Govind Krishnamoorthy - Academia.edu (original) (raw)
Uploads
Papers by Govind Krishnamoorthy
Education Sciences
Primary school teachers play a significant role in the support of children with mental health and... more Primary school teachers play a significant role in the support of children with mental health and developmental concerns, which can be comorbid or share similar symptomology. The literature suggests there is a deficiency in teacher mental health literacy (MHL), indicating that teachers often lack the knowledge and confidence to support childhood mental health. This study evaluated the success of the Mental Health Literacy for Educators Training Program for a subset of Queensland (QLD) primary school teachers, with a focus on the developmental areas of Attention Deficit Hyperactivity Disorder, Speech and Language Disorders, and Sensory Processing Disorders. The aim was to evaluate whether knowledge and confidence improved on training completion and to evaluate the satisfaction of the training. This research used a longitudinal design (pre- and post-training) with a sample of 81 QLD primary school teaching staff over a three-year period (2013–2015). The results showed that knowledge a...
Eating Disorders, Apr 17, 2023
IEEE Access
Autism Spectrum Disorder (ASD) is a type of neurodevelopmental disorder that affects the everyday... more Autism Spectrum Disorder (ASD) is a type of neurodevelopmental disorder that affects the everyday life of affected patients. Though it is considered hard to completely eradicate this disease, disease severity can be mitigated by taking early interventions. In this paper, we propose an effective framework for the evaluation of various Machine Learning (ML) techniques for the early detection of ASD. The proposed framework employs four different Feature Scaling (FS) strategies i.e., Quantile Transformer (QT), Power Transformer (PT), Normalizer, and Max Abs Scaler (MAS). Then, the feature-scaled datasets are classified through eight simple but effective ML algorithms like Ada Boost (AB), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Gaussian Naïve Bayes (GNB), Logistic Regression (LR), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA). Our experiments are performed on four standard ASD datasets (Toddlers, Adolescents, Children, and Adults). Comparing the classification outcomes using various statistical evaluation measures (Accuracy, Receiver Operating Characteristic: ROC curve, F1-score, Precision, Recall, Mathews Correlation Coefficient: MCC, Kappa score, and Log loss), the best-performing classification methods, and the best FS techniques for each ASD dataset are identified. After analyzing the experimental outcomes of different classifiers on feature-scaled ASD datasets, it is found that AB predicted ASD with the highest accuracy of 99.25%, and 97.95% for Toddlers and Children, respectively and LDA predicted ASD with the highest accuracy of 97.12% and 99.03% for Adolescents and Adults datasets, respectively. These highest accuracies are achieved while scaling Toddlers and Children with normalizer FS and Adolescents and Adults with the QT FS method. Afterward, the ASD risk factors are calculated, and the most important attributes are ranked according to their importance values using four different Feature Selection Techniques (FSTs) i.e., Info Gain Attribute Evaluator (IGAE), Gain Ratio Attribute Evaluator (GRAE), Relief F Attribute Evaluator (RFAE), and Correlation Attribute Evaluator (CAE). These detailed experimental evaluations indicate that proper finetuning of the ML methods can play an essential role in predicting ASD in people of different ages. We argue that the detailed feature importance analysis in this paper will guide the decision-making of healthcare practitioners while screening ASD cases. The proposed framework has achieved promising results compared to existing approaches for the early detection of ASD.
European Eating Disorders Review
Preventing School Failure: Alternative Education for Children and Youth
University of Southern Queensland, 2020
The SAGE Encyclopedia of Contemporary Early Childhood Education, Jul 29, 2016
University of Southern Queensland, 2020
University of Southern Queensland, 2020
University of Southern Queensland, 2020
University of Southern Queensland, 2020
University of Southern Queensland, 2020
University of Southern Queensland, 2020
University of Southern Queensland, 2020
University of Southern Queensland, 2020
International Journal of Environmental Research and Public Health
There is an increasing population of youths that report mental health issues. Research has shown ... more There is an increasing population of youths that report mental health issues. Research has shown that youths are reluctant to seek help for various reasons. A majority of those who do seek help are using digital mental health supports. Subsequently, efforts to promote youth mental health have focused on the use of digital applications as a means of overcoming barriers related to factors including stigma and lack of available services. The worldwide move toward recovery-oriented care led to emerging research on personal recovery amongst youths with mental health concerns. This study sought to address the need for recovery-oriented digital resources for youths. It utilised a qualitative design methodology to develop a rich interpretation of how youths are using digital interventions to support their mental health recovery journey. It sought to understand how existing digital applications are useful for youth recovery and identified characteristics associated with recovery and engageme...
University of Southern Queensland, 2020
Frontiers in Education
There is growing awareness of the impact of intergenerational trauma and community disadvantage o... more There is growing awareness of the impact of intergenerational trauma and community disadvantage on the educational achievement of Aboriginal and Torres Strait Islander (First Nations) children in Australia. Scholars have identified the need for culturally responsive and trauma-informed approaches to complement existing disciplinary and behavior support practices utilized in schools. This pilot research project explored the experiences of primary school teachers who were supported to implement trauma-informed practices in a regional primary school with a large number of First Nations students. Qualitative interviews with eight teachers were conducted after a 3-year (2017–2020) implementation of the Trauma-Informed Behavior Support (TIBS) program. Using a thematic analysis approach, the study identified the following themes: changes in teacher knowledge about the impact of intergenerational trauma, acknowledgment of the multi-systemic influences on student behavior difficulties, incre...
