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Matthew A. Kraft; Danielle Sanderson Edwards; Marisa Cannata – Annenberg Institute for School Reform at Brown University, 2024
Public school systems across the U.S. have made major investments in tutoring to support students' academic recovery in the wake of the COVID-19 pandemic. We evaluate a large urban district's efforts to design, implement, and scale a district-operated, standards-based tutoring program across three years. We draw on extensive interviews and survey…
Descriptors: Public Schools, COVID-19, Pandemics, Tutorial Programs
Yang An; Yushi Duan; Yuchen Zhang – International Journal of Information and Communication Technology Education, 2024
Higher education informatization (HEI) is an interdisciplinary field that examines the use and integration of information and communication technologies (ICTs) in higher education. This paper provides a bibliometric and visual analysis of the research trends, patterns, and topics in this field. Using the Web of Science database, the authors…
Descriptors: Bibliometrics, Educational Research, Higher Education, Information Technology
Schwartzman, Roy; Ferraro, Brianna – Education, 2020
With an estimated 11 percent of all undergraduate students (at least two million) having a disability and the numbers of such students increasing (National Council on Disability, 2015), oral communication tutoring centers face an escalating imperative to account for this population in their instructional practices. The Multidimensional Attitudes…
Descriptors: Undergraduate Students, Students with Disabilities, Speech Communication, Academic Support Services
Maniktala, Mehak; Cody, Christa; Isvik, Amy; Lytle, Nicholas; Chi, Min; Barnes, Tiffany – Journal of Educational Data Mining, 2020
Determining "when" and "whether" to provide personalized support is a well-known challenge called the assistance dilemma. A core problem in solving the assistance dilemma is the need to discover when students are unproductive so that the tutor can intervene. Such a task is particularly challenging for open-ended domains, even…
Descriptors: Intelligent Tutoring Systems, Problem Solving, Helping Relationship, Prediction
Balyan, Renu; McCarthy, Kathryn S.; McNamara, Danielle S. – International Journal of Artificial Intelligence in Education, 2020
For decades, educators have relied on readability metrics that tend to oversimplify dimensions of text difficulty. This study examines the potential of applying advanced artificial intelligence methods to the educational problem of assessing text difficulty. The combination of hierarchical machine learning and natural language processing (NLP) is…
Descriptors: Natural Language Processing, Artificial Intelligence, Man Machine Systems, Classification
Kobakhidze, Magda Nutsa – Globalisation, Societies and Education, 2020
This study focuses on the changed roles and symbolic identities of Myanmar teachers under the forces of marketisation and the privatisation of education. Private tutoring by school teachers is used as an example of what society perceives to be the incursion into education of market-oriented values that are incompatible with traditional morals. The…
Descriptors: Teacher Role, Professional Identity, Privatization, Tutoring
Briant, Elizabeth; Doherty, Catherine; Dooley, Karen; English, Rebecca – Pedagogy, Culture and Society, 2020
Australia's private tutoring market is expanding in a context where parents' trust in school personnel as educational experts is vulnerable. Simultaneously, a parentocratic logic is nudging parents to infuse the resources at their disposal into their pedagogic work in order to achieve the educational outcomes that they wish for their children.…
Descriptors: Parent Attitudes, Advertising, Tutoring, Private Education
Dooley, Karen; Liu, Liwei Livia; Yin, Yue Melody – Discourse: Studies in the Cultural Politics of Education, 2020
This article reports a study of the supply of private supplementary tuition in literacy for primary school students. The data set consisted of websites and advertisements of 46 suppliers active in an area of suburban Australia with a substantial population of migrants from East Asia. To conceptualise the supply of private tuition as edu-business,…
Descriptors: Foreign Countries, Private Education, Tutoring, Supplementary Education
Grubišic, Ani; Žitko, Branko; Stankov, Slavomir – Journal of Technology and Science Education, 2020
In intelligent e-learning systems that adapt a learning and teaching process to student knowledge, it is important to adapt the system as quickly as possible. However, adaptation is not possible until the student model is initialized. In this paper, a new approach to student model initialization using domain knowledge representative subset is…
Descriptors: Electronic Learning, Educational Technology, Models, Intelligent Tutoring Systems
Patikorn, Thanaporn; Heffernan, Neil T. – Grantee Submission, 2020
It has been shown in multiple studies that expert-created on-demand assistance, such as hint messages, improves student learning in online learning environments. However, there are also evident that certain types of assistance may be detrimental to student learning. In addition, creating and maintaining on-demand assistance are hard and…
Descriptors: Electronic Publishing, Group Experience, Tutoring, Electronic Learning
Balyan, Renu; McCarthy, Kathryn S.; McNamara, Danielle S. – Grantee Submission, 2020
For decades, educators have relied on readability metrics that tend to oversimplify dimensions of text difficulty. This study examines the potential of applying advanced artificial intelligence methods to the educational problem of assessing text difficulty. The combination of hierarchical machine learning and natural language processing (NLP) is…
Descriptors: Natural Language Processing, Artificial Intelligence, Man Machine Systems, Classification
Li, Haiying; Graesser, Art C. – Grantee Submission, 2020
This study investigated how computer agents' language style affects summary writing in an Intelligent Tutoring System, called CSAL AutoTutor. Participants interacted with two computer agents in one of three language styles: (1) a "formal" language style; (2) an "informal" language style; and (3) a "mixed" language…
Descriptors: Intelligent Tutoring Systems, Language Styles, Writing (Composition), Writing Improvement
Elaine Allensworth; Nate Schwartz – EdResearch for Recovery Project, 2020
This brief is one in a series aimed at providing K-12 education decision makers and advocates with an evidence base to ground discussions about how to best serve students during and following the novel coronavirus pandemic. Learning losses are likely to show up differently across grades and subjects, with intensive recovery needs concentrated in…
Descriptors: Retention (Psychology), Intervention, COVID-19, Pandemics
Lydia Rainey; Paul Hill; Robin Lake; Lisa Chu; Daniel Silver – Center on Reinventing Public Education, 2025
In the aftermath of the COVID-19 pandemic, student achievement levels remains low and adult approval of public schools is at its lowest point in decades. For nearly all student groups, but especially those who have been historically underserved, recovery remains unfinished business and a generation of American students is at risk of never catching…
Descriptors: Students, Teachers, Educational Development, Educational Improvement
Robin Lake; Paul Hill – Center on Reinventing Public Education, 2025
Five years after the start of the Covid-19 pandemic, student achievement levels are in free fall. According to the National Assessment of Educational Progress (NAEP) results released in late January 2025, fewer than one-third of students nationwide scored at proficiency levels in reading and math. Achievement gaps by race, income, and other…
Descriptors: Students, Teachers, Educational Development, Educational Improvement

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