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Khan, Ijaz; Ahmad, Abdul Rahim; Jabeur, Nafaa; Mahdi, Mohammed Najah – Smart Learning Environments, 2021
A major problem an instructor experiences is the systematic monitoring of students' academic progress in a course. The moment the students, with unsatisfactory academic progress, are identified the instructor can take measures to offer additional support to the struggling students. The fact is that the modern-day educational institutes tend to…
Descriptors: Artificial Intelligence, Academic Achievement, Progress Monitoring, Data Collection
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Yan, Hongxin; Lin, Fuhua; Kinshuk – International Journal of Artificial Intelligence in Education, 2021
Online education is growing because of its benefits and advantages that students enjoy. Educational technologies (e.g., learning analytics, student modelling, and intelligent tutoring systems) bring great potential to online education. Many online courses, particularly in self-paced online learning (SPOL), face some inherent barriers such as…
Descriptors: Learning Analytics, Independent Study, Online Courses, Electronic Learning
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Pargman, Teresa Cerratto; McGrath, Cormac – Journal of Learning Analytics, 2021
Ethics is a prominent topic in learning analytics that has been commented on from conceptual viewpoints. For a broad range of emerging technologies, systematic literature reviews have proven fruitful by pinpointing research directions, knowledge gaps, and future research work guidance. With these outcomes in mind, we conducted a systematic…
Descriptors: Ethics, Learning Analytics, Higher Education, Educational Research
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Revilla, Melanie; Couper, Mick P.; Paura, Ezequiel; Ochoa, Carlos – Field Methods, 2021
Passive data from a tracking application (or "meter") installed on participants' devices to register the URLs visited have great potential for studying people's online activities. However, given privacy concerns, obtaining cooperation installing a meter can be difficult and lead to selection bias. Therefore, in this article, we address…
Descriptors: Participation, Computer Use, Internet, Data Collection
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Johnson, Sara K. – New Directions for Child and Adolescent Development, 2021
Developmental scientists are often interested in subgroups of people who share commonalities in aspects of development; these subgroups often cannot be captured directly but instead must be inferred from other information. Mixture models can be used in these situations. Two specific types of mixture models, latent profile transition analyses and…
Descriptors: Profiles, Child Development, Developmental Psychology, Models
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Cernat, Alexandru; Sakshaug, Joseph W. – Field Methods, 2021
Increasingly surveys are using interviewers to collect objective health measures, also known as biomeasures, to replace or supplement traditional self-reported health measures. However, the extent to which interviewers affect the (im)precision of biomeasurements is largely unknown. This article investigates interviewer effects on several…
Descriptors: Interviews, Surveys, Data Collection, Health
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Pelletier, Kathe; Hutt, Chris – Change: The Magazine of Higher Learning, 2021
Digital transformation (Dx) refers to a series of deep and coordinated culture, workforce, and technology shifts that enable new educational and operating models and transform an institution's business model, strategic directions, and value proposition. Dx initiatives on many campuses are anchored in student success goals. As a result of this…
Descriptors: Technology Uses in Education, Educational Change, Faculty Advisers, College Students
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Feldman-Maggor, Yael; Barhoom, Sagiv; Blonder, Ron; Tuvi-Arad, Inbal – Education and Information Technologies, 2021
Research based on educational data mining conducted at academic institutions is often limited by the institutional policy with regard to the type of learning management system and the detail level of its activity reports. Often, researchers deal with only raw data. Such data normally contain numerous fictitious user activities that can create a…
Descriptors: Data Analysis, Educational Research, Data Processing, Learning Analytics
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Cernat, Alexandru; Sakshaug, Joseph W.; Chandola, Tarani; Nazroo, James; Shlomo, Natalie – International Journal of Social Research Methodology, 2021
Collecting biological data in representative surveys is becoming more common due to their potential to inform research and policy. Nevertheless, using nurses to collect these data can lead to unintended effects. In this paper, we investigate how nurses influence the non-response process by looking at five waves of data coming from two surveys in…
Descriptors: Surveys, Nurses, Data Collection, Foreign Countries
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Macak, Martin; Kruzelova, Daniela; Chren, Stanislav; Buhnova, Barbora – Education and Information Technologies, 2021
Understanding the processes in education, such as the student learning behavior within a specific course, is a key to continuous course improvement. In online learning systems, students' learning can be tracked and examined based on data collected by the systems themselves. However, it is non-trivial to decide how to extract the desired students'…
Descriptors: Student Projects, Learning Analytics, Data Collection, Computer Science Education
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Vo, Thi Ngoc Chau; Nguyen, Phung – IEEE Transactions on Learning Technologies, 2021
A course-level early final study status prediction task is to predict as soon as possible the final success of each student after studying a course. It is significant because each successful course accomplishment is required for a degree. Further, early predictions provide enough time to make necessary changes for ultimate success. This article…
Descriptors: Prediction, Academic Achievement, Data Collection, Learning Processes
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Dawadi, Saraswati; Shrestha, Sagun; Giri, Ram A. – Journal of Practical Studies in Education, 2021
The article positions mixed-method research (MMR) as a principled complementary research method to the traditional quantitative and qualitative research approaches. By situating MMR in an analysis of some of the common research paradigms, the article presents it as a natural choice in order to complement and cater to the increasingly complex needs…
Descriptors: Mixed Methods Research, Statistical Analysis, Qualitative Research, Data Collection
Daugherty, Lindsay; Anderson, Drew M. – RAND Corporation, 2021
This appendix supplements the report "Stackable Credential Pipelines in Ohio: Evidence on Programs and Earnings Outcomes" (ED613593). In the appendix, the authors provide more details about the data and empirical approach, additional information about the samples, and some alternative results from analyses related to those that appear in…
Descriptors: Credentials, Postsecondary Education, Research Methodology, Data Collection
Safir, Shane; Dugan, Jamila – Corwin, 2021
Education can be transformed if we eradicate our fixation on big data like standardized test scores as the supreme measure of equity and learning. Instead of the focus being on "fixing" and "filling" academic gaps, we must envision and rebuild the system from the "student" up--with classrooms, schools and systems…
Descriptors: Data Collection, Equal Education, Educational Change, Data Use
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Oliver-Quelennec, Katia; Bouchet, François; Carron, Thibault; Pinçon, Claire – International Association for Development of the Information Society, 2021
In-person sessions of participative design are commonly used in the field of Learning Analytics, but to reach students not always available on-site (e.g. during a pandemic), they have to be adapted to online-only context. Card-based tools are a common co-design method to collect users' needs, but this tangible format limits data collection and…
Descriptors: Learning Analytics, Educational Technology, Data Collection, Universities
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