Using KNN Method for Educational and Vocational Guidance

Haji, Essaid El; Azmani, Abdellah; Harzli, Mohamed El

doi:10.5120/17578-8335

Cited by 3 publications

(4 citation statements)

References 7 publications

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“…The K-Nearest Neighbor (KNN) model conducts non-parametric classification by utilizing a pre-existing dataset, eliminating the need for a distinct training procedure [ 25 ]. For a new observation, the model determines the k-nearest data points and assigns the class that appears most frequently among these data points as the classification result for the new observation [ 5 , 25 ].…”

Section: Methodsmentioning

confidence: 99%

“…The K-Nearest Neighbor (KNN) model conducts non-parametric classification by utilizing a pre-existing dataset, eliminating the need for a distinct training procedure [25].…”

Section: K-nearest Neighborsmentioning

confidence: 99%

“…For a new observation, the model determines the k-nearest data points and assigns the class that appears most frequently among these data points as the classification result for the new observation [5,25].…”

Section: K-nearest Neighborsmentioning

confidence: 99%

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Predicting Sudden Sensorineural Hearing Loss Recovery with Patient-Personalized Seigel’s Criteria Using Machine Learning

Shon,

Lim,

Chae

et al. 2024

Diagnostics

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Background: Accurate prognostic prediction is crucial for managing Idiopathic Sudden Sensorineural Hearing Loss (ISSHL). Previous studies developing ISSHL prognosis models often overlooked individual variability in hearing damage by relying on fixed frequency domains. This study aims to develop models predicting ISSHL prognosis one month after treatment, focusing on patient-specific hearing impairments. Methods: Patient-Personalized Seigel’s Criteria (PPSC) were developed considering patient-specific hearing impairment related to ISSHL criteria. We performed a statistical test to assess the shift in the recovery assessment when applying PPSC. The utilized dataset of 581 patients comprised demographic information, health records, laboratory testing, onset and treatment, and hearing levels. To reduce the model’s reliance on hearing level features, we used only the averages of hearing levels of the impaired frequencies. Then, model development, evaluation, and interpretation proceeded. Results: The chi-square test (p-value: 0.106) indicated that the shift in recovery assessment is not statistically significant. The soft-voting ensemble model was most effective, achieving an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.864 (95% CI: 0.801–0.927), with model interpretation based on the SHapley Additive exPlanations value. Conclusions: With PPSC, providing a hearing assessment comparable to traditional Seigel’s criteria, the developed models successfully predicted ISSHL recovery one month post-treatment by considering patient-specific impairments.

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Section: Methodsmentioning

confidence: 99%

“…The K-Nearest Neighbor (KNN) model conducts non-parametric classification by utilizing a pre-existing dataset, eliminating the need for a distinct training procedure [25].…”

Section: K-nearest Neighborsmentioning

confidence: 99%

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Predicting Sudden Sensorineural Hearing Loss Recovery with Patient-Personalized Seigel’s Criteria Using Machine Learning

Shon,

Lim,

Chae

et al. 2024

Diagnostics

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“…An expected outcome from the survey is the visualization of each indicator and their kind of data as the expectation of new parameters to observe, so JSMLA may acquire new lines of development towards the study results; expanding the metrics the system may represent in the future and provide meaning for other tools which exploit said data, such as Learnglish or any other tools alike [29,37].…”

Section: Surveymentioning

confidence: 99%

A Privacy-Oriented Local Web Learning Analytics JavaScript Library with a Configurable Schema to Analyze Any Edtech Log: Moodle’s Case Study

et al. 2021

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Educational institutions are transferring analytics computing to the cloud to reduce costs. Any data transfer and storage outside institutions involve serious privacy concerns, such as student identity exposure, rising untrusted and unnecessary third-party actors, data misuse, and data leakage. Institutions that adopt a “local first” approach instead of a “cloud computing first” approach can minimize these problems. The work aims to foster the use of local analytics computing by offering adequate nonexistent tools. Results are useful for any educational role, even investigators, to conduct data analysis locally. The novelty results are twofold: an open-source JavaScript library to analyze locally any educational log schema from any LMS; a front-end to analyze Moodle logs as proof of work of the library with different educational metrics and indicator visualizations. Nielsen heuristics user experience is executed to reduce possible users’ data literacy barrier. Visualizations are validated by surveying teachers with Likert and open-ended questions, which consider them to be of interest, but more different data sources can be added to improve indicators. The work reinforces that local educational data analysis is feasible, opens up new ways of analyzing data without data transfer to third parties while generating debate around the “local technologies first” approach adoption.

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