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A Rasch Analysis of Students' Academic Motivation toward Mathematics in an Adaptive Learning System.

作者信息

Lim Lyndon, Lim Seo Hong, Lim Wei Ying Rebekah

机构信息

Teaching & Learning Centre, Singapore University of Social Sciences, Singapore 599494, Singapore.

出版信息

Behav Sci (Basel). 2022 Jul 21;12(7):244. doi: 10.3390/bs12070244.

DOI:10.3390/bs12070244
PMID:35877313
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9311996/
Abstract

Despite the criticality of considering student academic motivation as it influences learning, research within the field of adaptive learning technologies for education has so far focused more on customising instruction to implement personalised learning, than investigating how personalised learning is associated with learners' motivation. Given this, a robust instrument is required to gather information about student academic motivation within the context of adaptive learning technologies. This study sought to validate the Academic Motivation Toward Mathematics Survey (AMTMS) currently used to measure motivation based on self-determination theory in mathematics education at pre-tertiary levels (grades 11 and 12) in Asia. A total of 196 participants recruited via availability sampling took part in this study, after interacting with an in-house mathematics adaptive learning system within a tertiary educational institution. The validation was performed based on modern test theory given that it overcomes some limitations of classical factor analytic approaches. Results supported the factorial structure of the AMTMS but 12 of the original 21 items had to be rescored to establish ordered thresholds. Further, the bifactor equivalent solution suggested the possibility of reporting a singular motivation index comprising the five factors within the AMTMS. Along with the results, this study offers researchers a robust and validated instrument to measure motivation toward mathematics that can be used within an adaptive learning environment.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e05c/9311996/d63bca29d3cb/behavsci-12-00244-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e05c/9311996/cee86f2585bb/behavsci-12-00244-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e05c/9311996/d7fe16c4384a/behavsci-12-00244-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e05c/9311996/d0336291c4d8/behavsci-12-00244-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e05c/9311996/d63bca29d3cb/behavsci-12-00244-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e05c/9311996/cee86f2585bb/behavsci-12-00244-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e05c/9311996/d7fe16c4384a/behavsci-12-00244-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e05c/9311996/d0336291c4d8/behavsci-12-00244-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e05c/9311996/d63bca29d3cb/behavsci-12-00244-g004.jpg

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引用本文的文献

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Measuring Learner Satisfaction of an Adaptive Learning System.衡量自适应学习系统的学习者满意度。
Behav Sci (Basel). 2022 Jul 30;12(8):264. doi: 10.3390/bs12080264.

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Psychol Rev. 2017 Nov;124(6):689-719. doi: 10.1037/rev0000082. Epub 2017 Sep 21.
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Empirical evaluation of a virtual laboratory approach to teach lactate dehydrogenase enzyme kinetics.
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Ann Med Surg (Lond). 2016 Apr 28;8:6-13. doi: 10.1016/j.amsu.2016.04.019. eCollection 2016 Jun.
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Evaluating bifactor models: Calculating and interpreting statistical indices.评估双因子模型:计算和解释统计指数。
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