Search results for: infantile autism

Authors: Sara J. Mills, Sally Howell

Abstract:

The field of special education has long struggled to bridge the research to practice gap. There is ample evidence from research of effective strategies for students with special needs, but these strategies are not routinely implemented in schools in ways that yield positive results for students. In recent years, the field of special education has turned its focus to implementation science. That is, discovering effective methods of implementing evidence-based practices in school settings. Teacher training is a critical factor in implementation. This study aimed to successfully implement Schema-Based Instruction (SBI) for math problem solving in four classrooms in a special primary school serving students with language deficits, including students with Autism Spectrum Disorders (ASD) and Intellectual Disabilities (ID). Using an action research design that allowed for adjustments and modification to be made over the year-long study, two cohorts of teachers across the school were trained and supported in six-week learning cycles to implement SBI in their classrooms. The learning cycles included a one-day training followed by six weeks of one-on-one or team coaching and three fortnightly cohort group meetings. After the first cohort of teachers completed the learning cycle, modifications and adjustments were made to lesson materials in an attempt to improve their effectiveness with the second cohort. Fourteen teachers participated in the study, including master special educators (n=3), special education instructors (n=5), and classroom assistants (n=6). Thirty-one students participated in the study (21 boys and 10 girls), ranging in age from 5 to 12 years (M = 9 years). Twenty-one students had a diagnosis of ASD, 20 had a diagnosis of mild or moderate ID, with 13 of these students having both ASD and ID. The remaining students had diagnosed language disorders. To evaluate the effectiveness of the implementation approach, both student and teacher data was collected. Student data included pre- and post-tests of math word problem solving. Teacher data included fidelity of treatment checklists and pre-post surveys of teacher attitudes and efficacy for teaching problem solving. Finally, artifacts were collected throughout the learning cycle. Results from cohort 1 and cohort 2 revealed similar outcomes. Students improved in the number of word problems they answered correctly and in the number of problem-solving steps completed independently. Fidelity of treatment data showed that teachers implemented SBI with acceptable levels of fidelity (M = 86%). Teachers also reported increases in the amount of time spent teaching problem solving, their confidence in teaching problem solving and their perception of students’ ability to solve math word problems. The artifacts collected during instruction indicated that teachers made modifications to allow their students to access the materials and to show what they knew. These findings are in line with research that shows student learning can improve when teacher professional development is provided over an extended period of time, actively involves teachers, and utilizes a variety of learning methods in classroom contexts. Further research is needed to evaluate whether these gains in teacher instruction and student achievement can be maintained over time once the professional development is completed.

Keywords: implementation science, mathematics problem solving, research-to-practice gap, schema based instruction

Procedia PDF Downloads 104

Authors: Nayer Mofidtabatabaei

Abstract:

Advancements in artificial intelligence (AI) have transformed various fields, including psychology and behavioral sciences. This paper explores the diverse ways in which AI is applied to enhance research, diagnosis, therapy, and understanding of human behavior and mental health. We discuss the potential benefits and challenges associated with AI in these fields, emphasizing the ethical considerations and the need for collaboration between AI researchers and psychological and behavioral science experts. Artificial Intelligence (AI) has gained prominence in recent years, revolutionizing multiple industries, including healthcare, finance, and entertainment. One area where AI holds significant promise is the field of psychology and behavioral sciences. AI applications in this domain range from improving the accuracy of diagnosis and treatment to understanding complex human behavior patterns. This paper aims to provide an overview of the various AI applications in psychological and behavioral sciences, highlighting their potential impact, challenges, and ethical considerations. Mental Health Diagnosis AI-driven tools, such as natural language processing and sentiment analysis, can analyze large datasets of text and speech to detect signs of mental health issues. For example, chatbots and virtual therapists can provide initial assessments and support to individuals suffering from anxiety or depression. Autism Spectrum Disorder (ASD) Diagnosis AI algorithms can assist in early ASD diagnosis by analyzing video and audio recordings of children's behavior. These tools help identify subtle behavioral markers, enabling earlier intervention and treatment. Personalized Therapy AI-based therapy platforms use personalized algorithms to adapt therapeutic interventions based on an individual's progress and needs. These platforms can provide continuous support and resources for patients, making therapy more accessible and effective. Virtual Reality Therapy Virtual reality (VR) combined with AI can create immersive therapeutic environments for treating phobias, PTSD, and social anxiety. AI algorithms can adapt VR scenarios in real-time to suit the patient's progress and comfort level. Data Analysis AI aids researchers in processing vast amounts of data, including survey responses, brain imaging, and genetic information. Privacy Concerns Collecting and analyzing personal data for AI applications in psychology and behavioral sciences raise significant privacy concerns. Researchers must ensure the ethical use and protection of sensitive information. Bias and Fairness AI algorithms can inherit biases present in training data, potentially leading to biased assessments or recommendations. Efforts to mitigate bias and ensure fairness in AI applications are crucial. Transparency and Accountability AI-driven decisions in psychology and behavioral sciences should be transparent and subject to accountability. Patients and practitioners should understand how AI algorithms operate and make decisions. AI applications in psychological and behavioral sciences have the potential to transform the field by enhancing diagnosis, therapy, and research. However, these advancements come with ethical challenges that require careful consideration. Collaboration between AI researchers and psychological and behavioral science experts is essential to harness AI's full potential while upholding ethical standards and privacy protections. The future of AI in psychology and behavioral sciences holds great promise, but it must be navigated with caution and responsibility.

