Faglige interesser
- Læringsanalyse
- Læringsteknologi, mobillæring og virtuell virkelighet
- Høyere utdanning
- Teknologistøttet læring
Yrkesbakgrunn
- 2022–: Postdoktor ved Institutt for pedagogikk, Det utdanningsvitenskapelige fakultet, Universitetet i Oslo (Norge)
- 2018–2022: Doktorgradsstipendiat ved Institutt for pedagogikk, Det utdanningsvitenskapelige fakultet, Universitetet i Oslo (Norge)
- 2017–2018: Studiekonsulent ved Faculty of Education, University of Auckland (New Zealand)
- 2015–2016: Vitenskapelig assistent og internasjonal studiekonsulent ved University of Adelaide (Australia)
- 2014: Forskningskoordinator i Link-Up Project, Makerere University, Child Health and Development Center (Uganda)
- 2014: Universitetslektor ved Kyambogo University (Uganda)
- 2012–2013: Forsker i PREPARE Project, Makerere University, Child Health and Development Center (Uganda)
Utdanningsbakgrunn
- 2018–2022: PhD, Institutt for pedagogikk, Det utdanningsvitenskapelige fakultet, Universitetet i Oslo (Norge)
- 2015–2016: Master of Education, University of Adelaide (Australia)
- 2010–2012: Master of Development Management, University of Agder (Norge)
- 2006–2009: Bachelor of Adult and Community Education, Makerere University (Uganda)
Undervisning
Nåværende
- MIED4020: Research Methods II
- MIED4010: Research Methods I
- MIED4190: Master Thesis
Tidligere
- HEM4112: Research Methods and Statistics I
- HEM4250: Thesis Proposal
- EDU4100: Introduction to Comparative and International Education
- EDU4201: Core Focus Areas in Comparative and International Education
- PED4550: Pedagogical innovations and communication
- HEM4210: History and Primary Processes of Higher Education
Emneord:
Pedagogikk,
IKT og læring,
Digitale læringsomgivelser,
Høyere utdanning,
Læringsanalyse
Publikasjoner
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Kaliisa, Rogers; Palmer, Edward & Miller, Julia
(2022).
Lecturers’ Perspectives on Mobile Learning in Higher Education: Experiences and Implementation Barriers.
Information Technology, Education and Society.
ISSN 1037-616X.
doi:
10.7459/ites/18.1.02.
Vis sammendrag
This paper reports the experiences of university lecturers towards mobile learning from two points of view: (i) lecturers’ needs and experiences in relation to mobile learning design and implementation, and (ii) possible barriers to their adoption in higher education classrooms. The findings from a qualitative case study that conducted semi-structured interviews with 12 lecturers from a developed and developing country contexts revealed; the need for strong commitment from institutional leadership, presence of an institutional and national mobile learning policy, a culture of innovation and willingness to revise the traditional curriculum to incorporate the new mobile pedagogies.
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Prinsloo, Paul & Kaliisa, Rogers
(2022).
Learning Analytics on the African Continent: An Emerging Research Focus and Practice.
Journal of Learning Analytics.
ISSN 1929-7750.
9(2),
s. 218–235.
doi:
10.18608/jla.2022.7539.
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Prinsloo, Paul & Kaliisa, Rogers
(2022).
Data privacy on the African continent: Opportunities, challenges and implications for learning analytics.
British Journal of Educational Technology (BJET).
ISSN 0007-1013.
s. 1–20.
doi:
10.1111/bjet.13226.
Fulltekst i vitenarkiv
Vis sammendrag
Whilst learning analytics is still nascent in most African higher education institutions, many African higher education institutions use learning platforms and analytic services from providers outside of the African continent. A critical consideration of the protection of data privacy on the African continent and its implications for learning analytics in African higher education is there-fore needed. In this paper, we map the current state of legal and regulatory environments and frameworks on privacy to establish their implications for learning analytics. This scoping review of privacy regulations in 32 African countries, complemented by 15 scholarly papers, revealed that there are numerous national and regional legislation and regulatory frameworks, provid-ing clear pointers pertaining to (student) data privacy to governments, higher education institutions and re-searchers. As such, the findings of this research have implications for African higher education to ensure not only legal compliance but also to oversee and safe-guard student data privacy as part of their fiduciary duty. This research provides crucial insights regarding the importance of context for thinking about the expansion and institutional adoption of learning analytics.
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Kaliisa, Rogers & Dolonen, Jan Arild
(2022).
CADA: a teacher-facing learning analytics dashboard to foster teachers’ awareness of students’ participation and discourse patterns in online discussions.
Technology, Knowledge and Learning.
ISSN 2211-1662.
doi:
10.1007/s10758-022-09598-7.
