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Xu, Jia; Wei, Tingting; Lv, Pin – International Educational Data Mining Society, 2022
In an Intelligent Tutoring System (ITS), problem (or question) difficulty is one of the most critical parameters, directly impacting problem design, test paper organization, result analysis, and even the fairness guarantee. However, it is very difficult to evaluate the problem difficulty by organized pre-tests or by expertise, because these…
Descriptors: Prediction, Programming, Natural Language Processing, Databases
Tran, Tuan M.; Hasegawa, Shinobu – International Association for Development of the Information Society, 2022
A learner model reflects learning patterns and characteristics of a learner. A learner model with learning history and its effectiveness plays a significant role in supporting a learner's understanding of their strengths and weaknesses of their way of learning in order to make proper adjustments for improvement. Nowadays, learners have been…
Descriptors: Markov Processes, Learning Processes, Models, Scores
Taylor V. Williams – ProQuest LLC, 2022
Clustering, a prevalent class of machine learning (ML) algorithms used in data mining and pattern-finding--has increasingly helped engineering education researchers and educators see and understand assessment patterns at scale. However, a challenge remains to make ML-enabled educational inferences that are useful and reliable for research or…
Descriptors: Multivariate Analysis, Data Analysis, Student Evaluation, Large Group Instruction
Aaron Bere; Patrick Chirilele; Rugare Chitiga – International Association for Development of the Information Society, 2022
The purpose of this paper is to present an empirical investigation of the critical determinants for the adoption of learning analytics in higher education. A conceptual model was proposed to understand better the adoption of learning analytics in higher education by teaching staff. Structural equation modelling is used for testing and validating…
Descriptors: Learning Analytics, Validity, Research Methodology, Higher Education
Danielle S. McNamara; Tracy Arner; Elizabeth Reilley; Paul Alvarado; Chani Clark; Thomas Fikes; Annie Hale; Betheny Weigele – Grantee Submission, 2022
Accounting for complex interactions between contextual variables and learners' individual differences in aptitudes and background requires building the means to connect and access learner data at large scales, across time, and in multiple contexts. This paper describes the ASU Learning@Scale (L@S) project to develop a digital learning network…
Descriptors: Electronic Learning, Educational Technology, Networks, Learning Analytics
Saint, John; Whitelock-Wainwright, Alexander; Gasevic, Dragan; Pardo, Abelardo – IEEE Transactions on Learning Technologies, 2020
The recent focus on learning analytics (LA) to analyze temporal dimensions of learning holds the promise of providing insights into latent constructs, such as learning strategy, self-regulated learning (SRL), and metacognition. These methods seek to provide an enriched view of learner behaviors beyond the scope of commonly used correlational or…
Descriptors: Undergraduate Students, Engineering Education, Learning Analytics, Learning Strategies
Emerson, Andrew; Cloude, Elizabeth B.; Azevedo, Roger; Lester, James – British Journal of Educational Technology, 2020
A distinctive feature of game-based learning environments is their capacity to create learning experiences that are both effective and engaging. Recent advances in sensor-based technologies such as facial expression analysis and gaze tracking have introduced the opportunity to leverage multimodal data streams for learning analytics. Learning…
Descriptors: Learning Analytics, Game Based Learning, Play, Eye Movements
Beile, Penny; Choudhury, Kanak; Mulvihill, Rachel; Wang, Morgan – College & Research Libraries, 2020
This large-scale study was conducted for the purposes of determining how representative library users are compared to the whole student population, to explore how library services contribute to student success, and to position the library to be included in the institution's learning analytics landscape. To that end, data were collected as students…
Descriptors: Academic Libraries, Library Services, Users (Information), College Students
Yu, Renzhe; Li, Qiujie; Fischer, Christian; Doroudi, Shayan; Xu, Di – International Educational Data Mining Society, 2020
In higher education, predictive analytics can provide actionable insights to diverse stakeholders such as administrators, instructors, and students. Separate feature sets are typically used for different prediction tasks, e.g., student activity logs for predicting in-course performance and registrar data for predicting long-term college success.…
Descriptors: Prediction, Accuracy, College Students, Success
Wei, Shuang – ProQuest LLC, 2020
CAL (Computer Assisted Learning) programs are widespread today in schools and families due to the effectiveness of CAL programs in improving students' learning and task performance. The flourishing of CAL programs in education has brought large amounts of students' learning data including log data, performance data, mouse movement data, eye…
Descriptors: Visualization, Problem Solving, Elementary School Students, Computer Assisted Instruction
Young, Nicholas T.; Caballero, Marcos D. – Journal of Educational Data Mining, 2021
We encounter variables with little variation often in educational data mining (EDM) due to the demographics of higher education and the questions we ask. Yet, little work has examined how to analyze such data. Therefore, we conducted a simulation study using logistic regression, penalized regression, and random forest. We systematically varied the…
Descriptors: Prediction, Models, Learning Analytics, Mathematics
Chen, Xieling; Zou, Di; Xie, Haoran; Wang, Fu Lee – International Journal of Educational Technology in Higher Education, 2021
Innovative information and communication technologies have reformed higher education from the traditional way to smart learning. Smart learning applies technological and social developments and facilitates effective personalized learning with innovative technologies, especially smart devices and online technologies. Smart learning has attracted…
Descriptors: Information Technology, Electronic Learning, Bibliometrics, Periodicals
Rets, Irina; Herodotou, Christothea; Bayer, Vaclav; Hlosta, Martin; Rienties, Bart – International Journal of Educational Technology in Higher Education, 2021
Learning analytics dashboards (LADs) can provide learners with insights about their study progress through visualisations of the learner and learning data. Despite their potential usefulness to support learning, very few studies on LADs have considered learners' needs and have engaged learners in the process of design and evaluation. Aligning with…
Descriptors: Learning Analytics, Educational Technology, Usability, College Students
Lin, Chi-Jen; Mubarok, Husni – Educational Technology & Society, 2021
One of the biggest challenges for EFL (English as Foreign Language) students to learn English is the lack of practicing environments. Although language researchers have attempted to conduct flipped classrooms to increase the practicing time in class, EFL students generally have difficulties interacting with peers and teachers in English in class.…
Descriptors: Learning Analytics, Cognitive Mapping, Artificial Intelligence, Computer Mediated Communication
Choi, Ikkyu; Deane, Paul – Language Assessment Quarterly, 2021
Keystroke logs provide a comprehensive record of observable writing processes. Previous studies examining the keystroke logs of young L1 English writers performing experimental writing tasks have identified writing processes features predictive of the quality of responses. Contrarily, large-scale studies on the dynamic and temporal nature of L2…
Descriptors: Writing Processes, Writing Evaluation, Computer Assisted Testing, Learning Analytics