Annotated Examples and Parameterized Exercises: Analyzing Students’ Behavior Patterns

Mirzaei, Mehrdad; Sahebi, Shaghayegh; Brusilovsky, Peter

doi:10.1007/978-3-030-23204-7_26

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…In this paper, we present and evaluate modifications to LDA, addressing the two aforementioned challenges, to focus on capturing topics that can characterize evolving interests specifically for COVID-19. Addressing such challenges enables several applications including monitoring the impact of a specific event on social, emotional, mental well-being and behaviours (Zamani et al, 2018a;Mirzaei et al, 2019). Building on previous work in online topic modeling (Canini et al, 2009), we propose the creation of short-interval dynamic topics that are updated on a regular basis (weekly or monthly).…”

Section: Introductionmentioning

confidence: 99%

Understanding Weekly COVID-19 Concerns through Dynamic Content-Specific LDA Topic Modeling

Zamani¹,

Schwartz²,

Eichstaedt³

et al. 2020

Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science

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The novelty and global scale of the COVID-19 pandemic has lead to rapid societal changes in a short span of time. As government policy and health measures shift, public perceptions and concerns also change, an evolution documented within discourse on social media. We propose a dynamic content-specific LDA topic modeling technique that can help to identify different domains of COVID-specific discourse that can be used to track societal shifts in concerns or views. Our experiments show that these model-derived topics are more coherent than standard LDA topics, and also provide new features that are more helpful in prediction of COVID-19 related outcomes including social mobility and unemployment rate.

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Section: Introductionmentioning

confidence: 99%

Understanding Weekly COVID-19 Concerns through Dynamic Content-Specific LDA Topic Modeling

Zamani¹,

Schwartz²,

Eichstaedt³

et al. 2020

Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science

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“…In education context, different methods such as machine learning [7][8][9] ,stochastic process [10,11] and matrix factorization based models [12,13] are used to evaluate students' academic performance. Studies have shown that factors such as student behavior is an indicator of performance [14]. Student performance can be defined as a variety of approaches to measure the persistence and success of students, including graduation rates, completion in a timely manner, and/or academic performance.…”

Section: Introductionmentioning

confidence: 99%

Multi-Institution Study of Student Demographics and Stickiness of Computing Majors in the USA

Zahedi,

EbrahimNejad,

Ross

et al.

2021 CoNECD Proceedings

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Monique Ross earned a doctoral degree in Engineering Education from Purdue University. She has a Bachelor's degree in Computer Engineering from Elizabethtown College, a Master's degree in Computer Science and Software Engineering from Auburn University, eleven years of experience in industry as a software engineer, and three years as a full-time faculty in the departments of computer science and engineering. Her interests focus on broadening participation in engineering through the exploration of: 1) race, gender, and identity in the engineering workplace; 2) discipline-based education research (with a focus on computer science and computer engineering courses) in order to inform pedagogical practices that garner interest and retain women and minorities in computer-related engineering fields.

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Automatic Content Analysis of Student Moral Discourse in a Collaborative Learning Activity

Álvarez

Zurita

Carvallo

et al. 2021

Lecture Notes in Computer Science

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Annotated Examples and Parameterized Exercises: Analyzing Students’ Behavior Patterns

Cited by 7 publications

References 9 publications

Understanding Weekly COVID-19 Concerns through Dynamic Content-Specific LDA Topic Modeling

Understanding Weekly COVID-19 Concerns through Dynamic Content-Specific LDA Topic Modeling

Multi-Institution Study of Student Demographics and Stickiness of Computing Majors in the USA

Automatic Content Analysis of Student Moral Discourse in a Collaborative Learning Activity

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