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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
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
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
Sarwar, Sohail; García-Castro, Raul; Qayyum, Zia Ul; Safyan, Muhammad; Munir, Rana Faisal – International Association for Development of the Information Society, 2017
Learner categorization has a pivotal role in making e-learning systems a success. However, learner characteristics exploited at abstract level of granularity by contemporary techniques cannot categorize the learners effectively. In this paper, an architecture of e-learning framework has been presented that exploits the machine learning based…
Descriptors: Student Characteristics, Profiles, Courseware, Electronic Learning
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
Delen, Dursun – Journal of College Student Retention: Research, Theory & Practice, 2012
Affecting university rankings, school reputation, and financial well-being, student retention has become one of the most important measures of success for higher education institutions. From the institutional perspective, improving student retention starts with a thorough understanding of the causes behind the attrition. Such an understanding is…
Descriptors: Higher Education, Student Attrition, School Holding Power, Prediction
Yu, Chong Ho; Digangi, Samuel; Jannasch-Pennell, Angel Kay; Kaprolet, Charles – Online Journal of Distance Learning Administration, 2008
The efficacy of online learning programs is tied to the suitability of the program in relation to the target audience. Based on the dataset that provides information on student enrollment, academic performance, and demographics extracted from a data warehouse of a large Southwest institution, this study explored the factors that could distinguish…
Descriptors: Online Courses, Data Collection, Research Methodology, Profiles
Smith, James E.; And Others – 1988
In the spring of 1980, the High School and Beyond (HSB) base year survey collected data from about 28,000 seniors and about 30,000 sophomores in more than 1,000 secondary schools. This supplement was designed to collect data concerning the methodology associated with the receipt of postsecondary financial aid within the first 4 years after high…
Descriptors: Cohort Analysis, College Students, Confidential Records, Data Collection