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Showing all 12 results Save | Export
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Weber, Felix – International Association for Development of the Information Society, 2019
The Future Skills Report about the future of learning and higher Education (Ehlers & Kellermann, 2019) defines a variety of skills in which the active learner plays a central role. Starting from this perspective, our idea is to promote future skills with a digital data-driven study assistant for. As a theoretical foundation research about…
Descriptors: Data Analysis, Higher Education, Goal Orientation, Self Management
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Fleischer, Yannik; Biehler, Rolf; Schulte, Carsten – Statistics Education Research Journal, 2022
This study examines modelling with machine learning. In the context of a yearlong data science course, the study explores how upper secondary students apply machine learning with Jupyter Notebooks and document the modelling process as a computational essay incorporating the different steps of the CRISP-DM cycle. The students' work is based on a…
Descriptors: Statistics Education, Educational Research, Electronic Learning, Secondary School Students
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Cleuziou, Guillaume; Flouvat, Frédéric – International Educational Data Mining Society, 2021
Improving the pedagogical effectiveness of programming training platforms is a hot topic that requires the construction of fine and exploitable representations of learners' programs. This article presents a new approach for learning program embeddings. Starting from the hypothesis that the function of a program, but also its "style", can…
Descriptors: Programming, Computer Science Education, Electronic Learning, Data Analysis
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Groos, Lukas; Maass, Kai; Graulich, Nicole – Journal of Chemical Education, 2021
More than ever, there is an increasing need for digital experimental learning environments in chemistry. The variety of digital learning approaches provided to students range from simple videos showing experiments to highly interactive virtual laboratories. Regardless of which approach is chosen, a digital learning environment should be adapted to…
Descriptors: Student Centered Learning, Electronic Learning, Science Experiments, Chemistry
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Camel, Valerie; Maillard, Marie-Noëlle; Descharles, Nicolas; Le Roux, Even; Cladière, Mathieu; Billault, Isabelle – Journal of Chemical Education, 2021
Educational resources that cover essential knowledge related to chemistry safety rules are openly accessible on the CHIMACTIV website (http://chimactiv.agroparistech.fr/). Organized into two online tracks, the content covers personal and collective protections, first aid, as well as the handling of hazardous chemicals. Being interactive and…
Descriptors: Open Educational Resources, Training Methods, Chemistry, Laboratory Safety
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V. Selvakumar; Tilak Pakki Venkata; Teja Pakki Venkata; Shubham Singh – South African Journal of Childhood Education, 2023
Background: The COVID-19 pandemic has brought attention to student psychological wellness. Because of isolation, lack of socialisation and intellectual and physical development from excessive media use, primary and secondary school students are at high risk for health problems. Aim: This study aimed to identify the most effective machine learning…
Descriptors: Elementary School Students, Middle School Students, Preferences, Online Courses
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Elbawab, Mohamed; Henriques, Roberto – Education and Information Technologies, 2023
Electronic learning (e-learning) is considered the new norm of learning. One of the significant drawbacks of e-learning in comparison to the traditional classroom is that teachers cannot monitor the students' attentiveness. Previous literature used physical facial features or emotional states in detecting attentiveness. Other studies proposed…
Descriptors: Students, Electronic Learning, Attention Span, Artificial Intelligence
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Abu Saa, Amjed; Al-Emran, Mostafa; Shaalan, Khaled – Technology, Knowledge and Learning, 2019
Predicting the students' performance has become a challenging task due to the increasing amount of data in educational systems. In keeping with this, identifying the factors affecting the students' performance in higher education, especially by using predictive data mining techniques, is still in short supply. This field of research is usually…
Descriptors: Performance Factors, Data Analysis, Higher Education, Academic Achievement
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Korkmaz, Ceren; Correia, Ana-Paula – Educational Media International, 2019
The purpose of this review is to investigate the trends in the body of research on machine learning in educational technologies, published between 2007 and 2017. The criteria for article selection were as follows: (1) study on machine learning in educational/learning technologies, (2) published between 2007-2017, (3) published in a peer-reviewed…
Descriptors: Electronic Learning, Educational Technology, Educational Trends, Automation
Prihar, Ethan; Haim, Aaron; Sales, Adam; Heffernan, Neil – Grantee Submission, 2022
Personalized learning stems from the idea that students benefit from instructional material tailored to their needs. Many online learning platforms purport to implement some form of personalized learning, often through on-demand tutoring or self-paced instruction, but to our knowledge none have a way to automatically explore for specific…
Descriptors: Individualized Instruction, Educational Technology, Technology Uses in Education, Electronic Learning
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Karkar, Ammar J. M.; Fatlawi, Hayder K.; Al-Jobouri, Ahmed A. – Electronic Journal of e-Learning, 2020
Electronic learning (e-learning) plays a significant role in improving the efficiency of the education process. However, in many cases in developing countries, technology transfer without consideration of technology acceptance factors has limited the impact of e-learning and the expected outcome of the education process. Therefore, this shift in…
Descriptors: Electronic Learning, Technology Integration, Developing Nations, Foreign Countries
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Charitopoulos, Angelos; Rangoussi, Maria; Koulouriotis, Dimitrios – International Journal of Artificial Intelligence in Education, 2020
The aim of this paper is to survey recent research publications that use Soft Computing methods to answer education-related problems based on the analysis of educational data 'mined' mainly from interactive/e-learning systems. Such systems are known to generate and store large volumes of data that can be exploited to assess the learner, the system…
Descriptors: Data Collection, Learning Analytics, Educational Research, Artificial Intelligence