Students’ conceptual understanding on acid-base titration and its relationship with drawing skills on a titration curve

Wahyuni, Tutik Sri; Purwanto, Kriesna Kharisma

doi:10.1088/1742-6596/1440/1/012018

Cited by 11 publications

(5 citation statements)

References 4 publications

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“…A closer examination of ICE tables is especially warranted, as this is a strategy long included in chemistry textbooks that is under-researched. Student understanding of titrations has been researched − more extensively than student understanding of ICE tables, but this study points suggests additional research is warranted with titrations as well.…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

Comparing the Performance of College Chemistry Students with ChatGPT for Calculations Involving Acids and Bases

Clark,

Anderson,

Dickson-Karn

et al. 2023

J. Chem. Educ.

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Student performance on open-response calculations involving acid and base solutions before and after instruction in general chemistry and analytical chemistry courses was compared with the output from the artificial intelligence chatbot ChatGPT. Applying a theoretical model of expertise for problem solving that includes problem conceptualization, problem strategy, and solution, it is found that students errors following instruction primarily involved problem conceptualization and the misapplication of heuristics like the Henderson−Hasselbalch equation When the same problems were used as input to ChatGPT the responses were comparable to worked examples found in general chemistry textbooks in terms of length and detail and usually displayed strong problem conceptualization. Response accuracy of the chatbot varied greatly for different topics, being best for calculations of pH for a strong acid or strong base and much lower for more complex problems involving titrations or aqueous salts. Chatbot and student errors differed in that the chatbot did not misapply heuristics but did make mathematical errors uncommon for students. The variability in the correctness of ChatGPT's responses and the nature of its errors vis-a-vis students will influence its potential use as an instructional resource for calculations involving acids and bases.

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Section: Limitations and Future Directionsmentioning

confidence: 99%

Comparing the Performance of College Chemistry Students with ChatGPT for Calculations Involving Acids and Bases

Clark,

Anderson,

Dickson-Karn

et al. 2023

J. Chem. Educ.

View full text Add to dashboard Cite

show abstract

“…R ² was also determined to measure how well the estimated step length matches the reference values, showing how much of the variation in step lengths is captured by our model. Pearson’s correlation coefficient greater than 0.8 was considered a strong correlation 66 . To determine the effect of the averaging technique on the RMSE, we performed a one-way analysis of variance (ANOVA) with a P value of 0.05.…”

Section: Methodsmentioning

confidence: 99%

A wearable sensor and machine learning estimate step length in older adults and patients with neurological disorders

Zadka,

Rabin,

Gazit

et al. 2024

npj Digit. Med.

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Step length is an important diagnostic and prognostic measure of health and disease. Wearable devices can estimate step length continuously (e.g., in clinic or real-world settings), however, the accuracy of current estimation methods is not yet optimal. We developed machine-learning models to estimate step length based on data derived from a single lower-back inertial measurement unit worn by 472 young and older adults with different neurological conditions, including Parkinson’s disease and healthy controls. Studying more than 80,000 steps, the best model showed high accuracy for a single step (root mean square error, RMSE = 6.08 cm, ICC(2,1) = 0.89) and higher accuracy when averaged over ten consecutive steps (RMSE = 4.79 cm, ICC(2,1) = 0.93), successfully reaching the predefined goal of an RMSE below 5 cm (often considered the minimal-clinically-important-difference). Combining machine-learning with a single, wearable sensor generates accurate step length measures, even in patients with neurologic disease. Additional research may be needed to further reduce the errors in certain conditions.

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“…2.1.3 Solar radiation Solar radiation plays an important role in the energy produced by solar panels. The recommended average value of solar radiation outside the Earth's atmosphere is 13661 W/m 2 which is used as the solar constant [30][31][32]. The total energy that reaches the earth's surface is the solar constant minus the radiation due to atmospheric absorption and reflection before reaching the earth.…”

Section: Working Principles Of Solar Panelsmentioning

confidence: 99%

“…Specifications for monocrystalline solar panels Relationship the value PearsonThe following table interprets the relationship from the value of Pearson's correlation coefficient in Table3[31].…”

mentioning

confidence: 99%

Predicting Photovoltaic Power Output with Convolutional Neural Networks: A Case Study in Cepu, Central Java, Indonesia

Asy’ari,

Nugraha,

Sahrin

et al. 2023

JESA

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The prediction of the general national electricity plan has stated that Indonesia's electricity needs in 2038 will increase to 1,000 TWh or the equivalent of 3.3 MWh per capita. If this trend continues, then by 2050, per capita energy consumption is expected to reach 7.7 MWh or 2,600 TWh. This prediction is important because it is to prepare power plant infrastructure throughout the region for the coming period.By applying and adding learning algorithms based on Convolutional Neural Networks (CNN) to developing solar power plants, CNN-based Forecast can increase accuracy by up to 30% and has the ability to train models up to 2 times faster than currently available algorithms. This new algorithm can more accurately detect leading demand indicators, such as pre-order information, increased product demand, price changes, and promotional spikes, to build more accurate forecasts.That before the discovery of CNN there was no algorithm that provided the most accurate estimates for all types of data. Traditional statistical models have been useful in predicting the demand for products that have regular demand patterns, such as summer or winter electricity production. However, statistical models cannot provide accurate forecasts for more complex scenarios, such as frequent changes in energy prices, differences between regional versus national demand, products with different selling speeds, and additions of new products.As for CNN, it is appropriate to predict the power from the panel, with a MAPE performance value of 18.7633%, MAE of 0.0176 and RMSE of 0.0466 so that the prediction system built has fulfilled the prediction of the targeted system.

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Students’ conceptual understanding on acid-base titration and its relationship with drawing skills on a titration curve

Cited by 11 publications

References 4 publications

Comparing the Performance of College Chemistry Students with ChatGPT for Calculations Involving Acids and Bases

Comparing the Performance of College Chemistry Students with ChatGPT for Calculations Involving Acids and Bases

A wearable sensor and machine learning estimate step length in older adults and patients with neurological disorders

Predicting Photovoltaic Power Output with Convolutional Neural Networks: A Case Study in Cepu, Central Java, Indonesia

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