Learning Analytics of Location-Based Learning: Understanding Students in Learning Trails
Wednesday 3 July: 5:30pm – 7:00pm, poster session
Presenters
Professor Lai Chuen, Paul Lam
The Chinese university of Hong Kong, Hong Kong
paul.lam@cuhk.edu.hk
Kevin Wong
The Chinese University of Hong Kong, Hong Kong
Overview
Mobile devices with adequate location-aware functionalities are so common nowadays that it is time to seriously consider location-based learning: technology-enhanced learning in which students learn while going to different locations with the learning materials given to them at the most relevant place and time. Corbeil & Corbeil (2007) and Woodill (2011) highlighted that technology brings the advantage of ‘free of space-time constraint.’
Engagement in the outdoor environment can be extremely engaging as students see, touch and manipulate real objects in the real world as they walk around and find information. New location-based learning platforms that do not require heavy programing are also built for use by common teachers. A typical learning trail created in these systems often contains instructions that direct students to visit a number of pre-defined locations for a common learning theme.
Learning Analytics is about the gathering and interpretation of the online footprint of learners for the purpose enabling teachers to better understand students. It can be regarded as a “data-intensive approach to education research” and has the “goal of enhancing education practice” (Baker & Inventado, 2014, p. 62). Up to now, most learning analytics are about data extracted from learning management systems such as Moodle. We envisage that location-based learning platforms could provide new data for understanding of learning in other scenarios. Apart from information such as students’ performance of items on the trails, the whereabouts of each of these happenings are also traceable, thus revealing a range of new analytic possibilities.
In this poster, we will present preliminary findings that attempt to relate learning behaviours outside the classroom with traditional views of learner types such as individual/group learners and surface/deep learners. The data came from two pilot studies involving about 100 students in five field trips. We found that students varied a great deal in the number of locations they attempted, the length of time spent on each spot and on the whole trip, the actual path they took (especially whether they have inspected the surroundings of each spot), and whether some learners visited the places in groups. The pilot study is an important first step to aim at a systematic approach to collect, analyse and interpret data for location-based learning.
References
Baker R.S., & Inventado P.S. (2014) Educational Data Mining and Learning Analytics. In: Larusson J., White B. (eds) Learning Analytics. Springer, New York, NY.
Corbeill, J. R. & Valdes-Corbeil, M. E. (2007). Are you ready for mobile learning? EDUCAUSE Quarterly, 30(2).
Woodill, G. (2011). The mobile learning edge: tools and technologies for developing your teams. [electronic resource]. New York : McGraw-Hill.
Presentation topic
Poster session
