William Cope
Adam P Rusch
Jason Berg
Duane Searsmith.png "Rita van Haren")
Rita van Haren
Eric Patnoudes
Maura Rodgers Brand
Jason Williams
Melanie Lamus
Heather A Ochmane-Learning Ecologies MOOC’s Updates
Learning analytics in higher education
Learning analytics
Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for understanding and optimizing learning and the environments in which it occurs, the field has grown so much since 2011, when learning analytics was introduced to the academic world.(1)
The data collected by the learning analytics is used to optimize the learning experience and leveraging decision-related to learning, teaching and management.(3)
Why learning analytics is becoming more and more important in educational settings?
Because of the technological involvements in academics, an abundance of data is generated from each activity of the learning. All the data available can be used for descriptive analytics (descriptive analytics is the most basic analytics, it is just simple clarified/cleaned data from all sources used to find out what is happening in the environment) and get the knowledge about the patterns etc (1).
Using the descriptive analytics for the predictive analytics how a learner is going to behave in future (in terms of attitude, connections, interactions and maybe sentiments). Using descriptive and predictive analytics can help learning and development experts understand finding patterns engagement, participation etc. that help them determine what content is useful or confusing or frustrating for a learner/user.(2)
What is predictive analytics-
[What is predictive analytics https://www.youtube.com/watch?v=GO8Cd2eUTVE]
Learning analytics is very important for the development of user-centred design and it is the first steps in creating a personalized learning path. It also helps in creating an individualized learning path for everyone that will suit their needs. Additionally, learning analytics also helps to improve the overall course and big positive impact on overall learning.
The following diagram summarizes possibilities with the learning analytics -
Common data sources (environments):
Students enrollment details (personal and demographic details)
Peer feedback
Feedback from tutor
Examination results
All the activities of the students (login to MOOC, log out activities on MOOC etc)
Crowdsourcing assessment
Machine feedback and machine mediated human feedback
It is important to get a bit understanding of the machine feedback, the feedback provided by machine or machine mediated human feedback that it is based on the collected data but it also collects feedback while provided the feedback. Another benefit is machine feedback is from multiple sources(4).
When collecting data for earning analytics, it captures teachers feedback, peer feedback, feedback from experts etc. All this feedback are important to capture the overall learning of a learner and if the feedback can be supported by the data, it can decide to make the learning environment more suitable for the learners and for the teachers.(1)
Learning analytics and future:
It is important to understand the learning analytics is only useful if the data interpretation of collected data and designing turns into meaningful actions. It has a lot to depend upon the good interpretation and with additions of other social and cultural determinants, especially while implementing its results for the future interpretation. Besides this, always remember the ethical and privacy issues associated with the use of personal data.(4)
References:
- Sclater N, Peasgood A, Mullan J. Learning analytics in higher education. London: Jisc. Accessed February. 2016 Apr;8(2017):176.
- https://www.youtube.com/watch?v=GO8Cd2eUTVE
- https://www.solaresearch.org/about/what-is-learning-analytics/
- Chan T, Sebok‐Syer S, Thoma B, Wise A, Sherbino J, Pusic M. Learning analytics in medical education assessment: the past, the present, and the future. AEM education and training. 2018 Apr;2(2):178-87.
Hi Deepesh,
If you aren't already following them, then subscribe to the LAK group on Google forums:
learninganalytics@googlegroups.com
G Siemens is active in this group and they organize an annual conference on Learning Analytics - which was online this year.
https://www.solaresearch.org/events/lak/lak21/
The field is still new and sharply criticized by some in the pedagogical field, probably because one of the more common themes in pedagogy is the need to humanize teaching and learning by transforming the roles of teacher and learner to be more collaborative and less adversarial. Learning Analytics appears to ignore this goal, or even be antagonistic to it.
I have some involvement in both, and don't necessarily agree that LAK isn't about teaching. A lot of the folks I saw at the LAK conference I attended were teachers.
That's a great recommendation Mark!
I was not aware of them, I am certainly gonna follow both of the groups.
Thank you very much for sharing this information with me.
You mention one common objection to LA, ethical and privacy issues associated with collection and analysis of personal data. There are a number of other objections that you do not acknowledge here.
One of the most obvious relates to what is being measured and whether or not it is, indeed measurable. This is a big question, I know. Given the current state of technology, LA is useful in a number of fairly narrow domains, such as the organization and provision of machine moderated feedback on specific performances. This may be closely associated with Machine Learning, a separate field. An example is the Google Translator feature available through the Chrome browser. This translator learns by user correction of its translations so that output is gradually improved. You can see this via particularly language pairs. The Arabic to English translator is much more accurate and colloquial than the English to Arabic translator, probably because the A-E translator is much more commonly used and users give it more feedback.
In the context of LA, we are looking for feedback to improve performance, but in this case it is the machine feedback improving human performance, rather than human feedback improving machine performance. So, one question is, where does machine expertise come from?
A more important question is pedagogical. We still do not know how human being learn. Human to human pedagogy is an actively developing field. It is also conceptually complex and challenging. LA is certainly a tool that can be used to improve our understanding and practice of pedagogy, but for this to happen, we probably need more active cross boundary activity among practitioners in these two fields.
Dear Mark,
Thank you very much for your comment.
You indeed provided a deep thoughts to the LA, which was not quite visible in the update. I would like to learn more about LA as when I started looking for the information online, I got fascinated with the amount of work has been done in recent time.
Once again thanks for the insightful comment, this is the way to learn more and more.