Parent resolution of diagnosis and intervention fidelity in a parent-delivered intervention for pre-school children with autism: A mixed methods study

Background: There is growing interest in parent-delivered interventions (PDI) for children with autism. Treatment fidelity has been associated with child outcomes in PDI but little is known about what impacts fidelity. One factor not previously examined is parents' resolution to the autism diagnosis which involves adjusting expectations about the child and sensitively responding to their cues, strengths and needs. Relatively little is known about resolution in the context of autism but there is evidence of an association between resolution and parent wellbeing. Method: The study adopted a mixed methods approach to examine whether there is an association between PDI fidelity and parent resolution to diagnosis, and whether resolution is associated with parent wellbeing. Parents of 31 preschool children diagnosed with autism who attended up to 12 Parent-delivered Early Start Denver Model (P-ESDM) coaching sessions participated in the study. A thematic analysis of parent interviews was also undertaken to identify themes raised by resolved and unresolved parents in interviews. Results: There was no difference in overall mean fidelity scores between resolved and unresolved parents. Those classified as resolved had lower depression scores and parenting stress scores than parents classified as unresolved. The qualitative analysis revealed that parents' perceptions of their child's progress and their hopes for the future appeared to distinguish resolved and unresolved parents. Conclusions: Findings suggest that parent wellbeing and child progress may predict resolution which was not related to parent treatment fidelity in this study. Parent wellbeing and resolution status should be assessed at entry to PDI.

Automatic Question Extractor and Answer Detector in a Recorded Webinar

Online learning becomes the primary method of education due to the novel coronavirus (COVID 19). This research paper describes the automatic extraction of domain-related questions in a lecture video, identifies the answers given by the lecturer for both voice-based questions and chat questions. The paper also presents a method to identify whether a lecturer gave a valid answer for the chat-based questions in the later section of the video. Additionally, this paper describes the approach to identify the most accurate solutions from the custom search engine-based responses. © 2022 IEEE.

Do Pandemic Related Datasets with High Artificial Control Still Follow the Benford’s Law?

Benford’s Law (BL) is being used extensively in research for several purposes including for the detection of potential manipulations of the data to detect fraud since datasets tend to follow the Benford’s distribution when they occur naturally without artificial control. The COVID-19 pandemic has heavily impacted business and non-business-related activities. Datasets related to the pandemic are being used in many different analyses to arrive at different conclusions. However, the credibility of the results and conclusions depend heavily on the accuracy of the datasets. The COVID-19 related datasets are obvious results of intense human intervention and artificial control efforts;therefore, the question arises as to whether Benford’s analysis can still be used to detect anomalous datasets among them? This research uses several publicly available datasets and uses predictive analytics to perform the Benford’s analysis. The applicability of BL is first verified using a regular dataset occurred prior to the pandemic, and then applied on COVID-19 related datasets to test the research hypothesis. The results demonstrate that even the datasets with sufficiently large sample sizes with considerable human intervention and artificial control follow the Benford’s distribution and that Benford’s analysis can still detect the anomalous datasets. The findings are anticipated to be useful for the data analysts and researchers and adds to the current literature gap. This paper may also serve as a class case study for the academia teaching data analytics. © IEOM Society International.

GENERAL SYSTEMS THEORY BASED CONCEPTUAL FRAMEWORK FOR RESTRUCTURING THE HIGHER EDUCATION INSTITUTION SYSTEM

With the sudden changes brought about with the COVID-19 pandemic, primarily the new legitimacy of online education, the traditional college and university boards are all actively discussing how the institutions can reform themselves in a strategic sense. This article presents a conceptual framework for viewing education institutions as an open system using the General Systems Theory (GST) and discusses how it could help improve the higher education system. The author concludes that the education institutions must carefully select the institution's components;should carefully and strategically manage the internal and external stakeholder network including all communications and informed decisions;accept that change of student perceptions is a norm and promote agility in the system operations, and also highlights that it is natural for systems to have specialized, outperforming departments/sections. The concepts discussed here could be further researched and would hopefully be of use for policy makers and administrators of higher education systems, while the author also wishes to spark a conversation amongst the citizens. © American Society for Engineering Management, 2021