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Hershkovitz, Arnon; Sitman, Raquel; Israel-Fishelson, Rotem; Eguíluz, Andoni; Garaizar, Pablo; Guenaga, Mariluz – Interactive Learning Environments, 2019
Many worldwide initiatives consider both creativity and computational thinking as crucial skills for future citizens, making them a priority for today's learners. We studied the associations between these two constructs among middle school students (N = 57), considering two types of creativity: a general creative thinking, and a specific…
Descriptors: Creativity, Creative Thinking, Computation, Computer Assisted Instruction
Klein, Carrie; Lester, Jaime; Nguyen, Thien; Justen, Abigail; Rangwala, Huzefa; Johri, Aditya – Journal of Educational Technology Systems, 2019
An instrumental case study was conducted at a large, public research university in the mid-Atlantic region of the United States to understand undergraduate use of learning analytics dashboard (LAD) interventions. Eighty-one undergraduate students participated in focus groups. Scenario-based questions, modeled on current and future LAD…
Descriptors: Learning Analytics, Undergraduate Students, Computer Uses in Education, Computer Interfaces
van Leeuwen, Anouschka – Educational Technology Research and Development, 2019
The flipped classroom model is a form of blended learning in which delivery of content occurs with online materials, and face-to-face meetings are used for teacher-guided practice. It is important that teachers stay up to date with the activities students engage in, which may be accomplished with the help of learning analytics (LA). This study…
Descriptors: Teacher Attitudes, Usability, Learning Analytics, Blended Learning
Guajardo Leal, Brenda Edith; Valenzuela González, Jaime Ricardo – Online Learning, 2019
MOOCs are characterized as being courses to which a large number of students enroll, but only a small fraction completes them. An understanding of students' engagement construct is essential to minimize dropout rates. This research is of a quantitative design and exploratory in nature and investigates the interaction between contextual factors…
Descriptors: Learner Engagement, Predictor Variables, Online Courses, Energy Conservation
Gardner, Josh; Yang, Yuming; Baker, Ryan S.; Brooks, Christopher – International Educational Data Mining Society, 2019
Replication of machine learning experiments can be a useful tool to evaluate how both "modeling" and "experimental design" contribute to experimental results; however, existing replication efforts focus almost entirely on modeling alone. In this work, we conduct a three-part replication case study of a state-of-the-art LSTM…
Descriptors: Online Courses, Large Group Instruction, Prediction, Models
Morsy, Sara; Karypis, George – International Educational Data Mining Society, 2019
Grade prediction for future courses not yet taken by students is important as it can help them and their advisers during the process of course selection as well as for designing personalized degree plans and modifying them based on their performance. One of the successful approaches for accurately predicting a student's grades in future courses is…
Descriptors: Grades (Scholastic), Models, Prediction, Predictor Variables
Figueroa, Christina A. – ProQuest LLC, 2019
Online information is not regulated for quality of content or accuracy; therefore, content found online is not always complete, accurate, or unbiased. While media-literacy education exists, educators often only see the final result of students' online research in the form of the assignment or a works cited page. For teachers to address the media…
Descriptors: Student Behavior, Student Research, Media Literacy, Learning Analytics
Yin, Chengjiu; Hwang, Gwo-Jen – Knowledge Management & E-Learning, 2018
E-books have been introduced to educational institutions in many countries. The use of e-books in traditional classrooms enables the recording of learning logs. Recently, researchers have begun to carry out learning analytics on the learning logs of e-books. However, there has been limited attention devoted to understanding the types of learning…
Descriptors: Electronic Publishing, Learning Analytics, Learning Strategies, Student Behavior
Wells, Jason; Spence, Aaron; McKenzie, Sophie – Journal of Information Technology Education: Research, 2021
Aim/Purpose: This paper focuses on understanding undergraduate computing student-learning behaviour through reviewing their online activity in a university online learning management system (LMS), along with their grade outcome, across three subjects. A specific focus is on the activity of students who failed the computing subjects. Background:…
Descriptors: Student Participation, Undergraduate Students, At Risk Students, Academic Failure
Mshayisa, Vusi Vincent; Basitere, Moses – Journal of Food Science Education, 2021
In STEM (science, technology, engineering, and mathematics) courses, undergraduate laboratory classes are vital for students to develop competencies such as critical observation, collaboration, critical thinking, technical, and problem-solving skills. Thus, for students to successfully acquire these competencies, preparation for laboratory classes…
Descriptors: Flipped Classroom, Teaching Methods, Student Attitudes, STEM Education
Tempelaar, Dirk; Rienties, Bart; Nguyen, Quan – Educational Technology & Society, 2021
Precision education requires two equally important conditions: accurate predictions of academic performance based on early observations of the learning process and the availability of relevant educational intervention options. The field of learning analytics (LA) has made important contributions to the realisation of the first condition,…
Descriptors: Learning Analytics, Individualized Instruction, Blended Learning, Electronic Learning
Kisling, Reid; Peterson, Andrew; Nisbet, Robert – Strategic Enrollment Management Quarterly, 2021
Data analytics is undergoing an evolution through effective data use to support both operational and learning analytics models. However, this evolution will require that institutional leaders transform their data systems to best support the needs of application modeling and use their intuition to help drive the development of better analytical…
Descriptors: Higher Education, Learning Analytics, Models, Instructional Leadership
Mansouri, Taha; ZareRavasan, Ahad; Ashrafi, Amir – Journal of Information Technology Education: Research, 2021
Aim/Purpose: This research aims to present a brand-new approach for student performance prediction using the Learning Fuzzy Cognitive Map (LFCM) approach. Background: Predicting student academic performance has long been an important research topic in many academic disciplines. Different mathematical models have been employed to predict student…
Descriptors: Cognitive Mapping, Models, Prediction, Performance Factors
Nahar, Khaledun; Shova, Boishakhe Islam; Ria, Tahmina; Rashid, Humayara Binte; Islam, A. H. M. Saiful – Education and Information Technologies, 2021
Information is everywhere in a hidden and scattered way. It becomes useful when we apply Data mining to extracts the hidden, meaningful, and potentially useful patterns from these vast data resources. Educational data mining ensures a quality education by analyzing educational data based on various aspects. In this paper, we have analyzed the…
Descriptors: Learning Analytics, College Students, Engineering Education, Data Collection
Monbec, Laetitia; Tilakaratna, Namala; Brooke, Mark; Lau, Siew Tiang; Chan, Yah Shih; Wu, Vivien – Assessment & Evaluation in Higher Education, 2021
This paper reports on an interdisciplinary pedagogical research project involving academic literacy experts and lecturers at a School of Nursing. Specifically, the paper focusses on the development of a data-driven analytical rubric to teach and assess critical reflections in year-one nursing. The purpose of the project was to support the teaching…
Descriptors: Interdisciplinary Approach, Nursing Education, Literacy, Academic Language