Data-Informed Learning Environments

The rapid growth in data generated and collected during educational activities has great potential to inform how we teach and learn. In creating 카지노사이트 data-informed learning environments, campuses can generate synergies between learning analytics and learning design that allow for real-time adjustment and long-term iterative improvement of digital and classroom-based learning environments.

Data collected in the course of learning activities offers the appealing opportunity for educators to understand how students actually engage in the academic experiences designed for them. This provides a more detailed, comprehensive, and (relatively) impartial version of the kind of classroom feedback that instructors have relied on informally for years. The availability of real-time diagnostic information can support instructors in making responsive adjustments to their teaching in the moment and lead to iterative improvements in learning designs over the long term.

Getting the Right Data

What data to gather to inform learning design depends on the kind of learning environment involved. People tend to think of learning analytics in the context of digital learning environments where it is relatively straightforward to collect data. It is not, however, necessarily straightforward to interpret what the data means. For example, one kind of common data to collect in online learning environments is clickstream data — a running log of what digital objects someone clicks on and when they do so. Such data can be compiled to look at class-wide patterns or identify individuals exhibiting particular behaviors, but it is important to think carefully about what such patterns mean in terms of student engagement and learning. Another kind of data generated online is student artifact data. When the student artifact is text based, as in the case of essays or discussion messages, then natural language processing and machine learning technologies can be used for the purpose of identifying common topics addressed, finding students with similar interests, or evaluating particular qualities of the texts. 바카라사이트

While digital learning environments are a natural site for learning analytics, a variety of interesting data can also be collected from classroom-based learning environments. Doing so is important to avoid overly privileging digital traces and ignoring important activity that occurs in the real world. Such multimodal learning analytics are based on data collected from physical spaces, such as speech, gaze, or gesture. There are also possibilities to use video data in combination with computer vision techniques and handwriting or sketch analysis of classroom-created student artifacts. Of course there are critical ethical and privacy issues to consider in collecting any of these kinds of data.

A final important kind of data is self-logged data, where learners voluntarily generate data about themselves. The difference in this data from that previously generated by self-report surveys is that it is collected in the moment (rather than reflectively) over multiple points in time. Frequent quick prompts can ask students to indicate what they are working on or their current confidence around the material at particular points in the learning process. This generates fine-grained data about student activity or attitudes that is more easily interpretable than clickstream data alone and can generate lots of potentially interesting patterns over time.

Ethics and Privacy

Before talking about how data can be used to inform the design of learning environments, it is important to consider ethical and privacy concerns. These issues come up any time data about students is collected and used. The core questions here relate to ownership (whose data is it?), access (who can see the data?), and impact (for what purposes is the data being used?). Such issues don’t just affect learning analytics in higher education, of course. Much data tracking and data usage go on in the commercial sector with minimal oversight. However, institutions of higher education have an elevated responsibility to students and need to be both careful and conscientious about what they do with student data. 온라인카지

One important principle here is transparency. Universities need to have clear and understandable policies that let students know what data is being captured and for what purposes it will and will not be used. In terms of access, one way to gain buy-in from students is by giving them the ability to see their own data so they understand the insight it can generate. There is a vast difference in feeling that data on you is being collected by some unseen entity and feeling involved and engaged in the process of using that data. In one of my research projects we found that if you establish an atmosphere of trust (including in this case the clear provision that the analytics were to be used to improve performance, not to evaluate it), then students can find the use of even very fine-grained data acceptable. Universities also need to consider whether opt-out or opt-in provisions are appropriate and what consequences non-participation will have for both students and the institution’s information base as a whole. While there are no simple answers, these are questions that every institution needs to think about if planning to use student data in learning analytics.

Data-Informed Learning Design

At a high level the number one thing that happens with data-informed learning environments is that you have much more information going into how you’re thinking about the design and helping you evaluate whether what you intend really happens. Many times we design learning environments with the best of intentions, but without many checks to see if what we expect actually results. In classroom-based teaching instructors may be able to use anecdotal reflection to get a holistic sense of things, but this often misses much of the picture. In online learning environments it is even more difficult to have a sense of how the students are engaging. With learning analytics we can set clear goals for what we hope happens during a learning experience and then check to see if this does indeed occur. We want to see if our designs are having the intended effects and creating the kinds of learning experiences we desire for our students; learning analytics gives us a powerful way to achieve this. See figure 1 for an example of discussion forum analytics both embedded directly into the learning system and extracted from it. The colored dots show where learners have been involved in the conversation (light and dark blue dots) and the parts they’ve missed (red dots). The numerical metrics on the right show that most students have been engaged in reading each other’s ideas, though one particular student has spent most of his time looking at his own contributions.

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