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Ning, Xiaoke – International Journal of Web-Based Learning and Teaching Technologies, 2023
With the vigorous development of intelligent campus construction, great changes have taken place in the development of information technology in colleges and universities from the previous digital to intelligent development. In the teaching process, the analysis of students' classroom learning has also changed from the previous manual observation…
Descriptors: College Students, Algorithms, Student Behavior, Artificial Intelligence
Hayama, Tessai; Odate, Hidetaka; Ishida, Naoto – International Journal on E-Learning, 2020
The field of learning analytics has been limited by its frequent dependence on learning logs created by students while learning. Most of the research has dealt with the relationships between learning during a course and the achieved results. Although students' in-class behavior affects learning achievement, this remains a challenging aspect to…
Descriptors: Student Behavior, Data Collection, Measurement Equipment, College Students
Melcher, Jennifer; Lavoie, Joel; Hays, Ryan; D'Mello, Ryan; Rauseo-Ricupero, Natali; Camacho, Erica; Rodriguez-Villa, Elena; Wisniewski, Hannah; Lagan, Sarah; Vaidyam, Aditya; Torous, John – Journal of American College Health, 2023
Objective: This study assessed the feasibility of capturing smartphone based digital phenotyping data in college students during the COVID-19 pandemic with the goal of understanding how digital biomarkers of behavior correlate with mental health. Participants: Participants were 100 students enrolled in 4-year universities. Methods: Each…
Descriptors: Telecommunications, Handheld Devices, College Students, COVID-19
Kai Li – International Association for Development of the Information Society, 2023
Assessing students' performance in online learning could be executed not only by the traditional forms of summative assessments such as using essays, assignments, and a final exam, etc. but also by more formative assessment approaches such as interaction activities, forum posts, etc. However, it is difficult for teachers to monitor and assess…
Descriptors: Student Evaluation, Online Courses, Electronic Learning, Computer Literacy
King, Seth A.; Dzenga, Chaidamoyo; Burch, Taneal; Kennedy, Krystal – Journal of Behavioral Education, 2021
Virtual reality (VR) places individuals within a simulated experience using an array of visual, auditory, and tactile interfaces. Research suggests VR, which facilitates the rehearsal of actual job duties and performance assessment during training, may improve professional development across a range of disciplines. Although studies incorporating…
Descriptors: Computer Simulation, Training, Behavior Change, College Students
Chinsook, Kittipong; Khajonmote, Withamon; Klintawon, Sununta; Sakulthai, Chaiyan; Leamsakul, Wicha; Jantakoon, Thada – Higher Education Studies, 2022
Big data is an important part of innovation that has recently attracted a lot of interest from academics and practitioners alike. Given the importance of the education industry, there is a growing trend to investigate the role of big data in this field. Much research has been undertaken to date in order to better understand the use of big data in…
Descriptors: Student Behavior, Learning Analytics, Computer Software, Rating Scales
Cenka, Baginda Anggun Nan; Santoso, Harry B.; Junus, Kasiyah – Knowledge Management & E-Learning, 2022
Online learning implementation has been growing year by year across countries, including Indonesia. Many higher education institutions use a Learning Management System (LMS) to facilitate online learning. Unfortunately, many issues arise during online learning implementation, such as a lack of student behaviour monitoring. This study adopts an…
Descriptors: Knowledge Management, Electronic Learning, Integrated Learning Systems, Student Behavior
Howlin, Colm P.; Dziuban, Charles D. – International Educational Data Mining Society, 2019
Clustering of educational data allows similar students to be grouped, in either crisp or fuzzy sets, based on their similarities. Standard approaches are well suited to identifying common student behaviors; however, by design, they put much less emphasis on less common behaviors or outliers. The approach presented in this paper employs fuzzing…
Descriptors: Data Collection, Student Behavior, Learning Strategies, Feedback (Response)
Igusa, Go – Journal of Education and Training Studies, 2018
This publication seeks to consider statistics education in Japan while referencing a student paper. The paper to be considered is as follows: "Gakusei no Koudou ga Gakusei Seikatsu Manzokudo ni Ataeru Eikyou" (The Influence of Student Behavior on the Degree of Satisfaction Perceived in Student Life).
Descriptors: Foreign Countries, College Students, Student Research, Statistics
Alturkistani, Abrar; Car, Josip; Majeed, Azeem; Brindley, David; Wells, Glenn; Meinert, Edward – International Association for Development of the Information Society, 2018
Massive Open Online Courses (MOOCs) are widely used to deliver specialized education and training in different fields. Determining the effectiveness of these courses is an integral part of delivering comprehensive, high-quality learning. This study is an evaluation of a MOOC offered by Imperial College London in collaboration with Health iQ…
Descriptors: Large Group Instruction, Online Courses, Educational Technology, Technology Uses in Education
Cohen, Anat – Educational Technology Research and Development, 2017
Persistence in learning processes is perceived as a central value; therefore, dropouts from studies are a prime concern for educators. This study focuses on the quantitative analysis of data accumulated on 362 students in three academic course website log files in the disciplines of mathematics and statistics, in order to examine whether student…
Descriptors: Academic Persistence, Predictor Variables, Dropouts, At Risk Students
Kahan, Tali; Soffer, Tal; Nachmias, Rafi – International Review of Research in Open and Distributed Learning, 2017
In recent years there has been a proliferation of massive open online courses (MOOCs), which provide unprecedented opportunities for lifelong learning. Registrants approach these courses with a variety of motivations for participation. Characterizing the different types of participation in MOOCs is fundamental in order to be able to better…
Descriptors: College Students, Student Behavior, Online Courses, Large Group Instruction
McBroom, Jessica; Jeffries, Bryn; Koprinska, Irena; Yacef, Kalina – International Educational Data Mining Society, 2016
Effective mining of data from online submission systems offers the potential to improve educational outcomes by identifying student habits and behaviours and their relationship with levels of achievement. In particular, it may assist in identifying students at risk of performing poorly, allowing for early intervention. In this paper we investigate…
Descriptors: Data Collection, Student Behavior, Academic Achievement, Correlation
Dvorak, Tomas; Jia, Miaoqing – Journal of Learning Analytics, 2016
This study analyzes the relationship between students' online work habits and academic performance. We utilize data from logs recorded by a course management system (CMS) in two courses at a small liberal arts college in the U.S. Both courses required the completion of a large number of online assignments. We measure three aspects of students'…
Descriptors: Online Courses, Educational Technology, Study Habits, Academic Achievement
Poitras, Eric; Doleck, Tenzin; Huang, Lingyun; Li, Shan; Lajoie, Susanne – Australasian Journal of Educational Technology, 2017
A primary concern of teacher technology education is for pre-service teachers to develop a sophisticated mental model of the affordances of technology that facilitates both teaching and learning with technology. One of the main obstacles to developing the requisite technological pedagogical content knowledge is the inherent challenge faced by…
Descriptors: Preservice Teachers, Teacher Education Programs, Technology Uses in Education, Educational Technology