research-article

A framework to support educational decision making in mobile learning

Authors:

Giovanni Fulantelli,

Davide Taibi, and

Marco ArrigoAuthors Info & Claims

Computers in Human Behavior, Volume 47, Issue C

Pages 50 - 59

https://doi.org/10.1016/j.chb.2014.05.045

Published: 01 June 2015 Publication History

Abstract

A task-interaction framework to support education decision making in mobile learning.Focus on relationships among learners' interactions and pedagogically relevant tasks.A case study demonstrates the application of the framework to m-learning scenarios.MeLOD, a mobile learning system supporting analytics of learner-produced data trails. Learning Analytics in Mobile Learning is a challenging research topic, due to the distinguishing features of mobile learning. In fact, mobile learning is characterized by the learners' mobility, the possibility of having localized data and information, the large amount of data that can be collected during a learning session, the affordances provided by the technologies and the social dynamics that characterize the context in which learning takes place. As a consequence, Learning Analytics in mobile learning requires original methodological approaches which enrich techniques already tested in different learning contexts (e.g., in Virtual Learning Environments) with specific strategies to deal with the complexity of mobile learning and manage the corresponding datasets. This paper presents a task-interaction framework to support educational decision-making in mobile learning. The framework is based on the relationships between the different types of interactions occurring in a mobile learning activity and the tasks which are pedagogically relevant for the learning activity. A case study has been designed to demonstrate the application of the task-interaction framework to learning scenarios based on the use of mobile devices.

References

[1]

A.F. Agudo-Peregrina, S. Iglesias-Pradas, M.A. Conde-González, A. Hernández-García, Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning, Computers in Human Behavior, 31 (2014) 542-550.

Digital Library

[2]

S. Ahmed, D. Parsons, Abductive science inquiry using mobile devices in the classroom, Computers & Education, 63 (2013) 62-72.

Digital Library

[3]

Aljohani, N. R., & Davis, H. C. (2012). Learning analytics in mobile and ubiquitous learning environments. In Proceedings of 11th world conference on mobile and contextual learning (mLearn 2012), Helsinki, Finland, 16-18 October 2012.

[4]

H. Alvarez, S.A. Rios, F. Aguilera, E. Merlo, L. Guerrero, Enhancing social network analysis with a concept based text mining approach to discover key members on a virtual community of practice, Lecture Notes in Computer Science, 6277 (2010) 591-600.

Digital Library

[5]

M. Arrigo, A. Kukulska-Hulme, I. Arnedillo-Sánchez, G. Kismihok, Meta-analyses from a collaborative project in mobile lifelong learning, British Educational Research Journal, 39 (2013) 222-247.

[6]

Bakharia, A., Dawson, S. (2011). SNAPP: A bird's-eye view of temporal participant interaction. In Proceedings of the 1st international conference on learning analytics and knowledge (LAK '11) (pp. 168-173). ACM, New York, NY, USA. http://dx.doi.org/10.1145/2090116.2090144.

Digital Library

[7]

C. Bizer, T. Heath, T. Berners-Lee, Linked data - The story so far. Special issue on linked data, International Journal on Semantic Web and Information Systems, 5 (2009) 1-22.

[8]

P. Carmichael, K. Jordan, Semantic web technologies for education - time for a 'turn to practice'?, Technology, Pedagogy and Education, 21 (2012) 153-169.

[9]

d'Aquin, M. & Jay, N. (2013). Interpreting data mining results with linked data for learning analytics: motivation, case study and directions. In D. Suthers & K. Verbert (Eds.), Proceedings of learning analytics and knowledge 2013 (pp. 155-164). ISBN: 978-1-4503-1785-6.

Digital Library

[10]

d'Aquin, M., Dietze, S., Drachsler, H., Taibi, D., Herder, E. (2013). Using linked data in learning analytics. In E-learning papers P.A.U. Education (Vol. 36). ISSN: 1887-1542.

[11]

Duval, E. (2012). Learning Analytics in mobile learning. In Learning in context workshop 2012, Brussels 26th and 27th of March.

[12]

A.L. Dyckhoff, D. Zielke, M. Bültmann, M.A. Chatti, U. Schroeder, Design and implementation of a learning analytics toolkit for teachers, Educational Technology & Society, 15 (2012) 58-76.

