each approach, comparing the results obtained by the introducing noise in the number of pending loads, with the purpose of simulate the robot's error in estimating the real number of pending tasks. The main contribution of this thesis can be found in the approach based on self-organization and division of labor in social insects. An experimental scenario for the coordination problem among multiple robots, the robustness of the approaches and the generation of dynamic tasks have been presented and discussed. The particular issues studied are: Threshold models: It presents the experiments conducted to test the response threshold model with the objective to analyze the system performance index, for the problem of the distribution of heterogeneous multitasks in multi-robot systems; also has been introduced additive noise in the number of pending loads and has been generated dynamic tasks over time. Learning automata methods: It describes the experiments to test the learning automata-based probabilistic algorithms. The approach was tested to evaluate the system performance index with additive noise and with dynamic tasks generation for the same problem of the distribution of heterogeneous multi-tasks in multi-robot systems. Ant colony optimization: The goal of the experiments presented is to test the ant colony optimization-based deterministic algorithms, to achieve the distribution of heterogeneous multi-tasks in multi-robot systems. In the experiments performed, the system performance index is evaluated by introducing additive noise and dynamic tasks generation over time.

Response threshold models and stochastic learning automata for self-coordination of heterogeneous multi-task distribution in multi-robot systems.

This paper focuses on the general problem of coordinating multiple robots. More specifically, it addresses the self-selection of heterogeneous specialized tasks by autonomous robots. In this paper we focus on a specifically distributed or decentralized approach as we are particularly interested in a decentralized solution where the robots themselves autonomously and in an individual manner, are responsible for selecting a particular task so that all the existing tasks are optimally distributed and executed. In this regard, we have established an experimental scenario to solve the corresponding multi-task distribution problem and we propose a solution using two different approaches by applying Response Threshold Models as well as Learning Automata-based probabilistic algorithms. We have evaluated the robustness of the algorithms, perturbing the number of pending loads to simulate the robot’s error in estimating the real number of pending tasks and also the dynamic generation of loads through time. The paper ends with a critical discussion of experimental results.

Teaching College Writing in a High School Setting: The Impact of Concurrent Enrollment on Teacher Learning and Practice

Thesis (Ph.D.)--University of Washington, 2016-06