Algorithm that identifies likely university dropouts is claimed to be 92% accurate.

So there's no sense arguing against education technology.

The rise of analytics in higher education raises questions about the responsible use of student data. Here are some of the issues and how institutions are dealing with them.

Ben Williamson The word ‘precision’ has become a synonym for the application of data to the analysis and treatment of a wide range of phenomena. ‘Precision medicine’ describes the use of detailed p…

Between the creation of a social rating system and street cameras with facial recognition capabilities, technology reports coming out of China have ...

An experiment with embedding "growth-mindset" messaging in software appeared to help students persist when they encountered difficulty—and raised ethical concerns.

Big data on personal choices can be helpful, but if not kept in check, it could also be dangerous.

Researchers took student card ID data and used it to infer whether students have routines and friends. This would not fly in many countries.

The experimental results show that the addition of campus integration and social behavior features refined using the ensemble method significantly improve prediction accuracy and recall. Traditional research in student retention is survey-based, relying on data collected from questionnaires, which is not optimal for proactive prediction and real-time decision (student intervention) support. Machine learning approaches have their own limitations. Therefore, in this research, we propose a big data approach to formulating a predictive model. We used commonly available (student demographic and academic) data in academic institutions augmented by derived implicit social networks from students’ university smart card transactions. Furthermore, we applied a sequence learning method to infer students’ campus integration from their purchasing behaviors. Since student retention data is highly imbalanced, we built a new ensemble classifier to predict students at-risk of dropping out. For model evaluation, we use a real-world dataset of smart card transactions from a large educational institution. The experimental results show that the addition of campus integration and social behavior features refined using the ensemble method significantly improve prediction accuracy and recall.

These days more and more colleges are setting up systems that automatically email students when an algorithm determines they are academically at risk. ...

Sudha Ram's research tracks students' social interactions and daily routines via their CatCard usage — and leverages that information to make predictions about freshman retention. Fast Forward, a

New software aims to help instructors understand students better, but raises privacy and practicality concerns.

In the socio-technical imaginary of higher education, algorithmic decision-making offers huge potential, but we also cannot deny the risks and ethical concerns. In fleeing from Frankenstein’s monst...

Interesting. Analytics sans actual patron data.

GIANT list of education and ed-tech breaches!