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The role of chunking in drawing Rey complex figure

Obaidellah UH, Cheng PC

Percept Mot Skills, 2015 Apr;120(2):535-55.
PMID: 25706345 DOI: 10.2466/24.PMS.120v17x6

The study investigated the effects of chunking and perceptual patterns that guide the drawings of Rey complex figure. Ten adult participants (M age=22.2 yr., SD=4.1) reproduced a single stimulus in four drawing modes including delayed recall, tracing, copying, and immediate recall across 10 sessions producing a total of 400 trials. It was hypothesized that the effect of chunking is most obvious in the free recall tasks than in the tracing or copying tasks. Measures such as pauses, patterns of drawings, and transitions among patterns of drawings suggested that participants used chunking to aid rapid learning of the diagram. The analysis of the participants' sequence of chunk production further revealed that they used a spatial schema to organize the chunks. Findings from this study provide additional evidence to support prior studies that claim graphical information is hierarchically organized.
Fulltext Exploring the opinions of secondary school students on the strengths and weaknesses of the school dental service in Selangor, Malaysia: a qualitative study

Ab Mumin N, Yusof ZYM, Marhazlinda J, Obaidellah U

BMC Oral Health, 2021 08 11;21(1):394.
PMID: 34380484 DOI: 10.1186/s12903-021-01741-7

BACKGROUND: The Malaysian School Dental Service (SDS) was introduced to provide systematic and comprehensive dental care to school students. The service encompasses promotive, preventive, and, curative dental care. This study aimed to undertake a process evaluation of the SDS based on the perspectives of government secondary school students in Selangor, Malaysia.
METHODS: The study adopted a qualitative approach to explore the opinions of secondary school students on the SDS implementation in their schools. Data from focus group discussions involving Form Two (14-year-olds) and Form Four (16-year-olds) students from the selected schools were transcribed verbatim and coded using the NVivo software before framework method analysis was conducted.
RESULTS: Among the strengths of the SDS were the convenience for students to undergo annual oral examination and dental treatment without having to visit dental clinics outside the school. The SDS also reduced possible financial burdens resulting from dental treatment costs, especially among students from low-income families. Furthermore, SDS helped to improve oral health awareness. However, the oral health education provided by the SDS personnel was deemed infrequent while the content and method of delivery were perceived to be less interesting. The poor attitude of the SDS personnel was also reported by the students.
CONCLUSION: The SDS provides effective and affordable dental care to secondary school students. However, the oral health promotion and education activities need to be improved to keep up with the evolving needs of the target audience.
Motivators and barriers to oral hygiene self-care among adolescents in Malaysia: A qualitative study

Ab Mumin N, Yusof ZYM, Marhazlinda J, Obaidellah U

Int J Dent Hyg, 2021 Oct 10.
PMID: 34628709 DOI: 10.1111/idh.12556

OBJECTIVE: Having good oral hygiene self-care, especially a regular toothbrushing habit will promote lifelong oral health. Therefore, understanding the factors that influence an adolescent's oral hygiene behaviour is important in developing effective oral health programmes for this age group. This study aimed to explore the motivators and barriers to adolescents' oral hygiene self-care by exploring the perspectives of secondary school students from three government schools in the state of Selangor, Malaysia.
METHODS: Focus group discussions (FGD) were conducted with Form 2 (14-years-old) and Form 4 (16-years-old) students from selected secondary schools in Selangor using a semi-structured topic guide until data saturation was reached. Data were transcribed verbatim and analysed using framework method analysis.
RESULTS: A total of 10 FGDs were conducted involving 77 adolescents. The motivators for good oral hygiene self-care were appearance, fear of oral disease, consequences of oral disease and past toothache experience. The barriers for oral hygiene self-care were poor attitude towards oral care, lack of confidence in toothbrushing skills, snacking habit and the taste of toothpaste.
CONCLUSION: Understanding the motivators and barriers to adolescents' oral hygiene self-care is the first step in designing effective oral health education messages. The findings from this study can be used as a guide for oral health education programmes and development of materials that fulfil the needs of the adolescent population.
Fulltext A novel online multi-task learning for COVID-19 multi-output spatio-temporal prediction

Wu Z, Loo CK, Obaidellah U, Pasupa K

Heliyon, 2023 Aug;9(8):e18771.
PMID: 37636411 DOI: 10.1016/j.heliyon.2023.e18771

In light of the ongoing COVID-19 pandemic, predicting its trend would significantly impact decision-making. However, this is not a straightforward task due to three main difficulties: temporal autocorrelation, spatial dependency, and concept drift caused by virus mutations and lockdown policies. Although machine learning has been extensively used in related work, no previous research has successfully addressed all three challenges simultaneously. To overcome this challenge, we developed a novel online multi-task regression algorithm that incorporates a chain structure to capture spatial dependency, the ADWIN drift detector to adapt to concept drift, and the lag time series feature to capture temporal autocorrelation. We conducted several comparative experiments based on the number of daily confirmed cases in 20 areas in California and affiliated cities. The results from our experiments demonstrate that our proposed model is superior in adapting to concept drift in COVID-19 data and capturing spatial dependencies across various regions. This leads to a significant improvement in prediction accuracy when compared to existing state-of-the-art batch machine learning methods, such as N-Beats, DeepAR, TCN, and LSTM.