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Muhammad Alhammami – Discover Education, 2024
This paper outlines the development of a Hardware Development Kit (HDK) for a remote training platform on FPGA Devices designed to provide university students pursuing degrees in electronic and informatics engineering (at the bachelor's, master's, and PhD levels) with the tools they need to learn and develop systems related to artificial…
Descriptors: Computer System Design, Distance Education, College Students, Engineering Education
Hong Xiao – International Journal of Web-Based Learning and Teaching Technologies, 2024
Relying on the background of big data, this paper introduces the blended teaching model into the secondary vocational Japanese oral classroom and explores whether the teaching model is conducive to the improvement of the secondary vocational Japanese oral learning effect and teaching effect. In order to make this research more scientific and…
Descriptors: Foreign Countries, Japanese, Language Teachers, Data Processing
Rybinski, Krzysztof – Higher Education Research and Development, 2022
This article develops a machine learning methodology to analyse the relationship between university accreditation and student experience. It is applied to 98 university accreditations conducted by the Quality Assurance Agency (QAA) in the UK in 2012-2018, and 263,025 university ratings in three categories posted by students on the website…
Descriptors: Program Evaluation, Accreditation (Institutions), Student Experience, College Students
Matthew McGrievy – ProQuest LLC, 2024
The purpose of this descriptive research study was to evaluate the web-based practice experience tracking system at a school of public health (SPH) at a large southeastern university. Applied practice experiences (APEs) are a key component of public health education, and schools and programs of public health in the United States must provide…
Descriptors: College Faculty, College Students, Public Health, Health Education
Cohausz, Lea; Tschalzev, Andrej; Bartelt, Christian; Stuckenschmidt, Heiner – International Educational Data Mining Society, 2023
Demographic features are commonly used in Educational Data Mining (EDM) research to predict at-risk students. Yet, the practice of using demographic features has to be considered extremely problematic due to the data's sensitive nature, but also because (historic and representation) biases likely exist in the training data, which leads to strong…
Descriptors: Information Retrieval, Data Processing, Pattern Recognition, Information Technology
Hemy Ramiel; Eran Fisher – Learning, Media and Technology, 2024
This paper adds an algorithmic epistemology perspective to previous works that examine the datafication of subjective social and emotional characteristics, perceptions, and behaviours. The paper employs a comparative epistemological approach to explore two behavioural educational platforms: RedCritter Teacher and Panorama Education. We unpack…
Descriptors: Epistemology, Social Emotional Learning, Data, Higher Education
Cominole, Melissa; Ritchie, Nichole Smith; Cooney, Jennifer – National Center for Education Statistics, 2021
This publication describes the methods and procedures used for the 2008/18 Baccalaureate and Beyond Longitudinal Study (B&B:08/18). The B&B graduates, who completed the requirements for a bachelor's degree during the 2007-08 academic year, were first surveyed as part of the 2008 National Postsecondary Student Aid Study (NPSAS:08), and then…
Descriptors: Bachelors Degrees, College Graduates, Longitudinal Studies, Data Collection
Varun Mandalapu – ProQuest LLC, 2021
Educational data mining focuses on exploring increasingly large-scale data from educational settings, such as Learning Management Systems (LMS), and developing computational methods to understand students' behaviors and learning settings better. There has been a multitude of research dedicated to studying the student learning process, leading to…
Descriptors: Models, Student Behavior, Learning Management Systems, Data Use
Chopra, Shivangi; Gautreau, Hannah; Khan, Abeer; Mirsafian, Melicaalsadat; Golab, Lukasz – International Educational Data Mining Society, 2018
It is well known that post-secondary science and engineering programs attract fewer female students. In this paper, we analyze gender differences through text mining of over 30,000 applications to the engineering faculty of a large North American university. We use syntactic and semantic analysis methods to highlight differences in motivation,…
Descriptors: Gender Differences, Undergraduate Students, Engineering Education, STEM Education
Weiand, Augusto; Manssour, Isabel Harb; Silveira, Milene Selbach – International Journal of Distance Education Technologies, 2019
With technological advances, distance education has been frequently discussed in recent years. The learning environments used in this course usually generates a great deal of data because of the large number of students and the various tasks involving their interaction. In order to facilitate the analysis of the data, the authors researched to…
Descriptors: Foreign Countries, Distance Education, Online Courses, Visualization
Singh, Archana – Education and Information Technologies, 2017
The youth power to speak their mind, recommendations and opinions about various issues on social media cannot be ignored. There is a generated by students on social media websites like, facebook, Orkut, twitter etc. This paper focusses on the extraction of knowledge from the data floated by the University students on social websites in different…
Descriptors: Social Media, College Students, Data Processing, Web Sites
Conijn, Rianne; Snijders, Chris; Kleingeld, Ad; Matzat, Uwe – IEEE Transactions on Learning Technologies, 2017
With the adoption of Learning Management Systems (LMSs) in educational institutions, a lot of data has become available describing students' online behavior. Many researchers have used these data to predict student performance. This has led to a rather diverse set of findings, possibly related to the diversity in courses and predictor variables…
Descriptors: Blended Learning, Predictor Variables, Predictive Validity, Predictive Measurement
Yin, Chengjiu; Hirokawa, Sachio; Yau, Jane Yin-Kim; Hashimoto, Kiyota; Tabata, Yoshiyuki; Nakatoh, Tetsuya – International Journal of Distance Education Technologies, 2013
To help researchers in building a knowledge foundation of their research fields which could be a time-consuming process, the authors have developed a Cross Tabulation Search Engine (CTSE). Its purpose is to assist researchers in 1) conducting research surveys, 2) efficiently and effectively retrieving information (such as important researchers,…
Descriptors: Search Engines, Online Searching, Surveys, Research
Abdous, M'hammed; He, Wu – British Journal of Educational Technology, 2011
Because of their capacity to sift through large amounts of data, text mining and data mining are enabling higher education institutions to reveal valuable patterns in students' learning behaviours without having to resort to traditional survey methods. In an effort to uncover live video streaming (LVS) students' technology related-problems and to…
Descriptors: Video Technology, Student Participation, Data Analysis, Learning Experience
Vialardi, Cesar; Bravo, Javier; Shafti, Leila; Ortigosa, Alvaro – International Working Group on Educational Data Mining, 2009
One of the main problems faced by university students is to take the right decision in relation to their academic itinerary based on available information (for example courses, schedules, sections, classrooms and professors). In this context, this work proposes the use of a recommendation system based on data mining techniques to help students to…
Descriptors: Data Analysis, Higher Education, Course Selection (Students), Enrollment
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