Discovering, applying and integrating self-knowledge : a grounded theory study of learning in life coaching

Professional coaching is a rapidly expanding field with interdisciplinary roots and broad application. However, despite abundant prescriptive literature, research into the process of coaching, and especially life coaching, is minimal. Similarly, although learning is inherently recognised in the process of coaching, and coaching is increasingly being recognised as a means of enhancing teaching and learning, the process of learning in coaching is little understood, and learning theory makes up only a small part of the evidence-based coaching literature. In this grounded theory study of life coaches and their clients, the process of learning in life coaching across a range of coaching models is examined and explained. The findings demonstrate how learning in life coaching emerged as a process of discovering, applying and integrating self-knowledge, which culminated in the development of self. This process occurred through eight key coaching processes shared between coaches and clients and combined a multitude of learning theory.

Mind change complexity of learning logic programs

The present paper motivates the study of mind change complexity for learning minimal models of length-bounded logic programs. It establishes ordinal mind change complexity bounds for learnability of these classes both from positive facts and from positive and negative facts. Building on Angluin’s notion of finite thickness and Wright’s work on finite elasticity, Shinohara defined the property of bounded finite thickness to give a sufficient condition for learnability of indexed families of computable languages from positive data. This paper shows that an effective version of Shinohara’s notion of bounded finite thickness gives sufficient conditions for learnability with ordinal mind change bound, both in the context of learnability from positive data and for learnability from complete (both positive and negative) data. Let Omega be a notation for the first limit ordinal. Then, it is shown that if a language defining framework yields a uniformly decidable family of languages and has effective bounded finite thickness, then for each natural number m >0, the class of languages defined by formal systems of length <= m: • is identifiable in the limit from positive data with a mind change bound of Omega (power)m; • is identifiable in the limit from both positive and negative data with an ordinal mind change bound of Omega × m. The above sufficient conditions are employed to give an ordinal mind change bound for learnability of minimal models of various classes of length-bounded Prolog programs, including Shapiro’s linear programs, Arimura and Shinohara’s depth-bounded linearly covering programs, and Krishna Rao’s depth-bounded linearly moded programs. It is also noted that the bound for learning from positive data is tight for the example classes considered.

Curriculum alignment: Exploring student perception of learning achievement measures

The importance of constructively aligned curriculum is well understood in higher education. Based on the principles of constructive alignment, this research considers whether student perception of learning achievement measures can be used to gain insights into how course activities and pedagogy are assisting or hindering students in accomplishing course learning goals. Students in a Marketing Principles course were asked to complete a voluntary survey rating their own progress on the intended learning goals for the course. Student perceptions of learning achievement were correlated with actual student learning, as measured by grade, suggesting that student perceptions of learning achievement measures are suitable for higher educators. Student perception of learning achievement measures provide an alternate means to understand whether students are learning what was intended, which is particularly useful for educators faced with large classes and associated restrictions on assessment. Further, these measures enable educators to simultaneously gather evidence to document the impact of teaching innovations on student learning. Further implications for faculty and future research are offered.

A study of parents’ conceptions of their roles as home educators of their children

Home education is a growing phenomenon in Australia. It is the practice whereby parents engage in the full time education of their children at home. This study used a phenomenographic approach to identify and analyse how home educating parents conceive of their roles as home educators. Data analysis presented an outcome space of the parents‘ qualitatively different conceptions of their roles as home educators. This outcome space exemplifies the phenomenon of the roles of parent home educators. This thesis reports on the qualitatively different ways in which a group of 27 home educating parents viewed their roles in the education of their children. Four categories of description of parent home educator roles emerged from the analysis. These parents saw themselves in the role of a (1) learner, as they needed to gain knowledge and skills in order to both commence and to continue home education. Further, they perceived of themselves as (2) partners, usually with their spouse, in an educational partnership, which provided the family‘s educational infrastructure. They also saw themselves in the role of (3) teachers of their children, facilitating their education and development. Finally, they conceived of themselves as (4) educational pioneers in their communities. These four categories were linked and differentiated from each other by three key themes or dimensions of variation. These were the themes of (1) educational influence; (2) educational example; and (3) spirituality, which impacted both their families and the wider community. The findings of the study indicate that home educators experience their roles in four critically different ways, each of which contributes to their family educational enterprise. The findings suggest that home educators, are bona fide educators and that they access parental qualities that provide a form of education which differs from the educational practices characteristic of the majority of Australians. The study has the potential to generate further understandings of home education for home educators and for the wider community. It may also inform policy makers in the fields of education, social welfare, and the law, where there is a vested interest in the education and welfare of children and families.

Assurance of learning : the role of work integrated learning and industry partners

In the partnering with students and industry it is important for universities to recognize and value the nature of knowledge and learning that emanates from work integrated learning experiences is different to formal university based learning. Learning is not a by-product of work rather learning is fundamental to engaging in work practice. Work integrated learning experiences provide unique opportunities for students to integrate theory and practice through the solving of real world problems. This paper reports findings to date of a project that sought to identify key issues and practices faced by academics, industry partners and students engaged in the provision and experience of work integrated learning within an undergraduate creative industries program at a major metropolitan university. In this paper, those findings are focused on some of the particular qualities and issues related to the assessment of learning at and through the work integrated experience. The findings suggest that the assessment strategies needed to better value the knowledges and practices of the Creative Industries. The paper also makes recommendations about how industry partners might best contribute to the assessment of students’ developing capabilities and to continuous reflection on courses and the assurance of learning agenda.

Open problems in the security of learning

Machine learning has become a valuable tool for detecting and preventing malicious activity. However, as more applications employ machine learning techniques in adversarial decision-making situations, increasingly powerful attacks become possible against machine learning systems. In this paper, we present three broad research directions towards the end of developing truly secure learning. First, we suggest that finding bounds on adversarial influence is important to understand the limits of what an attacker can and cannot do to a learning system. Second, we investigate the value of adversarial capabilities-the success of an attack depends largely on what types of information and influence the attacker has. Finally, we propose directions in technologies for secure learning and suggest lines of investigation into secure techniques for learning in adversarial environments. We intend this paper to foster discussion about the security of machine learning, and we believe that the research directions we propose represent the most important directions to pursue in the quest for secure learning.