Last week when covering Learning Analytics (LA) I found it difficult to relate the topic to my field and how it could support my methods of teaching and the teaching template created for the Scottish Qualification Association (SQA) Higher Dance course. I decided to experiment and create something that I could evaluate and discuss. As my role is not on a full-time basis in one location I have the advantage of assessing grades from a few institutes which involve a Private Dance School, a course based within a Performing Arts College where students attend from local High Schools on certain afternoons and a High School with a dance department. I combined the students from each institute and the technology used by the teacher and the students were measured in correlation to their overall end of year grade. Limitations were present in the quality of data and the lack of data related to the volume of online activity as not all activity could be recorded. The level of feedback was measured by the amount of times students were given tutor feedback (in person and through online communication) and the progression of each students choreographic content through implementation of that specific feedback. The feedback was measured alongside their progress in technical ability and performance. The data was recorded by their teachers and can therefore be classed as a biased approach. However, looking at the infographic we can see that feedback helped the student to increase their overall grade. The overall grade was comprised of theory and practical and if students were not technically strong they could focus on the choreographic content and written review to increase their marks. Likewise if the student found the creativity of choreography challenging and had limited literacy skills, the technical performance gave opportunity to still achieve a strong grade. The infographic unfortunately does not capture this data and highlights the issue of generalisation. The information provided can be misinterpreted dependent on ones knowledge of the course and perception of dance.
Learners construct knowledge by using (cognitive, digital, and physical) tools to perform operations on raw information in order to create products of learning. I’m not able to identify weak points in this particular LA on their learning activities or the topics they have struggled with but through communication I was able to provide instructive and process related feedback on how to improve their learning and final assignments. If I am to reflect the LA above I can not change the course learning design, as the SQA course is already set out prior to meeting the group. I can, however, emphasis the importance of teacher and student communication and on-going feedback.
” The choices learners make are influenced by the (internal an external) conditions, which in turn can affect standards of the learners use in their metacognitive monitoring and control” (Gašević, Dawson & Siemens , 2014, p4).
Although I have used a visual demonstration of learning analytics the process and collection of data can be mis-understood or seen as unreliable. It is important to know where the data is coming form and acknowledge a risk of a biased approach to the generation of data and the interpretation of the readings. As Siemens (2013, p.1395) states “the learning process is essentially social and cannot be completely reduced to algorithms”.
Boyd, Danah, & Crawford, K. (2012). CRITICAL QUESTIONS FOR THE BIG DATA. Information, Communication & society, 15(5), 662-679. DOI: 10.1080/1369118x.2012.678878
Gašević, D., Dawson, S., & Siemens, G. (2014). Let’s not forget: Learning analytics are about learning. TECH TRENDS TECH TRENDS, 59(1), 64-71. DOI:10.1007/s11528-014-0822-x
Seimens, G. (2013). Learning Analytics: The emergence of a discipline. American Behavioural Scientist, 57(10), 1380-1400. DOI: 10.1177/0002764213498851
Just Pinned to Learning Analytics Mscedc: Learning Analytics Higher Dance | @Piktochart Infographic http://ift.tt/2nkdPfy