Olga Viberg and Olle Bälter will, in cooperation with InnoEnergy, present a webinar on Learning analytics on November 21 at 10 am.
Are you already involved in Learning Analytics or want to find out how the new big data era can support your teaching or research activities? Maybe you are skeptical about the current state of Learning Analytics and whether it actually leads to improved learning outcomes? Is it already deployed widely, and used ethically?
Join our free webinar on 21/11 at 10 am to find out and to discover success stories as well as hands-on techniques for data-based improvements of learning material such as used at Stanford Graduate School of Education and Carnegie Mellon University.
This study is a systematic literature review of learning analytics in higher education. It is published in the journal of Computers in Human Behavior and is freely available at https://www.sciencedirect.com/science/article/pii/S0747563218303492
In this study, we found that:
1. Most learning analytics research undertake a descriptive approach.
2. Interpretative and experimental studies prevail.
3. Overall there is little evidence that shows improvements in learner practice.
4. The identified potential for improving learning support and teaching is high.
5. There is a shift towards a deeper understanding of students’ learning experiences.
Learning analytics can improve learning practice by transforming the ways we support learning processes. This study is based on the analysis of 252 papers on learning analytics in higher education published between 2012 and 2018. The main research question is: What is the current scientific knowledge about the application of learning analytics in higher education? The focus is on research approaches, methods and the evidence for learning analytics. The evidence was examined in relation to four earlier validated propositions: whether learning analytics i) improve learning outcomes, ii) support learning and teaching, iii) are deployed widely, and iv) are used ethically. The results demonstrate that overall there is little evidence that shows improvements in students’ learning outcomes (9%) as well as learning support and teaching (35%). Similarly, little evidence was found for the third (6%) and the forth (18%) proposition. Despite the fact that the identified potential for improving learner practice is high, we cannot currently see much transfer of the suggested potential into higher educational practice over the years. However, the analysis of the existing evidence for learning analytics indicates that there is a shift towards a deeper understanding of students’ learning experiences for the last years.
Finally the proceedings from the SustainIT conference (“The 5:th IFIP Conference on Sustainable Internet and Sustainability” have been published. One of my papers were within the TEL area, and conserved a serious game we developed for learning about household energy consumption.
Serious games have attracted much attention recently and are used to in an engaging way support for example education and behavior change. In this paper, we present a serious game designed for helping people learn about their own energy consumption and to support behavior change towards more sustainable energy habits. We have designed the game for all the four Bartle Player Types, a taxonomy used to identify different motivations for playing games. Engagement of the participants has been evaluated using the Intrinsic Motivation Inventory, and we have measured self-assessed future behavior change. We found a statistically significant positive correlation between self-assessed future behavior change and perceived value/usefulness of the application independent of player type. Our study indicates the player type “Achievers” might perform better using this type of application and find it more enjoyable, but that it can be useful for learning energy conserving behavior independent of player type.
Read the full paper at https://ieeexplore.ieee.org/document/8379794/