[on behalf of Vitomir Kovanović]
*apologies for multiple copies*
Dear all,
We have a pleasure to invite you to submit your manuscript for the
upcoming *Special
Section on Learning Analytics for Primary and Secondary Schools* at
the *Journal
of Learning Analytics* (JLA, https://learning-analytics.info). The Journal
of Learning Analytics is a peer-reviewed, open-access journal,
disseminating the highest quality research in the field and indexed by
Scopus and Web of Science. It is the official publication of the Society
for Learning Analytics Research (SoLAR, https://solaresearch.org) and the
first journal dedicated to research into the challenges of collecting,
analysing and reporting data with the specific intent to improve learning.
See below the full details of the call for papers.
Kind regards,
Vitomir
*NOTE: *We invite *research papers, practitioner reports, and data and tool
reports* (for the details of different submission types see
https://epress.lib.uts.edu.au/journals/index.php/JLA/about/editorialPolicies#sectionPolicies
).
Special Section on Learning Analytics for Primary and Secondary Schools
https://epress.lib.uts.edu.au/journals/index.php/JLA/announcement/view/161
*GUEST EDITORS*Vitomir Kovanović, University of South Australia, Australia,
vitomir.kovanovic@xxxxxxxxxxxx
Claudia Mazziotti, Technical University Munich, Germany,
claudia.mazziotti@xxxxxx
Jason Lodge, The University of Queensland, Australia, jason.lodge@xxxxxxxxx
*AIMS & SCOPE*Over the past decade, there has been substantial growth in
the adoption of learning analytics and data-driven techniques for improving
teaching and learning. Learning analytics have been used for monitoring,
supporting, and assessing different forms of learning and teaching and thus
opened the possibility to make data-driven decisions of how to improve
student learning. By investigating varying online and blended learning
modalities and addressing issues around student retention, students at
risks and study approaches, most of the previous work, however, has focused
on tertiary education and issues that were specific for that educational
context. With the broader adoption of educational technologies in primary
and secondary education and the emergence of new classroom-focused
technologies, there has been a growing awareness of the potentials of
learning analytics for supporting students and diagnosing their learning
progress in pre-university contexts.
The focus of this special section is on investigating, developing, and
evaluating state-of-the-art learning analytics approaches within the
primary and secondary school settings. We specifically invite both *research
and practitioner contributions* that go beyond the pure development of
analytics systems by also addressing the specific challenges and
opportunities of the school context later target group. We further invite
contributions that make connections between analytical systems to
contemporary pedagogies and educational theories. Finally, we also invite *data
and tool report submissions* which provide an overview of the educational
datasets, tools and methods that focus on the use of learning analytics
within the primary and secondary school context.
*Advancing the use of Learning Analytics within Schools*Although the use of
educational technologies has been growing in K-12 sector (Horn & Staker,
2011; Voogt et al., 2018), learning analytics has been, for the most part,
used to support tertiary learning and teaching (Li et al., 2015; Sancho et
al., 2015). While there are many reasons for this, more constrained
resources for supporting analytics implementation and limited expertise in
data analytics within schools are likely contributing factors. The
collection and analysis of data is also far more sensitive topic within the
primary and secondary contexts (Gunawardena, 2017), due to significant
concerns around privacy and ethical use of data by parents, teachers, legal
authorities and social activists (Singer, 2014).
While the adoption of analytics has drawn sharp criticism (McRae, 2014;
Roberts-Mahoney et al., 2016; Selwyn & Facer, 2013), there is also a
growing realisation of the unique opportunities that analytics provide in
supporting contemporary teaching and learning. The 2017 Horizon K–12 report
(Freeman et al., 2017) estimated 2–3 years as the time to broader adoption
of learning analytics within primary and secondary domains, with main
opportunities being to “predict learner outcomes, trigger interventions or
curricular adaptations, and even prescribe new pathways or strategies to
improve student success” (p. 44). Similar benefits and opportunities were
noted by the earlier US Department of Education report (Bienkowski et al.,
2012) and also more recent Gonski report in Australia (Gonski et al.,
2018), who emphasise the power of data and analytics to provide more
personalised learning experiences and improve student learning outcomes.
Moreover, there have been substantial developments within the learning
analytics field itself; The development of multimodal learning analytics
(MMLA)(Ochoa, 2017) as well novel classroom-based analytics systems (Lodge
et al., 2018), provided analytical approaches that are far more suitable
for primary and secondary school contexts use. Since 2018, a full-day
pre-conference event on learning analytics adoption within schools has been
running at LAK conference, witnessing strong interest by schoolteachers,
administrators, policymakers, and industry representatives.
The goal of this special issue is to build upon the current momentum around
learning analytics use within schools and provide a place for researchers
to report their current efforts in this domain. We invite *research
studies, practitioner reports, and data and tool reports* that focus on the
use of learning analytics for supporting primary and secondary school
learning. The aim is to showcase the latest developments in learning
analytics use within schools, and empirical evidence on the effectiveness
of its use for supporting learning and teaching in the school context. We
also seek studies that aim to foster innovative pedagogical approaches
through the adoption of learning analytics as well as improve theoretical
understanding of teaching and learning in these spaces. Finally, given the
early stage of learning analytics adoption within schools, we especially
encourage studies which provide practical suggestions for the
implementation of analytical systems in school settings and examination of
the barriers of their adoption and areas for future work.
*TOPICS OF INTEREST*Some areas of interest are, but not limited to, one of
the following topics:
- *Analytics tools and systems:* Studies that focus on new and
state-of-the-art learning analytics tools and platforms for supporting
school learning and teaching (e.g., teacher dashboards).
