Uncovering themes in personalized learning: Using natural language processing to analyze school interviews

McHugh, David; Shaw, Sarah A.; Moore, Travis R.; Ye, Leafia Zi; Romero-Masters, Philip; Halverson, Richard

doi:10.1080/15391523.2020.1752337

Cited by 12 publications

(9 citation statements)

References 19 publications

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“…This was better than expected, as university syllabi usually do not have fixed norms. A possible reason for the success may be that the TD-IDF identified the difference and similarity in documents primarily through word frequency, and more representative distinguishable keywords simplified the distinctions among documents (McHugh et al 2020 ). The syllabus describes the use of digital competence skills and tools, often including unique terms such as internet, e-mail, upload, software simulation, and source code (Stanny et al 2015 ).…”

Section: Resultsmentioning

confidence: 99%

Assessment of the digital competencies of university instructors through use of the machine learning method

Yang

2023

SN Soc Sci

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The explosion of COVID-19 has brought new challenges to the education industry, especially higher education. Digital competency is becoming an essential competency for higher education instructors, and how to assess instructors' digital competency is attracting increasing attention in higher education. However, most studies have used self-report questionnaires or manual reviews to assess digital competencies, which are time-consuming and potentially biased, and there is a current need for valid and effective assessment methods. To address this issue, this study uses machine learning to analyze syllabi to assess the extent to which university instructors have incorporated digital competency into their courses. The results show that not only is the proposed method feasible, but the results of the assessment using machine learning are highly consistent with those of the human assessment. This approach contributes to the assessment of digital competency in higher education institutions and provides evidence that can be used as a reference for future research on the development of digital competency in higher education institutions.

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Section: Resultsmentioning

confidence: 99%

Assessment of the digital competencies of university instructors through use of the machine learning method

Yang

2023

SN Soc Sci

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“…Additionally, we excluded empirical studies that focused on exploring contextual factors impacting technology‐enhanced PL rather than specific PL designs. For example, we excluded studies focusing on investigations of driving and constraining factors associated with likelihood of PL implementation (e.g., Robinson & Sebba, 2010), challenges faced by stakeholders in PL implementation (e.g., Bingham et al, 2016; Daruwala et al, 2020; Kallio & Halverson, 2020) as well as perceptions towards PL as school‐level initiatives, policies, and concepts (e.g., Courcier, 2007; Hallman, 2018; McHugh et al, 2020). Application of these inclusion and exclusion criteria yielded a final dataset consisting of 61 empirical studies that were published between 2006 and 2020 and reported specific PL designs and strategies supported by technology in PK‐12 educational settings.…”

Section: Methodsmentioning

confidence: 99%

Integrating instructional designs of personalized learning through the lens of universal design for learning

Zhang

Carter

Basham

et al. 2022

Computer Assisted Learning

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Background: Personalized learning (PL), conceptualized as an education innovation that tailors learning to meet diverse student needs, has drawn increased attention from different fields of study, such as education, learning sciences, and computer science. Regardless, there is a lack of a comprehensive understanding of PL instructional designs supported by technology.Objectives: This systematic literature review aimed to provide an empirical integration of PL designs and implementation across disciplines through the lens of the universal design for learning (UDL) framework. As an instructional framework that guides the design to embrace learner variability, UDL holds the potential to provide foundational considerations for instructional design components of PL.Methods: We used four databases including ERIC, OmniFile Full Text Select, Academic Search Complete, and Web of Science to search empirical studies investigating technology-supported PL in PK-12 school-based settings. In total, 61 studies published between 2006 and 2020 were included in the review. We analysed PL design and implementation features and aligned these features to the UDL framework.Results and Conclusions: Our results indicated that current empirical PL design features were widely mapped onto the UDL framework. Most current PL studies investigated segmented aspects such as student attributes (e.g., interest, motivation, self-regulation) and instructional practices (e.g., facilitating goal setting) that could contribute to PL design. Rarely did studies investigate how PL was operationalized in a system integrating various design components that consider learner characteristics, instructional support, and contextual implementation factors.Implications: The analysis of alignment between PL designs within the UDL framework can serve as a starting point for stakeholders to take a more holistic approach to designing PL experiences for all learners.

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“…The way that museums and galleries are organized has changed dramatically. NLP algorithms have created virtual, interactive displays that offer personalized learning experiences, increasing accessibility and engagement [50]. The fields of mental health support and personal fitness coaching have also evolved.…”

Section: Exploring the Impact Of Nlp Collaboration With The Metaverse...mentioning

confidence: 99%

Natural Language Processing Influence on Digital Socialization and Linguistic Interactions in the Integration of the Metaverse in Regular Social Life

Sumon,

Uddin,

Akter

et al. 2024

Electronics

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The Metaverse and Natural Language Processing (NLP) technologies have combined to fundamentally change the nature of digital sociability. Our understanding of social interaction needs to be reevaluated as the Metaverse’s influence spreads into more areas of daily life, such as AI-driven gaming, interactive training companions, museum exhibits, personalized fitness coaching, virtual mental health assistance, language translation services, virtual tour guiding, and virtual conferencing. This study analyzes how NLP is changing social relationships in these Metaverse applications. We examine how NLP algorithms influence societal norms, individual behaviors, interpersonal connections, and improve the user experience using a multi-method approach incorporating user surveys and sentiment analysis. Our study’s findings show how NLP can enhance interactive experiences while also pointing out related issues like potential bias and moral problems. Our study provides a foundational analysis, shedding light on the challenges of negotiating a social environment in the Metaverse that is molded by cutting-edge NLP. It offers stakeholders in academia and public policy essential assistance that helps them understand and manage the complex ramifications of this changing socio-technological paradigm.

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Uncovering themes in personalized learning: Using natural language processing to analyze school interviews

Cited by 12 publications

References 19 publications

Assessment of the digital competencies of university instructors through use of the machine learning method

Assessment of the digital competencies of university instructors through use of the machine learning method

Integrating instructional designs of personalized learning through the lens of universal design for learning

Natural Language Processing Influence on Digital Socialization and Linguistic Interactions in the Integration of the Metaverse in Regular Social Life

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