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Khosravi, Hassan; Cooper, Kendra M. L. – Journal of Learning Analytics, 2018
Educational environments continue to evolve rapidly to address the needs of diverse, growing student populations while embracing advances in pedagogy and technology. In this changing landscape, ensuring consistency among the assessments for different offerings of a course (within or across terms), providing meaningful feedback about student…
Descriptors: Graphs, Academic Achievement, Student Evaluation, Models
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Chiu, Ming Ming – Journal of Learning Analytics, 2018
Learning analysts often consider whether learning processes across time are related (1) to one another or (2) to learning outcomes at higher levels. For example, are a group's temporal sequences of talk (e.g., correct evaluation [right arrow] correct, new idea) during its problem solving related to its group solution? I show how to address these…
Descriptors: Statistical Analysis, Models, Data Analysis, Regression (Statistics)
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Fiel, Jeremy; Lawless, Kimberly A.; Brown, Scott W. – Journal of Learning Analytics, 2018
One feature of self-paced online courses is greater learner control over the timing of their work in a course. However, the greater timing flexibility that learners enjoy in such environments may play a different role in the learning process than has been previously observed in formal online or face-to-face courses. As such, the study of work…
Descriptors: Pacing, Individualized Instruction, Online Courses, Faculty Development
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Hart, Sara A.; Daucourt, Mia; Ganley, Colleen M. – Journal of Learning Analytics, 2017
In this study, we explore student achievement in a semester-long flipped Calculus II course, combining various predictor measures related to student attitudes (math anxiety, math confidence, math interest, math importance) and cognitive skills (spatial skills, approximate number system), as well as student engagement with the online system…
Descriptors: Academic Achievement, Calculus, Mathematics Instruction, Educational Technology
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Andrade, Alejandro; Danish, Joshua A.; Maltese, Adam V. – Journal of Learning Analytics, 2017
Interactive learning environments with body-centric technologies lie at the intersection of the design of embodied learning activities and multimodal learning analytics. Sensing technologies can generate large amounts of fine-grained data automatically captured from student movements. Researchers can use these fine-grained data to create a…
Descriptors: Measurement, Interaction, Models, Educational Environment
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Epp, Carrie Demmans; Phirangee, Krystle; Hewitt, Jim – Journal of Learning Analytics, 2017
Identifying which online behaviours and interactions are associated with student perceptions of being supported will enable a deeper understanding of how those activities contribute to learning experiences. Student language is one aspect of their interaction in need of greater exploration within discourse-based online learning environments. As a…
Descriptors: Student Behavior, Computer Mediated Communication, Form Classes (Languages), Language Usage
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Sharma, Kshitij; Chavez-Demoulin, Valérie; Dillenbourg, Pierre – Journal of Learning Analytics, 2017
The statistics used in education research are based on central trends such as the mean or standard deviation, discarding outliers. This paper adopts another viewpoint that has emerged in statistics, called extreme value theory (EVT). EVT claims that the bulk of normal distribution is comprised mainly of uninteresting variations while the most…
Descriptors: Foreign Countries, Educational Research, Statistical Distributions, Theories
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Siadaty, Melody; Gaševic, Dragan; Hatala, Marek – Journal of Learning Analytics, 2016
To keep pace with today's rapidly growing knowledge-driven society, productive self-regulation of one's learning processes are essential. We introduce and discuss a trace-based measurement protocol to measure the effects of scaffolding interventions on self-regulated learning (SRL) processes. It guides tracing of learners' actions in a learning…
Descriptors: Metacognition, Learning Processes, Intervention, Technology Uses in Education
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Douglas, Kerrie A.; Bermel, Peter; Alam, Md Monzurul; Madhavan, Krishna – Journal of Learning Analytics, 2016
MOOCs attract a large number of learners with largely unknown diversity in terms of motivation, ability, and goals. To understand more about learners in highly technical engineering MOOCs, this study investigates patterns of learners' (n = 337) behaviour and performance in the Nanophotonic Modelling MOOC, offered through nanoHUB-U. The authors…
Descriptors: Online Courses, Large Group Instruction, Distance Education, Technology Uses in Education
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Gray, Geraldine; McGuinness, Colm; Owende, Philip; Hofmann, Markus – Journal of Learning Analytics, 2016
This paper reports on a study to predict students at risk of failing based on data available prior to commencement of first year. The study was conducted over three years, 2010 to 2012, on a student population from a range of academic disciplines, n=1,207. Data was gathered from both student enrollment data and an online, self-reporting,…
Descriptors: Prediction, At Risk Students, Academic Failure, College Freshmen
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Sonnenberg, Christoph; Bannert, Maria – Journal of Learning Analytics, 2015
According to research examining self-regulated learning (SRL), we regard individual regulation as a specific sequence of regulatory activities. Ideally, students perform various learning activities, such as analyzing, monitoring, and evaluating cognitive and motivational aspects during learning. Metacognitive prompts can foster SRL by inducing…
Descriptors: Metacognition, Cues, Control Groups, Outcomes of Education
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Lowes, Susan; Lin, Peiyi; Kinghorn, Brian – Journal of Learning Analytics, 2015
As enrolment in online courses has grown and LMS data has become accessible for analysis, researchers have begun to examine the link between in-course behaviours and course outcomes. This paper explores the use of readily available LMS data generated by approximately 700 students enrolled in the 12 online courses offered by Pamoja Education, the…
Descriptors: Integrated Learning Systems, Student Behavior, Online Courses, Asynchronous Communication
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Colthorpe, Kay; Zimbardi, Kirsten; Ainscough, Louise; Anderson, Stephen – Journal of Learning Analytics, 2015
It is well established that a student's capacity to regulate his or her own learning is a key determinant of academic success, suggesting that interventions targeting improvements in self-regulation will have a positive impact on academic performance. However, to evaluate the success of such interventions, the self-regulatory characteristics of…
Descriptors: Data Analysis, Data Collection, Educational Research, Self Control
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Kovanovic, Vitomir; Gaševic, Dragan; Dawson, Shane; Joksimovic, Srecko; Baker, Ryan S.; Hatala, Marek – Journal of Learning Analytics, 2015
With widespread adoption of Learning Management Systems (LMS) and other learning technology, large amounts of data--commonly known as trace data--are readily accessible to researchers. Trace data has been extensively used to calculate time that students spend on different learning activities--typically referred to as time-on-task. These measures…
Descriptors: Time on Task, Computation, Validity, Data Analysis
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Kovanovic, Vitomir; Gaševic, Dragan; Hatala, Marek – Journal of Learning Analytics, 2014
This paper describes doctoral research that focuses on the development of a learning analytics framework for inquiry-based digital learning. Building on the Community of Inquiry model (CoI)--a foundation commonly used in the research and practice of digital learning and teaching--this research builds on the existing body of knowledge in two…
Descriptors: Communities of Practice, Doctoral Dissertations, Electronic Learning, Active Learning
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