Student Information System and Performance Retrieval Through Dashboard
Open accessAbstract
The principle focal point of information mining is to gather diverse information from databases or information ware houses and the data gathered that had never been known, it is legitimate and operational. Instructive establishments can utilize this to keep up all the data of understudy scholastics effectively which is basically imperative. The execution of understudies in their scholastics is a defining moment for their brightest vocation. Foreseeing understudy scholarly execution has been a basic research point in Educational Data Mining (EDM) which utilizes machine learning and information mining methods to look at information from illuminating settings. Estimating understudy scholarly execution is trying since it relies upon different elements. Grouping and Prediction are among the significant procedures in information mining and assumes an essential part in EDM. The requirement for this is to empower the college to mediate and give help to low achievers as right on time as conceivable. In this examination we build up a grouping model usingC4.5 calculation for area savvy execution assessment framework for designing understudies. It additionally brings network between educators, understudies and guardians by keeping them refreshed with their kid execution consistently. The entire framework will be accessible through a protected, online interface inserted in school sit.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Classification, modelling and prediction are an essential part of educational data mining.
“Classification, modelling and prediction are an essential part of educational data mining (Phani Krishna, Mani Kumar, & Aruna Sri, 2018).”
From Systematic review of research on artificial intelligence applications in higher education – where are the educators? (2019) · cites this paper as Phani Krishna, Mani Kumar, & Aruna Sri, 2018
Cited by
Sorted by citations
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-019-0171-0 · Cited by 7,242 · see citing works
Artificial intelligence innovation in education: A twenty-year data-driven historical analysis
Guan, C., Mou, J., & Jiang, Z. (2020) · International Journal of Innovation Studies · doi:10.1016/j.ijis.2020.09.001 · Cited by 665 · see citing works
Empowering Educators to Be AI-Ready
Luckin, R., Cukurova, M., Kent, C., & du Boulay, B. (2022) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2022.100076 · Cited by 422 · see citing works
- Authors
- V.Phani Krishna, K., Mani Kumar, M., & S.G.Aruna Sri, P.
- Venue
- International Journal of Engineering & Technology
- Publisher
- Science Publishing Corporation
- Published
- 2018-03-18
- Volume
- 7
- Issue
- 2.7
- Pages
- 682
- ISSN
- 2227-524X (ISSN-L)
- OpenAlex
- W2801882844
- Cited by
- 18
- References
- 0
- Citations by year
- 2025: 1 · 2024: 3 · 2023: 5 · 2022: 2 · 2020: 6 · 2019: 1
- FWCI
- 2.21
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- International Journal of Engineering & Technology (publishedVersion, cc-by)
- Science Publishing Corporation Inc. - International Journal of Engineering & Technology (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Edcuational Technology Systems
- Keywords
- student information system, student performance prediction, educational data mining, C4.5 decision tree, student progress monitoring, parental notification, school management system, low-achieving students
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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