Advancing Learning through Human-Machine Collaboration: Insights from a Meta-Analytic Structural Equation Modelling/Ostvarivanje napretka u učenju suradnjom čovjeka i stroja: saznanja dobivena metaanalitičkim modeliranjem strukturne jednadžbe
DOI:
https://doi.org/10.15516/cje.v28i3.42889Keywords:
artificial intelligence in education; human-machine collaborative learning; influence factor; MASEM; structural equation modelAbstract
Abstract
Human-Machine Collaborative Learning (HMCL) has gained increasing attention in education due to its potential to enhance learning outcomes through the integration of artificial intelligence (AI) and advanced technologies. This study employed the meta-analytic structural equation model (MASEM) to explore the relationships among the variables in the HMCL, and the data were derived from 15 studies retrieved. The MASEM results show that ease of use, satisfaction level, and learning motivation all positively and significantly influence academic performance, while student satisfaction positively and significantly affects learning motivation. The path coefficients revealed key relationships: ease of use to academic achievement (β=0.132), satisfaction to learning motivation (β=1.354), satisfaction to academic achievement (β=0.621), and learning motivation to academic achievement (β=0.332). The MASEM results delineate a clear chain, underscoring the complex interplay between affective, motivational, and cognitive dimensions of learning. Future research should focus on developing robust theoretical frameworks and investigating long-term impacts to further advance HMCL integration into diverse educational contexts.
Keywords: artificial intelligence in education; human-machine collaborative learning; influence factor; MASEM; structural equation model
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Sažetak
Učenje temeljeno na suradnji čovjeka i stroja privuklo je veliku pozornost u području obrazovanja jer ima potencijal da poveća usvojenost ishoda učenja putem integracije umjetne inteligencije i naprednih tehnologija. U ovome je istraživanju korišteno metaanalitičko modeliranje strukturne jednadžbe kako bi se ispitala veza između varijabli u učenju temeljenom na suradnji čovjeka i stroja. Podatci za analizu dobiveni su iz 15 odabranih studija. Rezultati metaanalitičkoga modeliranja strukturne jednadžbe pokazuju da lakoća korištenja, razina zadovoljstva te motivacija za učenje imaju pozitivan i značajan utjecaj na akademska postignuća, dok zadovoljstvo studenata pozitivno i značajno utječe na motivaciju za učenje. Koeficijenti putanje otkrili su ključne veze između: lakoće korištenja i akademskih postignuća (β = 0,132), zadovoljstva i motivacije za učenje (β = 1,354), zadovoljstva i akademskih postignuća (β = 0,621) te motivacije za učenje i akademskih postignuća (β = 0,332). Rezultati metaanalitičkoga modeliranja strukturne jednadžbe pokazuju jasan lanac, naglašavajući složenu interakciju između afektivne, motivacijske i kognitivne dimenzije učenja. Buduća bi se istraživanja trebala usmjeriti na izradu čvrstih teorijskih okvira i istraživanje dugoročnih utjecaja kako bi se unaprijedila daljnja integracija učenja temeljenoga na suradnji čovjeka i stroja u različite obrazovne kontekste.
Ključne riječi: faktori utjecaja; metaanalitičko modeliranje strukturne jednadžbe; model strukturne jednadžbe; učenje temeljeno na suradnji čovjeka i stroja; umjetna inteligencija u obrazovanju
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