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Ryan S. Baker; Stephen Hutt; Nigel Bosch; Jaclyn Ocumpaugh; Gautam Biswas; Luc Paquette; J. M. Alexandra Andres; Nidhi Nasiar; Anabil Munshi – Educational Technology Research and Development, 2024
In this paper, we propose a new method for selecting cases for in situ, immediate interview research: detector-driven classroom interviewing (DDCI). Published work in educational data mining and learning analytics has yielded highly scalable measures that can detect key aspects of student interaction with computer-based learning in close to…
Descriptors: Electronic Learning, Anxiety, Metacognition, Data Collection
Prihar, Ethan; Vanacore, Kirk; Sales, Adam; Heffernan, Neil – International Educational Data Mining Society, 2023
There is a growing need to empirically evaluate the quality of online instructional interventions at scale. In response, some online learning platforms have begun to implement rapid A/B testing of instructional interventions. In these scenarios, students participate in series of randomized experiments that evaluate problem-level interventions in…
Descriptors: Electronic Learning, Intervention, Instructional Effectiveness, Data Collection
Bimal Aklesh Kumar; Sailesh Saras Chand; Munil Shiva Goundar – International Journal of Information and Learning Technology, 2024
Purpose: Mobile learning has seen tremendous growth over the years. Like any other software application, usability is one of the key concerns in its successful implementation. There is a lack of study that provides a comprehensive overview of usability testing of mobile learning applications. Motivated by this a mapping study is conducted.…
Descriptors: Usability, Electronic Learning, Computer Software, Computer Oriented Programs
Xia, Xiaona; Qi, Wanxue – Education and Information Technologies, 2023
Interactive learning is a two-way learning method of learners independently by using computer and network technology. In the interactive relationships, interactive learning plays a role for learners to achieve the learning purpose, interactive learning has become an important effect of online learning, but it also has many problems that need to be…
Descriptors: Foreign Countries, Identification, Interaction, Learning Processes
Lorusso, Nicholas S.; Gemmellaro, M. Denise – Biochemistry and Molecular Biology Education, 2023
One significant impact of the COVID-19 pandemic for educators in forensic science was adapting what is traditionally a very applied field to a virtual learning environment. Because of this, science classes with a practical laboratory component had to implement significant adjustments to ensure that student learning objectives were still met,…
Descriptors: Crime, Science Education, Distance Education, Electronic Learning
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
Nayak, Padmalaya; Vaheed, Sk.; Gupta, Surbhi; Mohan, Neeraj – Education and Information Technologies, 2023
Students' academic performance prediction is one of the most important applications of Educational Data Mining (EDM) that helps to improve the quality of the education process. The attainment of student outcomes in an Outcome-based Education (OBE) system adds invaluable rewards to facilitate corrective measures to the learning processes.…
Descriptors: Predictor Variables, Academic Achievement, Data Collection, Information Retrieval
Karen Dan Wang – ProQuest LLC, 2023
Digital learning environments are becoming increasingly ubiquitous as a wide range of EdTech products and services enter classrooms and households across the globe. One salient attribute of these environments is their capacity to generate large amounts of data as students interact with the technology. These data logs can help construct a detailed…
Descriptors: Educational Technology, Electronic Learning, Data Collection, Problem Solving
Educational Data Mining: An Application of a Predictive Model of Online Student Enrollment Decisions
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
David P. Reid; Timothy D. Drysdale – IEEE Transactions on Learning Technologies, 2024
The designs of many student-facing learning analytics (SFLA) dashboards are insufficiently informed by educational research and lack rigorous evaluation in authentic learning contexts, including during remote laboratory practical work. In this article, we present and evaluate an SFLA dashboard designed using the principles of formative assessment…
Descriptors: Learning Analytics, Laboratory Experiments, Electronic Learning, Feedback (Response)
Saba Sareminia; Vida Mohammadi Dehcheshmeh – International Journal of Information and Learning Technology, 2024
Purpose: Although E-learning has been in use for over two decades, running parallel to traditional learning systems, it has gained increased attention due to its vital role in universities in the wake of the COVID-19 pandemic. The primary challenge within E-learning pertains to the maintenance of sustainable effectiveness and the assurance of…
Descriptors: Educational Improvement, Electronic Learning, Personality Traits, Models
Jensen, Lasse X.; Bearman, Margaret; Boud, David; Konradsen, Flemming – Higher Education: The International Journal of Higher Education Research, 2022
To understand how the digitalization of higher education influences the inter-relationship between students, teachers, and their broader contexts, research must account for the social, cultural, political, and embodied aspects of teaching and learning in digital environments. "Digital ethnography" is a research method that can generate…
Descriptors: Electronic Learning, Ethnography, Higher Education, Research Methodology
Yavuz Akbulut; Abdullah Saykili; Aylin Öztürk; Aras Bozkurt – International Review of Research in Open and Distributed Learning, 2023
Online surveys are widely used in social science research as well as in empirical studies of open, online, and distance education. However, students' responses are likely to be at odds with their actual behavior. In this context, we examined the discrepancies between self-reported use and actual use (i.e., learning analytics data) among 20,646…
Descriptors: Measurement Techniques, Electronic Learning, Distance Education, Open Education
Xing, Wanli; Du, Dongping; Bakhshi, Ali; Chiu, Kuo-Chun; Du, Hanxiang – IEEE Transactions on Learning Technologies, 2021
Predictive modeling in online education is a popular topic in learning analytics research and practice. This study proposes a novel predictive modeling method to improve model transferability over time within the same course and across different courses. The research gaps addressed are limited evidence showing whether a predictive model built on…
Descriptors: Electronic Learning, Bayesian Statistics, Prediction, Models
Jeremy Roschelle; Adam Schellinger – Digital Promise, 2024
SEERNet digital learning platforms (DLPs) are developing new infrastructure to support research in authentic contexts where student learning is happening. In order to contextualize this work within the larger field, we trace historical precedents along four main categories: data repositories, data collection services, research design interfaces,…
Descriptors: Electronic Learning, Data Collection, Research Design, Communities of Practice