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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
Billington, Catherine; Rivero, Gonzalo; Jannett, Andrew; Chen, Jiating – Field Methods, 2022
During data collection, field interviewers often append notes or comments to a case in open text fields to request updates to case-level data. Processing these comments can improve data quality, but many are non-actionable, and processing remains a costly manual task. This article presents a case study using a novel application of machine learning…
Descriptors: Artificial Intelligence, Interviews, Data Collection, Notetaking
David DiSabito; Lisa Hansen; Thomas Mennella; Josephine Rodriguez – New Directions for Teaching and Learning, 2025
This chapter investigates the integration of generative AI (GenAI), specifically ChatGPT, into institutional and course-level assessment at Western New England University. It explores the potential of GenAI to streamline the assessment process, making it more efficient, equitable, and objective. Through the development of a proprietary GenAI tool,…
Descriptors: Artificial Intelligence, Technology Uses in Education, Man Machine Systems, Educational Assessment
Wilson, Cristina G.; Qian, Feifei; Jerolmack, Douglas J.; Roberts, Sonia; Ham, Jonathan; Koditschek, Daniel; Shipley, Thomas F. – Cognitive Research: Principles and Implications, 2021
How do scientists generate and weight candidate queries for hypothesis testing, and how does learning from observations or experimental data impact query selection? Field sciences offer a compelling context to ask these questions because query selection and adaptation involves consideration of the spatiotemporal arrangement of data, and therefore…
Descriptors: Hypothesis Testing, Data Collection, Information Seeking, Decision Making
Charitopoulos, Angelos; Rangoussi, Maria; Koulouriotis, Dimitrios – International Journal of Artificial Intelligence in Education, 2020
The aim of this paper is to survey recent research publications that use Soft Computing methods to answer education-related problems based on the analysis of educational data 'mined' mainly from interactive/e-learning systems. Such systems are known to generate and store large volumes of data that can be exploited to assess the learner, the system…
Descriptors: Data Collection, Learning Analytics, Educational Research, Artificial Intelligence
Di Mitri, Daniele; Schneider, Jan; Specht, Marcus; Drachsler, Hendrik – Journal of Computer Assisted Learning, 2018
Multimodality in learning analytics and learning science is under the spotlight. The landscape of sensors and wearable trackers that can be used for learning support is evolving rapidly, as well as data collection and analysis methods. Multimodal data can now be collected and processed in real time at an unprecedented scale. With sensors, it is…
Descriptors: Educational Research, Data Collection, Data Analysis, Learning Modalities
Couland, Quentin; Hamon, Ludovic; George, Sébastien – International Association for Development of the Information Society, 2018
More and more domains such as industry, sport, medicine, Human Computer Interaction (HCI) and education analyze user motions to observe human behavior, follow and predict its action, intention and emotion, to interact with computer systems and enhance user experience in Virtual (VR) and Augmented Reality (AR). In the context of human learning of…
Descriptors: Motion, Teaching Methods, Human Posture, Data Collection
Prinsloo, Paul – E-Learning and Digital Media, 2017
In the socio-technical imaginary of higher education, algorithmic decision-making offers huge potential, but we also cannot deny the risks and ethical concerns. In fleeing from Frankenstein's monster, there is a real possibility that we will meet Kafka on our path, and not find our way out of the maze of ethical considerations in the nexus between…
Descriptors: Mathematics, Decision Making, Higher Education, Data Collection
Padilla, Thomas – OCLC Online Computer Library Center, Inc., 2019
Responsible Operations is intended to help chart library community engagement with data science, machine learning, and artificial intelligence (AI) and was developed in partnership with an advisory group and a landscape group comprised of more than 70 librarians and professionals from universities, libraries, museums, archives, and other…
Descriptors: Data Collection, Data Analysis, Artificial Intelligence, Educational Technology
Schneider, Bertrand; Blikstein, Paulo – Journal of Educational Data Mining, 2015
In this paper, we describe multimodal learning analytics (MMLA) techniques to analyze data collected around an interactive learning environment. In a previous study (Schneider & Blikstein, submitted), we designed and evaluated a Tangible User Interface (TUI) where dyads of students were asked to learn about the human hearing system by…
Descriptors: Educational Research, Data Collection, Data Analysis, Educational Environment
Chou, Jyh Rong – EURASIA Journal of Mathematics, Science & Technology Education, 2016
The touch mouse is a new type of computer mouse that provides users with a new way of touch-based environment to interact with computers. For more than a decade, user experience (UX) has grown into a core concept of human-computer interaction (HCI), describing a user's perceptions and responses that result from the use of a product in a particular…
Descriptors: Computer Peripherals, Computer Uses in Education, Handheld Devices, Educational Technology
Worsley, Marcelo; Blikstein, Paulo – Journal of Learning Analytics, 2014
Learning analytics and educational data mining are introducing a number of new techniques and frameworks for studying learning. The scalability and complexity of these novel techniques has afforded new ways for enacting education research and has helped scholars gain new insights into human cognition and learning. Nonetheless, there remain some…
Descriptors: Data Analysis, Data Collection, Engineering, Design
Peer reviewedFerguson, Douglas; And Others – Information Technology and Libraries, 1982
Describes the scope, objectives, and status of a study of user responses to public online catalogs conducted in 1982 with Council on Library Resources (CLR) funding. The framework for evaluating the user-catalog interface, data collection, and questionnaire content are discussed, and a copy of the survey questionnaire is appended. (Author/JL)
Descriptors: College Libraries, Data Collection, Design Preferences, Higher Education
Francis, Larry; Weaver, Tamar – 1977
Certain features of the PLATO IV system allow the gathering of large quantities and varieties of information describing the interaction of students with computer based education courseware. Two kinds of information can be provided: (1) data concerning single student interaction, which must be analyzed manually; and (2) data summarizing activities…
Descriptors: Computer Assisted Instruction, Data Analysis, Data Collection, Educational Diagnosis
Larson, Ray R. – 1983
This report focuses on a discussion of findings from analyses of computer transaction logs contributed by four of the online catalog systems used in the Public Access Project. It supplements the analyses of User and Non-User Questionnaires by providing analyses of data from the systems themselves. The four systems contributing data to this…
Descriptors: Data Collection, Design Requirements, Library Automation, Library Catalogs
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