Dropout management in online learning systems

Dash, Rupanwita; Ranjan, Kumar Rakesh; Rossmann, Alexander

doi:10.1080/0144929x.2021.1910730

Cited by 9 publications

(3 citation statements)

References 106 publications

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“…This answers the direct impact of the outbreak of the pandemic, as reported by literature ( McKinsey and Company, 2020 ). Since this outbreak implied the transition to online education, which is closely related to higher dropout rates than face-to-face programs ( Dash et al., 2021 ; Grau-Valldosera and Minguillón, 2014 ), the results obtained can be explained.…”

Section: Resultsmentioning

confidence: 94%

“…One of the main challenges of online education is student dropout. Online programs have shown higher dropout rates than face-to-face programs ( Dash et al., 2021 ; Grau-Valldosera and Minguillón, 2014 ). There are many conceptions for the term “student dropout” ( Xavier and Meneses, 2020 ), but all of them are often related to the variable time ( Grau-Valldosera and Minguillón, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

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Exploring the relationship between mental health-related problems and undergraduate student dropout: A case study within a civil engineering program

Savio

Velarde

Pachas

2022

Heliyon

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Section: Resultsmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Exploring the relationship between mental health-related problems and undergraduate student dropout: A case study within a civil engineering program

Savio

Velarde

Pachas

2022

Heliyon

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“…The main goal of both works is to engage learners. Dash et al (2021) examined the role of communication from users on dropout from digital learning systems. The findings indicate a "main effect of negative sentiment on dropout rates but no effect of positive sentiment on preventing dropout behavior.…”

Section: Related Workmentioning

confidence: 99%

Using Sentiment Analysis to Identify Student Emotional State to Avoid Dropout in E-Learning

Bóbó

Campos

Ströele

et al. 2022

International Journal of Distance Education Technologies

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Dropping out of school comes from a long-term disengagement process with social and economic consequences. Being able to predict students' behavior earlier can minimize their failures and disengagement. This article presents the SASys architecture, based on a lexical approach and a polarized frame network. Its main goal is to define the author's sentiment in texts and increase the assertiveness of detecting the sentence's emotional state by adding authors' information and preferences. The author's emotional state begins with the phrase extraction from Virtual Learning Environments; then, pre-processing techniques are applied in the text, which is submitted to the complex frame network to identify words with polarity and the author's text sentiment. The flow ends with the identification of the author's emotional state. The proposal was evaluated by a case study, applying the Sentiment Analysis approach to the students' school dropout problem. The results point to the feasibility of the proposal for asserting the student's emotional state and detection of students' risks of dropout.

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Investment in employee developmental climate and employees' continued online learning behaviors: A social influence perspective

Tan,

Zhu,

Zhang

2024

Human Resource Management

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In the contemporary digital age, continued online learning behaviors have become indispensable for fostering employee development. However, the learning and development literature predominantly focuses on traditional instructor‐led approaches. It remains unclear how organizations can shape employees' learning behaviors, particularly in the context of online learning. The current study clarifies this underexplored research area by investigating how organizational investment in employee developmental climate (IEDC) can promote employees' continued online learning behaviors. Drawing on social influence theory and employing a multilevel research design, we find empirical support for our research model, which specifies that IEDC positively affects employees' continued online learning behaviors through the effect of normative pressure. The findings also reveal the moderating role of online learning facilitating conditions. Specifically, in firms characterized by higher levels of online learning facilitating conditions, the positive relationship between IEDC and normative pressure becomes more pronounced when compared to organizations with lower levels of such facilitating conditions.

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Dropout management in online learning systems

Cited by 9 publications

References 106 publications

Exploring the relationship between mental health-related problems and undergraduate student dropout: A case study within a civil engineering program

Exploring the relationship between mental health-related problems and undergraduate student dropout: A case study within a civil engineering program

Using Sentiment Analysis to Identify Student Emotional State to Avoid Dropout in E-Learning

Investment in employee developmental climate and employees' continued online learning behaviors: A social influence perspective

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