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Poitras, Eric; Butcher, Kirsten R.; Orr, Matthew; Hudson, Michelle A.; Larson, Madlyn – Interactive Learning Environments, 2022
This study mined student interactions with visual representations as a means to automate assessment of learning in a complex, inquiry-based learning environment. Log trace data of 143 middle school students' interactions with an interactive map in Research Quest (an inquiry-based, online learning environment) were analyzed. Students used the…
Descriptors: Middle School Students, Electronic Learning, Maps, Science Instruction
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Iwasaki, Patsy Y. – IAFOR Journal of Education, 2021
Information about the overall documentary filmmaking process is available; however, there is a lack of literature and educational resources about how to conduct subject research and data collection. As documentary filmmaking becomes an increasingly democratic endeavor due to technology, and information distribution and education use increases,…
Descriptors: Film Study, Documentaries, Computer Assisted Instruction, Teaching Methods
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Aguilar, J.; Buendia, O.; Pinto, A.; Gutiérrez, J. – Interactive Learning Environments, 2022
Social Learning Analytics (SLA) seeks to obtain hidden information in large amounts of data, usually of an educational nature. SLA focuses mainly on the analysis of social networks (Social Network Analysis, SNA) and the Web, to discover patterns of interaction and behavior of educational social actors. This paper incorporates the SLA in a smart…
Descriptors: Learning Analytics, Cognitive Style, Socialization, Social Networks
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Bull, Susan; Kay, Judy – International Journal of Artificial Intelligence in Education, 2016
The SMILI? (Student Models that Invite the Learner In) Open Learner Model Framework was created to provide a coherent picture of the many and diverse forms of Open Learner Models (OLMs). The aim was for SMILI? to provide researchers with a systematic way to describe, compare and critique OLMs. We expected it to highlight those areas where there…
Descriptors: Educational Research, Data Collection, Data Analysis, Intelligent Tutoring Systems
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Reich, Justin; Tingley, Dustin; Leder-Luis, Jetson; Roberts, Margaret E.; Stewart, Brandon M. – Journal of Learning Analytics, 2015
Dealing with the vast quantities of text that students generate in Massive Open Online Courses (MOOCs) and other large-scale online learning environments is a daunting challenge. Computational tools are needed to help instructional teams uncover themes and patterns as students write in forums, assignments, and surveys. This paper introduces to the…
Descriptors: Large Group Instruction, Online Courses, Data Collection, Data Analysis
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Bull, Susan; Wasson, Barbara – ReCALL, 2016
This paper introduces an open learner model approach to learning analytics to combine the variety of data available from the range of applications and technologies in language learning, for visualisation of language learning competences to learners and teachers in the European language context. Specific examples are provided as illustrations…
Descriptors: Competence, Visualization, Educational Research, Data Collection
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Leblay, Joffrey; Rabah, Mourad; Champagnat, Ronan; Nowakowski, Samuel – International Association for Development of the Information Society, 2018
How can we learn to use properly business software, digital environments, games or intelligent tutoring systems (ITS)? Mainly, we assume that the new user will learn by doing. But what about the efficiency of such a method? Our approach proposes an answer by introducing on-line coaching. In learning process, learners may need guidance to help them…
Descriptors: Intelligent Tutoring Systems, Coaching (Performance), Efficiency, Learning Processes
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Junokas, M. J.; Lindgren, R.; Kang, J.; Morphew, J. W. – Journal of Computer Assisted Learning, 2018
Gestural recognition systems are important tools for leveraging movement-based interactions in multimodal learning environments but personalizing these interactions has proven difficult. We offer an adaptable model that uses multimodal analytics, enabling students to define their physical interactions with computer-assisted learning environments.…
Descriptors: Nonverbal Communication, Multimedia Instruction, Computer Assisted Instruction, Data Analysis
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Kinnebrew, John S.; Segedy, James R.; Biswas, Gautam – IEEE Transactions on Learning Technologies, 2017
Research in computer-based learning environments has long recognized the vital role of adaptivity in promoting effective, individualized learning among students. Adaptive scaffolding capabilities are particularly important in open-ended learning environments, which provide students with opportunities for solving authentic and complex problems, and…
Descriptors: Computer Assisted Instruction, Problem Solving, Learning, Student Behavior
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Qazdar, Aimad; Er-Raha, Brahim; Cherkaoui, Chihab; Mammass, Driss – Education and Information Technologies, 2019
The use of machine learning with educational data mining (EDM) to predict learner performance has always been an important research area. Predicting academic results is one of the solutions that aims to monitor the progress of students and anticipates students at risk of failing the academic pathways. In this paper, we present a framework for…
Descriptors: Data Analysis, Academic Achievement, At Risk Students, High School Students
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Lally, Diane; Forbes, Cory – International Journal of Science Education, 2019
Introductory undergraduate courses present an opportunity to use disciplinary concepts in solving authentic problems. Making complex natural systems accessible to students through computer-based models allows them to practice making evidence-based predictions and communicate understanding. Despite the importance of modelling tools in formal…
Descriptors: Models, Water, Introductory Courses, Undergraduate Students
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Papanikolaou, Kyparisia A. – IEEE Transactions on Learning Technologies, 2015
In this paper, we discuss how externalizing learners' interaction behavior may support learners' explorations in an adaptive educational hypermedia environment that provides activity-oriented content. In particular, we propose a model for producing interpretative views of learners' interaction behavior and we further apply this model to…
Descriptors: Student Behavior, Interaction, Hypermedia, Educational Technology
Nye, Benjamin D.; Morrison, Donald M.; Samei, Borhan – International Educational Data Mining Society, 2015
Archived transcripts from tens of millions of online human tutoring sessions potentially contain important knowledge about how online tutors help, or fail to help, students learn. However, without ways of automatically analyzing these large corpora, any knowledge in this data will remain buried. One way to approach this issue is to train an…
Descriptors: Tutoring, Instructional Effectiveness, Tutors, Models
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Chan, Carol K. K. – Metacognition and Learning, 2012
This discussion paper for this special issue examines co-regulation of learning in computer-supported collaborative learning (CSCL) environments extending research on self-regulated learning in computer-based environments. The discussion employs a socio-cognitive perspective focusing on social and collective views of learning to examine how…
Descriptors: Discussion, Construct Validity, Validity, Metacognition
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Xianwei, Gao; Samuel, Moses; Asmawi, Adelina – Malaysian Online Journal of Educational Technology, 2016
The purpose of this study was to explore critical thinking skills in peer feedback for Business English writing in order to facilitate the quality of peer feedback and quality of Business English writing. "Critical peer feedback" was conceptualized with the integration of "critical thinking" and "peer feedback" in…
Descriptors: Foreign Countries, Undergraduate Students, Models, Critical Thinking
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