Keywords: artificial intelligence, psychological sciences, behavioral sciences, diagnosis and therapy, ethical considerations

Procedia PDF Downloads 38

Authors: Paul J. McKay

Abstract:

Large-scale genome-wide association studies (GWAS) investigating genetic contributions to sexual orientation and gender identity are largely lacking and may reduce stigma experienced in the LGBTQ+ community by providing an underlying biological explanation for queerness. While there is a growing consensus within the scientific community that genetic makeup contributes – at least in part – to sexual orientation and gender identity, there is a marked lack of genomics research exploring polygenic contributions to queerness. Based on recent (2019) findings from a large-scale GWAS investigating the genetic architecture of same-sex sexual behavior, and various additional peer-reviewed publications detailing novel insights into the molecular mechanisms of sexual orientation and gender identity, we hypothesize that sexual orientation and gender identity are complex, multifactorial, and polygenic; meaning that many genetic factors contribute to these phenomena, and environmental factors play a possible role through epigenetic modulation. In recent years, large-scale GWAS studies have been paramount to our modern understanding of many other complex human traits, such as in the case of autism spectrum disorder (ASD). Despite possible benefits of such research, including reduced stigma towards queer people, improved outcomes for LGBTQ+ in familial, socio-cultural, and political contexts, and improved access to healthcare (particularly for trans populations); important risks and considerations remain surrounding this type of research. To mitigate possibilities such as invalidation of the queer identities of existing LGBTQ+ individuals, genetic discrimination, or the possibility of euthanasia of embryos with a genetic predisposition to queerness (through reproductive technologies like IVF and/or gene-editing in utero), we propose a community-engaged research (CER) framework which emphasizes the privacy and confidentiality of research participants. Importantly, the historical legacy of scientific research attempting to pathologize queerness (in particular, falsely equating gender variance to mental illness) must be acknowledged to ensure any future research conducted in this realm does not propagate notions of homophobia, transphobia or stigma against queer people. Ultimately, in a world where same-sex sexual activity is criminalized in 69 UN member states, with 67 of these states imposing imprisonment, 8 imposing public flogging, 6 (Brunei, Iran, Mauritania, Nigeria, Saudi Arabia, Yemen) invoking the death penalty, and another 5 (Afghanistan, Pakistan, Qatar, Somalia, United Arab Emirates) possibly invoking the death penalty, the importance of this research cannot be understated, as finding a biological basis for queerness would directly oppose the harmful rhetoric that “being LGBTQ+ is a choice.” Anti-trans legislation is similarly widespread: In the United States in 2022 alone (as of Oct. 13), 155 anti-trans bills have been introduced preventing trans girls and women from playing on female sports teams, barring trans youth from using bathrooms and locker rooms that align with their gender identity, banning access to gender affirming medical care (e.g., hormone-replacement therapy, gender-affirming surgeries), and imposing legal restrictions on name changes. Understanding that a general lack of knowledge about the biological basis of queerness may be a contributing factor to the societal stigma faced by gender and sexual orientation minorities, we propose the initiation of large-scale GWAS studies investigating the genetic basis of gender identity and sexual orientation.

Keywords: genome-wide association studies (GWAS), sexual and gender minorities (SGM), polygenicity, community-engaged research (CER)

Procedia PDF Downloads 48