[13]

Educause Center for Applied Research ECAR (2012). ECAR study of undergraduate students and information technology. Louisville, CO: Educause Center for Applied Research. <http://net.educause.edu/ir/library/pdf/ERS1208/ERS1208.pdf> Retrieved 25.06.13.

[14]

Y. Engeström, Learning by expanding: An activity-theoretical approach to developmental research, Orienta-Konsulit Oy, Helsinki, 1987.

[15]

Erétéo, G., Buffa, M., Gandon, F., Grohan, P., Leitzelman, M. & Sander, P. (2008). A state of the art on social network analysis and its applications on a semantic web. In Proceedings of SDoW2008, workshop at ISWC 2008.

[16]

R. Ferguson, Learning analytics: drivers, developments and challenges, International Journal of Technology Enhanced Learning, 4 (2012) 304-317.

Digital Library

[17]

R. Ferguson, S. Buckingham Shum, Social learning analytics: five approaches, in: Proceedings of the 2nd international conference on learning analytics & knowledge (Vancouver, 29 April-2 May 2012), ACM Press, 2012.

Digital Library

[18]

D. Frohberg, C. Göth, G. Schwabe, Mobile Learning projects-a critical analysis of the state of the art, Journal of Computer Assisted Learning, 25 (2009) 307-331.

[19]

Gasevic, D. (2012). LinkedData and its potential for Learning Analytics. In Proceedings of the 1st international workshop on learning analytics and linked data (29.04-02.05.2012 Vancouver Canada).

[20]

D.C. Hillman, D.J. Willis, C.N. Gunawardena, Learner-interface interaction in distance education: An extension of contemporary models and strategies for practitioners, American Journal of Distance Education, 8 (1994) 30-42.

[21]

D. Hung, Situated cognition and problem-based learning: implications for learning and instruction with technology, Journal of Interactive Learning Research, 13 (2002) 393-414.

[22]

G.J. Hwang, C.C. Tsai, Research trend in mobile and ubiquitous learning: a review of publications in selected journal from 2001 to 2010, British Journal of Education Technology, 42 (2011) E65-E70.

[23]

IAB Europe (2012). Mediascope Europe 2012: Online Media Consumption in Europe. <http://www.iabeurope.eu>.

[24]

Johnson, L., Adams Becker, S., Cummins, M., Estrada, V., Freeman, A., & Ludgate, H. (2013). NMC Horizon Report: 2013 Higher Education Edition. Austin, Texas: The New Media Consortium. <http://www.nmc.org/pdf/2013-horizon-report-HE.pdf> Retrieved 26.06.13.

[25]

R.D. Johnson, S. Hornik, E. Salas, An empirical examination of factors contributing to the creation of successful e-learning environments, International Journal of Human-Computer Studies, 66 (2008) 356-369.

Digital Library

[26]

A.C. Jones, E. Scanlon, G. Clough, Mobile learning: Two case studies of supporting inquiry learning in informal and semiformal settings, Computers & Education, 61 (2013) 21-32.

Digital Library

[27]

Long, P. D. & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education (EDUCAUSE Review). <http://www.educause.edu/ero/article/penetrating-fog-analytics-learning-and-education> Retrieved 2006.3.

[28]

Manyika, J. (2011). Big data: The next frontier for innovation, competition, and productivity. executive summary, McKinsey Global Institute, May 2011. <http://www.mckinsey.com/mgi/publications/big_data/pdfs/MGI_big_data_exec_summary.pdf>.

[29]

Maturana, R. A., Alvarado, M. E., López-Sola, S., Ibáñez, M. J., Ruiz Elósegui, L. (2013). Linked data based applications for learning analytics research: Faceted searches, enriched contexts, graph browsing and dynamic graphic visualisation of data. In Proceedings of the LAK Data Challenge, held at LAK2013 - The Third Conference on Learning Analytics and Knowledge, April 2013. CEUR WS Proceedings (Vol. 974).

[30]

Merino, P. J. M., Valiente, J. A. R. & Kloos, C. D. (2013). Inferring higher level learning information from low level data for the Khan Academy platform. In D. Suthers & K. Verbert (Eds.), LAK (pp. 112-116). ACM. ISBN: 978-1-4503-1785-6.

Digital Library

[31]

M.G. Moore, Three types of interaction, American Journal of Distance Education, 3 (1989) 1-6.

