This study examined the question, ‘What is the impact of a digital math intervention on secondary ELL
students’ mathematical capabilities and perceptions of their future possibilities?’ The hypothesis was
that through its direct effect on increasing students’ math ability and its indirect effect on increasing
students’ perceived math self-efficacy, the digital intervention affects students’ perceptions of their
functionings and future possibilities. A path analysis, with qualitative data nested into the design, was
used to analyze the conceptualized relationships. The study was conducted with 50 ninth-and-10thgrade
Hispanic students in a Colorado high school, over 6 months. The primary finding was that
through its direct effect on increasing students’ math ability and its indirect effect on increasing students’
perceived math self-efficacy, the digital intervention improved students’ perceptions of their functionings
and future possibilities. What this study specifically underscores is the importance of taking
a coherent and purposeful approach toward the design of digital student-directed educational technology,
especially for ELL students who may have specific learning needs.
This article details how to learn advanced undergraduate level mathematics solely from self-study via textbooks, lecture notes and videos found on the internet.
This article details how to learn advanced undergraduate level mathematics solely from self-study via textbooks, lecture notes and videos found on the internet.
Z. Long, Y. Lu, und B. Dong. (2018)cite arxiv:1812.04426Comment: 16 pages, 15 figures. arXiv admin note: substantial text overlap with arXiv:1710.09668.
L. Biggio, T. Bendinelli, A. Neitz, A. Lucchi, und G. Parascandolo. Proceedings of the 38th International Conference on Machine Learning, Volume 139 von Proceedings of Machine Learning Research, Seite 936--945. PMLR, (18--24 Jul 2021)
L. Biggio, T. Bendinelli, A. Neitz, A. Lucchi, und G. Parascandolo. Proceedings of the 38th International Conference on Machine Learning, Volume 139 von Proceedings of Machine Learning Research, Seite 936--945. PMLR, (18--24 Jul 2021)
F. Ciaglia, F. Di Cosmo, M. Laudato, und G. Marmo. (2017)cite arxiv:1705.02974Comment: 42 pages, 6 figures, accepted for publication in International Journal of Geometric Methods in Modern Physics.