Fulltekst i vitenarkiv
Vis sammendrag
Despite the potential of learning analytics (LA) to support teachers’ everyday practice, its adoption has not been fully embraced due to the limited involvement of teachers as co-designers of LA systems and interventions. This is the focus of the study described in this paper. Following a design-based research (DBR) approach and guided by concepts from the socio-cultural perspective and human-computer interaction (HCI), we design, test, and evaluate a teacher-facing LA dashboard, the Canvas Discussion Analytics Dashboard (CADA), in real educational settings. The goal of this dashboard is to support teachers’ roles in online environments through insights into students’ participation and discourse patterns. We evaluate CADA through 10 in-depth interviews with university teachers to examine their experiences using CADA in seven blended undergraduate and graduate courses over a one-year period. The findings suggest that engaging teachers throughout the analytics tool design process and giving them control/agency over LA tools can favour their adoption in practice. Additionally, the alignment of dashboard metrics with relevant theoretical constructs allows teachers to monitor the learning designs and make course design changes on the fly. The teachers in this study emphasise the need for LA dashboards to provide actionable insights by moving beyond what things are toward how things should be. This study has several contributions. First, we make an artefact contribution (e.g. CADA), an LA dashboard to support teachers with insights into students’ online discussions. Second, by leveraging theory, and working with the teachers to develop and implement a dashboard in authentic teaching environments, we make an empirical, theoretical and methodological contribution to the field of learning analytics and technology-enhanced learning. We synthesise these through practical design and implementation considerations for researchers, dashboard developers, and higher education institutions.
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Kaliisa, Rogers; Rienties, Bart; Mørch, Anders & Kluge, Anders
(2022).
Social learning analytics in computer-supported collaborative learning environments: A systematic review of empirical studies.
Computers & Education Open (CAEO).
doi:
10.1016/j.caeo.2022.100073.
Fulltekst i vitenarkiv
Vis sammendrag
Social learning analytics (SLA) is a promising approach for identifying students’ social learning processes in computer-supported collaborative learning (CSCL) environments. To identify the main characteristics of SLA, gaps and future opportunities for this emerging approach, we systematically identified and analyzed 36 SLA related studies conducted between 2011 and 2020. We focus on SLA implementation and methodological characteristics, educational focus, and the studies’ theoretical perspectives. The results show the predominance
of SLA in formal and fully online settings with social network analysis (SNA) a dominant analytical technique.
Most SLA studies aimed to understand students’ learning processes and applied the social constructivist perspective as a lens to interpret students’ learning behaviours. However, (i) few studies involve teachers in developing SLA tools, and rarely share SLA visualizations with teachers to support teaching decisions; (ii) some SLA studies are atheoretical; and (iii) the number of SLA studies integrating more than one analytical approach remains limited. Moreover, (iv) few studies leveraged innovative network approaches (e.g., epistemic network analysis, multimodal network analysis), and (v) studies rarely focused on temporal patterns of students’ interactions to assess how students’ social and knowledge networks evolve over time. Based on the findings and the gaps identified, we present methodological, theoretical and practical recommendations for conducting research and creating tools that can advance the field of SLA.
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Damsa, Crina-Ioana; Muukkonen, Hanni; van Leeuwen, Anouschka; Gašević, Dragan; Janssen, Jeroen & Esterhazy, Rachelle
[Vis alle 10 forfattere av denne artikkelen]
(2021).
Analyzing and Conceptualizing Collaborative Learning with Digital Knowledge Objects .
Proceedings of the annual meeting of the ISSS.
ISSN 1999-6918.
s. 41–44.
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Misiejuk, Kamila; Scianna, Jennifer; Kaliisa, Rogers; Vachuska, Karl & Shaffer, David Williamson
(2021).
Incorporating Sentiment Analysis with Epistemic Network Analysis to Enhance Discourse Analysis of Twitter Data.
I Ruis, Andrew R & Seung, Lee. B (Red.),
Advances in Quantitative Ethnography.
Springer Nature.
ISSN 978-3-030-67788-6.
s. 375–389.
doi:
%20https:/doi.org/10.1007/978-3-030-67788-6_26.
Vis sammendrag
While there has been much growth in the use of microblogging platforms (e.g., Twitter) to share information on a range of topics, researchers struggle
to analyze the large volumes of data produced on such platforms. Established methods such as Sentiment Analysis (SA) have been criticized over their inaccuracy
and limited analytical depth. In this exploratory methodological paper, we propose
a combination of SA with Epistemic Network Analysis (ENA) as an alternative
approach for providing richer qualitative and quantitative insights into Twitter
discourse. We illustrate the application and potential use of these approaches by
visualizing the differences between tweets directed or discussing Democrats and
Republicans after the COVID-19 Stimulus Package announcement in the US. SA
was integrated into ENA models in two ways: as a part of the blocking variable
and as a set of codes. Our results suggest that incorporating SA into ENA allowed
for a better understanding of how groups viewed the components of the stimulus
issue by splitting them by sentiment and enabled a meaningful inclusion of data
with singular subject focus into the ENA models.
