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Esteban Villalobos; Mar Perez-Sanagustin; Roger Azevedo; Cedric Sanza; Julien Broisin – IEEE Transactions on Learning Technologies, 2024
Blended learning (BL) has become increasingly popular in higher education institutions. Despite its popularity and the advances in methodologies for the detection of learning tactics and strategies from trace data, little is known about how they apply to BL settings and, therefore, how students use them to plan, organize, monitor, and regulate…
Descriptors: Metacognition, Learning Strategies, Blended Learning, Instructional Design
Qiyun Wang; Qian Huang – IEEE Transactions on Learning Technologies, 2024
Blended synchronous learning enables online learners to participate in class activities from geographically separated sites. Due to various challenges, however, online learners are often harder to be engaged and their engagement levels are lower than that of classroom counterparts. This review summarized and synthesized the challenges that led to…
Descriptors: Journal Articles, Blended Learning, Synchronous Communication, Barriers
Analysis and Prediction of Students' Performance in a Computer-Based Course through Real-Time Events
Lucia Uguina-Gadella; Iria Estevez-Ayres; Jesus Arias Fisteus; Carlos Alario-Hoyos; Carlos Delgado Kloos – IEEE Transactions on Learning Technologies, 2024
Students learn not only directly from their teachers and books, but also by using their computers, tablets, and phones. Monitoring these learning environments creates new opportunities for teachers to track students' progress. In particular, this article is based on gathering real-time events as students interact with learning tools and materials…
Descriptors: Predictor Variables, Academic Achievement, Computer Assisted Instruction, Electronic Learning
Han, Feifei; Ellis, Robert A.; Pardo, Abelardo – IEEE Transactions on Learning Technologies, 2022
This article uses digital traces to help identify students' online learning strategies by making a clear distinction between the descriptive features (the proportional distribution of students' different online learning actions) and quantitative aspects (the total number of the online learning sessions), a distinction that has not been properly…
Descriptors: Electronic Learning, Learning Strategies, Student Behavior, Educational Environment
Zoe C. M. Davidson; Shuping Dang; Xenofon Vasilakos – IEEE Transactions on Learning Technologies, 2024
Raspberry Pi Pico, based on chip RP2040, is an easy-to-use development microcontroller board that can provide flexible input/output functions and meets the teaching needs of basic electronics to first-year university undergraduates. This article presents our blended laboratory design using Raspberry Pi Pico for the course unit Digital Circuits and…
Descriptors: College Freshmen, Electronics, Technology Education, Scientific Concepts
Wan, Pengfei; Wang, Xiaoming; Lin, Yaguang; Pang, Guangyao – IEEE Transactions on Learning Technologies, 2021
Learners' autonomous learning is at the heart of modern education, and the convenient network brings new opportunities for it. We notice that learners mainly use the combination of online and offline learning methods to complete the entire autonomous learning process, but most of the existing models cannot effectively describe the complex process…
Descriptors: Independent Study, Personal Autonomy, Learning Processes, Electronic Learning
Albo, Laia; Hernandez-Leo, Davinia – IEEE Transactions on Learning Technologies, 2021
This article presents an evaluation of edCrumble, a blended learning authoring tool for teachers. The tool visually represents learning designs and integrates data analytics to scaffold teacher design decisions. In addition to assessing the usability of edCrumble using Usability Metric for User Experience questionnaire, analyses of participant…
Descriptors: Programming, Blended Learning, Teaching Methods, Instructional Design
Ellis, Robert A.; Han, Feifei; Pardo, Abelardo – IEEE Transactions on Learning Technologies, 2019
Collaboration is an increasingly important and difficult skill for graduate engineers to develop. While universities provide some measures of collaboration ability of students on graduation, there is still some dissatisfaction with the level of preparedness of students for collaborative activity in the workplace. This paper presents a case study…
Descriptors: Engineering Education, College Freshmen, Blended Learning, Cooperation
Hernandez, Josefina; Rodriguez, Fernanda; Hilliger, Isabel; Perez-Sanagustin, Mar – IEEE Transactions on Learning Technologies, 2019
The effectiveness of remedial mathematics courses in post-secondary education has been a controversial topic for years. Higher Education institutions need their students to have basic understandings of the subjects to be imparted in the first semesters, but since they come with different backgrounds and prior knowledge, this is not always possible…
Descriptors: Online Courses, Remedial Mathematics, Adoption (Ideas), Student Attitudes
Jevremovic, Aleksandar; Shimic, Goran; Veinovic, Mladen; Ristic, Nenad – IEEE Transactions on Learning Technologies, 2017
The case study presented in this paper describes the pedagogical aspects and experience gathered while using an e-learning tool named IPA-PBL. Its main purpose is to provide additional motivation for adopting theoretical principles and procedures in a computer networks course. In the proposed model, the sequencing of activities of the learning…
Descriptors: Problem Based Learning, Computer Networks, Case Studies, Electronic Learning
Gitinabard, Niki; Xu, Yiqiao; Heckman, Sarah; Barnes, Tiffany; Lynch, Collin F. – IEEE Transactions on Learning Technologies, 2019
Blended courses that mix in-person instruction with online platforms are increasingly common in secondary education. These platforms record a rich amount of data on students' study habits and social interactions. Prior research has shown that these metrics are correlated with students performance in face-to-face classes. However, predictive models…
Descriptors: Blended Learning, Educational Technology, Technology Uses in Education, Prediction
Fincham, Ed; Gasevic, Dragan; Jovanovic, Jelena; Pardo, Abelardo – IEEE Transactions on Learning Technologies, 2019
Research into self-regulated learning has traditionally relied upon self-reported data. While there is a rich body of literature that has extracted invaluable information from such sources, it suffers from a number of shortcomings. For instance, it has been shown that surveys often provide insight into students' perceptions about learning rather…
Descriptors: Study Habits, Learning Strategies, Independent Study, Educational Research
Govaerts, Sten; Holzer, Adrian; Kocher, Bruno; Vozniuk, Andrii; Garbinato, Benoit; Gillet, Denis – IEEE Transactions on Learning Technologies, 2018
Improving face-to-face (f2f) interaction in large classrooms is a challenging task as student participation can be hard to initiate. Thanks to the wide adoption of personal mobile devices, it is possible to blend digital and face-to-face interaction and integrate co-located social media applications in the classroom. To better understand how such…
Descriptors: Blended Learning, Social Media, Computer Oriented Programs, Technology Uses in Education
Wan, Han; Liu, Kangxu; Yu, Qiaoye; Gao, Xiaopeng – IEEE Transactions on Learning Technologies, 2019
Most educational institutions adopted the hybrid teaching mode through learning management systems. The logging data/clickstream could describe learners' online behavior. Many researchers have used them to predict students' performance, which has led to a diverse set of findings, but how to use insights from captured data to enhance learning…
Descriptors: Educational Practices, Learner Engagement, Identification, Study Habits
Conijn, Rianne; Snijders, Chris; Kleingeld, Ad; Matzat, Uwe – IEEE Transactions on Learning Technologies, 2017
With the adoption of Learning Management Systems (LMSs) in educational institutions, a lot of data has become available describing students' online behavior. Many researchers have used these data to predict student performance. This has led to a rather diverse set of findings, possibly related to the diversity in courses and predictor variables…
Descriptors: Blended Learning, Predictor Variables, Predictive Validity, Predictive Measurement
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