NotesFAQContact Us
Collection
Advanced
Search Tips
Showing all 7 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Marijn Martens; Ralf De Wolf; Lieven De Marez – Technology, Knowledge and Learning, 2025
Algorithmic decision-making systems such as Learning Analytics (LA) are widely used in an educational setting ranging from kindergarten to university. Most research focuses on how LA is used and adopted by teachers. However, the perspective of students and parents who experience the (in)direct consequences of these systems is underexplored. This…
Descriptors: Algorithms, Decision Making, Learning Analytics, Secondary School Students
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Wenyi Lu; Joseph Griffin; Troy D. Sadler; James Laffey; Sean P. Goggins – Journal of Learning Analytics, 2025
Game-based learning (GBL) is increasingly recognized as an effective tool for teaching diverse skills, particularly in science education, due to its interactive, engaging, and motivational qualities, along with timely assessments and intelligent feedback. However, more empirical studies are needed to facilitate its wider application in school…
Descriptors: Game Based Learning, Predictor Variables, Evaluation Methods, Educational Games
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Makhlouf, Jihed; Mine, Tsunenori – Journal of Educational Data Mining, 2020
In recent years, we have seen the continuous and rapid increase of job openings in Science, Technology, Engineering and Math (STEM)-related fields. Unfortunately, these positions are not met with an equal number of workers ready to fill them. Efforts are being made to find durable solutions for this phenomena, and they start by encouraging young…
Descriptors: Learning Analytics, STEM Education, Science Careers, Career Choice
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Almeda, Ma. Victoria; Baker, Ryan S. – Journal of Educational Data Mining, 2020
Given the increasing need for skilled workers in science, technology, engineering, and mathematics (STEM), there is a burgeoning interest to encourage young students to pursue a career in STEM fields. Middle school is an opportune time to guide students' interests towards STEM disciplines, as they begin to think about and plan for their career…
Descriptors: Student Participation, Predictor Variables, STEM Education, Science Careers
Peer reviewed Peer reviewed
Direct linkDirect link
Wu, Fati; Lai, Song – Distance Education, 2019
Open, flexible and distance learning has become part of mainstream education in China. Using a blended learning program in a Chinese high school as the case, this study adopted data-mining approaches to establish predictive models using personality traits. Results showed that, for students with high OE and low extraversion, and students who are…
Descriptors: Personality Traits, Learning Analytics, Foreign Countries, At Risk Students
Peer reviewed Peer reviewed
Direct linkDirect link
Senkbeil, Martin – Education and Information Technologies, 2022
This study examined the incremental validity of different information and communication technologies (ICT)-related person characteristics over and above intelligence and and prior achievement when predicting ICT literacy across a period of three years. Relative weights analyses were performed to determine the relative contribution of each…
Descriptors: Technological Literacy, Information Technology, Validity, Individual Characteristics
Peer reviewed Peer reviewed
Direct linkDirect link
Baker, Ryan S.; Berning, Andrew W.; Gowda, Sujith M.; Zhang, Shizhu; Hawn, Aaron – Journal of Education for Students Placed at Risk, 2020
Dropout remains a persistent challenge within high school education. In this paper, we present a case study on automatically detecting whether a student is at-risk of dropout within a diverse school district in Texas. We predict whether a student will drop out in a future school year from data on students' discipline, attendance, course-taking,…
Descriptors: At Risk Students, High School Students, Dropout Prevention, Student Diversity