[32]

D. Persico, F. Pozzi, L. Sarti, Design patterns for monitoring and evaluating CSCL processes, Computers in Human Behavior, 25 (2009) 1020-1027.

Digital Library

[33]

Richards, G. & DeVries, I. (2011). Revisiting formative evaluation: Dynamic monitoring for the improvement of learning activity design and delivery. In Proceedings of the 1st international conference on learning analytics and knowledge (LAK'11) (pp. 157-162).

Digital Library

[34]

Rios, S. A., Aguilera, F., Bustos, F., Omitola, T., & Shadbolt, N. (2011). Leveraging social network analysis with topic models and the semantic web. In Proceeding of WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (Vol. 03, pp. 339-342). http://dx.doi.org/10.1109/WI-IAT.2011.127.

Digital Library

[35]

D.A. Schön, The reflective practitioner: How professionals think in action, Basic Book, NewYork, 1983.

[36]

M. Sharples, J. Taylor, G. Vavoula, A theory of learning for the mobile age, in: The sage handbook of e-learning research, Sage, London, 2007, pp. 221-247.

[37]

Siemens, G. (2010). What Are Learning Analytics? http://www.elearnspace.org/blog/2010/08/25/what-are-learning-analytics/ (accessed on 24.09.10).

[38]

Softic, S., Taraghi, B., De Vocht, L. (2013). Activities and Trends Analytics in a Widget based PLE using Semantic Technologies. In Proceedings of CSEDU 2013-5th international conference on computer supported education, 6-8 May, Aachen, Germany.

[39]

SoLAR (2012). Society for learning analytics research. <http://www.solaresearch.org/about/>.

[40]

J.M. Spector, Emerging educational technologies and research directions, Educational Technology & Society, 16 (2013) 21-30.

[41]

J. Taylor, M. Sharples, C.O. Malley, G. Vavoula, J. Waycott, Towards a task model for mobile learning: a dialectical approach, International Journal of Learning Technology, 2 (2006) 138-158.

Digital Library

[42]

J. Traxler, Defining, discussing, and evaluating mobile learning: The moving finger writes and having writ¿, International Review of Research in Open and Distance Learning, 8 (2007).

[43]

G. Vavoula, M. Sharples, Meeting the challenges in evaluating mobile learning: A 3-level evaluation framework, International Journal of Mobile and Blended Learning, 1 (2009) 54-75.

[44]

Woolf, B. P. (Ed.) (2010). A roadmap for education technology. Washington, DC: National Science Foundation. <http://www.cra.org/ccc/files/docs/groe/GROE%20Roadmap%20for%20Education%20Technology%20Final%20Report.pdf> Retrieved on 18.06.3.

[45]

W.-H. Wu, Y.-C. Jim Wu, C.-Y. Chen, H.-Y. Kao, Review of trends from mobile learning studies: A meta-analysis, Computers & Education, 59 (2012) 817-827.

Digital Library

[46]

Zaïane, O. R. (2001). Web usage mining for a better web-based learning environment. In Proceedings of the 4th IASTED international conference on advanced technology for education (CATE'01), 27-28 June, Banff, Canada.

Cited By

Lee-Cultura SSharma KGiannakos M(2024)Multimodal Teacher Dashboards: Challenges and Opportunities of Enhancing Teacher Insights Through a Case StudyIEEE Transactions on Learning Technologies10.1109/TLT.2023.327684817(181-201)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TLT.2023.3276848
Khalil MPrinsloo PSlade SWise AMartinez-Maldonado RHilliger I(2022)A Comparison of Learning Analytics Frameworks: a Systematic ReviewLAK22: 12th International Learning Analytics and Knowledge Conference10.1145/3506860.3506878(152-163)Online publication date: 21-Mar-2022
https://dl.acm.org/doi/10.1145/3506860.3506878
Liu MYu D(2022)Towards intelligent E-learning systemsEducation and Information Technologies10.1007/s10639-022-11479-628:7(7845-7876)Online publication date: 12-Dec-2022
https://dl.acm.org/doi/10.1007/s10639-022-11479-6
Show More Cited By

Index Terms

A framework to support educational decision making in mobile learning
1. Applied computing
  1. Education
  2. Enterprise computing

Index terms have been assigned to the content through auto-classification.

