Scholarly Writing

The integration of technological tools into mathematics courses:  A Systematic Literature Review  

Higher education students, particularly at community colleges, often view core mathematics courses, such as College Algebra, Precalculus, and Statistics, as unrelated to their degree plans and career goals, leading to decreased motivation and engagement. This systematic literature review explores the integration of technology in mathematics education as a potential solution to this issue, focusing on tools, such as programming languages, Computational Thinking (CT) platforms, and interactive applications. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we analyzed literature sourced from international academic databases, encompassing studies published between 2013 and 2024. Our findings reveal that technological tools can bridge the gap between abstract mathematical concepts and real-world applications, enhancing student motivation, engagement, and performance. Specifically, programming languages like Python and R, as well as CT platforms, such as Scratch, promote critical thinking and problem-solving skills, making mathematics more accessible and relevant. Data analysis tools such as RStudio, not only boost engagement but also provide students with hands-on experience that aligns with workforce demands. However, long-term studies on the durability of these skills are limited, and time constraints within standard curricula remain a challenge for broader implementation. Future research should employ longitudinal designs to better understand the lasting impact of technology-enhanced learning on students' academic and career trajectories. Additionally, investing in faculty development and aligning technology tools with course-specific needs will be essential for sustainable, impactful integration. These strategic efforts can enhance mathematics education, making it more relevant and supportive of diverse student goals and professional success.

International Journal of Science and Mathematics Education - Springer
(Under Review)

 Understanding How Students Perceive Digital Tools:

A Review Using the Technology Acceptance Model and the Computer Attitude Questionnaire

This literature review explores how students in college mathematics and statistics courses perceive and engage with digital tools using the Technology Acceptance Model and the Computer Attitude Questionnaire. It highlights challenges in student motivation and the underexplored role of institutional support programs in influencing technology engagement. The study identifies theoretical and methodological gaps to guide future empirical research and improve educational technology integration in higher education. 

Villalobos, O., Gamez, J. & Kinshuk, K. (2025). Understanding How Students Perceive Digital Tools: A Review Using the Technology Acceptance Model and the Computer Attitude Questionnaire. In T. Bastiaens (Ed.), Proceedings of EdMedia + Innovate Learning (pp. 555-559). Barcelona, Spain: Association for the Advancement of Computing in Education (AACE). https://www.learntechlib.org/primary/p/226186/. 

RETROALIMENTACIÓN INMEDIATA Y APRENDIZAJE PERSONALIZADO PARA UN APOYO EQUITATIVO EN MATEMÁTICAS CON IA

El artículo explica que muchos estudiantes llegan a matemáticas universitarias con poca preparación y con acceso limitado a apoyo constante, lo que puede aumentar la ansiedad y reducir la motivación. Propone que un tutor de matemáticas con IA puede ofrecer aprendizaje personalizado y retroalimentación inmediata para apoyar mejor a estudiantes con necesidades diversas. En conjunto, plantea que esta integración puede mejorar el aprendizaje y contribuir a mayor equidad en cursos de matemáticas.

publicación pendiente: https://www.iadisportal.org/digital-library/iadis-conferencia-ibero-americana-computa%C3%A7%C3%A3o-aplicada-ciaca)

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Immediate Feedback and Personalized Learning for Equitable Support in Mathematics with AI

The paper argues that many students enter college math underprepared and without consistent support, which can raise anxiety and lower motivation. It suggests that an AI-based math tutor can provide personalized learning and immediate feedback to better support diverse learners. Overall, it frames AI integration as a way to improve learning and advance equity in college mathematics. 

(Pending publication: https://www.iadisportal.org/digital-library/iadis-conferencia-ibero-americana-computa%C3%A7%C3%A3o-aplicada-ciaca

Task-Technology Fit in Action: Understanding Learning-Task Alignment in Math Classes at Community Colleges

The rapid expansion of online learning in community college math courses has intensified the need to understand how educational technologies align with students' learning processes. Grounded in the Task–Technology Fit (TTF) framework, this study examines Learning–Task Fit, defined as the degree to which digital tools support the cognitive and procedural demands of instructional math learning tasks. Hence, the purpose of this research is to investigate the dimensionality and reliability of the Learning–Task Fit construct and to identify the key factors shaping students' learning experiences in technology-mediated math learning environments. Data were collected from 134 students enrolled in gateway math courses at a local community college. Moreover, an exploratory factor analysis (EFA) using principal component analysis as the extraction method yielded a single-factor solution, the Learning-Task Fit construct, with strong loadings across six items and an eigenvalue of 3.19, accounting for 53.21% of the variance. Internal consistency was high (Cronbach's α = 0.817), indicating the construct's reliability. Thus, the findings provide strong empirical evidence for the unidimensional and psychometric soundness of the Learning–Task Fit scale in math courses across all modalities at community colleges. Whence, the study highlights the importance of aligning educational technologies with the structure of learning tasks to enhance student engagement, persistence, and performance.