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Kaliisa, Rogers; Misiejuk, Kamila; Arastoopour Irgens, Golnaz & Misfeldt, Morten
(2021).
Scoping the Emerging Field of Quantitative Ethnography: Opportunities, Challenges and Future Directions.
I Ruis, Andrew R & Seung, Lee. B (Red.),
Advances in Quantitative Ethnography.
Springer Nature.
ISSN 978-3-030-67788-6.
s. 3–17.
doi:
%20https:/doi.org/10.1007/978-3-030-67788-6_1.
Fulltekst i vitenarkiv
Vis sammendrag
Quantitative Ethnography (QE) is an emerging methodological approach that combines ethnographic and statistical tools to analyze both Big Data
and smaller data to study human behavior and interactions. This paper presents
a methodological scoping review of 60 studies employing QE approaches with
an intention to characterize and establish where the boundaries of QE might and
should be in order to establish the identity of the field. The key finding is that QE
researchers have enough commonality in their approach to the analysis of human
behavior with a strong focus on grounded analysis, the validity of codes and
consistency between quantitative models and qualitative analysis. Nonetheless,
in order to reach a larger audience, the QE community should attend to a number of conceptual and methodological issues (e.g. interpretability). We believe
that the strength of work from individual researchers reported in this review and
initiatives such as the recently established International Society for Quantitative
Ethnography (ISQE) can present a powerful force to shape the identity of the QE
community
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Kaliisa, Rogers; Kluge, Anders & Mørch, Anders Irving
(2020).
Combining Checkpoint and Process Learning Analytics to Support Learning Design Decisions in Blended Learning Environments.
Journal of Learning Analytics.
ISSN 1929-7750.
7(3),
s. 33–47.
doi:
10.18608/JLA.2020.73.4.
Fulltekst i vitenarkiv
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Kaliisa, Rogers & Picard, Michelle
(2019).
Mobile learning policy and practice in Africa: Towards inclusive and equitable access to higher education.
Australasian Journal of Educational Technology.
ISSN 1449-3098.
35(6),
s. 1–14.
doi:
10.14742/ajet.5562.
Fulltekst i vitenarkiv
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Se alle arbeider i Cristin
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Kaliisa, Rogers; Anna, Gillespie; Herodotou, Christothea; Kluge, Anders & Rienties, Bart
(2021).
Teachers’ Perspectives on the Promises,
Needs and Challenges of Learning
Analytics Dashboards: Insights
from Institutions Offering Blended
and Distance Learning.
Springer Nature.
ISBN 978-3-030-81222-5.
20 s.
Se alle arbeider i Cristin
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Esterhazy, Rachelle; Kaliisa, Rogers; Sanchez, Daniel; Langford, Malcolm & Damsa, Crina I.
(2023).
Multimodal collaboration analytics in collaborative problem solving – a scoping review.
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Kaliisa, Rogers & Damsa, Crina I.
(2023).
Supporting teachers in hybrid learning environments: The role of learning analytics.
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Kaliisa, Rogers; Mørch, Anders & Kluge, Anders
(2021).
Correction to: ‘My Point of Departure for Analytics is Extreme Skepticism’: Implications Derived from An Investigation of University Teachers’ Learning Analytics Perspectives and Design Practices (Technology, Knowledge and Learning, (2021), 10.1007/s10758-020-09488-w).
Technology, Knowledge and Learning.
ISSN 2211-1662.
doi:
10.1007/s10758-021-09574-7.
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Mørch, Anders Irving; Andersen, Renate; Kaliisa, Rogers & Litherland, Kristina
(2020).
Mixed methods with social network analysis for networked
learning: Lessons learned from three case studies.
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Kaliisa, Rogers
(2020).
Unpacking the Bi-directional relationship between learning analytics and learning design in blended learning environments.
Vis sammendrag
This paper suggests a technology-supported teacher-led approach that includes leveraging learning analytics (LA) to support data-informed learning design (LD) decisions in blended learning environments. The context of this study is three to five blended Bachelors courses using the Canvas learning management system (LMS) at two public universities in Norway. This is a design-based research study, employing quantitative ethnography approaches. Data will be collected from multiple sources, i.e. course analytics, discussion forums, interviews, teachers LD representations, and in-class observations. The analysis will be conducted using social and epistemic network analysis, automated discourse analysis, inferential statistics and inductive thematic analysis. This PhD project is anticipated to contribute towards an empirically based theoretical discussion about the potential affordances of LA to transform LD from a craft into a sounder and more evidence-based field of research and practice.
(4) (PDF) Unpacking the Bi-directional relationship between learning analytics and learning design in blended learning environments. Available from: https://www.researchgate.net/publication/340133140_Unpacking_the_Bi-directional_relationship_between_learning_analytics_and_learning_design_in_blended_learning_environments [accessed Mar 30 2020].
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Se alle arbeider i Cristin
Publisert
26. apr. 2018 11:58
- Sist endret
9. aug. 2023 15:04