Personalized learning in iSTART: Past modifications and future design

McCarthy, Kathryn S.; Watanabe, Micah; Dai, Jie; McNamara, Danielle S.

doi:10.1080/15391523.2020.1716201

Cited by 38 publications

(29 citation statements)

References 48 publications

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“…Based on a comprehensive observation of the personalization program, it should include personalized learning materials, personalized graphical user interface, personalized learning activities, personalized learning strategy assistance, and personalized collaboration [17]. ey use intelligent agents to personalize the learning environment from three different perspectives: the system, the learner, and the teacher [18].…”

Section: Introductionmentioning

confidence: 99%

Personalized Online Education Learning Strategies Based on Transfer Learning Emotion Classification Model

Wang

Shi

2021

Security and Communication Networks

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Due to the epidemic, online course learning has become a major learning method for students worldwide. Analyzing its massive data from the massive online education platforms becomes a challenge because most learners watch online instructional videos. Thus, analyzing learners’ learning behaviors is beneficial to implement personalized online learning strategies with sentiment classification models. To this end, we propose a context-aware network model based on transfer learning that aims to predict learner performance by solving learners’ problems and improving the educational process, contributing to a comprehensive analysis of such student behavior and exploring various learning models in MOOC video interactions. In addition, we visualize and analyze MOOC video interactions, enabling course instructors and education professionals to analyze clickstream data generated by learners interacting with course videos. The experimental results show that, in the process of “massive data mining,” personalized learning strategies of this model can efficiently enhance students’ interest in learning and enable different types of students to develop personalized online education learning strategies.

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Section: Introductionmentioning

confidence: 99%

Personalized Online Education Learning Strategies Based on Transfer Learning Emotion Classification Model

Wang

Shi

2021

Security and Communication Networks

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“…To do so, a systematic investigation can consider learner/user experience and perceptions in addition to efficacy measures (Sedrakyan et al, 2020). For example, the Design Implementation Framework (McCarthy, Watanabe, Dai, & McNamara, 2020; Stone et al, 2018) encourages instructional and educational technology designers to engage in multiple iterative cycles of design and implementation that consider the experiences and outcomes of multiple end users (e.g., teachers, students, researchers). Thus, this work must also consider effects across various individual differences such as gender, prior knowledge and previous technology experience.…”

Section: Discussionmentioning

confidence: 99%

Improving summary writing through formative feedback in a technology‐enhanced learning environment

Kim

McCarthy

2020

Computer Assisted Learning

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Summary writing is a useful instructional tool for learning. However, summary writing is a challenge to many students. This mixed‐method study examined the potential of the Student Mental Model Analyzer for Research and Teaching (SMART) system to help students produce summaries that reflect key concepts and relations in a text. SMART uses the students' summary to generate a multi‐dimensional 3S (surface, structure, semantic) evaluation of the students' mental model. This model is then used to drive feedback to help students revise their summary. The current study is an initial investigation examining whether writing and revising in SMART improves students' summary quality. Students (n = 38) in a graduate‐level online course used SMART for seven reading assignments. The 38 students submitted a total of 357 summaries in response to the seven readings. In 47 cases, students produced both an initial draft and a modified revision. These 47 cases were selected for analysis. In the quantitative phase, MANOVA results indicated that students' summaries improved along the 3S dimensions from initial draft to revision. In the qualitative phase, inspection of exemplar cases revealed how students' mental models changed towards more robust and cohesive knowledge structure for texts.

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“…Es particularmente útil en los Sistemas Tutores Inteligentes orientados a lenguaje. Las versiones de i-START (Interactive Strategy Training for Active Reading and Thinking) utilizan LSA para asignar un puntaje de forma automática al escrito generado por los estudiantes con respecto al texto original (Crossley et al, 2015;Allen et al, 2015;McCarthy et al, 2020).…”

Section: Análisis Semántico Latenteunclassified

Construcción de contenido para un Sistema Tutor Inteligente en idiomas: un estudio piloto con el corpus OneStopEnglish

Escobar-Acevedo

Guerrero-García

2022

Rev. colomb. comput.

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Durante la adquisición de un idioma extranjero, la lectura representa una de las oportunidades de acercamiento al lenguaje. Sin embargo, los textos inadecuados pueden desencadenar una experiencia contraproducente para un estudiante, por ello, en los cursos regulares, los docentes utilizan su experiencia o la de un equipo editorial para seleccionar las lecturas. En un sistema automático como en un Sistema Tutor Inteligente, es prioritario realizar recomendaciones adecuadas al perfil del alumno. No basta conocer el nivel de idioma del texto, El presente trabajo aplica herramientas para clasificar una muestra de textos extraídos del corpus OneStopEnglish conforme al Marco Común de Referencia Europeo, crea grupos temáticos con análisis semántico latente (LSA), y aplica tres métricas populares de lecturabilidad como un referente para recomendar textos a los estudiantes.

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Personalized learning in iSTART: Past modifications and future design

Cited by 38 publications

References 48 publications

Personalized Online Education Learning Strategies Based on Transfer Learning Emotion Classification Model

Personalized Online Education Learning Strategies Based on Transfer Learning Emotion Classification Model

Improving summary writing through formative feedback in a technology‐enhanced learning environment

Construcción de contenido para un Sistema Tutor Inteligente en idiomas: un estudio piloto con el corpus OneStopEnglish

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