Big Data Support for Problem Solving Method in Mass Spectrometry Topic in Modern Analytical Chemistry Course

Muis, Irmayanti; Wonorahardjo, Surjani; Budiasih, Endang

doi:10.3991/ijim.v15i09.21569

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…Big data analytics is an apparatus that empowers medical services associations to arrive at this objective by enhancing EMRs through scientific calculations. In this manner they can give medical services benefits all the more productively and precisely, streamline existing activities, and forestall some medical dangers [9]. Undoubtedly, the utilization of big data analytics inside EMRs is established in the idea of data life cycle structure that comprises of three parts: data assortment, data stockpiling, and data use.…”

Section: Big Data Characteristicsmentioning

confidence: 99%

A Knowledge Based Analysis on Big Data Analytics in Optimizing Electronic Medical Records in Private Hospitals

Gattan

2021

Int. J. Onl. Eng.

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The current examination is to break down the information dependent on the Big Data Analytics in enhancing electronic medical records in clinics and wellbeing focuses. Numerous emergency clinics and medical services habitats are experiencing incapable utilization of enormous information investigation in streamlining electronic medical records (EMRs) to create great bits of knowledge for their clinical practices. Hierarchical conduct and the exercises associated with the medical field are the principal factors in improving the utilization of Big Data Analytics in EMRs. The examination centres around exploring the how the models and methods of information can accomplish enormous information life cycle in EMRs. The examination likewise uncovers the information put together investigation with respect to EMRs use and capacity that assists with improving the significant usage of huge information rehearses. The investigation contributes on advanced wellbeing rehearses by investigating the appropriate adaption of scientific instruments to EMRs to shape the significant utilization of large information examination with EMRs.

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Section: Big Data Characteristicsmentioning

confidence: 99%

A Knowledge Based Analysis on Big Data Analytics in Optimizing Electronic Medical Records in Private Hospitals

Gattan

2021

Int. J. Onl. Eng.

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“…However, the utilization of big data in education extends beyond conventional data analysis and monitoring of student performance [10][11][12][13]. Recent research has begun to focus on the progression of thought based on big data and the evolution of student learning preferences [14,15]. For instance, in finance, where students are required to possess high levels of analytical and critical thinking abilities, researchers have started to observe how students' learning preferences evolve over time and attempt to identify potential correlations between these changes and students' progression of thought.…”

Section: Introductionmentioning

confidence: 99%

A Model to Predict and Analyze Students' Learning Preferences and their Cognitive Development through Educational Big Data

2023

Int. J. Emerg. Technol. Learn.

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Underpinned by the accelerated progression of information technology, the role of educational big data in information gathering and analysis has been underscored, particularly so in finance, a discipline embedded in logic and analysis. Patterns in student learning and behavioral data, when examined, can afford educators invaluable insights to shape efficacious teaching strategies. Contemporary research probing into the dynamics of student learning preference evolution and cognitive advancement appears to over-depend on static data, often falling short of effectively addressing the intricate data structures in educational big data. In this light, it becomes imperative to delve into the temporal shifts in student learning preferences and their link to cognitive advancement. In this context, a novel dynamic trustaware preference evolution model is brought to the fore, with the potential to precisely track variations in learning preferences of finance students and elucidate their correlation with cognitive advancement. A correlation model is erected, laying bare the reciprocal interaction between the metamorphosis of student learning preferences and cognitive progression. This pioneering approach eclipses the constraints inherent in extant research methodologies, rendering deeper comprehension to educators. Findings from regression analysis divulge the association between the transformative journey of learning preferences and cognitive advancement, holding far-reaching implications for educational practices. These revelations can capacitate educators to fine-tune their teaching approaches in line with student development, fostering personalized learning ecosystems. This research further holds significant merits for addressing complexities within finance education, aiding in the cultivation of adept professionals capable of navigating the fluid landscape of modern finance.

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“…As a part of Science, Technology, Engineering, and Mathematics, the technology education approach can be used to improve students' and teachers' 21stcentury skills [2]. However, the patented spectrometers are not affordable for educational purposes, especially in schools [3,4].…”

Section: Introductionmentioning

confidence: 99%

A low-cost spectrometer to analyse the purity of honey

Bestari¹,

Sarwanto²,

Pujayanto³

et al. 2021

Phys. Educ.

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Light spectrum dispersion is an exciting subjectin science because of its beautiful atmospheric colour phenomenon which attracts students. However, to see the phenomenon is not easy since it needs a spectrometer, which is commonly expensive. Therefore, the present study aims to describe a low-cost spectrometer for investigating lighting spectrum and analysing the purity of honey as a pedagogical students’ project. The spectrometer was constructed from a webcam connected to a laptop, a free spectrometer software, DVD disk, and black cardboard. The calibration of the developed spectrometer used an Argon (Ar) lamp. Afterwards, measurement tests were carried out by using Neon (Ne) and Xenon (Xe) lamps. A white light-emitting diode was used as a light source to measure several types of honey wavelengths. The results from the experiment show that the wavelength of Ar, Ne, and Xe are (503 ± 4) nm, (463 ± 3) nm, and (451 ± 3) nm respectively. The measurement accuracy of the spectrometer is 99.5%. In addition, the commercial honey characterisations show that pure honey tends to have an excellent and smooth spectrum with one peak. On the contrary, the adulteries honeys show a rough wave with many peaks representing the inhomogeneous ingredients. It is found that the average accuracy for honey spectrum is 99.8%. Moreover, the low-cost spectrometer can be built and used easily by students for educational purposes.

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Big Data Support for Problem Solving Method in Mass Spectrometry Topic in Modern Analytical Chemistry Course

Cited by 3 publications

References 11 publications

A Knowledge Based Analysis on Big Data Analytics in Optimizing Electronic Medical Records in Private Hospitals

A Knowledge Based Analysis on Big Data Analytics in Optimizing Electronic Medical Records in Private Hospitals

A Model to Predict and Analyze Students' Learning Preferences and their Cognitive Development through Educational Big Data

A low-cost spectrometer to analyse the purity of honey

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