The current landscape of learning analytics in higher education

By Olga Viberg, Mathias Hatakka, Olle Bälter, Anna Mavroudi. Computers in Human Behavior, 2018

596 citations recorded by the source corpus.

Learning analytics can improve learning practice by transforming the ways we support learning processes. This study is based on the analysis of 252 papers on learning analytics in higher education ...

Subfields: Computer Science Applications

View Paper: https://www.semanticscholar.org/paper/17abdcb1da177cefb81d7d76dc801129f1d828f0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Science Applications

1. M. Yağcı
2. Abdullah Alshanqiti
3. Abdallah Namoun

Citations in the corpus

• Predicting academic performance of students from VLE big data using deep learning models (2020, background): “The progression of the accumulated educational data has stimulated the emergence of several research communities, such as learning analytics to predict learners’ behavior and providing indicators for optimized policy formulations (Azcona & Smeaton, 2017; Viberg et al., 2018; Capuano & Toti 2019).”
• Promises and challenges of generative artificial intelligence for human learning (2024, unknown): “These AI­generated data could impede existing learning analytic methods that rely on such data to model the learning process 96 .”
• Educational data mining: prediction of students' academic performance using machine learning algorithms (2022, methodology): “The processing of educational data yields improvements in many areas such as predicting student behaviour, analytical learning, and new approaches to education policies (Capuano & Toti, 2019; Viberg et al., 2018).”
• Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review (2020, background): “, EDM) are proclaimed to improve the attainment of student learning outcomes [108].”