Isbn: 9786630137132 - machine learning in higher education: bibliometric systematic review protocol 2024-2026 (9 risultati)

Condizione: New.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condizione: new. Paperback. To design a systematic review protocol with bibliometric analysis on the use of machine learning models to predict academic performance and dropout risk in university students during the period 2024-2026.A systematic review is proposed in accordance with PRISMA 2020 and PRISMA-S, with searches in Scopus, Web of Science, ScienceDirect, SpringerLink, ERIC, PubMed, IEEE Xplore, Dialnet and REDALYC. The protocol defines eligibility criteria, Boolean chains, debugging strategy, extraction matrix, methodological quality assessment and synthesis plan. Records, duplicates, exclusions, included studies and bibliometric distributions should be documented only after running searches in primary databases and verifying metadata, full text and quartiles.The review will examine supervised and assembly algorithms, scholarly and digital data sources, predictive metrics, interpretability, explainability, and early warning systems. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 52 pp. Englisch.

Paperback. Condizione: new. Paperback. To design a systematic review protocol with bibliometric analysis on the use of machine learning models to predict academic performance and dropout risk in university students during the period 2024-2026.A systematic review is proposed in accordance with PRISMA 2020 and PRISMA-S, with searches in Scopus, Web of Science, ScienceDirect, SpringerLink, ERIC, PubMed, IEEE Xplore, Dialnet and REDALYC. The protocol defines eligibility criteria, Boolean chains, debugging strategy, extraction matrix, methodological quality assessment and synthesis plan. Records, duplicates, exclusions, included studies and bibliometric distributions should be documented only after running searches in primary databases and verifying metadata, full text and quartiles.The review will examine supervised and assembly algorithms, scholarly and digital data sources, predictive metrics, interpretability, explainability, and early warning systems. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware 52 pp. Englisch.

Taschenbuch. Condizione: Neu. Machine learning in higher education | Bibliometric Systematic Review Protocol 2024-2026 | Ariel Herrera (u. a.) | Taschenbuch | Englisch | 2026 | Our Knowledge Publishing | EAN 9786630137132 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.…

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - To design a systematic review protocol with bibliometric analysis on the use of machine learning models to predict academic performance and dropout risk in university students during the period 2024-2026.A systematic review is proposed in accordance with PRISMA 2020 and PRISMA-S, with searches in Scopus, Web of Science, ScienceDirect, SpringerLink, ERIC, PubMed, IEEE Xplore, Dialnet and REDALYC. The protocol defines eligibility criteria, Boolean chains, debugging strategy, extraction matrix, methodological quality assessment and synthesis plan. Records, duplicates, exclusions, included studies and bibliometric distributions should be documented only after running searches in primary databases and verifying metadata, full text and quartiles.The review will examine supervised and assembly algorithms, scholarly and digital data sources, predictive metrics, interpretability, explainability, and early warning systems.…