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Exploring Physics Education in the Classroom and the Laboratory with Multimodal Learning Analytics

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Poster

Exploring Physics Education in the Classroom and the Laboratory with Multimodal Learning Analytics

Abstract

We investigate how students learn in an expeirmental physics course and how they perform in the laboratory through network analysis and multimodal learning analytics (MMLA) that capture the log-data from the digital learning activities, and interaction between students and objects from video and audio. Our research aim is to examine how the students’ actions change from the classroom to the laboratory to understand how the coursework can further support the laboratory work in real scientific experiments.
Exploring Physics Education in the Classroom and the
Laboratory with Multimodal Learning Analytics
Jesper Bruun
University ofCopenhangen
jbruun@ind.ku.dk
Daniel Spikol
Malmö University
daniel.spikol@mah.se
Linda Udby
University of Copenhagen
udby@nbi.dk
Introduction
There is a good amount of evidence set out in recent reports that
show the rising importance of working with other agents, both
people and machines, to solve complex problems across sub-
jects.
In the case of physics education with the focus on neutron
sciences in preparation for the European Spallation Source
(ESS), many teaching/learning initiatives have been launched
that allow for collaborative problem-solving in authentic con-
texts, the laboratories.
Our research aim is to examine how the students’ actions change
from the classroom to the laboratory to understand how the
coursework can further support the laboratory work in real
scientic experiments.
Approach
We are investigating student behaviour in a neutron scattering science
course using a combination of server logs, MMLA and observations of
learners in the classroom and authentic experimental environments.
We expect some students to display behaviour which can be identied
as in-depth learning strategies, while other students display behav-
iours more associated with surface learning. However, a given student
may display in-depth learning strategies at one point in time, and sur-
face learning strategies at other points in time.
Concurrent with logging and analysing online behaviour, we will use
video and audio recording student interactions with online course ma-
terial during class and during group work that will be analysed by
human and machine with ongoing MMLA work that explores group col-
laboration
Data Collection
Data Analysis Human
Data Analysis Machine
Results Ideas
Time: Classroon to the Lab
Network Analysis Coding Non-Verbal Student Interaction Coding Interview Coding
LMS Data Network Analysis Coding Non-Verbal Student Interaction Coding
... Another avenue of research in play is a design-based approach. Here, educational researchers design online and blended learning materials for science courses while at the same time monitoring clickstreams, videotaping lessons, and audiotaping student discussions for joint multimodal analyses [29]. On such project is the Virtual Neutrons for Teaching project (eneutrons.org), in which students learn neutron scattering via online textbooks and quizzes [30] [31]. ...
Article
Full-text available
Information and communication technologies are increasingly mediating learning and teaching practices as well as how educational institutions are handling their administrative work. As such, students and teachers are leaving large amounts of digital footprints and traces in various educational apps and learning management platforms, and educational administrators register various processes and outcomes in digital administrative systems. It is against such a background we in recent years have seen the emergence of the fast-growing and multi-disciplinary field of learning analytics. In this paper, we examine the research efforts that have been conducted in the field of learning analytics in Austria, Finland, Norway, Germany, Spain, and Sweden. More specifically, we report on developed national policies, infrastructures and competence centers, as well as major research projects and developed research strands within the selected countries. The main conclusions of this paper are that the work of researchers around Europe has not led to national adoption or European level strategies for learning analytics. Furthermore, most countries have not established national policies for learners’ data or guidelines that govern the ethical usage of data in research or education. We also conclude that learning analytics research on the pre-university level to a high extent have been overlooked. In the same vein, learning analytics has not received enough focus form national and European national bodies. Such funding is necessary for taking steps towards data-driven development of education.
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