Challenges and opportunities of multimodal data in human learning
The computer science students' perspective
Bibliographic Data
| ID | 21499271 |
|---|---|
| Authors | Katerina Mangaroska (0000-0002-7853-0429, Department of Computer Science, Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim Norway), Roberto Martínez‐Maldonado (0000-0002-8375-1816, Faculty of Information Technologies Monash University Clayton Victoria Australia), Boban Vesin (0000-0002-6490-4311, School of Business University of South‐Eastern Norway Vestfold Norway, corresponding author), Dragan Gašević (0000-0001-9265-1908, Faculty of Information Technologies Monash University Clayton Victoria Australia) |
| Year | 2021 |
| Volume | 37 |
| Issue | 4 |
| Pages | 1030-1047 |
| Publication date | 2021-08-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Computer Assisted Learning (JOURNAL) |
| Journal identifiers | ISSN: 0266-4909 • E-ISSN: 1365-2729 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/jcal.12542 |
| OpenAlex | W3135540143 |
| Language | EN |
| Citations received | 7 |
| References cited | 93 |
Multimodal data have the potential to explore emerging learning practices that extend human cognitive capacities. A critical issue stretching in many multimodal learning analytics (MLA) systems and studies is the current focus aimed at supporting researchers to model learner behaviours, rather than directly supporting learners. Moreover, many MLA systems are designed and deployed without learners' involvement. We argue that in order to create MLA interfaces that directly support learning, we need to gain an expanded understanding of how multimodal data can support learners' authentic needs. We present a qualitative study in which 40 computer science students were tracked in an authentic learning activity using wearable and static sensors. Our findings outline learners' curated representations about multimodal data and the non‐technical challenges in using these data in their learning practice. The paper discusses 10 dimensions that can serve as guidelines for researchers and designers to create effective and ethically aware student‐facing MLA innovations
Data science · Human–computer interaction · Learning analytics · Multimodal learning · Multimodality · Wearable computer · World Wide Web · Computer Science · E-Learning and Knowledge Management · Innovative Teaching and Learning Methods · Online Learning and Analytics · Artificial Intelligence
Designed to Death? The Tensions Underpinning Design in Educational Discourse
Effects of flipped English learning designs on learning outcomes and cognitive load
Capturing cognitive load management during authentic virtual reality flight training with behavioural and physiological indicators
Innovation Resistance in EdTech
Mapping from proximity traces to socio‐spatial behaviours and student progression at the school
The role of indoor positioning analytics in assessment of simulation‐based learning
Multimodal learning analytics—In‐between student privacy and encroachment
User Centered System Design
Routledge International Handbook of Participatory Design
Doing a thematic analysis
Intelligent tutoring systems
Perceiving Learning at a Glance
Ethical and privacy principles for learning analytics
Focus Group Interview
Learning Analytics for Learning Design
The current landscape of learning analytics in higher education
A Systematic Review of Empirical Studies on Learning Analytics Dashboards
Dynamics of affective states during complex learning
Interest, Prior Knowledge, and Learning
Defining and Measuring Engagement and Learning in Science
A Taxonomy of Privacy
EEG alpha and theta oscillations reflect cognitive and memory performance
Where is the teacher? Digital analytics for classroom proxemics
Multimodal Learning Analytics research with young children
Reframing classroom sensing
Student Vulnerability, Agency and Learning Analytics
Group Child Interviews as a Research Tool
Using thematic analysis in psychology
The Challenges of Defining and Measuring Student Engagement in Science
Learning Analytics
Learning Analytics
Verification Strategies for Establishing Reliability and Validity in Qualitative Research
Targeted
| Unique citing works | 7 |
|---|---|
| Citations per year | 1,75 |
| Citation span | 2022 - 2025 (4) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 7 |