Students' performance in interactive environments: an intelligent model [PeerJ]

Modern approaches in education technology, which make use of advanced resources such as electronic books, infographics, and mobile applications, are progressing to improve education quality and learning levels, especially during the spread of the coronavirus, which resulted in the closure of schools, universities, and all educational facilities. To adapt to new developments, students’ performance must be tracked in order to closely monitor all unfavorable barriers that may affect their academic progress. Educational data mining (EDM) is one of the most popular methods for predicting a student’s performance. It helps monitoring and improving students’ results. Therefore, in the current study, a model has been developed so that students can be informed about the results of the computer networks course in the middle of the second semester and 11 machine algorithms (out of five classes). A questionnaire was used to determine the effectiveness of using infographics for teaching a computer networks course, as the results proved the effectiveness of infographics as a technique for teaching computer networks. The Moodle (Modular Object-Oriented Dynamic Learning Environment) educational platform was used to present the course because of its distinctive characteristics that allow interaction between the student and the teacher, especially during the COVID-19 pandemic. In addition, the different methods of classification in data mining were used to determine the best practices used to predict students’ performance using the weka program, where the results proved the effectiveness of the true positive direction of functions, multilayer perceptron, random forest trees, random tree and supplied test set, f-measure algorithms are the best ways to categories.

Utilizing random forest algorithm for early detection of academic underperformance in open learning environments [PeerJ]

Early detection of student degree-level academic performance using educational data mining [PeerJ]

The role of citizen science mobile apps in facilitating a contemporary digital agora

PDF) Early detection of student degree-level academic performance using educational data mining

Frontiers Facilitating Peer Interaction Regulation in Online Settings: The Role of Social Presence, Social Space and Sociability

Student learning performance prediction based on online behavior: an empirical study during the COVID-19 pandemic [PeerJ]

Natural language processing applied to mental illness detection: a narrative review

CRISP-DM phases and key outputs (adapted from Chapman et al. (2000)).

A primitive-based representation of dance: modulations by experience and perceptual validity

The evaluation of university management performance using the CS-RBM algorithm [PeerJ]

Leveraging ChatGPT for Enhancing Critical Thinking Skills

Personalization-based deep hybrid E-learning model for online course recommendation system [PeerJ]

System model. Full-size  DOI: 10.7717/peerj-cs.11011101/fig-2