Accepted as Brief Paper

Validating the Extended Technology Acceptance Model through Exploratory Factor Analysis (EFA): The Role of Educational Support in Online Homework Systems for Gateway Courses at Community Colleges

The growing use of online homework platforms such as MyLab Math, ALEKS, WebAssign, and others in gateway math courses has transformed how students practice, receive feedback, and interact with their coursework at community colleges. Many learners, however, still face challenges in adopting and using these systems effectively. The study applies an extended version of the Technology Acceptance Model (TAM) to explore how students perceive and accept online homework tools, incorporating Educational Support (EdS) alongside the core constructs of Perceived Usefulness (PU) and Perceived Ease of Use (PEU). Data from approximately 200 students were analyzed using exploratory factor analysis (EFA) and reliability testing. Findings revealed a stable three-factor structure with strong internal consistency across all constructs, confirming the model's validity and highlighting the importance of instructional and technical support in helping students stay engaged and successful with online math tools. The results offer insights into improving how online homework systems are used in math courses and into guiding future confirmatory factor analysis (CFA) studies using the extended TAM framework.

Accepted as Brief Paper

Integrating CAQ and TAM to Understand Technology Acceptance in Gateway Math Courses Taught at Community Colleges

This study explores how student confidence and technology acceptance interact to shape engagement and success in gateway math courses at community colleges. Grounded in the Technology Acceptance Model (TAM) and the Computer Attitude Questionnaire (CAQ), the research explores students' perceived usefulness, perceived ease of use, and confidence toward educational technologies such as online homework systems. A total of 500 student responses were collected and analyzed using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to evaluate construct validity and reliability. Furthermore, results revealed a clear three-factor structure (CAQ, PU, PEU) with strong internal consistency (α = .83–.91) and excellent model fit (CFI = 0.995, RMSEA = 0.065), with no significant differences observed across demographic groups. Additionally, findings provide evidence that TAM and CAQ can be effectively combined to better understand students' technology perceptions and guide the development of instructional strategies for online learning.

Accepted as Full Paper

Confirmatory Factor Analysis of the Extended Technology Acceptance Model in Online Gateway Courses at Community Colleges

Many community college students face challenges staying engaged in math courses across all modalities, where homework systems serve as the main source of practice and feedback. The Technology Acceptance Model provides a strong framework for understanding how students form beliefs about technology by emphasizing their perceptions of usefulness and ease of use. Hence, building upon earlier exploratory analysis, this study applied Confirmatory Factor Analysis to validate the Extended Technology Acceptance Model among community college students enrolled in gateway math courses, incorporating the constructs of Perceived Usefulness, Perceived Ease of Use, and Educational Support to explain how instructional and environmental supports influence technology acceptance. Using a new sample of 300 students, the analysis showed strong model fit with significant factor loadings across all constructs. Two items with low loadings were removed from the Educational Support construct, which improved coherence and overall model performance while maintaining acceptable internal consistency. Thus, findings confirm the reliability of the Extended Technology Acceptance Model and emphasize the importance of contextual and instructional supports in fostering engagement with online mathematics systems.

Accepted as Full Paper

Evaluating the Technology Acceptance Model for Measuring Student Attitudes Toward Educational Tools in Gateway Math Classes at Community Colleges

StudenT engagement and persistence in gateway math classes remain challenges in STEM pathways, particularly in technology-mediated learning environments. This study evaluates the suitability of the Technology Acceptance Model (TAM) for measuring community college students’ attitudes toward technology tools used in online math classes. Survey and institutional data were collected from over 150 undergraduate students enrolled in online math courses at a community college. Using validated instruments grounded in the TAM and the Computer Attitude Questionnaire, the study examined perceived usefulness, perceived ease of use, and confidence as predictors of engagement and persistence. Results indicate that perceived usefulness was the strongest predictor of engagement, while perceived ease of use and confidence were significantly associated with persistence outcomes. Overall, students reported positive attitudes toward technology-supported instruction, with variability across learners. Such findings highlight the value of technology acceptance measures for understanding persistence mechanisms and informing instructional practices in community college mathematics. 

Under Review

PENDING

PENDING