Читать книгу Innovando la educación en la tecnología - Группа авторов - Страница 26

The collection and processing of data for decision-making in engineering education is not without problems. In fact, there are important risks to worry about, which have been highlighted by numerous experts on learning analytics, educational data mining, and data protection policies, (Dringus, 2012) (Khalil, Taraghi, & Ebner, 2016), as well as certain ethical considerations (Slade, & Tait, 2019). Some of these risks are briefly described in this section, although each of them would deserve an entire paper for discussion.

A first problem refers to the secure storage of collected data. Many institutional education systems are not prepared to store large amounts of data in a secure way and are likely to have breaches that can compromise important data, including students’ personal data. Relying on third-party services for data storage in the cloud can also lead to an inappropriate use of the data collected by these service providers.

A second problem refers to data privacy. It is very important to have an institutional strategy that clearly states what data needs to be collected from students and who has access to the data. Many educational institutions are not aware of all the data they collect (or can potentially collect); do not have mechanisms to control who has access to the data collected; and/ or do not have an internal policy to facilitate that the right people can make proper informed decisions, being able to access the data they are entitled to.

A third problem refers to always getting explicit consent from the students for collecting data. It is very important that students (who actually own the data) know at all times what data are being collected from them and for what purpose the data are going to be used. In addition, there are international laws such as the GDPR (General Data Protection Regulation) in Europe that require, among other things, the removal of data collected upon request by its owner. Many educational institutions do not have data collection ethics committees and are not prepared to delete collected data upon request.

A fourth problem refers to transparency, or rather lack of transparency, of many algorithms and systems which are private and whose code is not open. The lack of transparency prevents algorithms and systems from being audited to better understand how they work. Even if one relies on third-party algorithms and systems to make informed decisions, it is important to know how the results and visualizations they provide were obtained.

A fifth problem refers to bias. Many artificial intelligence systems use data collected in the past to make their calculations and predictions. However, data collected in the past may have important biases, such as a gender imbalance, which could lead to promoting more male students versus female students or vice versa. In general, it is important to take minorities into account when using data from the past as input so that these are not penalized.

Finally, it is important to bear in mind that humans may misinterpret the results and visualizations obtained from the data processed. Sometimes there are people who deliberately twist the data to fit a pre-designed theory, instead of discarding this theory if data advise to do so. Actually, from a very large dataset, and taking only a subset of it, erroneous and unreproducible conclusions can be very easily reached.