The dinoaur that lost its head:
A contribution to a framework for
using Learning Analytics in Learning Design
This presentation will be published (as a full paper) in the international online open access journal for Designs for Learning
Rene B Christiansen Karsten Gynther
Rasmus Leth Vergmann Jørnø Morten Rasmus Puck
the why: LA into LD
LA into LD; enroute to (digital) data saturated learning environments:
“the computer has been around a while...It was not until very recently that we became able to speak of the digital paths of learning in a learning
environment in which all study activity is traceable via digital footprints
“the ‘aim to improve learning effectiveness’” (Mort er al. 2017) We talk about TEACHER EMPOWERMENT
LA LD
INFORM DECISION MAKING:
“[a] methodology for enabling
teachers/designers to make more informed decisions in how they go about designing learning
activities and
interventions, which is pedagogically informed and
makes effective use of appropriate resources and
technologies”
INFORM DECISION MAKING:
“ … LA sees data as capable of informing
decision making for teachers, students and
mentors alike … “
How can Learning Analytics support the professional teacher in his or her work with learning designs?
the what: research question
● The teaching and learning environment are, in themselves, data-saturated.
● The moderators and participants do not have to produce extraneous data to satisfy any learning analytics
ambitions.
● The digital platform provides data that are easily adaptable to learning analytics analysis.
● The digital resources needed to generate and analyse the data are manageable.
[The figures used here have been produced using IBM SPSS software, which can easily handle the generated data from the Moodle platform]
the how: a data-saturated
learning environment is needed:
(such as a) MOOC
The MOOC was executed twice. (spring 2017: 288 signed up, 183 logged on. Fall 2017: 280 signed up, and 188 logged on. The data used in this article stems from round two.
In terms of cohort analysis, we are aware of five different groups of participants:
a) professionals working in the school system
b) professionals working outside the school system c) teacher and pedagogy students
d) teachers connected to group ‘c’
(filtered out)e) the moderators that have designed and moderated the MOOC
(filtered out)more methodology and ways of...
the data and the active learners
Of the 188 logged-on participants, 141 were defined as ‘learning active’. Our criteria for being learning-active was that (a)
participant must have a sum total of five or more clicks on any resource in the MOOC
.The following analysis pertains only to the 141 learning active participants.
Only 7 out of 141 received 5 badges or more, thus qualifying for a diploma.
- A large group of participants was nowhere near ‘course completion’ and seemed to be either unable to collect badges or indifferent to doing so.
- For a different, smaller group, badges appeared to play an ancillary role in their motivation as they all not only reached the required number of badges but received significantly more badges than necessary
- A small group was “few clicks away” from a diploma, but failed in doing so -
maybe because their motivation for joining laid elsewhere (content?)
Explanations for MOOC dropouts
● No intention to complete
● No time
● Too difficult
● No skills
● Bad experience
● Late start
● Peer review
(Onah, Sinclair & Boyatt, 2014)
A different explanation
● Intended design
● Implemented design (actual use)
● Attained design
(McKenney & Reeves, 2012)
Student behavior
Workload 1 module = 2 x 37 hour
week
Actual student behavior
Short bursts of intense work covering all available assignments and ressources.
Actual student behavior
Minimizing number of log-ins and maximizing activity when logged-in.
Actual student behavior
Aim to be Study efficient
Student efficiency qua
Actual student activity
Module 4
Why?
Bad R.O.I.
A
Study(time) Economy
Takeaway 1
Takeaway 2
Intended design ≠
Implemented design
Takeaway 3
Success ≠ Diploma
Takeaway 4
Data only informs existing (design)praxis
Thank you for your attention
Contact Info:
Morten Rasmus Puch, mrpu@ucl.dk Karsten Gynther, kgy@pha.dk
René B. Christiansen, rbc@pha.dk Rasmus Leth Jørnø, ralj@pha.dk