Recommendations

A semantic approach to mobile learning analytics
TEEM '13: Proceedings of the First International Conference on Technological Ecosystem for Enhancing Multiculturality

Mobile learning has reached a considerable level of maturity in recent years, and its role is widely acknowledged in school contexts, university, vocational training, formal and non-formal learning settings, and more generally as an opportunity for ...
Read More
Mobile Learning to Support Self-Regulated Learning: A Theoretical Review

This paper discusses the possibilities of using and designing mobile technology for learning purposes coupled with learning analytics to support self-regulated learning (SRL). Being able to self-regulate one's own learning is important for academic ...
Read More
Considering mobile device constraints and context-awareness in adaptive mobile learning for flipped classroom

Today, the mobile technologies and Information and Communication Technology development opened the door on new methods and pedagogies of learning. We are talking here about the mobile learning and the flipped classroom approaches. The flipped classroom ...
Read More

Comments

Information & Contributors

Information

Published In

cover image Computers in Human Behavior

Computers in Human Behavior Volume 47, Issue C

June 2015

182 pages

ISSN:0747-5632

Issue’s Table of Contents

Copyright © Elsevier Ltd.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 June 2015

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

15
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Other Metrics

View Author Metrics

Citations

Cited By

Lee-Cultura SSharma KGiannakos M(2024)Multimodal Teacher Dashboards: Challenges and Opportunities of Enhancing Teacher Insights Through a Case StudyIEEE Transactions on Learning Technologies10.1109/TLT.2023.327684817(181-201)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TLT.2023.3276848
Khalil MPrinsloo PSlade SWise AMartinez-Maldonado RHilliger I(2022)A Comparison of Learning Analytics Frameworks: a Systematic ReviewLAK22: 12th International Learning Analytics and Knowledge Conference10.1145/3506860.3506878(152-163)Online publication date: 21-Mar-2022
https://dl.acm.org/doi/10.1145/3506860.3506878
Liu MYu D(2022)Towards intelligent E-learning systemsEducation and Information Technologies10.1007/s10639-022-11479-628:7(7845-7876)Online publication date: 12-Dec-2022
https://dl.acm.org/doi/10.1007/s10639-022-11479-6
Kumar BSharma B(2020)Context aware mobile learning application development: A systematic literature reviewEducation and Information Technologies10.1007/s10639-019-10045-x25:3(2221-2239)Online publication date: 1-May-2020
https://dl.acm.org/doi/10.1007/s10639-019-10045-x
Shabaninejad SKhosravi HLeemans SSadiq SIndulska M(2020)Recommending Insightful Drill-Downs Based on Learning Processes for Learning Analytics DashboardsArtificial Intelligence in Education10.1007/978-3-030-52237-7_39(486-499)Online publication date: 6-Jul-2020
https://dl.acm.org/doi/10.1007/978-3-030-52237-7_39
Sun KChen M(2019)Utilizing Free Augmented Reality App for Learning Geometry at Elementary School in TaiwanInternational Journal of Distance Education Technologies10.4018/IJDET.201910010317:4(36-53)Online publication date: 1-Oct-2019
https://dl.acm.org/doi/10.4018/IJDET.2019100103
Acquila-Natale EIglesias-Pradas SHernández-García ÁChaparro-Peláez JRodríguez-Ruiz I(2019)MWDEXProceedings of the Seventh International Conference on Technological Ecosystems for Enhancing Multiculturality10.1145/3362789.3362899(297-303)Online publication date: 16-Oct-2019
https://dl.acm.org/doi/10.1145/3362789.3362899
Wan AOmar MSan L(2018)Augmented Reality Technology for Year 10 Chemistry ClassInternational Journal of Computer-Assisted Language Learning and Teaching10.4018/IJCALLT.20181001048:4(45-64)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.4018/IJCALLT.2018100104
Schwendimann BRodriguez-Triana MVozniuk APrieto LBoroujeni MHolzer AGillet DDillenbourg P(2017)Perceiving Learning at a GlanceIEEE Transactions on Learning Technologies10.1109/TLT.2016.259952210:1(30-41)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1109/TLT.2016.2599522
Snchez-Prieto JOlmos-Miguelez SGarca-Pealvo F(2017)MLearning and pre-service teachersComputers in Human Behavior10.1016/j.chb.2016.09.06172:C(644-654)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1016/j.chb.2016.09.061
Show More Cited By

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents