Improving the prediction accuracy in blended learning environment using synthetic minority oversampling technique

Dimić, Gabrijela; Rančić, Dejan; Maček, Nemanja; Spalević, Petar; Drąsutė, Vida

doi:10.1108/idd-08-2018-0036

Cited by 8 publications

(4 citation statements)

References 24 publications

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“…As custom corporate eLearning content is built on demand, it is tailored for specific requirements and corresponds to certain corporate practices (Dimic et al, 2019).…”

Section: Tailoring For Custom Needsmentioning

confidence: 99%

Enabling digital growth through continuous education of project managers: A framework for collaborative, complementary, sustained, and simultaneous learning in software engineering organizations

Atanasijević,

Zahar,

Rančić

et al. 2023

Vojnotehnički glasnik

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Introduction/purpose: This paper introduces a novel PMO EDUCT framework that encompasses Collaborative, Complementary, Sustained, and Simultaneous learning explicitly tailored for the dynamic landscape of software engineering. This paper contributes to the ongoing discourse on education and professional development in the digital era by illuminating the challenges faced, the framework developed, and the measurable outcomes achieved. The presented framework underscores the need for agility, adaptability, and a relentless pursuit of knowledge as fundamental tenets for success in an ever-evolving landscape. Methods: Focusing on a large software engineering organization, this study explores the multifaceted aspects of project management education, methodology enhancement, and team optimization. The proposed methodology combines qualitative and quantitative approaches to explore the educational process area in a global organization and generate a flexible and measurable framework to improve corporate knowledge growth. The framework aims to provide learners the most flexibility about learning paths, schedules, collaboration, and best practice exchange while guaranteeing proper alignment with frequent market changes and emerging PM skills. Results: Findings indicate that personalised, continuous learning, facilitated through collaborative environments and custom-tailored content, yields significant benefits. Through analysis of key performance indicators (KPIs), this study showcases improved project onboarding, enhanced team collaboration, and the cultivation of a culture of innovation. Conclusion: This paper presents the PMO EDUCT framework that organizations can use to create a learning culture and empower their employees to succeed in the constantly evolving field of software development. By adopting collaborative, complementary, sustained, and simultaneous learning principles, organizations can develop a workforce with the essential skills of critical thinking, collaboration, and adaptability necessary for success in the digital era.

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“…As custom corporate eLearning content is built on demand, it is tailored for specific requirements and corresponds to certain corporate practices (Dimic et al, 2019).…”

Section: Tailoring For Custom Needsmentioning

confidence: 99%

Enabling digital growth through continuous education of project managers: A framework for collaborative, complementary, sustained, and simultaneous learning in software engineering organizations

Atanasijević,

Zahar,

Rančić

et al. 2023

Vojnotehnički glasnik

View full text Add to dashboard Cite

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“…In [Ramaswami and Bhaskaran 2009] the authors examined the how filtered FS techniques influence the predictive accuracy of student performance in India using demographical, socioeconomic, and school performance data. In [Dimic et al 2019] the data from a grade management platform in an engineering faculty in Serbia was used to predict student performance using CS-filtering methods alongside relief feature scoring, and information gain. Some works have also examined combinations of FS filters and classifiers to understand which one exhibited the best performance [Rachburee and Punlumjeak 2015;Abid et al 2018;Enaro and Chakraborty 2020;Das et al 2020;Muchuchuti et al 2020].…”

Section: Related Workmentioning

confidence: 99%

“…• 393 2018; Gopalakrishnan et al 2018;Febro 2019;Huang et al 2019;Dimic et al 2019;Enaro and Chakraborty 2020;Das et al 2020;Chaudhury and Tripathy 2020;Muchuchuti et al 2020;Chaves et al 2021]. Other methods employ wrapper approaches that use genetic algorithms (GA) to select the best set of attributes [Wafi et al 2019;Almasri et al 2020;Farissi et al 2020;Santos et al 2020].…”

Section: Introductionmentioning

confidence: 99%

A Genetic Algorithm with Flexible Fitness Function for Feature Selection in Educational Data: Comparative Evaluation

Albuquerque

Tarrataca

Brandão

et al. 2022

jidm

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Educational Data Mining is an interdisciplinary field that helps understand educational phenomena through computational techniques. The databases of educational institutions are usually extensive, possessing many descriptive attributes that make the prediction process complex. In addition, the data can be sparse, redundant, irrelevant, and noisy, which can degrade the predictive quality of the models and affect computational performance. One way to simplify the problem is to identify the least important attributes and omit them from the modeling process. This can be performed by employing attribute selection techniques. This work evaluates different feature selection techniques applied to open educational data and paired alongside a genetic algorithm with a flexible fitness function. The methods and results described herein extend a previously published paper by: (i) describing a larger set of computational experiments; (ii) performing a hypothesis test over different classifiers; and (iii) presenting a more in-depth literature revision. The results obtained indicate an improvement in the classification process.

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“…Technology innovation is transforming the world at an astonishing speed, including the field of education. The development of learning technologies has enabled teachers to track student learning in fully online, blended, as well as brick and mortar learning environments by recording student behaviors via technological means (Hung et al , 2017; Dimic et al , 2019). By analyzing the vast amount of educational data that are generated and collected in the process of teaching and learning, decision-makers such as instructors, students and administrators can obtain a holistic view of learning progress and trigger corresponding evidence-based or data-based personalized interventions or recommendations (Hung et al , 2017).…”

Section: Introductionmentioning

confidence: 99%

Educational data mining: a systematic review of research and emerging trends

Yang

Hung

et al. 2020

IDD

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Purpose Educational data mining (EDM) and learning analytics, which are highly related subjects but have different definitions and focuses, have enabled instructors to obtain a holistic view of student progress and trigger corresponding decision-making. Furthermore, the automation part of EDM is closer to the concept of artificial intelligence. Due to the wide applications of artificial intelligence in assorted fields, the authors are curious about the state-of-art of related applications in Education. Design/methodology/approach This study focused on systematically reviewing 1,219 EDM studies that were searched from five digital databases based on a strict search procedure. Although 33 reviews were attempted to synthesize research literature, several research gaps were identified. A comprehensive and systematic review report is needed to show us: what research trends can be revealed and what major research topics and open issues are existed in EDM research. Findings Results show that the EDM research has moved toward the early majority stage; EDM publications are mainly contributed by “actual analysis” category; machine learning or even deep learning algorithms have been widely adopted, but collecting actual larger data sets for EDM research is rare, especially in K-12. Four major research topics, including prediction of performance, decision support for teachers and learners, detection of behaviors and learner modeling and comparison or optimization of algorithms, have been identified. Some open issues and future research directions in EDM field are also put forward. Research limitations/implications Limitations for this search method include the likelihood of missing EDM research that was not captured through these portals. Originality/value This systematic review has not only reported the research trends of EDM but also discussed open issues to direct future research. Finally, it is concluded that the state-of-art of EDM research is far from the ideal of artificial intelligence and the automatic support part for teaching and learning in EDM may need improvement in the future work.

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Improving the prediction accuracy in blended learning environment using synthetic minority oversampling technique

Cited by 8 publications

References 24 publications

Enabling digital growth through continuous education of project managers: A framework for collaborative, complementary, sustained, and simultaneous learning in software engineering organizations

Enabling digital growth through continuous education of project managers: A framework for collaborative, complementary, sustained, and simultaneous learning in software engineering organizations

A Genetic Algorithm with Flexible Fitness Function for Feature Selection in Educational Data: Comparative Evaluation

Educational data mining: a systematic review of research and emerging trends

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