RETRACTED ARTICLE: Prediction of individual learning style in e-learning systems: opportunities and limitations in dental education

Movchun, Vasily; Lushkov, Richard; Pronkin, Nikolay

doi:10.1007/s10639-020-10372-4

Cited by 16 publications

(9 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…ML algorithms are widely used for prediction purposes and applied to provide solutions to questions such as global solar radiation [9], accuracy in determining the mortality rate in COVID-19 patients [10], and efficient processes for manufacturing industries [11]. In addition, ML algorithms assist the educational sector to evaluate student performance [12], forecast student dropout rates in any course [13], and understand students' unique learning styles [14]. Due to ML's vast and dynamic implementation and its capability to learn from any dataset, and predict and classify future transactions, we have selected multiple ML algorithms for this study.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

et al. 2021

View full text Add to dashboard Cite

Electronic learning management systems provide live environments for students and faculty members to connect with their institutional online portals and perform educational activities virtually. Although modern technologies proactively support these online sessions, students’ active participation remains a challenge that has been discussed in previous research. Additionally, one concern for both parents and teachers is how to accurately measure student performance using different attributes collected during online sessions. Therefore, the research idea undertaken in this study is to understand and predict the performance of the students based on features extracted from electronic learning management systems. The dataset chosen in this study belongs to one of the learning management systems providing a number of features predicting student’s performance. The integrated machine learning model proposed in this research can be useful to make proactive and intelligent decisions according to student performance evaluated through the electronic system’s data. The proposed model consists of five traditional machine learning algorithms, which are further enhanced by applying four ensemble techniques: bagging, boosting, stacking, and voting. The overall F1 scores of the single models are as follows: DT (0.675), RF (0.777), GBT (0.714), NB (0.654), and KNN (0.664). The model performance has shown remarkable improvement using ensemble approaches. The stacking model by combining all five classifiers has outperformed and recorded the highest F1 score (0.8195) among other ensemble methods. The integration of the ML models has improved the prediction ratio and performed better than all other ensemble approaches. The proposed model can be useful for predicting student performance and helping educators to make informed decisions by proactively notifying the students.

show abstract

Section: Introductionmentioning

confidence: 99%

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In both dental education [ 31 , 32 ] and medical education [ 33 ], distance education has been shown to be as effective as traditional in-person education. Therefore, blended learning, which combines distance education and conventional teaching methods, was advocated even before the COVID-19 pandemic [ 34 , 35 ].…”

Section: Resultsmentioning

confidence: 99%

Challenges and Opportunities for Dental Education from COVID-19

Cheng

Wang

2022

Dentistry Journal

View full text Add to dashboard Cite

With the ongoing COVID-19 pandemic, dental education has been profoundly affected by this crisis. First of all, COVID-19 brought physical and psychological health problems to dental students and educators. In addition, both non-clinical teaching and clinical-based training experienced challenges, ranging from fully online educational content to limited dental training, students’ research was delayed in achieving project milestones and there was hesitancy in respect of the COVID-19 vaccine. On the other hand, the COVID-19 pandemic has increased the demand for teledentistry and dental emergency treatment, and brought awareness of the advantages and high-speed development of distance education. This review aims to present these challenges and opportunities for dental education, and suggest how dental institutions should prepare for the future demand for dental education.

show abstract

“…Cloud computing is needed in every urban infrastructure today, including transportation, energy, economic and social development, the environment, health and education [20]. Without cloud integration, it is almost impossible to visualize the infrastructural and social modernization of a city, aimed at improving public safety, efficient energy consumption, and developing a cohesive society.…”

Section: Methodsmentioning

confidence: 99%

Assessment of the Impact of Cloud Technologies on Social Life in the Era of Digitalization

Panfilova

Lukyanova

Pronkin

et al. 2021

Int. J. Interact. Mob. Technol.

Self Cite

View full text Add to dashboard Cite

<span lang="EN-US">Due to the expansion of new digital trends in the strategic development of territorial units, it became necessary to structure the socio-economic effects from the introduction of innovative technologies grounded on cloud computing. Realized the identification of cloud technologies impact on the social life in the era of digitalization, an online conference was held between representatives of government, business, science, and education of the Republics of Karelia, Komi, Adygea, Dagestan, Ingushetia, Kalmykia, Karachay-Cherkessia, Krasnodar Krai and Stavropol Krai (Russian Federation). In the context of innovative possibilities of platform economy, in discussion were identified the key effects of the cloud solutions implementation in the Russian Federation regions development prospect strategy. Namely, development of social and human capital, improvement of the quality and standard of living in a region, improvement of social interaction, development of social constructivism, economic potential, and competitiveness of social capital. It has been reported that the positive social effects of cloud technologies at the level of a territorial unit are achieved by increasing digital literacy of the population and opening up new opportunities and platforms for social interaction and constructivism; providing the population with convenient services and open data for participation in active social, economic and political life. Cloud technologies help to form a digital educational environment, modernize educational programs for the preparation of high-quality labor resources, develop digital infrastructure of individual settlements, create user content to preserve socio-cultural identity, and popularize a region in the global information space. Revealed that the possibilities of cloud technologies increase the population quality of living and ensure the growth of the efficiency and productivity indicators of labor resources. The listed above are achieved by expanding the marketing capabilities of business structures, providing them with new business models and digital services; optimization of resource management, digitization of business processes, and platform organization of labor. The capabilities of cloud solutions allow for the formation of flexible and effective interregional and international cooperation in a region, having a positive impact on regional economy and social capital development.</span>

show abstract

RETRACTED ARTICLE: Prediction of individual learning style in e-learning systems: opportunities and limitations in dental education

Cited by 16 publications

References 27 publications

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

Challenges and Opportunities for Dental Education from COVID-19

Assessment of the Impact of Cloud Technologies on Social Life in the Era of Digitalization

Contact Info

Product

Resources

About