Education Sciences
Primary school teachers play a significant role in the support of children with mental health and... more Primary school teachers play a significant role in the support of children with mental health and developmental concerns, which can be comorbid or share similar symptomology. The literature suggests there is a deficiency in teacher mental health literacy (MHL), indicating that teachers often lack the knowledge and confidence to support childhood mental health. This study evaluated the success of the Mental Health Literacy for Educators Training Program for a subset of Queensland (QLD) primary school teachers, with a focus on the developmental areas of Attention Deficit Hyperactivity Disorder, Speech and Language Disorders, and Sensory Processing Disorders. The aim was to evaluate whether knowledge and confidence improved on training completion and to evaluate the satisfaction of the training. This research used a longitudinal design (pre- and post-training) with a sample of 81 QLD primary school teaching staff over a three-year period (2013–2015). The results showed that knowledge a...
Eating Disorders, Apr 17, 2023
IEEE Access
Autism Spectrum Disorder (ASD) is a type of neurodevelopmental disorder that affects the everyday... more Autism Spectrum Disorder (ASD) is a type of neurodevelopmental disorder that affects the everyday life of affected patients. Though it is considered hard to completely eradicate this disease, disease severity can be mitigated by taking early interventions. In this paper, we propose an effective framework for the evaluation of various Machine Learning (ML) techniques for the early detection of ASD. The proposed framework employs four different Feature Scaling (FS) strategies i.e., Quantile Transformer (QT), Power Transformer (PT), Normalizer, and Max Abs Scaler (MAS). Then, the feature-scaled datasets are classified through eight simple but effective ML algorithms like Ada Boost (AB), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Gaussian Naïve Bayes (GNB), Logistic Regression (LR), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA). Our experiments are performed on four standard ASD datasets (Toddlers, Adolescents, Children, and Adults). Comparing the classification outcomes using various statistical evaluation measures (Accuracy, Receiver Operating Characteristic: ROC curve, F1-score, Precision, Recall, Mathews Correlation Coefficient: MCC, Kappa score, and Log loss), the best-performing classification methods, and the best FS techniques for each ASD dataset are identified. After analyzing the experimental outcomes of different classifiers on feature-scaled ASD datasets, it is found that AB predicted ASD with the highest accuracy of 99.25%, and 97.95% for Toddlers and Children, respectively and LDA predicted ASD with the highest accuracy of 97.12% and 99.03% for Adolescents and Adults datasets, respectively. These highest accuracies are achieved while scaling Toddlers and Children with normalizer FS and Adolescents and Adults with the QT FS method. Afterward, the ASD risk factors are calculated, and the most important attributes are ranked according to their importance values using four different Feature Selection Techniques (FSTs) i.e., Info Gain Attribute Evaluator (IGAE), Gain Ratio Attribute Evaluator (GRAE), Relief F Attribute Evaluator (RFAE), and Correlation Attribute Evaluator (CAE). These detailed experimental evaluations indicate that proper finetuning of the ML methods can play an essential role in predicting ASD in people of different ages. We argue that the detailed feature importance analysis in this paper will guide the decision-making of healthcare practitioners while screening ASD cases. The proposed framework has achieved promising results compared to existing approaches for the early detection of ASD.
European Eating Disorders Review
Preventing School Failure: Alternative Education for Children and Youth
University of Southern Queensland, 2020
The SAGE Encyclopedia of Contemporary Early Childhood Education, Jul 29, 2016
University of Southern Queensland, 2020
University of Southern Queensland, 2020
University of Southern Queensland, 2020
University of Southern Queensland, 2020
University of Southern Queensland, 2020
University of Southern Queensland, 2020
University of Southern Queensland, 2020
University of Southern Queensland, 2020
International Journal of Environmental Research and Public Health
There is an increasing population of youths that report mental health issues. Research has shown ... more There is an increasing population of youths that report mental health issues. Research has shown that youths are reluctant to seek help for various reasons. A majority of those who do seek help are using digital mental health supports. Subsequently, efforts to promote youth mental health have focused on the use of digital applications as a means of overcoming barriers related to factors including stigma and lack of available services. The worldwide move toward recovery-oriented care led to emerging research on personal recovery amongst youths with mental health concerns. This study sought to address the need for recovery-oriented digital resources for youths. It utilised a qualitative design methodology to develop a rich interpretation of how youths are using digital interventions to support their mental health recovery journey. It sought to understand how existing digital applications are useful for youth recovery and identified characteristics associated with recovery and engageme...
University of Southern Queensland, 2020
Frontiers in Education
There is growing awareness of the impact of intergenerational trauma and community disadvantage o... more There is growing awareness of the impact of intergenerational trauma and community disadvantage on the educational achievement of Aboriginal and Torres Strait Islander (First Nations) children in Australia. Scholars have identified the need for culturally responsive and trauma-informed approaches to complement existing disciplinary and behavior support practices utilized in schools. This pilot research project explored the experiences of primary school teachers who were supported to implement trauma-informed practices in a regional primary school with a large number of First Nations students. Qualitative interviews with eight teachers were conducted after a 3-year (2017–2020) implementation of the Trauma-Informed Behavior Support (TIBS) program. Using a thematic analysis approach, the study identified the following themes: changes in teacher knowledge about the impact of intergenerational trauma, acknowledgment of the multi-systemic influences on student behavior difficulties, incre...