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Sun, Geng; Lin, Jiayin; Shen, Jun; Cui, Tingru; Xu, Dongming; Kayastha, Mahesh – British Journal of Educational Technology, 2020
Improving both the quantity and quality of existing data are placed at the center of research for adaptive micro open learning. To cover this research gap, our work targets on the current scarcity of both data and rules that represent open learning activities. An evolutionary rule generator is constructed, which consists of an outer loop and an…
Descriptors: Learning Activities, Data Analysis, Open Education, Computer Software
Dindar, Muhterem; Järvelä, Sanna; Haataja, Eetu – British Journal of Educational Technology, 2020
There is a growing body of research on physiological synchrony (PS) in Collaborative Problem Solving (CPS). However, the current literature presents inconclusive findings about the way in which PS is reflected in cognitive and affective group processes and performance. In light of this, this study investigates the relationship between PS and…
Descriptors: College Students, Metacognition, Physiology, Problem Solving
Pishtari, Gerti; Rodríguez-Triana, María J.; Sarmiento-Márquez, Edna M.; Pérez-Sanagustín, Mar; Ruiz-Calleja, Adolfo; Santos, Patricia; P. Prieto, Luis; Serrano-Iglesias, Sergio; Väljataga, Terje – British Journal of Educational Technology, 2020
Mobile and Ubiquitous Learning (m/u-learning) are finding an increasing adoption in education. They are often distinguished by hybrid learning environments that encompass elements of formal and informal learning, in activities that happen in distributed settings (indoors and outdoors), across physical and virtual spaces. Despite their purported…
Descriptors: Telecommunications, Handheld Devices, Teaching Methods, Learning Processes
Cohen, Anat; Shimony, Udi; Nachmias, Rafi; Soffer, Tal – British Journal of Educational Technology, 2019
This study explores and characterizes learners' participation patterns in MOOC forums, as well as the factors that correlate with learners' participation. Educational data mining and learning analytics methods were used to retrieve and analyze the learners' interpersonal interaction data, which had accumulated in the Coursera log files. The…
Descriptors: Online Courses, Student Participation, Correlation, Mass Instruction
Ahn, Byunghoon; Harley, Jason M. – British Journal of Educational Technology, 2020
Learning analytics (LA) incorporates analyzing cognitive, social and emotional processes in learning scenarios to make informed decisions regarding instructional design and delivery. Research has highlighted important roles that emotions play in learning. We have extended this field of research by exploring the role of emotions in a relatively…
Descriptors: Nonverbal Communication, Computer Software, LGBTQ People, Data Analysis
Montgomery, Amanda P.; Mousavi, Amin; Carbonaro, Michael; Hayward, Denyse V.; Dunn, William – British Journal of Educational Technology, 2019
Blended learning (BL) is a popular e-Learning model in higher education that has the potential to take advantage of learning analytics (LA) to support student learning. This study utilized LA to investigate fourth-year undergraduates' (n = 157) use of self-regulated learning (SRL) within the online components of a previously unexamined BL…
Descriptors: Blended Learning, Educational Technology, Higher Education, Undergraduate Students
Kadijevich, Djordje M. – British Journal of Educational Technology, 2015
Because the relationship between computer use and achievement is still puzzling, there is a need to prepare and analyze good quality datasets on computer use and achievement. Such a dataset can be derived from TIMSS data. This paper describes how this dataset can be prepared. It also gives an example of how the dataset may be analyzed. The…
Descriptors: Mathematics Achievement, Computer Use, Test Items, Problem Sets
Li, Lan; Liu, Xiongyi; Steckelberg, Allen L. – British Journal of Educational Technology, 2010
This study investigated the relationship between the quality of peer assessment and the quality of student projects in a technology application course for teacher education students. Forty-three undergraduate student participants completed the assigned projects. During the peer assessment process, students first anonymously rated and commented on…
Descriptors: Feedback (Response), Undergraduate Students, Student Projects, Data Analysis