- *School adoption, implementation and scale-up:* Studies that provide
insights into the current state of analytics adoption in schools, key
challenges and opportunities on an institutional and professional level, as
well as reports on the current learning analytics adoption efforts.
- *Practical implications: *Studies that focus on developing
recommendations for practice using robust empirical evidence around
analytics use within primary and secondary schools.
- *Theory building:* Contributions that focus on advancing theoretical
understanding of learning analytics use for supporting learning and
teaching within school settings by building upon existing educational and
learning science-related theories.
- *Methods:* Contributions that describe methodological approaches and
challenges that are grounded in theory, and serve the purpose of combining
different multimodal data for using learning analytics within the primary
and secondary school context.
- *Critical perspectives:* Contributions which provide critical and
balanced assessment and examination of learning analytics use and adoption
within school settings.
*SUBMISSION INSTRUCTIONS*Prospective authors may contact the section
editors with queries. Final submissions will take place through JLA’s
online submission system at http://learning-analytics.info When submitting
a paper, select the section “Special Section: Learning Analytics for
Primary and Secondary Schools". All submissions should follow JLA’s
standard manuscript guidelines and template available on the journal
website and will undergo double-blind peer review.
*TIMELINE *
Full manuscripts due: September 20, 2020
Completion of first review round: November 2020
Revised/final manuscripts due: December 2020
Completion of second review round (if needed): February 2021
Revised/final manuscripts due: March 2021
Publication of special issue: July/August 2021, Issue 8(2)
*REFERENCES*
Bienkowski, M. A., Feng, M., & Means, B. (2012). *Enhancing Teaching and
Learning Through Educational Data Mining and Learning Analytics: An Issue
Brief*. U.S. Department of Education.
Freeman, A., Adams Becker, S., Cummins, M., Davis, A., & Hall Giesinger, C.
(2017). *NMC/CoSN Horizon Report: 2017 K–12 Edition*. The New Media
Consortium.
Gonski, D., Arcus, T., Boston, K., Gould, V., Johnson, W., O’Brien, L.,
Perry, L.-A., & Roberts, M. (2018). *Through Growth to Achievement: Review
to Achieve Educational Excellence in Australian Schools*. Department of
Education and Training.
Gunawardena, A. (2017). Brief survey of analytics in K12 and higher
education. *International Journal on Innovations in Online Education*, *1*
(1). https://doi.org/10.1615/IntJInnovOnlineEdu.v1.i1.80
Horn, M. B., & Staker, H. (2011). *The rise of K-12 blended learning*.
Innosight Institute.
https://www.christenseninstitute.org/wp-content/uploads/2013/04/The-rise-of-K-12-blended-learning.emerging-models.pdf
Li, K. C., Lam, H. K., & Lam, S. S. (2015). A review of learning analytics
in educational research. In J. Lam, K. K. Ng, S. K. S. Cheung, T. L. Wong,
K. C. Li, & F. L. Wang (Eds.), *Technology in Education.
Technology-Mediated Proactive Learning* (pp. 173–184). Springer.
https://doi.org/10.1007/978-3-662-48978-9_17
Lodge, J. M., Horvath, J. C., & Corrin, L. (Eds.). (2018). *Learning
Analytics in the Classroom: Translating Learning Analytics Research for
Teachers*. Routledge.
McRae, P. (2014). Rebirth of the teaching machine through the seduction of
data analytics: This time it’s personal. *Revista Intercambio*, 6, 28–32.
Ochoa, X. (2017). Multimodal learning analytics. In C. Lang, G. Siemens, A.
F. Wise, & D. Gaševic (Eds.), *The Handbook of Learning Analytics* (1st
ed., pp. 129–141). Society for Learning Analytics Research (SoLAR).
http://solaresearch.org/hla-17/hla17-chapter1
Roberts-Mahoney, H., Means, A. J., & Garrison, M. J. (2016). Netflixing
human capital development: Personalized learning technology and the
corporatization of K-12 education. *Journal of Education Policy*, *31*(4),
405–420. https://doi.org/10.1080/02680939.2015.1132774
Sancho, M.-R., Cañabate, A., & Sabate, F. (2015). Contextualizing learning
analytics for secondary schools at micro level. *2015 International
Conference on Interactive Collaborative and Blended Learning (ICBL)*,
70–75. https://doi.org/10.1109/ICBL.2015.7387638
Selwyn, N., & Facer, K. (2013). *The Politics of Education and Technology:
Conflicts, Controversies, and Connections*. Springer.
Singer, N. (2014). *InBloom Student Data Repository to Close*. The New York
Times Bits Blog.
http://bits.blogs.nytimes.com/2014/04/21/inbloom-student-data-repository-to-close/
Voogt, J., Knezek, G., Christensen, R., & Lai, K.-W. (2018). Developing an
understanding of the impact of digital technologies on teaching and
learning in an ever-changing landscape. In J. Voogt, G. Knezek, R.
Christensen, & K.-W. Lai (Eds.), *Second Handbook of Information Technology
in Primary and Secondary Education*(pp. 3–12). Springer International
Publishing. https://doi.org/10.1007/978-3-319-71054-9_113
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Dr Vitomir Kovanović
Research Fellow,* School of Education, University of South Australia*
Data Scientist,* Teaching Innovation Unit, University of South Australia*
Visiting Researcher, *LINK Lab, University of Texas Arlington, USA*
Secretary,* Society for Learning Analytics Research (SoLAR)*
Academic Editor (Education, Pedagogy),* PLOS One*
University of South Australia <http://unisa.edu.au/>
GPO Box 2471, Adelaide SA 5001
CRICOS Provider Number 00121B
Office: City West Campus Room BH3-21 <https://goo.gl/maps/TeFoTBJ8nrM2>
Web: http://vitomir.kovanovic.info