NotesFAQContact Us
Collection
Advanced
Search Tips
Audience
Laws, Policies, & Programs
Assessments and Surveys
Massachusetts Comprehensive…1
What Works Clearinghouse Rating
Showing 1 to 15 of 41 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Cheryl Burleigh; Andrea M. Wilson – Journal of Educational Technology Systems, 2024
With the advent of readily accessible generative artificial intelligence (GAI), a concern exists within the academic community that research data collected in the context of conducting doctoral dissertation research is authentic. The purpose of the present study was to explore the role of GAI in the production of new research paying particular…
Descriptors: Artificial Intelligence, Data Collection, Doctoral Dissertations, Research Methodology
Peer reviewed Peer reviewed
Direct linkDirect link
Juliana Elisa Raffaghelli; Marc Romero Carbonell; Teresa Romeu-Fontanillas – Information and Learning Sciences, 2024
Purpose: It has been demonstrated that AI-powered, data-driven tools' usage is not universal, but deeply linked to socio-cultural contexts. The purpose of this paper is to display the need of adopting situated lenses, relating to specific personal and professional learning about data protection and privacy. Design/methodology/approach: The authors…
Descriptors: Artificial Intelligence, Data Collection, Information Literacy, Intervention
Peer reviewed Peer reviewed
Direct linkDirect link
Ning, Xiaoke – International Journal of Web-Based Learning and Teaching Technologies, 2023
With the vigorous development of intelligent campus construction, great changes have taken place in the development of information technology in colleges and universities from the previous digital to intelligent development. In the teaching process, the analysis of students' classroom learning has also changed from the previous manual observation…
Descriptors: College Students, Algorithms, Student Behavior, Artificial Intelligence
Peer reviewed Peer reviewed
Direct linkDirect link
Catherine Ferguson – Issues in Educational Research, 2025
The use of artificial intelligence (AI) in higher education has mostly focused on issues associated with teaching and assessment. In this paper I used AI to support the analysis of data which consisted of public comments on a newspaper article. This small, low risk research was chosen to demonstrate the potential use of AI and how it may support…
Descriptors: Artificial Intelligence, Data Analysis, Technology Uses in Education, Higher Education
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Frank Stinar; Zihan Xiong; Nigel Bosch – Journal of Educational Data Mining, 2024
Educational data mining has allowed for large improvements in educational outcomes and understanding of educational processes. However, there remains a constant tension between educational data mining advances and protecting student privacy while using educational datasets. Publicly available datasets have facilitated numerous research projects…
Descriptors: Foreign Countries, College Students, Secondary School Students, Data Collection
Peer reviewed Peer reviewed
Direct linkDirect link
Nehyba, Jan; Štefánik, Michal – Education and Information Technologies, 2023
Social sciences expose many cognitively complex, highly qualified, or fuzzy problems, whose resolution relies primarily on expert judgement rather than automated systems. One of such instances that we study in this work is a reflection analysis in the writings of student teachers. We share a hands-on experience on how these challenges can be…
Descriptors: Models, Language, Reflection, Writing (Composition)
Peer reviewed Peer reviewed
Direct linkDirect link
Sghir, Nabila; Adadi, Amina; Lahmer, Mohammed – Education and Information Technologies, 2023
The last few years have witnessed an upsurge in the number of studies using Machine and Deep learning models to predict vital academic outcomes based on different kinds and sources of student-related data, with the goal of improving the learning process from all perspectives. This has led to the emergence of predictive modelling as a core practice…
Descriptors: Prediction, Learning Analytics, Artificial Intelligence, Data Collection
Peer reviewed Peer reviewed
Direct linkDirect link
Chang Liu; Charles Downing – Journal of Information Systems Education, 2024
This teaching tip describes using Microsoft Power BI Desktop in a class to analyze unstructured data from an exit survey of prior students from a Master of Science in Management Information Systems program. Results from a short survey administered to these students showed that the students, using the no-code Power BI, were able to accomplish their…
Descriptors: Graduate Students, Program Effectiveness, Information Science, Management Information Systems
Peer reviewed Peer reviewed
Direct linkDirect link
Elena Drugova; Irina Zhuravleva; Ulyana Zakharova; Adel Latipov – Journal of Computer Assisted Learning, 2024
Background: Driven by the ongoing need to provide high-quality learning and teaching, universities recently have shown an increased interest in using learning analytics (LA) for improving learning design (LD). However, the evidence of such improvements is scarce, and the maturity of such research is unclear. Objectives: This study is aimed to…
Descriptors: Learning Analytics, Instructional Design, Higher Education, Instructional Improvement
Cody Gene Singer – ProQuest LLC, 2023
College and university enrollment has decreased nationwide every year for more than a decade as educational consumers increasingly question the value of higher education and discover alternatives to the traditional university system. Enrollment professionals seeking growth are tasked to develop and implement innovative solutions to address…
Descriptors: Data Collection, Predictor Variables, Electronic Learning, Enrollment
Peer reviewed Peer reviewed
Direct linkDirect link
Leif Sundberg; Jonny Holmström – Journal of Information Systems Education, 2024
With recent advances in artificial intelligence (AI), machine learning (ML) has been identified as particularly useful for organizations seeking to create value from data. However, as ML is commonly associated with technical professions, such as computer science and engineering, incorporating training in the use of ML into non-technical…
Descriptors: Artificial Intelligence, Conventional Instruction, Data Collection, Models
Peer reviewed Peer reviewed
Direct linkDirect link
Phillips, Tanner M.; Saleh, Asmalina; Ozogul, Gamze – International Journal of Artificial Intelligence in Education, 2023
Encouraging teachers to reflect on their instructional practices and course design has been shown to be an effective means of improving instruction and student learning. However, the process of encouraging reflection is difficult; reflection requires quality data, thoughtful analysis, and contextualized interpretation. Because of this, research on…
Descriptors: Reflection, Artificial Intelligence, Natural Language Processing, Data Collection
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Federica Picasso; Javiera Atenas; Leo Havemann; Anna Serbati – Open Praxis, 2024
The development of critical data and artificial intelligence (AI) literacy has become a key focus in current discussions in Higher Education, thus it is necessary to develop and advance capacity building, reflectiveness and awareness across disciplines to critically address the possibilities and challenges presented by data and AI. In this paper,…
Descriptors: Undergraduate Students, College Faculty, Artificial Intelligence, Data Collection
Peer reviewed Peer reviewed
Direct linkDirect link
Jingjing Long; Jiaxin Lin – Education and Information Technologies, 2024
English language learning students in China often feel challenged to learn English due to lack of motivation and confidence, pronunciation and grammar difference, lack of practice and people to communicate with etc., which affects students mental health. Adopting Big data and AI will help in overcoming these limitations as it provides personalized…
Descriptors: Foreign Countries, English Language Learners, College Students, Mental Health
Peer reviewed Peer reviewed
Direct linkDirect link
Li, Maximilian Xiling; Nadj, Mario; Maedche, Alexander; Ifenthaler, Dirk; Wöhler, Johannes – Technology, Knowledge and Learning, 2022
With the advent of physiological computing systems, new avenues are emerging for the field of learning analytics related to the potential integration of physiological data. To this end, we developed a physiological computing infrastructure to collect physiological data, surveys, and browsing behavior data to capture students' learning journey in…
Descriptors: Physiology, Computation, Artificial Intelligence, Psychological Patterns
Previous Page | Next Page »
Pages: 1  |  2  |  3