title: Improving self-awareness and engagement through group coaching

authors: Sutton, Anna

abstract: Group coaching has potential to enhance individual and organisational outcomes but demonstrating its effectiveness remains difficult. Practitioners are often caught between trying to demonstrate ROI and knowing that many of the benefits to clients are not captured by this bottom-line. In this paper, we evaluate a group coaching programme by assessing its impact on self-awareness development and work engagement. Using a pre and post-test design, we find that group coaching improves self-awareness outcomes (reflective self-development, acceptance of self and others, proactivity at work) and employee engagement. Further, coaching sessions provided a supportive environment in which to explore the sometimeschallenging emotions associated with increased self-awareness.

text: Improving self-awareness and engagement through group coaching Group coaching has potential to enhance individual and organisational outcomes but demonstrating its effectiveness remains difficult. Practitioners are often caught between trying to demonstrate ROI and knowing that many of the benefits to clients are not captured by this bottom-line. In this paper, we evaluate a group coaching programme by assessing its impact on self-awareness development and work engagement. Using a pre and post-test design, we find that group coaching improves self-awareness outcomes (reflective self-development, acceptance of self and others, proactivity at work) and employee engagement. Further, coaching sessions provided a supportive environment in which to explore the sometimeschallenging emotions associated with increased self-awareness.

year: 2022-01-01

title: Handling numeric attributes in Hoeffding trees

authors: Pfahringer, Bernhard

subjects: computer science

abstract: For conventional machine learning classification algorithms handling numeric attributes is relatively straightforward. Unsupervised and supervised solutions exist that either segment the data into pre-defined bins or sort the data and search for the best split points. Unfortunately, none of these solutions carry over particularly well to a data stream environment. Solutions for data streams have been proposed by several authors but as yet none have been compared empirically. In this paper we investigate a range of methods for multi-class tree-based classification where the handling of numeric attributes takes place as the tree is constructed. To this end, we extend an existing approximation approach, based on simple Gaussian approximation. We then compare this method with four approaches from the literature arriving at eight final algorithm configurations for testing. The solutions cover a range of options from perfectly accurate and memory intensive to highly approximate. All methods are tested using the Hoeffding tree classification algorithm. Surprisingly, the experimental comparison shows that the most approximate methods produce the most accurate trees by allowing for faster tree growth.

text: Handling numeric attributes in Hoeffding trees For conventional machine learning classification algorithms handling numeric attributes is relatively straightforward. Unsupervised and supervised solutions exist that either segment the data into pre-defined bins or sort the data and search for the best split points. Unfortunately, none of these solutions carry over particularly well to a data stream environment. Solutions for data streams have been proposed by several authors but as yet none have been compared empirically. In this paper we investigate a range of methods for multi-class tree-based classification where the handling of numeric attributes takes place as the tree is constructed. To this end, we extend an existing approximation approach, based on simple Gaussian approximation. We then compare this method with four approaches from the literature arriving at eight final algorithm configurations for testing. The solutions cover a range of options from perfectly accurate and memory intensive to highly approximate. All methods are tested using the Hoeffding tree classification algorithm. Surprisingly, the experimental comparison shows that the most approximate methods produce the most accurate trees by allowing for faster tree growth.

year: 2008

title: Taku haumaru me te kurī: He aratohu mō te haumaru i waenga i te tamariki (8-9 pea ngā tau) me te kurī - Te Reo Maori

authors: Carter, Jennifer

subjects: dog safety tips

abstract: This safety booklet for older children (about 8-9yrs) will provide the best level of understanding of how to avoid pet dog attacks which can injure, maim and even kill children. This research-based educational resource provides practical, valid and reliable guidelines for safe interaction between children and pet dogs in their homes or known to them. The content of this booklet has been drawn from ‘Stories with happy endings: Preventing pet dog attacks on children’, the 2007-2008 Summer Scholarship report prepared for the Child Accident Prevention Foundation of New Zealand (CAPFNZ). Obtain a copy of the report from http://hdl.handle.net/10289/768

text: Taku haumaru me te kurī: He aratohu mō te haumaru i waenga i te tamariki (8-9 pea ngā tau) me te kurī - Te Reo Maori This safety booklet for older children (about 8-9yrs) will provide the best level of understanding of how to avoid pet dog attacks which can injure, maim and even kill children. This research-based educational resource provides practical, valid and reliable guidelines for safe interaction between children and pet dogs in their homes or known to them. The content of this booklet has been drawn from ‘Stories with happy endings: Preventing pet dog attacks on children’, the 2007-2008 Summer Scholarship report prepared for the Child Accident Prevention Foundation of New Zealand (CAPFNZ). Obtain a copy of the report from http://hdl.handle.net/10289/768

year: 2008