Big Data and analytics in higher education: Opportunities and challenges

By Ben Kai Daniel. British Journal of Educational Technology, 2015

594 citations recorded by the source corpus.

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Challenges and Future Directions of Big Data and Artificial Intelligence in Education (2020, methodology): “Recently, the adoption of big data and AI in the psychology of learning and teaching has been trending as a novel method in cutting-edge educational research (Daniel, 2015; Starcic, 2019).”
The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and millennial generation teachers? (2023, background): “These insights can be used to inform the development of targeted interventions, course materials, and learning strategies that cater to the diverse needs and preferences of students (Daniel, 2015).”
Reviewing and exploring innovative ubiquitous learning tools in higher education (2019, background): “…massive data that are generated each second in the social network, regulation of the innovative-based learning model in such learning activities is done to offer the entire beneficial values to allow improving strengthen pervasive knowledge-based learning (Anshari et al. 2016; Daniel 2015).”
Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review (2020, background): “Student academic performance in higher education (HE) is researched extensively to tackle academic underachievement, increased university dropout rates, graduation delays, among other tenacious challenges [1].”