Blogseries Learning Analytics part 5
How can we engage students with learning analytics?
In the previous blogs we have focused on the privacy issues behind learning analytics, teachers and the benefits of learning analytics in general. Dr Bart Rienties, director of the learning analytics programme at the Open University says that “We’re trying to use data to improve our understanding of how students learn. We want to understand the story behind that data.” While we recently presented our concept to students, it became obvious that students have a strong opinion about this topic too. The conversations lead us to ask ourselves a question: Are students interested in putting their effort into learning analytics?
Data can help students to make their educational decisions while on campus
By talking to many Dutch students as well as our own experience, whilst studying at a university, students want to spend their study time wisely. They want to receiving feedback from teachers as soon as possible and not spending much time with course evaluation forms to improve education for next year. Also, studying only the relevant topics and getting a personal attention is on their expectation list. Universities are putting a lot of their resources and energy into student monitoring and course evaluation using learning analytics. However, without the students input, the full potential of learning analytics won't be reached. And so, to improve the collective understanding of learning analytics, we would like to illustrate a few practises on how to present learning analytics to students.
One of the first advices on how to make learning analytics attractive to students is providing them with meaningful data. As a student you are owevhemed with countless documents and emails with high importance however they rarly take your personal situation into account. Data of many students have been collected already, and this data can help students to make their educational decisions while on campus. As a student, you would benefit from the experience and choised of all prior students, instead of from the two or three people that they now talk to about their experiences. This would for instance make the decision process easier while deciding between majoring in physics or majoring in marketing.
Secondly, speaking from my own experience, students like to be understood. Learning analytics can provide a way in which we might use data to understand every student much more deeply. To give an example, as a student you write a lot of reports and essays in college. These documents contain a lot of information about the understanding of topics, but also on your writing skills. Currently, after submitting an assignment, teacher assesses it, you receive a grade and … and the path ends there. What if we would incorporate learning analytics into this process? By connecting essays from first classes until you graduate will result in creating a portrait of that student and how their level of understanding and writing skills have has grown. And most importantly - as a student you would be always understood.
Universities have to get a balance between providing good instructional support to students, but not overwhelming them with evaluation forms.
Although we have discussed privacy issues in the last article, we would like to touch upon it a little today too. While agreeing upon the usage of data at the institution, students show little or no concern on the usage of these data. This might be caused by the environment of increased sharing through social media, which creates a landscape of “digital promiscuity” (Murphy, 2014). After having a conversation with few students, many expressed that it should be their choice whether or not they are tracked. They need to be able to opt out when needed. Some students were concerned about being monitored in a Big Brother fashion. At the same time, many students were surprised that universities weren’t using it to a fuller degree already. A learning point from that, would be to always inform the student on the actions taken while agreeing to use their data.
As we are reaching the conclusion, the key is to find a "happy medium". Universities have to get a balance between providing good instructional support to students, but not overwhelming them with evaluation forms from which the students are unlikely to see results. Secondly, by portraying learning analytics as a tool for help through examples listed above, students will not only gain trust however also see the importance of their input. Only in this way we can reach a collective understanding of the impact of analytics on teaching and learning.