Online Learning in Bangladesh during COVID-19: Perceived Effectiveness, Challenges, and Suggestions

Rahaman, Md. Saidur; Moral, Iqbal Hossain; Rahman, Dr. Md. Mizanur; Sahabuddin, Mohammad; Samuel, Adedeji Babatunji

doi:10.52631/jemds.v1i3.51

Cited by 10 publications

(18 citation statements)

References 15 publications

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“…From the Mental health of hawkers during COVID-19 respondents, people such as "O" and "P" had 3-6 members, including partners in their family, and they were solely responsible for looking after them. The pandemic situation made their earnings reduced, and they were not able to fulfil their family members' needs (Rahaman et al, 2021a(Rahaman et al, , 2021b(Rahaman et al, , 2021c(Rahaman et al, , 2021d. They had the pressure to feed their family, and they were not able to do that.…”

Section: Discussion Of the Findingsmentioning

confidence: 99%

“…But, as an informal societal community, hawkers struggle to manage their daily necessities (Bhuiyan et al, 2020). After the spread of the COVID-19 pandemic, the government enforced the lockdown, and all business sectors were shut down, including educational and business organizations (Rahaman et al, 2021a(Rahaman et al, , 2021b(Rahaman et al, , 2021c(Rahaman et al, , 2021d; hawker businesses are no exception. The livelihoods of the hawker community have degraded since then.…”

Section: Background Of the Studymentioning

confidence: 99%

“…Some people were worried about what they would do if the virus attacked them, as they could not afford proper medical facilities (Salamzadeh and Dana, 2021). Others were afraid to lose their loved ones, and many suffered from hunger (Rahaman et al, 2021a(Rahaman et al, , 2021b(Rahaman et al, , 2021c(Rahaman et al, , 2021dPaul et al, 2021). Also, they fail to borrow money because no one wants to help them.…”

Section: Hawkers In Bangladesh During Covid-19mentioning

confidence: 99%

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Mental health of hawkers during COVID-19: a marginal community in Bangladesh

Moral

Rahaman

Imran

et al. 2022

JEC

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Purpose Globally, the COVID-19 pandemic impacts the financial condition and the mental health of millions of workers from various informal sectors. This study aims to look into the hawkers’ community’s mental health and living conditions in Bangladesh during COVID-19. Design/methodology/approach The researchers have applied the purposive sampling technique to choose ten hawkers from Khulna city, a district in the southern region of Bangladesh. An in-depth interview was taken in the Bengali language in an unstructured manner and lasted 30–40 min per respondent. Findings The findings showed that the Hawkers’ income reduced, and specifically, during the pandemic, they had earned half of what they usually made before. Besides, they could not open their stores because law enforcement agencies imposed restrictions on opening business centres during the lockdown except for some emergency necessities shops. This restriction led the hawkers to stop selling their products because there was a high chance of spreading the virus through the products they sold. Due to income reduction, they had to eat cheap food, which caused their health problems. Consequently, this community mentally got depressed. Practical implications Policymakers in Bangladesh might think about enacting more effective measures to provide some extrinsic and intrinsic support in improving the mental health of the hawkers’ community. Originality/value To the best of the authors’ knowledge, this is the first study on the mental of the hawkers’ community during COVID-19.

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Section: Discussion Of the Findingsmentioning

confidence: 99%

Section: Background Of the Studymentioning

confidence: 99%

Section: Hawkers In Bangladesh During Covid-19mentioning

confidence: 99%

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Mental health of hawkers during COVID-19: a marginal community in Bangladesh

Moral

Rahaman

Imran

et al. 2022

JEC

Self Cite

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“…Arcade, Puzzle) into the Games category. Besides, during the pandemic, students attended classes through apps like Zoom and Google Meet [73]. Hence, we kept such apps in the Education category due to the student profession of all participants.…”

Section: Data Of the App Categoriesmentioning

confidence: 99%

“…In our work, Zoom, Google Meet, Google Classroom, etc. apps were in this category, and in Bangladesh, these apps have been used for online learning since the start of the pandemic [73].…”

Section: The Implication Of the Findingsmentioning

confidence: 99%

A Minimal and Faster System to Identify Depression Through Smartphone: An Explainable Machine Learning-Based Approach

Ahmed¹,

Ahmed²

2023

Preprint

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Background: The robust pervasive device-based existing systems to detect depression developed in recent years requiring data collected over a long period of time may not be effective in cases where early detection is crucial. In addition, due to the requirement of running systems in the background for prolonged periods, the existing promising systems may not be resource efficient. As a result, these systems can be infeasible in low-resource settings.Objective: Our main objective was to develop a minimal system to identify depression that works on data retrieved in the fastest possible time. Another objective of this study was to explain the machine learning (ML) models which performed best in identifying depression.Methods: We developed a faster tool that retrieves the past 7 days’ app usage data in a second (mean=0.31 second, SD=1.10 second) . In our study, 100 students from Bangladesh participated and our tool collected their app usage data and responses to the Patient Health Questionnaire-9 (PHQ-9) scale. To identify the depressed and non-depressed participants, we developed a diverse set of ML models including linear, tree-based, and neural network-based models. We selected the important features by the Stable approach along with the 3 main types of feature selection (FS) approaches: Filter, Wrapper, and Embedded. We developed and validated the models using nested cross validation method. Additionally, we explained the best ML models through the SHapley Additive exPlanations (SHAP) method.Results: Leveraging only the app usage data retrieved in a second, our Light GBM model using the Stable approach selected features identified 82.4% depressed correctly (precision=75%, F1 score=78.5%). Moreover, after comprehensive exploration, we presented a parsimonious Stacking model where around 5 features selected by the all-relevant FS approach Boruta were used in each iteration of validation and had a maximum precision of 77.4% (balanced accuracy=77.95%). Feature importance analysis presents app usage behavioral markers containing the diurnal usage patterns as more important compared to the aggregated data-based markers. Apart from these, SHAP analysis on our best models presented the behavioral markers that have a relation with depression. For instance, the non-depressed students’ spending time on Education apps was higher on weekdays while depressed students used a higher number of Photo & Video apps and also had a higher deviation in using Photo & Video apps over the day of the weekend.Conclusions: Due to the faster and minimalistic nature, our system may make a worthwhile contribution to identifying depression in underdeveloped and developing regions. In addition, our detailed discussion about the implication of findings can facilitate the development of resource insensitive systems, in better understanding the depressed students and taking steps in intervention.

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