Abstract:

The scope of this paper will include the use of Learning Analytics in a Next Generation Digital Learning Environment (NGDLE) and will describe a working pan-European NGDLE created in the recent Up2University (Up2U) Horizon 2020 project to help high school students transition to university. From 2018, this modularly designed NGDLE has been used in secondary education in many pilot countries. In this study, the Up2U NGDLE was adapted for use in an intensive postgraduate course which shifted online in response to the emergency situation resulting from the general lockdown in Italy in March 2020.The central purpose of our presentation is to show how Learning Analytics and Up2U NGDLE helped the professor who taught this course to adapt his teaching to this sudden shift to an online learning environment.

A sample of 25 students who were attending their first year of a 2-year long MA course participated in this online course which was based on 5 lessons in 2 weeks. Each lesson was 4 hours long, based on two parts - first part was based on a webinar-style traditional approach followed by a second 2-hour part based on a PBL approach, using a Moodle-based environment as the main virtual support. In the second part of each lesson, students switched to another platform (CommonSpaces) hosted in the same NGDLE to do their PBL activities. This approach allowed students to exploit one important aspect of the NGDLE principle: the Learning Analytics system as the common base for all tools hosted in the environment. In our presentation, we will show how the Up2U platform gives teachers the opportunity to test and track students’ use of Moodle-based tools as well as CommonSpaces via Learning Locker, a system capable of collecting data from xApi-abled tools as well as from a more traditional data provider like Matomo (Abbey, 2016).

Learning Analytics are seen as an important tool to support effective self-regulated study (Schumacher & Ifenthaler, 2018) and their importance has been shown in asynchronous courses (Kim, Yoon, Jo, & Branch, 2018). This study will provide additional evidence of this by showing how the Up2U NGDLE generates Learning Analytics that can (1) help students to support self- regulated study and (2) teachers to track their students’ activities even in a synchronous online context that also manages to create a F2F atmosphere. Finally, we will show that a blended or hybrid approach to collecting Learning Data (Ellis, Han, & Pardo, 2017) represents a highly effective way to ensure that students remain active throughout the classes as well as collect student feedback that could inform and improve both the teaching and learning processes.

References:
[1] Abbey, D. (2016). Learning Locker on Github. Retrieved from https://github.com/LearningLocker/learninglocker/releases
[2] Ellis, R. A., Han, F., & Pardo, A. (2017). Improving learning analytics - Combining observational and self-report data on student learning. Educational Technology and Society, 20(3), 158–169.
[3] Kim, D., Yoon, M., Jo, I. H., & Branch, R. M. (2018). Learning analytics to support self-regulated learning in asynchronous online courses: A case study at a women’s university in South Korea. Computers and Education, 127, 233–251. https://doi.org/10.1016/j.compedu.2018.08.023
[4] Schumacher, C., & Ifenthaler, D. (2018). Features students really expect from learning analytics. Computers in Human Behavior, 78, 397–407. https://doi.org/10.1016/j.chb.2017.06.030

Keywords:

Learning Analytics, COVID-19 Coronavirus, Remote learning.