Hsu et al., 2022
ViewPDF| Publication | Publication Date | Title |
|---|---|---|
| Grájeda et al. | Assessing student-perceived impact of using artificial intelligence tools: Construction of a synthetic index of application in higher education | |
| Wang et al. | Effects of social-interactive engagement on the dropout ratio in online learning: insights from MOOC | |
| Gardner et al. | Student success prediction in MOOCs | |
| Ahmad et al. | Connecting the dots–A literature review on learning analytics indicators from a learning design perspective | |
| Gelan et al. | Affordances and limitations of learning analytics for computer-assisted language learning: A case study of the VITAL project | |
| Brooks et al. | The data-assisted approach to building intelligent technology-enhanced learning environments | |
| Costa et al. | Monitoring academic performance based on learning analytics and ontology: A systematic review | |
| Yogev et al. | Classifying and visualizing students' cognitive engagement in course readings | |
| Khalil | Learning analytics in massive open online courses | |
| Jyothy et al. | Exploring large language models as an integrated tool for learning, teaching, and research through the Fogg Behavior Model: A comprehensive mixed-methods analysis | |
| Alves | Making diagnostic inferences about student performance on the Alberta education diagnostic mathematics project: An application of the Attribute Hierarchy Method | |
| Bojic et al. | Empowering health care education through learning analytics: in-depth scoping review | |
| Balaban et al. | Post hoc identification of student groups: Combining user modeling with cluster analysis | |
| Lakho et al. | Development of an integrated blended learning model and its performance prediction on students’ learning using Bayesian network | |
| Bhaduri | NLP in Engineering Education-Demonstrating the use of Natural Language Processing Techniques for Use in Engineering Education Classrooms and Research | |
| Godinez et al. | A Gaussian-Bernoulli mixed Naïve Bayes approach to predict students’ academic procrastination tendencies in online mathematics learning | |
| Khine | Educational Data Mining and Learning Analytics | |
| Knöös et al. | Sentiment Analysis of MOOC learner reviews: What motivates learners to complete a course? | |
| Hsu et al. | Editorial–Volume 23, Issue 1 Special Issue: AI e-Learning and Online Curriculum | |
| Knight | Students' abilities to critique scientific evidence when reading and writing scientific arguments | |
| Azcona | Artificial intelligence in computer science and mathematics education | |
| Brooks | A data-assisted approach to supporting instructional interventions in technology enhanced learning environments | |
| King et al. | An Examination of Students’ Moral Character Experiences Using the Four Component Model and Self-Evolution Theory | |
| Canale | Artificial Intelligence methodologies to early predict student outcome and enrich learning material | |
| Çelikbağ | PREDICTING STUDENT PERFORMANCE IN ONLINE ENGLISH LANGUAGE LEARNING DURING CHALLENGING TIMES THROUGH LEARNING ANALYTICS |